Tag Archives: demography

Visualizing family modernization, 1900-2016

After this post about small multiple graphs, and partly inspired by two news reports I was interviewed for — this Salt Lake Tribune story about teen marriage, and this New York Times report mapping age at first birth — I made some historical data figures.

These visualizations use decennial census data from 1900 to 1990, and then American Community Survey data for 2001, 2010, and 2016; all data from IPUMS.org. (I didn’t use the 2000 Census because marital status is messed up in that data, with a lot of people who should be never married coded as married, spouse absent; 2001 ACS gets it done.)

An important, simple way of illustrating the myth-making around the 1950s is with marriage age. Contrary to the myth that the 1950s was “traditional,” a long data series show the period to be unique. The two trends here, teen marriage and divorce, both show the modernization of family life, with increasing individual self-determination and less restricted family choices for women.

First, I show the proportion of teenage women married in each state, for each decade from 1900 to 2016. The measure I used for this is the proportion of 19- and 20-year-olds who have ever been married (that is, including those married, divorced, and widowed). It’s impossible to tell exactly how many people were married before their 20th birthday, which would be a technical definition of teen marriage, but the average of 19 and 20 should do it, since it includes some people are on the first day of their 19th year, and some people are on the last day of their 20th, for an average close to exact age 20.

I start with a small multiple graph of the trend on this measure in every state (click all figures to enlarge). Here the states are ordered by the level of teen marriage in 2016, from Maine lowest (<1%) to Utah (14%):

teen marriage 1900-2016

This is useful for seeing that the basic pattern is universal: starting the century lower and rising to a peak in 1960, then declining steeply to the present. But that similarity, and smaller range in the latest data, make it hard to see the large relative differences across states now. Here are the 2016 levels, showing those disparities clearly:

teen marriage states 2016.xlsx

Neither the small multiples nor the bars help you see the regional patterns and variations. So here’s an animated map that shows both the scale of change and the pattern of variation.


This makes clear the stark South/non-South divide, and how the Northeast led the decline in early marriage. Also, you can see that Utah, which is such a standout now, did not have historically high teen marriage levels, the state just hasn’t matched the decline seen nationally. Their premodernism emerged only in relief.


Here I again used a prevalence measure. This is just the number of people whose marital status is divorced, divided by the number of married people (including separated and divorced). It’s a little better than just the percentage divorced in the population, because it’s at least scaled by marriage prevalence. But it doesn’t count divorces happening, and it doesn’t count people who divorced and then remarried (so it will under-represent divorce to the extent that people remarry). Also, if divorced people die younger than married people, it could be messed up at older ages. Anyway, it’s the best thing I could think of for divorce rates by state all the way back to 1900.

So, here’s the small multiple graph, showing the trend in divorce prevalence for all states from 1900 to 2016:


That looks like impressive uniformity: gradual increase until 1970, then a steep upward turn to the present. These are again ordered by the 2016 value, from Utah at less than 20% to New Mexico at more than 30% — smaller variation than we saw in teen marriage. That steep increase looks dramatic in the animated map, which also reveals the regional patterns:



The strategy for both trends is to download microdata samples from all years, then collapse the files down to state averages by decade. The linear figures are Stata scatter plots by state. The animated maps use maptile in Stata (by Michael Stepner) to make separate image files for each map, which I then imported into Photoshop to make the animations (following this tutorial).

The downloaded data, codebooks, Stata code, and images, are all available in an Open Science Framework project here. Feel free to adapt and use. Happy to hear suggestions and alternative techniques in the comments.


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Fertility trends explained, 2017 edition

Not really, but some thoughts and a bunch of figures on the 2017 fertility situation.

There was a big drop in the U.S. fertility rate in 2017. As measured by the total fertility rate (TFR), which is a projection of lifetime births for the average woman based on one year’s data, the drop was 3.1%, from 1.82 projected births per woman to 1.76. (See this measure explained, and learn how to calculate it yourself, in my blockbuster video, “Total Fertility Rate.”) To put that change in perspective, here is the trend in TFR back to 1940, followed by a plot of the annual changes since 1971:



That drop in 2017 is the biggest since the last recession started. In fact, we have seen no drop that big that’s not associated with a time of national economic distress, at least since the Baby Boom. In 2010, I noted that the drop in fertility at that time preceded the official start of the recession and the big unemployment spike. There is now some more systematic evidence (pointed out by Karen Benjamin Guzzo) that fertility falls before economic indicators turn down. Which makes this New York Times headline a little funny, “US Births Hit a 30-Year Low, Despite Good Economy.” This is a pretty solid warning sign, although not definitive, of an economic downturn coming in the next year or so. (On the other hand, maybe it’s a Trump effect, as people are just freaking out and not thinking positively about the future; something to think about.)

Whatever the role of immediate economic conditions, the long-term trend is toward later births, which is generally going to mean fewer births — both because people who want later births tend to want fewer births, and because some people run out of time if they start late. And that is not wholly separable from economic factors, of course. People (especially women) delay childbearing to improve their economic situation, as they improve their economic situation when they delay births (if they have the right suite of economic opportunities). To show this trend, I’ve been updating this figure for a few years (you’ll find it, and a description, in my book Enduring Bonds).

change in birthrates by age 1989-2016.xlsx

The real reason I made this figure was to highlight the interconnected nature of teen births. Birth rates for teens have fallen dramatically, but it’s been along with drops among younger women generally, and increases among older women — it’s about delaying births overall. Note, however, that 2017 is the first time since the depths of the last recession that birth rates fell for all age groups except women over age 40.

So, sell stock now. But it is hard to know for sure what’s a local temporal reaction and what’s just the way things are going nowadays. For that it’s useful to compare the U.S. to other countries. The next figure shows the U.S. and 15 other hand-picked countries, from World Bank data. Rising fertility in the decade before the last recession wasn’t so unusual. We are a little like Spain and France in this figure, who had rising fertility then and falling now. But Germany and Japan are still rising, at least through 2016. All this is at below-replacement levels (about 2.0), meaning eventually these rates lead to population decline, in the absence of immigration. The figure really shows the amazing fertility transformation of the last half century, especially in giant countries like China, India, and Brazil. Who would have thought we’d live to see Brazil have lower fertility rates than the U.S.? It’s been that way for more than a decade (click to enlarge).

country fertilitiy trends.xlsx

Anyway, it’s my position that our below-replacement fertility levels are themselves nothing to worry about at present. There are still lots of people who want to move here (or, there were before Trump). And we can live with low fertility for a long time before the population starts to decline in a meaningful way. Eventually it will be a good idea to stop perpetual population growth anyway, so we may as well start working on it. This is better than trying to shape domestic policy to increase birth rates.

That said, there is an argument that Americans are having fewer children than they want to because of our stone age work-family policies, especially poor family leave support and the high costs of good childcare. I’m sure that’s happening to some degree, but it’s still the case that more privileged people, who should be able to overcome those things more readily — people with college degrees and Whites — have lower fertility rates than people who are getting squeezed more. People who assume their kids are going to college are naturally concerned with rising higher education costs, both their own loan payments and their kids’ future payments. So it’s a mixed bag story. Here are the predictors of childbearing for women ages 15-44 in the 2016 American Community Survey. These are the probabilities of having had a birth in the previous 12 months, estimated (with logistic regression) at the mean of all the variables shown.*

birth model simple 2016.xlsx

Interesting that there’s only a small foreign-born fertility edge in this multivariate model. In the unadjusted data, 7.4% of foreign-born versus 6.0% of U.S.-born women had a baby, but that’s mostly accounted for by their age, education, and race/ethnicity.

To summarize: 2017 was a big year for fertility decline (at all but the highest ages), the economy is probably about to tank, and the U.S. fertility rate is still relatively high for our income level, especially for racial-ethnic minorities.

Happy to have your thoughts in the comments. For more, check the fertility tag.

* Here’s the Stata code for the regression analysis. It’s just some simple recodes of the ACS data from IPUMS.org. Start with a file of women ages 15-44, with the variables you see here, and then do this to it:

recode educd (0/61=1) (62/64=2) (65/90=3) (101/116=4), gen(edcat)
label define edlbl 1 "Less than high school"
label define edlbl 2 "High school graduate", add
label define edlbl 3 "Some college", add
label define edlbl 4 "BA or higher", add
label values edcat edlbl
gen raceth=race
replace raceth=4 if race==5 | race==6 /* now 4 is all API */
replace raceth=5 if hispan>0
drop if race>5
label define raceth_lbl 1 "White"
label define raceth_lbl 2 "Black", add
label define raceth_lbl 3 "AIAN", add
label define raceth_lbl 4 "API", add
label define raceth_lbl 5 "Hispanic", add
label values raceth raceth_lbl
egen agecat=cut(age), at(15(5)50)
gen forborn=citizen!=0
gen birth=fertyr==2
logit birth i.agecat i.raceth i.forborn i.edcat i.marst [weight=perwt]
margins i.agecat i.raceth i.forborn i.edcat i.marst


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Who are you gonna marry? That one big assumption marriage promotion gets totally wrong

First preamble, then new analysis.

One critique of the marriage promotion movement is that it ignores the problem of available spouses, especially for Black women. Joanna Pepin and I addressed this with an analysis of marriage markets in this paper. White women ages 20-45, who are more than twice as likely to marry as Black women, live in metro areas with an average of 118 unmarried White men per 100 unmarried White women. Black women, on the other hand, face markets with only 78 single men per 100 single women. This is one reason for the difference in marriage rates; given very low rates of intermarriage, especially for Black women, some women essentially can’t marry.

But surely some people are still passing up potential marriages, or so the marriage promoters would have us believe, and in so doing they undermine their own futures and those of their children. Even if you can get past the sex ratio problem, you still have the issue of the benefits of marriage. Of course married people, and their kids, are better off on average. (There are great methodological lessons to be learned from their big lie use of this fact.) But who gets those benefits? The intellectual water-carriers of the movement, principally Brad Wilcox and his co-authors, always describe the benefits of increasing marriage as if the next marriage to occur will provide the same benefits as the average existing marriage. I wrote about how this wrong in Enduring Bonds:

The idea that the “benefits” of marriage—that is, the observed association between marriage and nonpoverty—would accrue to single mothers if they “simply” married their current partners is bonkers. The notion of a “marriage market” is not perfect, but there is something like a marriage queue that arranges people from most likely to least likely to marry. When you say, “Married people are better off than single people,” a big part of what you’re observing is that, on average, the richer, healthier, better-at-relationships people are at the front of that queue, more likely to marry and then to display what look like the benefits of marriage. Those at the back of the queue, who are more (if not totally) “unmarriageable,” clearly aren’t going to have those highly beneficial marriages if they “simply” marry the closest person.

In fact, I assume this problem has gotten worse as marriage has become more selective, as “it’s increasingly the most well off who are getting and staying married,” and those who aren’t marrying “may not have the assets that lead to marriage benefits: skills, wealth, social networks, and so on.”

Note on race

People who promote marriage don’t like to talk about race, but if it weren’t for race — and racism — they would never have gotten as far as they have in selling their agenda. They use supposedly race-neutral language to talk about fatherhood and a “culture of marriage” and “sustainably escaping poverty,” in ways that are all highly relevant to Black families and racial disparities. If you think the problem of marriage is that poor people are not marrying enough, you should not avoid the fact that you’re talking about race. Black women, especially mothers, are much less likely to be married than most other groups of women, even at the same level of income or education (last I checked Black college graduates were 5-times more likely than White college graduates to be single when they had a baby). So, don’t avoid that this is about race, own it  — the demographic facts and political machinations in this area are all highly interwoven with race. I do this analysis, like the paper Joanna and I did, separately for Black and White women, because that’s the main faultline in this area. The code I share below is adaptable to use with other groups as well.

Data illustration

In this data exercise I try to operationalize something like that marriage market queue, to show that women who are least likely to marry are also least likely to enter an economically beneficial marriage if they did marry. See how you like this, and let me know what you think. Or take the data and code and come up with a different way of doing it.

The logic is to take a sample of never-married women, and women who just got married in the last year, and predict membership in the latter group. This generates a predicted probability of marrying for each woman, and it means I can look at the never-married women and see which among them are more or less likely to marry in a given year. For example, based on the models below, I would estimate that a Black woman under age 25, with less than a BA degree, who had a job with less-than-average earnings, has a 0.4% probability of marrying in one year. On the other hand, if she were age 25+, with a BA degree and above-average earnings, her chance of marrying rises to 3.5% per year. (Round numbers.)*

Next, I look at the husbands of women who married men in the year prior to the survey, and I assign them economic scores on an 11-point scale (this is totally arbitrary): up to four points for education, up to four points for earnings, and up to three points for employment level (weeks and hours worked in the previous year). So, a woman whose husband has a high school education, earned $30,000 last year, and worked full-time, year-round, would have 7 points.

Finally, I show the relationship between the odds of marriage for women who didn’t get married and the economic score of the men they would have married if they did.

There are two descriptive conclusions, which I assumed I would find: (1) women who get married marry men with better economic scores than the women who don’t get married would if they did get married; and, (2) the greater the odds of marriage, the better the economic prospects of the man they would marry. The substantive conclusion from this is that marriage promotion, if it could get more people to marry, would pull from the women on the lower rungs of marriage probability, so those new marriages would be less economically beneficial than the average marriage, and the use of married people’s characteristics to project the benefits of marriage for unmarried people is wrong. Like I said, I already believed this, so this is a way of confirming it or showing the extent to which it fits my expectations. (Or, I could be wrong.)

Here are the details.

I use the 2012-2016 five-year American Community Survey data from IPUMS.org (for larger sample). The sample is women ages 18-44, not living in group quarters, single-race Black or White, non-Hispanic, and US-born. I further limited the sample to those who never married, and those who are married for the first time in the previous 12 months. That condition — just married — is the dependent variable in a model predicting odds of first marriage. (Women with female spouses or partners are excluded, too.) The variables used to predict marriage are age (and its square), education, earnings in the previous year (logged), and having no earnings in the previous year (these women are most likely to marry), disability status, metro area residence, and state dummy variables. It’s a simple model, not trying for statistical efficiency but rather the best prediction of marriage odds. Then I use the same set of variables, limiting the analysis to just-married women, to predict their husbands’ economic scores. The regression models are in a table at the end.**

Figure 1 shows how the prediction models assign marriage probabilities. White women have much higher odds of marrying, and those who married have higher odds than those who didn’t, which is reassuring. In particular, a large proportion of never-married Black women are predicted to have very low odds of marrying (click to enlarge).


Figure 2 shows the distribution of husbands’ economic scores for Black and White women who married and those who didn’t. The women who didn’t marry have lower predicted husband scores, with the model giving them husbands with a mode of about 7.0 for Whites and 6.5 for Blacks (click to enlarge).


Finally, the last figure includes only never-married women. It shows the relationship between predicted marriage probability and predicted husband score, using median splines. So, for example, the average unmarried Black woman has a marriage probability of about 1.7%. Figure 3 shows that her predicted husband would have a median score of about 6.4. So he could be a full-time, full-year worker with a high school education, earning $19,000 per year, which would not be enough to lift her and one child out of poverty. The average never-married White woman has a predicted marriage probability of 5.1%, and her imaginary husband has a score of about 7.4 (e.g., a similar husband, but earning $25,000 per year).


Figure 3 implies  what I thought was obvious at the beginning: the further down the marriage market queue you go, the worse the economic prospects of the men they would marry, if there were men for them to marry (whom they wanted to marry, and who wanted to marry them).

I will now be holding my breath while marriage promotion activists develop a more sensible set of assumptions for their assessment of the benefits of the promoted marriages they assure us they will be able to conjure if only we give them a few billion more dollars.

I’m posting the data and code used on the Open Science Framework, here. Please feel free to work with it and let me know what you come up with!

* This looks pretty similar to what Dohoon Lee did in this paper, including his figures, and since I was on his dissertation committee, and read his paper, which has similar figures, I credit him with this idea — I should have remembered earlier.

** Here are the regression models used to (1) predict marriage, and then (2) predict husband’s economic scores.

marriage models.xlsx



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Theology majors marry each other a lot, but business majors don’t (and other tales of BAs and marriage)

The American Community Survey collects data on the college majors of people who’ve graduated college. This excellent data has lots of untapped potential for family research, because it tells us something about people’s character and experience that we don’t have from any other variables in this massive annual dataset. (It even asks about a second major, but I’m not getting into that.)

To illustrate this, I did two data exercises that combine college major with marital events, in this case marriage. Looking at people who just married in the previous year, and college major, I ask: Which majors are most and least likely to marry each other, and which majors are most likely to marry people who aren’t college graduates?

I combined eight years of the ACS (2009-2016), which gave me a sample of 27,806 college graduates who got married in the year before they were surveyed (to someone of the other sex). Then I cross-tabbed the major of wife and major of husband, and produced a table of frequencies. To see how majors marry each other, I calculated a ratio of observed to expected frequencies in each cell on the table.

Example: With weights (rounding here), there were a total of 2,737,000 BA-BA marriages. I got 168,00 business majors marrying each other, out of 614,000 male and 462,000 female business majors marrying altogether. So I figured the expected number of business-business pairs was the proportion of all marrying men that were business majors (.22) times the number of women that were business majors (461,904), for an expected number of 103,677 pairs. Because there were 168,163 business-business pairs, the ratio is 1.6.  (When I got the same answer flipping the genders, I figured it was probably right, but if you’ve got a different or better way of doing it, I wouldn’t be surprised!)

It turns out business majors, which are the most numerous of all majors (sigh), have the lowest tendency to marry each other of any major pair. The most homophilous major is theology, where the ratio is a whopping 31. (You have to watch out for the very small cells though; I didn’t calculate confidence intervals.) You can compare them with the rest of the pairs along the diagonal in this heat map (generated with conditional formatting in Excel):

spouse major matching

Of course, not all people with college degrees marry others with college degrees. In the old days it was more common for a man with higher education to marry a woman without than the reverse. Now that more women have BAs, I find in this sample that 35% of the women with BAs married men without BAs, compared to just 22% of BA-wielding men who married “down.” But the rates of down-marriage vary a lot depending on what kind of BA people have. So I made the next figure, which shows the proportion of male and female BAs, by major, marrying people without BAs (with markers scaled to the size of each major). At the extreme, almost 60% of the female criminal justice majors who married ended up with a man without a BA (quite a bit higher than the proportion of male crim majors who did the same). On the other hand, engineering had the lowest overall rate of down-marriage. Is that a good thing about engineering? Something people should look at!

spouse matching which BAs marry down

We could do a lot with this, right? If you’re interested in this data, and the code I used, I put up data and Stata code zips for each of these analyses (including the spreadsheet): BA matching, BA’s down-marrying. Free to use!


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Unequal marriage markets for Black and White women

Joanna Pepin and I have posted a new paper titled, “Unequal marriage markets: Sex ratios and first marriage among Black and White women.” In the paper, we find that the marriage markets of Black and White women are very different, with Black women living in metropolitan areas that have many fewer single men than White women do. And, in a regression model with other important predictors of marriage, this unmarried sex ratio is strongly associated with the odds of marrying.

We count this as evidence on the side of “structure” over “culture” in the debates over the decline in marriage. Here’s the main result, showing Black and White women in 172 metro areas (scaled for size), and the difference in sex ratios (the horizontal spread), the difference in marriage rates (the vertical spread), and the statistical effect of sex ratios on marriage (the slopes).


In a nutshell: As you move from left to right, there are more men, and higher odds of marriage. And almost all the White women are up and to the right compared with the Black women. One implication is that this could be one reason why marriage promotion programs in the welfare system aren’t working.

There are a couple of noteworthy innovations here. First, we used the American Community Survey marital events data, which is marriage happening (did you get married in the last year?) rather than just existing (are you married?). This is a better way to assess what might influence marriage. Second, young people, especially single young people who might be getting married, move around a lot. So what is their marriage market? It’s impossible to say exactly, but we define it as the metro area where they lived one year earlier, rather than just where they live now. (This is especially important because the people who move may move because they just got married.)

The paper is on SocArXiv, where if you follow the links you get to the project page, where we put most of the data and code. The paper is under review now, and we’d love to know if you find any mistakes or have any suggestions.

(This began with a blog post four years ago in which I critiqued a NYT Magazine piece by Anne Lowrie about using marriage to cure poverty. Then we presented a first pass at the Population Association in 2014, and I put some of the descriptive statistics in my textbook, and we made a short video out of it, in which I said, “So, larger social forces — the economy, job discrimination, incarceration policies, and health disparities — all impinge on the ability of individuals to shape their own family lives.” Along the way, I presented some about it here and there, while thinking of new ways to measure marriage inequalities.)


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How I engaged my way to excellent research success and you can too

kid on string phone in front of computer screen

Kid photo CC from MB Photography; collage by pnc.

Too often sociologists think of social media, or online communications generally, primarily as a way of broadcasting their ideas and building their audience, instead of as a way of deepening their engagement with different people and perspectives. You see this when academics start a twitter account right when their book is coming out. Nothing wrong with that, but it’s very limited. A crucial part of being a public scholar, public intellectual, or a public sociologist, etc., is reading, listening, and learning through engagement, and digital communication can enhance the metabolism of that process. Especially important is the chance to learn from people you don’t normally interact with. For all the complaints about social media bubbles, some true, social media also offers huge efficiencies for meeting and learning from new people.

As I’m writing an essay about this, I thought of my work on divorce as an example. So here’s that thread, condensed.

A divorce story

In 2008 I was teaching an undergraduate Family Sociology course at the University of North Carolina, and included a section on divorce based on other people’s research. I was also developing a proposal for my own textbook, which at the time framed family structures and events, including divorce, as consequences and causes of inequality. I was reading research about divorce along with many other family issues that were outside of my formal training and experience (the closest I had come to a family demography or family sociology course was a seminar on Gender, Work & Family in grad school).

Then in 2009, I wrote a post on my pretty new blog criticizing something bad the Brad Wilcox had written about divorce. I was trying to be newsy and current, and he was claiming that the recession was lowering divorce rates because hard times pulled people together. We didn’t yet know what would happen in the recession. (In the comments, Louise Roth suggested it would take time for divorces “caused” by the recession to show up, which turned out to be true.)

I kept on that path for a while, criticizing Wilcox again for similar work in 2011. By then — prompted by the combination of my reading, the blog debates, and the news coverage around families and the recession — I was working on a paper on divorce using the American Community Survey. I presented it at a demography meeting in the summer of 2011, then revised and presented it at the Population Association of America the following spring. I blogged about this a couple more times as I worked on it, using data on state variation, and Google searches, each time getting feedback from readers.

A version of the paper was rejected by Demography in the summer of 2011 (which generated useful reviews). Although now discredited as not peer-review-publishable (which no one knew), my commentary on divorce and the recession was nevertheless featured in an NPR story by Shankar Vedantam. Further inspired, I sent a new version of the paper (with new data) to Demographic Research, which also rejected it. I presented on the work a couple of times in 2012, getting feedback each time. By August 2012, with the paper still not “published,” I was quoted describing my “divorce/recession lull-rebound hypothesis” in New York magazine.

The news media pieces were not simply my work appearing in the news, in a one-directional manner, or me commenting on other people’s research, but rather me bringing data and informed commentary to stories the reporters were already working on. Their work influenced my work. And all along that news coverage was generating on- and offline conversations, as I found and shared work by other people working on these topics (like the National Center for Marriage and Family Research, and the Pew Research Center). Looking back over my tweets about divorce, I see that I covered divorce and religion, disabilities, economics, and race/ethnic inequality, and also critiqued media coverage. (Everything also got discussed on Facebook, in a smaller semi-private circle.)

By 2014 I finally got the paper — now with even newer data — published in a paywalled peer-reviewed journal, in Population Research and Policy Review. This involved writing the dreaded phrase, “Thank you very much for the opportunity to revise this paper again.” (Submitted October 2012, revision submitted August 2013, second revision submitted January 2014, final revision April 2014.) The paper, eventually titled, “Recession and Divorce in the United States, 2008-2011,” did improve over this time as new data provided better leverage on the question, and the reviewers actually made some good suggestions.

Also in 2014 the descriptive analysis was published in my textbook. The results were reported here and there, and expanded into the general area of family-recession studies, including this piece in the Conversation. I also developed a method of projecting lifetime divorce odds (basically 50%), for which I shared the data and code, which was reported on here. Along the way I also did some work on job characteristics and divorce (data and code, working paper). When I posted technical notes, I got interesting responses from people like economist Marina Adshade, whom I’ve never met.

So that’s an engagement story that includes teaching, the blogosphere and social media, news media, peer-reviewed publishing, conference presentations and colloquium talks. I did research, but also argued about politics and inequality, and taught and learned demography. It’s not a story of how I used social media, or the news media, to get the word out about my research, although that happened, too. The work product, not just the “publications,” were all public to varying degrees, and the discussions included all manner of students, sociologists, reporters, and interested blog or Twitter readers, most of whom I didn’t know or wouldn’t have met any other way.

So I can’t draw a line dividing the “engagement” and the “research,” because they weren’t separate processes.

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No, early marriage is not more common for college graduates

Update: IFS has taken down the report I critiqued here, and put up a revised report. They have added an editor’s note, which doesn’t mention me or link to this post:

Editor’s Note: This post is an update of a post published on March 14, 2018. The original post looked at marriage trends by education among all adults under age 25. It gave the misimpression that college graduates were more likely to be married young nowadays, compared to non-college graduates.

At the Institute for Family Studies, Director of Research Wendy Wang has a post up with the provocative title, “Early Marriage is Now More Common For College Graduates” (linking to the Internet Archive version).

She opens with this:

Getting married at a young age used to be more common among adults who didn’t go to college. But the pattern has reversed in the past decade or so. In 2016, 9.4% of college graduates ages 18 to 24 have ever been married, which is higher than the share among their peers without a college degree (7.9%), according to my analysis of the most recent Census data.

And then the dramatic conclusion:

“What this finding shows is that even at a young age, college-educated adults today are more likely than their peers without a college degree to be married. And this is new.”

That would be new, and surprising, if it were true, but it’s not.

Here’s the figure that supports the conclusion:


It shows that 9.4% of college graduates in the age range 18-24 have been married, compared with 7.9% of those who did not graduate from college. (The drop has been faster for non-graduates, but I’m setting aside the time trend for now.) Honestly, I guess you could say, based on this, that young college graduates are more likely than non-graduates to “be married,” but not really.

The problem is there are very very few college graduates in the ages 18-19. The American Community Survey, which they used here, reports only about 12,000 in the whole country, compared with 8.7 million people without college degrees ages 18-19 (this is based on the public use files that IPUMS.org uses; which is what I use in the analysis below). Wow! There are lots and lots of non-college graduates below age 20 (including almost everyone who will one day be a college graduate!), and very few of them are married. So it looks like the marriage rate is low for the group 18-24 overall. Here is the breakdown by age and marital status for the two groups: less than BA education, and BA or higher education — on the same population scale, to help illustrate the point:


If you pool all the years together, you get a higher marriage rate for the college graduates, mostly because there are so few college graduates in the younger ages when hardly anyone is married.

To show the whole thing in terms of marriage rates, here is the marital status for the two groups at every age from 15 (when ACS starts asking about marital status) to 54.


Ignoring 19-21, where there are a tiny number of college graduates, you see a much more sensible pattern: college graduates delay marriage longer, but then have higher rates at older ages (starting at age 28), for all the reasons we know marriage is ultimately more common among college graduates. In fact, if you used ages 15-24 (why not?), you get an even bigger difference — with 9.4% of college graduates married and just 5.7% of non-college graduates. Why not? In fact, what about ages 0-24? It would make almost as much sense.

Another way to do this is just to look at 24-year-olds. Since we’re talking about the ever-married status, and mortality is low at these ages, this is a case where the history is implied in the cross-sectional data. At age 24, as the figure shows, 19.9% of non-college graduates have been married, compared with 12.9% of college graduates. Early marriage is not more common for college graduates.

In general, I don’t recommend comparing college graduates and non-graduates, at least in cross-sectional data, below age 25. Lots of people finishing college below age 25 (and increasingly after that age as well). There is also an important issue of endogeneity here, which always makes education and age analysis tricky. Some people (mostly women) don’t finish college because they get married and have children).

Anyway, it looks to me like someone working for a pro-marriage organization saw what seemed like a story implying marriage is good (that’s why college graduates do it, after all), and one that also fits with the do-what-I-say-not-what-I-do criticism of liberals, who are supposedly not promoting marriage among poor people while they themselves love to get married (a critique made by Charles Murray, Brad Wilcox, and others). And, before thinking it through, they published it.

Mistakes happen. Fortunately, I dislike the Institute for Family Studies (see the whole series under this tag), and so I read it and pointed out this problem within a couple hours (first on Twitter, less than two hours after Wang tweeted it). It’s a social media post-publication peer review success story! If they correct it.


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