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%):

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:

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.

Divorce

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:

Technique

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.

More marriage promotion failure evidence

Photo PNC / Flickr CC: https://flic.kr/p/9xNLad

I have another entry in the Cato Unbound forum on the Success Sequence. This is a response to a post by Brad Wilcox, which responded to my initial post, “The failure of the success sequence.”

With that context, I reprint it here.

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In the second round of comments here, Brad Wilcox chose to focus on my argument that marriage promotion doesn’t work—that is, it doesn’t lead to more marriages. I have two brief responses to his comments.

First, Wilcox asserts that I have ignored salient evidence, and he mentions two studies. He writes:

But Cohen did not do justice to the existing literature on the HMI [Healthy Marriage Initiative] or of interventions like those used within it. For instance, he ignores evidence of modest success for the Oklahoma Marriage Initiative in fostering family stability (the longest running local effort working on this issue) and research that found that spending on the HMI was “positively associated with small changes in the percentage of married adults in the population” (italics in the original).

However, in my essay I linked to my book, Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible. There I dealt with the subject in much greater depth.

In particular, with regard to the claim that marriage promotion was associated with more marriage, the link is to this study (paywalled) in the journal Family Relations, by Alan Hawkins, Paul Amato, and Andrea Kinghorn. In my book I devote more than two pages to debunking this single study in detail. Since Wilcox appears not inclined to read my analysis in the book, I provide some key excerpts here:

[Hawkins, Amato, and Kinghorn] attempted to show that the marriage promotion money had beneficial effects at the population level.

They statistically compared state marriage promotion funding levels to the percentage of the population that was married and divorced, the number of children living with two parents or one parent, the nonmarital birth rate, and the poverty and near-poverty rates for the years 2000–2010. This kind of study offers an almost endless supply of subjective, post hoc decisions for researchers to make in their search for some relationship that passes the official cutoff for “statistical significance.” Here’s an example of one such choice these researchers made to find beneficial effects (no easy task, apparently): arbitrarily dividing the years covered into two separate periods. Here is their rationale: “We hypothesized that any HMI effects were weaker (or nonexistent) early in the decade (when funding levels were uniformly low) and stronger in the second half of the decade (when funding levels were at their peak).”

This is wrong. If funding levels were low and there was no effect in the early period, and then funding levels rose and effects emerged in the later period, then the analysis for all years should show that funding had an effect; that is the point of the analysis. This decision does not pass the smell test. Having determined that this decision would help them show that marriage promotion was good, they went on to report their beneficial effects, which were “significant” if you allowed them a 90 percent confidence (rather than the customary 95 percent, which is kosher under some house rules).

However, then they admitted their effects were significant only with Washington, D.C., included. Our nonstate capital city is a handy wiggle-room device for researchers studying state-level patterns; you can justify including it because it’s a real place, or you can justify excluding it because it’s not really a state. It turns out that the District of Columbia had per capita marriage promotion funding levels about nine times the average. With an improving family well-being profile during the period under study, this single case (out of fifty-one) could have a large statistical effect on the overall pattern. Statistical outliers are like the levers you learned about in physics—the further they are from the average, the more they can move the pile. To deal with this extreme outlier, they first cut the independent variable in half for D.C., bringing it down to about 4.4 times the mean and a third higher than the next most-extreme state, Oklahoma (itself pretty extreme). That change alone cut the number of significant effects on their outcomes down from six to three.

Then, performing a tragic coup de grâce on their own paper, they removed D.C. from the analysis altogether, and nothing was left. They didn’t quite see it that way, however: “But with the District of Columbia excluded from the data, all of the results were reduced to nonsignificance. Once again, most of the regression coefficients in this final analysis were comparable to those in Table 2 in direction and magnitude, but they were rendered nonsignificant by a further increase in the size of the standard errors.”

Really. These kinds of shenanigans give social scientists a bad name. (Everything that is nonsignificant is that way because of the [relative] size of the standard errors—that’s what nonsignificant means.) And what does “comparable in direction and magnitude” mean, exactly? This is the kind of statement one hopes the peer reviewers or editors would check closely. For example, with D.C. removed, the effect of marriage promotion on two-parent families fell 44 percent, and the effect on the poor/near-poor fell 78 percent. That’s “comparable” in the sense that they can be compared, but not in the sense that they are similar. Again, the authors helpfully explain that “the lack of significance can be explained by the larger standard errors.” That’s just another way of saying their model was ridiculously dependent on D.C. being in the sample and that removing it left them with nothing.

Oh well. Anyway, please keep giving the programs money, and us money for studying them: “In sum, the evidence from a variety of studies with different approaches targeting different populations suggests a potential for positive demographic change resulting from funding of [Marriage and Relationship Education] programs, but considerable uncertainty still remains. Given this uncertainty, more research is needed to determine whether these programs are accomplishing their goals and worthy of continued support.”

In short, this paper provides no evidence that HMI funding increased marriage rates or family wellbeing.

The other link Wilcox provides (“modest success for the Oklahoma Marriage Initiative”) goes to an essay on his website by the same Alan Hawkins. The evidence about Oklahoma’s “modest success” in that essay is limited to a broken link to another page on Wilcox’s site, and—I find this hard to even believe—an estimate of the effects of HMI funding in Oklahoma extrapolated from the paper I discussed above! That is, they took the very bad models from that paper and used them to predict how much the funding should have mattered in Oklahoma based on the level of funding there (and remember, Oklahoma was an outlier in that analysis). There was no estimate of the actual effect in Oklahoma. In fact, as I explained in a followup debunking, Oklahoma during this period experienced a greaterdecline in married-parent families than the rest of the country, even as they sucked up much more than their share of marriage promotion funds. This is, to put it mildly, not good social science. (The Oklahoma program, incidentally, is the subject of an excellent book by Melanie Heath: One Marriage Under God.)

Wilcox also argues that I am too demanding of federal programs, expecting demonstrable success. He concludes, “If the United States had adapted Cohen’s standard a half century ago, this would have resulted in the elimination of scores of federally funded programs that now garner hundreds of billions of dollars every year in public spending—from job training to Head Start.”

Amazingly, because Wilcox has made this argument before, I also addressed it in my book. Specifically, I wrote:

Of course, lots of programs fail. And, specifically, some studies have failed to show that kids whose parents were offered Head Start programs do better in the long run than those whose parents were not. But Head Start is offering a service to parents who want it, a service that most of them would buy on their own if it were not offered free. Head Start might fail at lifting children out of poverty while successfully providing a valuable, need-based service to low-income families.

As you can imagine, I am all for giving free marriage counseling to poor people if they want it (along with lots of other free stuff, including healthcare and childcare). And if they like it and keep using it, I might define that program as a success. But it’s not an antipoverty program.

Finally, in response to the idea that we just need more funding and more research to know if marriage promotion works, here’s my suggestion: in the studies testing marriage promotion programs, have a third group—in addition to the program and control group—who just get the cash equivalent to the cost of the service (a few thousand dollars). Then check to see how well the group getting the cash is doing compared with those getting the service. That’s the measure of whether this kind of policy is a success.

The failure of the success sequence

This essay was originally published as part of a forum on the success sequence sponsored by the Cato Institute, featuring Michael Tanner, Isabel Sawhill, and Brad Wilcox.

The success sequence is often (mistakenly) attributed to the 2009 book Creating an Opportunity Society by the Brookings Institution’s Ron Haskins and Isabel Sawhill. “First comes education,” they wrote. “Then comes a stable job that pays a decent wage, made decent by the addition of wage supplements and work supports if necessary. Finally comes marriage, followed by children.” They called for “marketing campaigns and educational programs to change social norms: to bring back the success sequence as the expected path for young Americans.”

The only issue here is marriage, as the rest is obvious to everyone. And in that regard this model of social change is wholly unproven and without precedent. Seat belt laws and anti-smoking campaigns, always cited by success sequence advocates, are not comparable. Those are daily habits easily addressed by legal regulations and tax policy (seat belts are required by law; with taxes, the price of cigarettes has more than tripled since 1980). The decline in marriage is a massive global trend driven by economic development and cultural adaptation. And the decline in teen pregnancy, to which success sequencers also point as a precedent for public information campaigns, flows with rather than against that underlying trend. As I detail in my new book, Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible, the drop in teen birth was part of the general increase in the age at which women have children, driven by the expansion of their educational and professional opportunities.

That idea of using public information campaigns to preach “marriage culture” echoed the futile proclamations of a previous generation. In a Hoover Institution symposium in 1996, former vice president Dan Quayle wrote that, “when it comes to strengthening families … we also desperately need help from nongovernment institutions like the media and the entertainment community.” Taking up the call with even more zeal, in 2001 Heritage Foundation fellow Patrick Fagan declared it was time to add three W’s to the common three R’s of schooling. “We need to stress something just as fundamental [as reading, writing, and arithmetic],” he wrote. “Call it the three W’s: work, wedlock and worship. … Put all three in the lives of parents and children, and they thrive.”* Five years later, another Heritage fellow said of the three W’s, “According to the social science data, if these three fundamentals are in place, government social policy is virtually unnecessary.” In 2012, the National Marriage Project, under director W. Bradford Wilcox, was again calling for “community-based and focused public service announcements” and a Hollywood “conversation” to promote marriage.

Meanwhile, slightly more liberal think tank denizens had discretely replaced “worship” with education, but they stuck to the basic idea that the problem with poor people is that they’re doing life wrong—and the “three somethings” formula. In a 2006 report for the National Campaign to Prevent Teen Pregnancy, Barbara Dafoe Whitehead and Marline Pearson wrote that it was time to “teach teens the rules of the success sequence,” which they defined as, “Finish high school, or better still, get a college degree; wait until your twenties to marry; and have children after you marry.” (Three things is a favorite formula of Chinese social engineers we well, as with Jiang Zemin’s “Three Represents” and Hu Jintao’s “Three Supremes”—but China combines such slogans with centralized education and state repression to increase their salience.)

Today, more than two decades after Quayle’s plea, 17 years after the three W’s, 12 years after the first “success sequence” proclamation, and one president after the National Marriage Project pitched its “President’s Marriage Agenda,” movement leaders are still calling for “Public and private social marketing campaigns on behalf of marriage and the ‘success sequence’,” to quote Wilcox and Wendy Wang’s latest report. Neither the policy nor the campaign to promote the policy have changed appreciably over the years, although the definition of the success sequence has varied from author to author. And in all this time, I could not find one academic study, outside of those published by think tanks, that seriously evaluates the claims of the success sequence.

What Could Go Wrong?

Today’s success sequence movement is puzzling in part because it fails to recognize—or admit—the extent to which its adherents already won. After the landmark 1996 welfare reform act, the federal government pumped more than $1 billion into national marriage promotion programs (the Healthy Marriage and the Responsible Fatherhood initiatives). This was cause for great celebration in the movement, as it should have been. In 2004, a Heritage Foundation report gushed, “The President’s Healthy Marriage Initiative is a future-oriented, preventive policy. It will foster better life-planning skills—encouraging couples to develop loving, committed marriages before bringing children into the world.”

It didn’t. The previous decade’s marriage promotion programs sent the same message the “success sequence” promoters do today. But where is the recognition that they failed? Rigorous evaluations of the marriage promotion efforts showed unequivocally that they produced no increase in marriage, not even among the people coerced into sitting for hours in relationship skills courses required to qualify for welfare benefits. As most readers probably know, in the years after welfare reform, marriage rates have continued to fall, and they have fallen fastest for those with less than a college education, the very population the programs were supposed to help. Even though pro-marriage billboards dotted the highways and FedEx delivered thousands of new-daddy care packages to hospitals. In fact, the only people more likely to marry after all these years of conservative activism are gays and lesbians. (This history is also reviewed in my book.)

Does this mean it’s bad advice to get an education, get a job, and find a permanent partner before having children? Of course not. But the success sequence is bad public policy, which is not the same thing at all. For public policy the question is, what will we accomplish with this money, compared with other things we could spend it on (or nothing at all)? Will the proposed campaigns have any positive effect on family outcomes? And if so, would they be better than some other way of spending money, like giving it to poor people, which is what most rich countries do, along with jobs, paid family leave, health care, and preschool education? Specifically, the rationale for spending money on these campaigns assumes that there are people who are on the fence about the success sequence, whose minds might be changed by the campaign, and that those altered decisions would lead to better outcomes in the future for those specific people. There is simply no evidence to support anything like that chain of events. Despite the ad nauseam repetition of the obvious fact that educated, employed, and (much less importantly) married people are less likely to be poor, there is no evidence at all that convincing people who are not one of those things of their importance will cause a reduction in poverty rates.

Given the well-documented desire of most young adults to finish high school, get a job, and get married—if the opportunity to follow that course presents itself—there is no reason to think the people reached by the proposed campaigns would not either already plan to follow the sequence or rightly suspect that it is not feasible for them. The decision to delay childbearing in hopes of marrying first rests on assumptions about the future—education, economics, relationships, health, stability—that the target population simply cannot makeabout their own destinies in today’s economic and social context. Improve the basic equation, the material expectations of young adults, and you won’t need a campaign to change behavior.

When women have more to lose, they delay parenthood. The college students in my classes, overwhelmingly women (I teach sociology of the family), almost all want to get married and then have children after they finish college. They understand that their marriage prospects will improve after college, and they don’t want children to interfere with their education or career launch. So, why shouldn’t we tell all women, especially those with poorer education and career prospects, to follow this course as well? Success sequencers believe it’s hypocritical to hoard this advice and only dispense it to the children of privilege. But you can’t wish away education, career, and marriage uncertainty or impose order on instability by force of will. If we’re not prepared to guarantee all women the same opportunities as those in my classes have, it’s not reasonable to demand the same attachment to the success sequence that those opportunities make feasible. In the absence of that guarantee, you’re simply asking, or requiring, poor people to delay (until “they’re ready,” in Sawhill’s terms, meaning not poor) or forego having children, one of the great joys of life, and something we should consider a human right.

In addition, what signals will a federal “success sequence” program send? What message will these campaigns send to people who are currently materially underserved by the welfare state, and people who don’t have the option to pursue the sequence because stable partners, education, or jobs aren’t available to them? What message will it send to the majority of Americans who are in a position to look down upon, and act against, those who become, in Sawhill’s chilling phrase, “norm breakers”?

And here race becomes especially salient. Black women have low marriage rates and black single mothers have high poverty rates. They face marriage markets with drastic shortages of eligible men, as Michael Tanner noted in the essay that opened this discussion. Not coincidentally, the history of welfare politics in the United States is intricately bound up with the history of racism against black women, who have been labeled pathological and congenitally dependent. The idea that delaying parenthood until marriage is a choice one makes is highly salient and prized by the white middle class, and the fact that black women often don’t have that choice makes them the objects of scorn for their perceived lax morals. The framing of the success sequence plays into this dynamic. For example, Ron Haskins has argued that welfare reform was needed to “[change] the values and the approach to life of people on welfare that they have to do their part.” The image of the poor welfare “taker” has a race and a gender in America.

In their book, Haskins and Sawhill proudly acknowledge that their cause was out of step with contemporary society. “To those who argue that this goal is old-fashioned or inconsistent with modern culture,” they wrote, “we argue that modern culture is inconsistent with the needs of children.” That may by a reasonable ideological position, but it’s no way to make public policy. The success sequence is a political meme repeated in highly similar form over more than a generation of public policy debates, without yet having any discernible impact for the better. The third “step” or “norm” in particular—marriage—has already been promoted with massive federal subsidies for almost two decades. The first two, education and jobs, are terrific ideas, obvious for good reasons, and not in need of much normative boosting, and we should turn our attention to improving the opportunity for more people to attain them.

* Thanks to Shawn Fremstad for this nugget.

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!

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* 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.

 

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):

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!

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!

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.)

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.

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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.