This is an update of previous reports with some new analysis at the end.
In my book Enduring Bonds I showed the distribution of income within different-sex married couples from 1970 to 2014. Here is the updated trend to 2017:
The change from 2014 is a modest continuation. Here’s the detail from 2017, with the couples reporting exactly-even incomes broken out in the middle:
In 2017, for different-sex couples with wife age 18-64:
26% of wives earn more than their husbands (up from 15% in 1990 and 7% in 1970).
The average wife-who-earns-more takes home 69% of the couple’s earnings. The average for higher-earning husbands is 79%.
It is 8.3-times more common for a husband to earn all the money than a wife (18.7% versus 2.3%).
In the book I offer the following summary:
Actually, this triplet pattern fits a lot of trends regarding gender inequality: yes, lots of change, but most of it decades ago, and not quite as fundamental as it looks.
New breakdown
At the request of Stephanie Coontz, I ran the 2017 numbers by income bracket (and including all ages). I broke the couples into the bottom 10% (under $27,000), the 10-25th percentile (to $47,000), the 25th-5th (to $80,000), the 50th-75th (to $130,000), the 75th-90th (to $202,000), and the top 10% ($202,000+). Here is the income distribution within couples for each income bracket, with a few points labeled for clarity:
A key point here is that although wives rarely earn the dominant share of income, most couples rely on the wife’s income to maintain their standard of living. For example, a couple at the median, $80,000, would have to drastically alter their lifestyle without the 40-49% share contributed by the wife’s income. Breadwinning in its 1950s connotation is is distracting from this contemporary reality, and we should probably drop the term.
In demography, there is a well-known phenomenon known as age-heaping, in which people round off their ages, or misremember them, and report them as numbers ending in 0 or 5. We have a measure, known as Whipple’s index, that estimates the extent to which this is occurring in a given dataset. To calculate this you take the number of people between ages 23 and 62 (inclusive), and compare it to five-times the number of those whose ages end in 0 or 5 (25, 30 … 60), so there are five-times as many total years as 0 and 5 years.
If the ratio of 0/5s to the total is less than 105, that’s “highly accurate” by the United Nations standard, a ratio 105 to 110 is “fairly accurate,” and in the range 110 to 125 age data should be considered “approximate.”
I previously showed that the American Community Survey’s (ACS) public use file has a Whipple index of 104, which is not so good for a major government survey in a rich country. The heaping in ACS apparently came from people who didn’t respond to email or mail questionnaires and had to be interviewed by Census Bureau staff by phone or in person. I’m not sure what you can do about that.
What about marriage?
The ACS has a great data on marriage and marital events, which I have used to analyze divorce trends, among other things. Key to the analysis of divorce patterns is the question, “When was this person last married?” (YRMARR) Recorded as a year date, this allows the analyst to take into account the duration of marriage preceding divorce or widowhood, the birth of children, and so on. It’s very important and useful information.
Unfortunately, it may also have an accuracy problem.
I used the ACS public use files made available by IPUMS.org, combining all years 2008-2017, the years they have included the variable YRMARR. The figure shows the number of people reported to have last married in each year from 1936 to 2015. The decadal years are highlighted in black. (The dropoff at the end is because I included surveys earlier than those years.)
Yikes! That looks like some decadal marriage year heaping. Note I didn’t highlight the years ending in 5, because those didn’t seem to be heaped upon.
To describe this phenomenon, I hereby invent the Decadally-Biased Marriage Recall index, or DBMR. This is 10-times the number of people married in years ending in 0, divided by the number of people married in all years (starting with a 6-year and ending with a 5-year). The ratio is multiplied by 100 to make it comparable to the Whipple index.
The DBMR for this figure (years 1936-2015) is 110.8. So there are 1.108-times as many people in those decadal years as you would expect from a continuous year function.
Maybe people really do get married more in decadal years. I was surprised to see a large heap at 2000, which is very recent so you might think there was good recall for those weddings. Maybe people got married that year because of the millennium hoopla. When you end the series at 1995, however, the DBMR is still 110.6. So maybe some people who would have gotten married at the end of 1999 waited till New Years day or something, or rushed to marry on New Year’s Eve 2000, but that’s not the issue.
Maybe this has to do with who is answering the survey. Do you know what year your parents got married? If you answered the survey for your household, and someone else lives with you, you might round off. This is worth pursuing. I restricted the sample to just those who were householders (the person in whose name the home is owned or rented), and still got a DBMR of 110.7. But that might not be the best test.
Another possibility is that people who started living together before they were married — which is most Americans these days — don’t answer YRMARR with their legal marriage date, but some rounded-off cohabitation date. I don’t know how to test that.
The divorce paper I posted two weeks ago, “The Coming Divorce Decline,” suddenly took off in the media the other day (blog post | paper | data and code). I’ve now written an op-ed about the findings for The Hill, including this:
I am ambivalent about these trends. Divorce is often painful and difficult, and most people want to avoid it. The vast majority of Americans aspire to a lifelong marriage (or equivalent relationship). So even if it’s a falling slice of the population, I’m not complaining that they’re happy. Still, in an increasingly unequal society and a winner-take-all economy, two-degree couples with lasting marriages may be a buffer for the select few, but they aren’t a solution to our wider problems.
Here’s my media scrapbook, with some comment about open science process at the end.
The story was first reported by Ben Steverman at Bloomberg, who took the time to read the paper, interview me at some length, send the paper to Susan Brown (a key expert on divorce trends) for comment, and produce figures from the data I provided. I was glad that his conclusion focused on the inequality angle from my interpretation:
“One of the reasons for the decline is that the married population is getting older and more highly educated,” Cohen said. Fewer people are getting married, and those who do are the sort of people who are least likely to get divorced, he said. “Marriage is more and more an achievement of status, rather than something that people do regardless of how they’re doing.”
Many poorer and less educated Americans are opting not to get married at all. They’re living together, and often raising kids together, but deciding not to tie the knot. And studies have shown these cohabiting relationships are less stable than they used to be.
Fewer divorces, therefore, aren’t only bad news for matrimonial lawyers but a sign of America’s widening chasm of inequality. Marriage is becoming a more durable, but far more exclusive, institution.
The Bloomberg headline was, “Millennials Are Causing the U.S. Divorce Rate to Plummet.” Which proved irresistible on social media. I didn’t use the terms “millennials” (which I oppose), or “plummet,” but they don’t fundamentally misrepresent the findings.
Naturally, though, the Bloomberg headline led to other people misrepresenting the paper, like Buzzfeed, which wrote, “Well, according to a new study, millennials are now also ‘killing’ divorce.” Neither I nor Bloomberg said anyone was “killing” divorce; that was just a Twitter joke someone made, but Buzzfeed was too metameta to pick up on that. On the other hand, never complain about a Buzzfeed link, and they did link to the paper itself (generating about 800 clicks in a few days).
Then Fox 5 in New York did a Skype interview with me, and hit the bar scene to talk over the results (additional footage courtesy of my daughter, because nowadays you provide your own b-roll):
The next day Todaydid the story, with additional information and reporting from Bowling Green’s National Center for Family and Marriage Research, and Pew.
The Maryland news office saw the buzz and did their own story, which helped push it out.
An article in Atlantic featured an interview with Andrew Cherlin putting the trends in historical context. Rachelle Hampton in Slate tied the divorce trend to a Brookings report showing marriage is increasingly tied to higher education. On KPCC, AirTalk hosted a discussion with Megan Sweeney and Steven Martin. On Wisconsin Public Radio, Stephanie Coontz widened the discussion to put changes in marriage and divorce in historical perspective.
Rush Limbaugh read from the Bloomberg article, and was just outraged: “Now, who but deranged people would look at it this way?”
How anybody thinks like this… You have to work to be this illogical. I don’t know where this kind of thing comes from, that a plummeting divorce rate is a bad sign for America in the left’s crazy world of inequality and social justice and their quest to make everybody the same. So that’s just an example of the… Folks, that is not… That kind of analysis — and this is a sociology professor at the University of Maryland. This is not stable. That kind of thinking is not… It’s just not normal. Yet there it is, and it’s out there, and it’s be widely reported by the Drive-By Media, probably applauded and supported by others. So where is this coming from? Where is all of this indecency coming from? Why? Why is it so taking over the American left?
The Limbaugh statement might have been behind this voicemail I received from someone who thinks I’m trying to “promote chaos” to “upend the social order”:
I had a much more reasonable discussion about marriage, divorce, and inequality in this interview with Lauren Gilger in KJZZ (Phoenix public radio).
The Chicago Tribuneeditorial board used the news to urge parents not to rush their children toward marriage:
This waiting trend may disturb older folks who followed a more traditional (rockier?) path and may be secretly, or not so secretly, wondering if there’s something wrong with their progeny. There isn’t. Remember: Unlike previous generations, many younger people have a ready supply of candidates at their fingertips in the era of Tinder and other dating apps. They can just keep swiping right. Our advice for parents impatient to marry off a son or daughter? Relax. The older they get, the less likely you’ll be stuck paying for the wedding.
The Catholic News Agency got an expert to chime in, “If only we could convince maybe more of them to enter into marriage, we’d be doing really well.”
I don’t know how TV or local news work, but somehow this is on a lot of TV stations. Here’s a selection.
Fox Business Network did a pretty thorough job.
Some local stations added their own reporting, like this one in Las Vegas:
And this one in Buffalo:
And this one in Boise, which brought in a therapist who says young people aren’t waiting as long to start couples therapy.
Jeff Waldorf on TYT Nation did an extended commentary, blaming capitalism:
Open science process
Two things about my process here might concern some people.
The first is promoting research that hasn’t been peer reviewed. USA Today was the only report I saw that specifically mentioned the study is not peer reviewed:
The study, which has not been published in a peer-reviewed journal, has been submitted for presentation at the 2019 Population Association of America meeting, an annual conference for demographers and sociologists to present research.
But, when Steverman interviewed me I emphasized to him that it was not peer-reviewed and urged him to consult other researchers before doing the story — he told me he had already sent it to Susan Brown. Having a good reporter consult a top expert who’s read the paper is as good a quality peer review as you often get. I don’t know everything Brown told him, but the quote he used apparently showed her endorsement of the main findings:
“The change among young people is particularly striking,” Susan Brown, a sociology professor at Bowling Green State University, said of Cohen’s results. “The characteristics of young married couples today signal a sustained decline [in divorce rates] in the coming years.”
For the story to be clear enough to become a news event, the research often has to be pretty simple. That’s the case here: what I’m doing is looking at an easily-identified trend and providing my interpretation of it. If this has to be peer-reviewed, then almost anything an academic says should be. Of course, I provided the publicly verifiable data and code, and there are a lot of people with the skills to check this if it concerned them.
On the other hand, there is a lot of research that is impossible to verify that gets reported. Prominent examples include the Alice Goffman ethnographic book and the Raj Chetty et al. analysis of confidential IRS data. These were big news events, but whether they were peer reviewed or not was irrelevant because the peer reviewers had no way to know if the studies were right. My conclusion is that sharing research is the right thing to do, and sharing it with as much supporting material as you can is the responsible way to do it.
The second concern is over the fact that I posted it while it was being considered for inclusion in the Population Association of America meetings. This is similar to posting a paper that is under review at a journal. Conference papers are not reviewed blind, however, so it’s not a problem of disclosing my identity, but maybe generating public pressure on the conference organizers to accept the paper. This happens in many forms with all kinds of open science. I think we need to see hiding research as a very costly choice, one that needs to be carefully justified — rather than the reverse. Putting this in the open is the best way to approach accountability. Now the work of the conference organizers, whose names are listed in the call for papers, can be judged fairly. And my behavior toward the organizers if they reject it can also be scrutinized and criticized.
Although I would love to have the paper in the conference, in this case I don’t need this paper to be accepted by PAA, as it has already gotten way more attention than I ever expected. PAA organizers have a tough job and often have to reject a lot of papers for reasons of thematic fit as well as quality. I won’t complain or hold any grudges if it gets rejected. There’s a lot of really good demography out there, and this paper is pretty rudimentary.
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.
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).
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.
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 bookCreating 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.
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.
