Tag Archives: single mothers

That thing where you have a lot of little graphs (single-parent edition)

Yesterday I was on an author-meets-critics panel for The Triple Bind of Single-Parent Families: Resources, Employment, and Policies to Improve Well-Being, a new collection edited by Rense Nieuwenhuis and Laurie Moldonado. The book is excellent — and it’s available free under Creative Commons license.

Most of the chapters are comparative, with data from multiple countries. I like looking at the figures, especially the ones like this, which give a quick general sense and let you see anomalies and outliers. I made a couple, too, which I share below, with code.


Here’s an example, showing the proportion of new births to mothers who aren’t married, by education, for U.S. states.  For this I used the 2012-2016 combined American Community Survey file, which I got from IPUMS.org. I created an sample extract that included only women who reported having a child in the previous year, which gives me about 177,000 cases over the five years. The only other variables are state, education, and marital status. I put the raw data file on the Open Science Framework here. Code below.

My first attempt was bar graphs for each state. This is easiest because Stata lets you do graph means with the bar command (click to enlarge).

marst fertyr educ by state

The code for this is very simple. I made a dummy variable for single, so the mean of that is the proportion single. Edcat is a four-category education variable.

gr bar (mean) single [weight=perwt], over(edcat) bar(1,color(green)) yti(“Proportion not married”) by(state)

The bar graph is easy, and good for scanning the data for weird cases or interesting stories. But maybe it isn’t ideal for presentation, because the bars run from one state to the next. Maybe little lines would be better. This takes another step, because it requires making the graph with twoway, which doesn’t want to calculate means on the fly. So I do a collapse to shrink the dataset down to just means of single by state and edcat.

collapse (mean) single psingle=single [fw=perwt], by(state edcat)

Then I use a scatter graph, with line connectors between the dots. I like this better:

marst fertyr educ by state lines

You can see the overall levels (e.g., high in DC, low in Utah) as well as the different slopes (flatter in New York, steeper in South Dakota), and it’s still clear that the single-mother incidence is lowest in every state for women with BA degrees.

Here’s the code for that graph. Note the weights are now baked into the means so I don’t need them in the graph command. And to add the labels to the scatter plot you have to specify you want that. Still very simple:

gr twoway scatter single edcat , xlab(1 2 3 4, valuelabel) yti(“Proportion not married”) lcolor(green) msymbol(O) connect(l) by(state)

Sadly, I can’t figure out how to put one title and footnote on the graph, rather than a tiny title and footnote on every state graph, so I left titles out of the code and I then added them by hand in the graph editor. Boo.

Here’s the full code:

set more off

quietly infix ///
 byte statefip 1-2 ///
 double perwt 3-12 ///
 byte marst 13-13 ///
 byte fertyr 14-14 ///
 byte educ 15-16 ///
 int educd 17-19 ///
 using "[PATHNAME]\usa_00366.dat"

/* the sample is all women who reported having a child in the previous year, FERTYR==2 */
replace perwt = perwt / 100

format perwt %10.2f

label var statefip "State (FIPS code)"
label var perwt "Person weight"
label var marst "Marital status"
label var educd "Educational attainment [detailed version]"

label define statefip_lbl 01 "Alabama"
label define statefip_lbl 02 "Alaska", add
label define statefip_lbl 04 "Arizona", add
label define statefip_lbl 05 "Arkansas", add
label define statefip_lbl 06 "California", add
label define statefip_lbl 08 "Colorado", add
label define statefip_lbl 09 "Connecticut", add
label define statefip_lbl 10 "Delaware", add
label define statefip_lbl 11 "District of Columbia", add
label define statefip_lbl 12 "Florida", add
label define statefip_lbl 13 "Georgia", add
label define statefip_lbl 15 "Hawaii", add
label define statefip_lbl 16 "Idaho", add
label define statefip_lbl 17 "Illinois", add
label define statefip_lbl 18 "Indiana", add
label define statefip_lbl 19 "Iowa", add
label define statefip_lbl 20 "Kansas", add
label define statefip_lbl 21 "Kentucky", add
label define statefip_lbl 22 "Louisiana", add
label define statefip_lbl 23 "Maine", add
label define statefip_lbl 24 "Maryland", add
label define statefip_lbl 25 "Massachusetts", add
label define statefip_lbl 26 "Michigan", add
label define statefip_lbl 27 "Minnesota", add
label define statefip_lbl 28 "Mississippi", add
label define statefip_lbl 29 "Missouri", add
label define statefip_lbl 30 "Montana", add
label define statefip_lbl 31 "Nebraska", add
label define statefip_lbl 32 "Nevada", add
label define statefip_lbl 33 "New Hampshire", add
label define statefip_lbl 34 "New Jersey", add
label define statefip_lbl 35 "New Mexico", add
label define statefip_lbl 36 "New York", add
label define statefip_lbl 37 "North Carolina", add
label define statefip_lbl 38 "North Dakota", add
label define statefip_lbl 39 "Ohio", add
label define statefip_lbl 40 "Oklahoma", add
label define statefip_lbl 41 "Oregon", add
label define statefip_lbl 42 "Pennsylvania", add
label define statefip_lbl 44 "Rhode Island", add
label define statefip_lbl 45 "South Carolina", add
label define statefip_lbl 46 "South Dakota", add
label define statefip_lbl 47 "Tennessee", add
label define statefip_lbl 48 "Texas", add
label define statefip_lbl 49 "Utah", add
label define statefip_lbl 50 "Vermont", add
label define statefip_lbl 51 "Virginia", add
label define statefip_lbl 53 "Washington", add
label define statefip_lbl 54 "West Virginia", add
label define statefip_lbl 55 "Wisconsin", add
label define statefip_lbl 56 "Wyoming", add
label define statefip_lbl 61 "Maine-New Hampshire-Vermont", add
label define statefip_lbl 62 "Massachusetts-Rhode Island", add
label define statefip_lbl 63 "Minnesota-Iowa-Missouri-Kansas-Nebraska-S.Dakota-N.Dakota", add
label define statefip_lbl 64 "Maryland-Delaware", add
label define statefip_lbl 65 "Montana-Idaho-Wyoming", add
label define statefip_lbl 66 "Utah-Nevada", add
label define statefip_lbl 67 "Arizona-New Mexico", add
label define statefip_lbl 68 "Alaska-Hawaii", add
label define statefip_lbl 72 "Puerto Rico", add
label define statefip_lbl 97 "Military/Mil. Reservation", add
label define statefip_lbl 99 "State not identified", add
label values statefip statefip_lbl

label define educd_lbl 000 "N/A or no schooling"
label define educd_lbl 001 "N/A", add
label define educd_lbl 002 "No schooling completed", add
label define educd_lbl 010 "Nursery school to grade 4", add
label define educd_lbl 011 "Nursery school, preschool", add
label define educd_lbl 012 "Kindergarten", add
label define educd_lbl 013 "Grade 1, 2, 3, or 4", add
label define educd_lbl 014 "Grade 1", add
label define educd_lbl 015 "Grade 2", add
label define educd_lbl 016 "Grade 3", add
label define educd_lbl 017 "Grade 4", add
label define educd_lbl 020 "Grade 5, 6, 7, or 8", add
label define educd_lbl 021 "Grade 5 or 6", add
label define educd_lbl 022 "Grade 5", add
label define educd_lbl 023 "Grade 6", add
label define educd_lbl 024 "Grade 7 or 8", add
label define educd_lbl 025 "Grade 7", add
label define educd_lbl 026 "Grade 8", add
label define educd_lbl 030 "Grade 9", add
label define educd_lbl 040 "Grade 10", add
label define educd_lbl 050 "Grade 11", add
label define educd_lbl 060 "Grade 12", add
label define educd_lbl 061 "12th grade, no diploma", add
label define educd_lbl 062 "High school graduate or GED", add
label define educd_lbl 063 "Regular high school diploma", add
label define educd_lbl 064 "GED or alternative credential", add
label define educd_lbl 065 "Some college, but less than 1 year", add
label define educd_lbl 070 "1 year of college", add
label define educd_lbl 071 "1 or more years of college credit, no degree", add
label define educd_lbl 080 "2 years of college", add
label define educd_lbl 081 "Associates degree, type not specified", add
label define educd_lbl 082 "Associates degree, occupational program", add
label define educd_lbl 083 "Associates degree, academic program", add
label define educd_lbl 090 "3 years of college", add
label define educd_lbl 100 "4 years of college", add
label define educd_lbl 101 "Bachelors degree", add
label define educd_lbl 110 "5+ years of college", add
label define educd_lbl 111 "6 years of college (6+ in 1960-1970)", add
label define educd_lbl 112 "7 years of college", add
label define educd_lbl 113 "8+ years of college", add
label define educd_lbl 114 "Masters degree", add
label define educd_lbl 115 "Professional degree beyond a bachelors degree", add
label define educd_lbl 116 "Doctoral degree", add
label define educd_lbl 999 "Missing", add
label values educd educd_lbl

recode educd (0/61=1) (62/64=2) (65/90=3) (101/116=4), gen(edcat)

label define edlbl 1 "<HS"
label define edlbl 2 "HS", add
label define edlbl 3 "SC", add
label define edlbl 4 "BA+", add
label values edcat edlbl

label define marst_lbl 1 "Married, spouse present"
label define marst_lbl 2 "Married, spouse absent", add
label define marst_lbl 3 "Separated", add
label define marst_lbl 4 "Divorced", add
label define marst_lbl 5 "Widowed", add
label define marst_lbl 6 "Never married/single", add
label values marst marst_lbl

gen married = marst==1 /* this is married spouse present */
gen single=marst>3 /* this is divorced, widowed, and never married */

gr bar (mean) single [weight=perwt], over(edcat) bar(1,color(green)) yti("Proportion not married") by(state)

collapse (mean) single psingle=single [fw=perwt], by(state edcat)

gr twoway scatter single edcat , xlab(1 2 3 4, valuelabel) yti("Proportion not married") lcolor(green) msymbol(O) connect(l) by(state)




Filed under Me @ work

More marriage promotion failure evidence

broken lightbulb

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.

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.


Filed under Me @ work

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.


Filed under Politics

The fathers behind teen births (or, statistical memes and motivated blind trust)

When makes people trust statistical memes? I don’t know of any research on this, but it looks like the recipe includes a combination of scientific-sounding specificity, good graphics, a source that looks credible, and – of course – a number that supports what people already believe (and want their Facebook friends to believe, too).

If that’s the problem, and assuming the market can’t figure out how to make journalism work, I have no solution except seizing the Internet and putting it under control of the Minister of Sociology, or, barring that, encouraging social scientists to get engaged, help reporters, and make all their good work available publicly, free, and fast.

Today’s cringe:


The blogger TeenMomNYC takes credit for creating this, and the Facebook version has been shared tens of thousands of times. Its popularity led to this story from Attn: “The Truth About Teenage ‘Baby Mamas’ is Quite Revealing.” (If anyone did want to study this issue, this is a neat case study, because she posted 8 “did you know” graphics on Facebook at the same time, and none of the others took off at all – why?)

I don’t know anything about TeenMomNYC, but I share her desire to stop stigmatizing and shaming young mothers. I wish her work were not necessary, but I applaud the effort. That said, I don’t necessarily think shaming young fathers (even if they’re not quite as young) is a solution to that, but that’s not the point. My point is, what is this statistic?

According to the footnote (thanks!), it comes from this 1995 National Academies report, and (except for changing “29” to “29.7”) it represents it accurately. From p. 205:

These data highlight an additional component of the sexual abuse picture— the evidence that an appreciable portion of the sexual relationships and resulting pregnancies of young adolescent girls are with older males, not peers. For example, using 1988 data from the NSFG and The Alan Guttmacher Institute, Glei (1994) has estimated that among girls who were mothers by the age of 15, 39 percent of the fathers were ages 20–29; for girls who had given birth to a child by age 17, the comparable figure was 53 percent. Although there are no data to measure what portion of such relationships include sexual coercion or violence, the significant age difference suggests an unequal power balance between the parties, which in turn could set the stage for less than voluntary sexual activity. As was recently said at a public meeting on teen pregnancy, “can you really call an unsupervised outing between a 13-year-old girl and a 24-year-old man a ‘date’?”

This is an important point, and was good information in 1995, when it cited a 1994 analysis of 1988 data, which asked women ages 15-44 a retrospective question. In other words, this refers to births that took place as early as 1958, or between 28 and 58 years ago. That is historical, and really shouldn’t be used like this today, given how much has changed regarding teen births.

The analysis is of the 1988 National Survey of Family Growth, a survey that was repeated as recently as 2011-2013. Someone who knows how to use NSFG should figure out the current state of the age gap between young mothers and fathers and let TeenMomNYC know.

Even if I didn’t know the true, current statistic, this would give me pause. Births to women before age 15 are extremely rare. The American Community Survey, which asks millions of women whether they have had a birth in the previous year, does not even ask the question of women younger than 15. The ACS reports there were 179,000 births in the previous year among women who were under 20 when interviewed, of which only 6,500 were to women age 15 at the interview. So that’s 3.7% of teen births, and 3 out of every thousand 15-year-old women. In 1958 this was much more common, and the social environment was much different.

Another issue is the age range of the fathers, 20-29, which is very wide when dealing with such young mothers. Look at the next phrase from the 1995 report: “girls who had given birth to a child by age 17, the comparable figure was 53 percent.” Realize that the great majority of girls who had a birth “by age 17” were 17 when they did, and the great majority of those men were probably close to 20. I’m not very positive about 20-year-old men having children with 17-year-old women, but it’s pretty different from 29-versus-13.

I can’t find the original source for this, but this report from the Resource Center for Adolescent Pregnancy Protection attributes this table to the California Center for Health Statistics in 2002, which shows that the father was age 20 or older  for 23% of women who had a birth before age 15. And of those, 93% were 20-24 (rather than 25+).


Anyway, this is a good case of a well-intentioned but under-resourced effort to sway people with true information, picked up by click-bait media and repeated because people think it will help them win arguments, not because they have any real reason to believe it’s true (or not true).

So I really hope someone with the resources, skills, and training to answer this question will produce the real numbers regarding father’s age for teen births, and post them, with accompanying non-technical language, along with their code, on the Open Science Framework (or other open-access repository).

Fixing the media and its economy is a tall order, but academics can do better if we put our energy into this work, reward it, and restructure our own system so that good information gets out better, faster and more reliably.

Related posts:


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I overspoke myself on Twitter

Possibly not the only time.

A blog called Random Critical Analysis (RCA) has posted, “On Philip Cohen’s knee-jerk response to Chetty’s “causal mobility” data and its association with single-motherhood.” I now must admit that I overspoke myself on Twitter.

But I think the blog post I wrote holds up OK. I complained in the post that the now-famous Chetty et al. analysis of intergenerational mobility had mishandled race, leading to people like David Leonhardt (and rightward from there) to conclude that the big story of hampered social mobility is family structure. It’s part of the overall pattern of polite society embracing the issue of economic inequality but also using that as a foil to avoid the issue of race inequality.

Brad Wilcox has seized on the Chetty analysis, repeating ad nauseum the quote that single parenthood is the “single strongest correlate of upward mobility.” My beef was, and is, that the analysis that was based on — which used the rate of single parenthood at the labor market level to predict intergenerational mobility — did not control for the racial composition of the labor market. That’s an obvious problem when your map of mobility looks like this:


When your analysis is ecological, that is, based only on aggregate characteristics, you have to be very cautious about drawing conclusions. It’s especially dicey in the Chetty case because the basic data, from tax, returns, includes family structure (because of parents’ marital status) but not race (which doesn’t go on your tax form). And that’s even more dicey because we know that at the individual level single parenthood is definitely not the “single strongest correlate of upward mobility.” I’ve been writing about this for years (follow the single-mother tag), but this figure from 2012 sums it up nicely (details in the old post):

You just have to keep that in perspective when you jump to an aggregate-level analysis. The difference between averages in Atlanta versus Salt Lake City — important as it is — is never going to be as big as the difference between a rich family and a poor family. Social parents’ class matters much more for determining children’s social class than does family structure.

Anyway, RCA is reworking my very simple analysis showing the effect of single motherhood rates was reduced by two-thirds when a single control for racial composition (percent Black) was added. That’s making the obvious point that, because single parenthood and percent Black in the local area are so strongly correlated, if you don’t take percent Black into account it looks like single parenthood has a huge, independent effect — which incorporates the effects of racism or other community factors associated with historical race composition. The new RCA post goes much further in the analysis, and concludes:

It ought to be pretty clear by now single-motherhood is capturing something quite powerful and that, contrary to Cohen’s strong assertions, it is not well explained by race.  If anything, single-motherhood mediates the black association much better than the reverse.

I’m not persuaded by the conclusion; you can evaluate it yourself. But the premise of the RCA post is actually not my blog post, but my tweets. As time went by I apparently became frustrated at the continued repetitions of the single mother thing by people who were ignoring my very clever post, and with the carelessness that distance allows I overstated my own claim, so I tweeted this,

The table and the highlighting are mine. What I should have paid attention to was my own next sentence after the underlined part: “That’s not an analysis, it’s just an argument for keeping percent Black in the more complex models.” I didn’t do a serious analysis — I just did enough to prove the point that racial composition should be in the model. Without that, you shouldn’t run around saying single parenthood is the most important factor. (RCA also believes I shouldn’t have said in the post that “Percent Black statistically explains the relationship between single motherhood and intergenerational immobility.”  I think “explains” is defensible, in that the effect is no longer statistically distinguishable from zero at the conventional level, but it’s clearly not the same as proving there is no effect, so I’ll take the criticism, too.)

I actually first did the little analysis in an earlier post, debunking a univariate analysis by Scott Winship and Donald Schneider. In that case I concluded: “This [my analysis] is not a rigorous examination of the cause of intergenerational immobility. It is just debunking one bivariate story that is too easily picked up by the forces of bad.” That seems about right.

Anyway, in conclusion, it was incorrect based on what I did for me to tweet, “the single mother effect in Chetty is all in the % Black effect.” I should just say single parenthood hasn’t been proven to matter as much as its partisans say it has. Even if it’s less effective in a tweet. This is a common frustration, that it takes more work to debunk something than to bunk it in the first place. But that’s not a good excuse.

Finally, I’m grateful that what I write matters enough that someone would go to the trouble of testing my claims to hold me accountable.


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Lifetime chance of marrying for Black and White women

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

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

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

Predictions off

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

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

Mass incarceration

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

Some new projections

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

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

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

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

NHBW life tables 2010.xlsx

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

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

NHBW life tables 2010.xlsx

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

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

NHBW life tables 2010.xlsx

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

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

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

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

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


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Vox interview on the Moynihan chilling effect

Jenée Desmond-Harris from Vox.com interviewed me about the Moynihan backlash post. The piece is here. In it she links to this blog, but not to the specific post. If you’re looking for that, it’s here.

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