We won our First Amendment lawsuit against President Trump

unblocked

Federal judge Naomi Reice Buchwald ruled yesterday that the president is violating our First Amendment rights when he blocked me and six other plaintiffs for disagreeing with him on Twitter. The details and decision are available here. Congratulations and deep appreciation to the legal team at the Knight First Amendment Institute, especially Katie Fallow, Jameel Jaffer, Alex Abdo, and Carrie DeCell (sorry for those I’m missing).

I described my participation in the suit and my tweets last year here, and the oral arguments in March here.

Judge Buchwald’s introduction to the decision is great:

This case requires us to consider whether a public official may, consistent with the First Amendment, “block” a person from his Twitter account in response to the political views that person has expressed, and whether the analysis differs because that public official is the President of the United States. The answer to both questions is no.

She went on to issue declaratory relief, meaning she told the president he’s breaking the law, rather than injunctive relief (an order to act), writing:

It is emphatically the province and duty of the judicial department to say what the law is,” Marbury v. Madison, 5 U.S. (1 Cranch) 137, 177 (1803), and we have held that the President’s blocking of the individual plaintiffs is unconstitutional under the First Amendment. Because no government official is above the law and because all government officials are presumed to follow the law once the judiciary has said what the law is, we must assume that the President and [social media director Dan] Scavino will remedy the blocking we have held to be unconstitutional.

That remains to be seen, of course (I’m still blocked at this writing).

Here are a couple of snippets of analysis.

From Wired:

“In an age when we’re seeing so many norms broken by government regarding free speech, this is an important and right decision,” says [Danielle Citron, a law professor at the University of Maryland]. “It sends a message that we’re not going to destroy free speech norms.”

[David Greene, a senior staff attorney and civil liberties director at the Electronic Frontier Foundation] says he hopes the ruling warns other elected officials who are blocking constituents on social media to stop. “We routinely get a ton of people complaining to us about similar practices,” he says. “I hope they take it as a message that you have to stop doing this.”

From the Mercury News:

“The First Amendment prohibits government officials from suppressing speech on the basis of viewpoint,” said Katie Fallow, senior staff attorney at the institute, in a statement Wednesday. “The court’s application of that principle here should guide all of the public officials who are communicating with their constituents through social media.”

Erwin Chemerinsky, dean of Berkeley Law at UC Berkeley, agrees.

“The judge followed clear law: A government official cannot give selective access of this sort,” Chereminsky said.

From the San Francisco Chronicle:

Knight staff attorney Carrie DeCell said the organization was pleased with the decision, but expects the White House to appeal. “Twitter is a new communications platform, but First Amendment principles are foundations,” DeCell said. “Public discourse is increasingly taking place online.”

DeCell said the case could have implications for all public officials using social media — not just Trump’s account. “The reasoning in the court decisions, we think, should inform public officials’ activities on our social media pages throughout the country,” she said.

My co-plaintiffs have also written on the decision. See Rebecca Pilar Buckwalter Poza in Daily Kos:

Public officials are relying on social media more and more to communicate to constituents. As that shift accelerates, it’s imperative that courts recognize that the First Amendment protects against viewpoint discrimination in digital public forums like the @realdonaldtrump account just as it does in more traditional town halls. An official’s Twitter account is often the central forum for direct political debate with and among constituents, a tenet of democracy.

and Holly Figueroa O’Reilly in the Guardian:

Twitter is as public a forum as a town hall meeting. By blocking people who disagree with him, he’s not only blocking our right to petition our government and access important information, but he distorts that public forum by purging critical voices. It’s like a senator throwing someone out of a town hall because they held up a “disagree” sign.

The New York Times also did a piece on other people Trump blocked (the public doesn’t know how many such people there are), one of whom called the decision “incredibly vindicating.”

I agree. The decision is a breath of democracy fresh air.

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Fertility trends explained, 2017 edition

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

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

tfr4017

tfrchanges

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

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

change in birthrates by age 1989-2016.xlsx

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

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

country fertilitiy trends.xlsx

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

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

birth model simple 2016.xlsx

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

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

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


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

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

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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.

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Breaking: In 2017 names, Donald, Alexa, and Mary plummet; Malia booms

Time to update name trends, with the release of the 2017 data files from the Social Security Administration.

My hot take: Mary is back on the skids; Donald is going down, Alexa is over, and Malia shows that the resilience of humanity is not. Here are the details.

In Enduring Bonds I extend the Mary trend back to 1780, using Census data as well as Social Security records (and now is [always] an excellent time to get a review copy and consider it for your classes). The story is the mother of all naming trends, an unparalleled decline in name popularity, reflecting both the decline of conformity as an aesthetic and changes in how people see religion, parenting, and lots of other things. Then, for a couple years — 2013-2015 — it looked like maybe all the attention I gave the fate of Mary had prompted a revival, but now things are looking even bleaker than before, down another 4.3%. Here’s an updated version of the chart from the book:

mary names.xlsx

Meanwhile, the decline of The Donald has taken on a new urgency. Although the name has been taking for a long time (its association with unpleasant character didn’t start in 2016), but last year’s decline was impressive, at -4.3%. Not a cliff, but a solid slide (this one’s on a log scale so you can see the detail):

names.xlsx

You have to feel for people who named their daughters Alexa, and the Alexas themselves, before Amazon sullied their names. Did they not think of the consequences for these people? In the last year Alexa essentially ended as a (human) name, possibly the worst two-year case in U.S. history of name contamination. [Correction] Another bad year for Alexa. After a 21.3% drop in 2016, another 74% 19.5% last year:

alexa.xlsx

Finally, someone better tell the deplorables to start naming their daughters Ivanka, because in 2017 about nine-times more people are named their daughters Malia (1416) than Ivanka (167). Malia, up 15.4% last year:

names.xlsx

On my OSF project I’ve shared the names data, the Mary code (Stata), and SAS code for making individual name trends. The whole series of posts is under the names tag.

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

First preamble, then new analysis.

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

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

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

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

Note on race

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

Data illustration

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

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

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

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

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

Here are the details.

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

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

f1

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).

f2

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).

f3

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

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

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


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

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

marriage models.xlsx

 

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

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

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

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

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

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

spouse major matching

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

spouse matching which BAs marry down

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

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Unequal marriage markets for Black and White women

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

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

mmpif2

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

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

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

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

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