We won our First Amendment lawsuit against President Trump

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.

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:

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):

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:

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:

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.

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

First preamble, then new analysis.

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

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

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

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

Note on race

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

Data illustration

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

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

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

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

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

Here are the details.

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

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

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

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

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

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

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

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

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

 

Unequal marriage markets for Black and White women

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

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

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

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

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

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

Let’s improve the ASA/Sage journal author agreement

I have spoken well of the policy that permits authors to post preprint versions of their papers before submitting them to journals of the American Sociological Association. That means you can get your work out more broadly while it’s going through the review process. The rule says:

ASA authors may post working versions of their papers on their personal web sites and non-peer-reviewed repositories. Such postings are not considered by ASA as previous publication.

The policy goes on to ask that authors modify their posted papers to acknowledge publication if they are subsequently published. That’s all reasonable. This is why SocArXiv and other services offer authors the opportunity to link their papers to the DOI (record locator) for the published version, should it become available. This allows citation aggregators such as Google Scholar link the records.

Unfortunately, the good part of this policy is undermined by the ASA / Sage author agreement that authors sign when their paper is accepted. It transfers the copyright of the paper to ASA, and sets conditions under which authors can distribute the paper in the future. The key passage here is this:

1. Subject to the conditions in this paragraph, without further permission each Contributor may …

• At any time, circulate or post on any repository or website, the version of the Contribution that Contributors submitted to the Journal (i.e. the version before peer-review) or an abstract of the Contribution.
• No sooner than 12 months after initial publication, post on any non-commercial repository or website the version of the Contribution that was accepted for publication.

This is not good. It means that if you post a paper publicly, e.g., on SocArXiv, and then submit it to ASA, you can’t update it to the revised version as your paper moves through the process. Only 12 months after ASA publishes it can you update the preprint version to match the version that the journal approved.

This policy, if followed, would produce multiple bad outcomes.

One scenario is that people post papers publicly, and submit them to ASA journals for review. Over the course of the next year or so, the paper is substantially revised and eventually published, but the preprint version is not updated until a full year after that, often two years after the initial submission. That means readers don’t get to see the improved version, and authors have to live with people reading and sharing their unimproved work. This discourages people from sharing their papers in the first place.

In the other scenario, people update their preprints as the paper goes through the revision process, so they and their readers get the benefit of access to the latest work. However, when the paper is accepted authors are expected to remove from public view that revised paper, and only share the pre-review version. If this were feasible, it would be terrible for science and the public interest, as well as the author’s career interests. Of course, this isn’t really feasible — you can’t unring the bell of internet distribution (SocArXiv and other preprint services do not allow removing papers, which would corrupt the scholarly record.) This would also discourage people from sharing their papers in the first place.

So, what possible reason can there be for this policy? It is clearly intended to punish the public in order to buttress the revenue stream of Sage, which returns some of its profits to ASA, at the expense of our libraries, which pay for subscriptions to ASA journals.

I assume this policy is never enforced, as I’ve never heard of it, but I don’t know that for a fact. It’s also possible that whoever wrote the Publications policy I linked above didn’t realize that it contradicted the Sage author agreement, which basically no one reads. I also assume that such a policy does not in fact have any effect on Sage’s profits, or the profits that it kick backs to ASA. So it’s probably useless, but if it has any effects at all they’re bad, by discouraging people from distributing their work. ASA should change this author agreement.

I will be on the ballot for the ASA Publications Committee this spring. If elected, I will add making this change to my platform, which I outlined here. If I’m not elected, I’ll try to do this anyway.

Review essay: Public engagement and the influence imperative

I have written a review essay at the invitation of Contemporary Sociology. Here’s a preprint version on SocArXiv: https://osf.io/preprints/socarxiv/v27xk/.

This is the abstract. Feedback welcome!

Public engagement and the influence imperative

Abstract: A review essay discussing three advice books for social scientists. Sociologists, in responding to the imperative to make their work more influential, must go beyond doing “public sociology” to embrace doing sociology “in public” (Healy 2017). Rather than using public engagement primarily for publicity – to make our research matter – we should use engagement to help us do research that matters in the first place. Next, I caution that the drive to be professionally rewarded for public intellectualism is fraught with conflicts that may be irreconcilable. To be a public intellectual today requires being both public in one’s intellectual life and intellectual in one’s public life, and for academics in the era of the “market university” (Berman 2011), trying to get paid for that leads to a neoliberal trap. Finally, I argue for a move beyond personal strategies toward the development of the open scholarship as an institutional response that ultimately may be responsible for sociology’s survival.

Here is the SocArXiv citation:

Cohen, Philip N., 2018. “Public Engagement and the Influence Imperative”. SocArXiv. April 7. doi:10.17605/OSF.IO/V27XK.

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

Kid photo CC from MB Photography; collage by pnc.

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

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

A divorce story

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

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

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

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

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

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

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

From my presentation, “The open scholarship media strategy for maximum success.”

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

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