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 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)



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Against Trump’s family separation policy


The policy of separating parents from their children when they enter the country without permission has generated a spike of outrage and shock that’s actually noticeable over the background level of outrage and shock.

At the Council on Contemporary Families we don’t take formal policy positions or make partisan appeals, but the board (on which I sit) decided to organize a statement of opposition for individual family researchers and experts to sign. We passed a hundred signatories after the first few hours. You can sign it here, or view the list of signatories here. Here’s the text, and then I have a few comments.

Family Scholars and Experts Statement of Opposition to Policy of Separating Immigrant Families

We write as family scholars and experts to express our opposition to the Trump Administration policy of separating immigrant parents and children at the border as they enter the United States to seek refuge. This practice is an inhumane mistreatment of those seeking refuge from danger or persecution, and goes against international law. As scholars and experts devoted to identifying and sharing information relevant to policies to improve individual and family wellbeing, we deplore the Administration’s callous disregard of the overwhelming scientific information demonstrating the harm of separating children from their parents. This practice is known to be extremely traumatic for dependent children who stand a strong likelihood of experiencing lasting negative consequences from the sudden and inexplicable loss of their caregiver. Government should only separate children from their parents as a last resort when children are in danger of imminent harm. We urge the Administration to reconsider and reverse this policy. Although the Council on Contemporary Families (CCF) as an organization does not take partisan positions or advocate for policy, the CCF Board has decided to circulate this statement so that individual like-minded scholars and experts may join together to express their views publicly.


The policy has been a vivid showcase of human cruelty, racist political manipulation, hypocrisy, and misdirection.

The human cruelty is most important. The people working for the U.S. government that carry out this policy seem to be no more or less evil than rank-and-file Nazi concentration camp guards. They rip children from the arms of their parents — parents risking life and limb to give their children a chance at safety, or a better life — sometimes under false pretenses, and rationalize their actions as somehow in the service of social order, or the law, or the will of their superiors. Human-tip: quit your job before you follow such orders.

The racist political manipulation comes from the top, where Trump and his legions of lying liars repeat lies about illegal immigrants overrunning our borders, bringing violence and mayhem and taking American jobs and welfare. These lies find fertile ground in the consciousness of people who already don’t consider Latino immigrants to deserving of basic human rights and protections because they don’t see their humanity. Things I’ve heard on Twitter from supporters of the policy include:

The hypocrisy is well represented by the invocation of the Bible to justify these atrocities, a literal chapter and verse repetition of the godless defenses of slavery, Nazism, and apartheid perpetrated by Trumpism’s (recent) ancestors. In the typical up-is-down-wrong-is-right formulation of Trumpism, Elizabeth Bruenig writes, “[Jeff] Sessions and [Sarah] Sanders radically depart from the Christian religion, inventing a faith that makes order itself the highest good and authorizes secular governments to achieve it.”

The misdirection runs beneath all Trumpism’s atrocities, in this case simply inventing a story that the current policy is the result of Democrats’ “horrible and cruel legislative agenda.” This is part of the demagoguery playbook, which predictably cycles from it’s-not-true to it’s-no-big-deal to Obama-was-worse to nothing matters. (When I tweeted a link to the statement above, a Trump supporter asked, “They do realize they’re here illegally?” and then, “So why the hard push now except to smear the President?”) “We are following the law,” said federal prosecutor Ryan Patrick, before possibly accidentally confirming, “Well, it is a policy choice by the president and by the attorney general.”


Patrick’s interview is a nauseating testament to how this authoritarianism is corrupting human integrity, as he describes the policy as an attempt to restore fairness to law enforcement:

“I’ve heard the attorney general say – it is not – in his estimation, it is not equitable or fair to simply, like I said, wave a wand over an entire population of crossers just because they come in in a family unit or they have a child with them and we simply ignore them on the criminal prosecution. They’re still crossing the border illegally.”

And what about the documented atrocities?

“I think some of these stories are outliers. This is not the norm. I don’t think this is a standard operating procedure on how all of the agents conduct their business. There’s going to be some situations that are going to be regrettable or that break your heart or – and it is unfortunate.”

OK, so not everyone experiences the very worst abuses. And what about the legal protections of the accused and their separated children?

“So when apprehended, if they’re a family unit, they’re given a card in English and in Spanish that has different 1-800 numbers for them to be able to contact. And there’s also a text line. There’s an email address, if they have access to those in their different holding facilities, where they can track not only their own case but also the location of their child.”

OK, so, Kafka. And about that due process for children?

“And then, when it comes to the juveniles who are in HHS custody, there are some space limitations with attorneys. At any time in the process, they can hire their own attorney.”

And finally, putting it all together: it’s not so bad, but really it’s their fault, and law and order, so.

So, obviously, there are still family units being broken up. But the average stay of those children in those facilities is less than 20 days. It would be – it would be incredibly difficult, if I was a parent, to see my child one of the situations. But at the same time, it also is difficult to wrap my mind around – and I’m not in their situation – but they’re also taking incredible risk to their own life and safety on crossing the border illegally in the way that they do, with their children, and putting them in danger.

This policy is the bad turning the blind against the innocent. It’s vile and inhumane. No one has to tolerate this system of atrocities, and that includes all of us.


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Actually unblocked by Trump, as DOJ appeals ruling

We won our lawsuit on May 23, with a federal judge ruling that Trump blocking me and seven others violates the First Amendment. Now, as of June 4, I am actually unblocked by the president’s @realdonaldtrump account, as are the other plaintiffs. At the same time, the Department of Justice filed a notice of their intent to appeal the ruling to the United States Court of Appeals for the Second Circuit.


Meanwhile, an unknown number of other people remain blocked by the president. The Knight First Amendment Institute, which is representing us, has asked other people who are blocked to contact them at: I would love this case to end up extending to others blocked by Trump, and other public officials.


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More marriage promotion failure evidence

broken lightbulb

Photo PNC / Flickr CC:

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.


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Breaking: Matt Richtel book homepage bogus statistic removed

For at least three years, the website for New York Times reporter Matt Richtel’s book, A Deadly Wandering, about the dangers of texting and driving, has prominently featured a bogus internet meme statistic claiming that 11 teens per die from texting and driving accidents every day. I first debunked it in 2014, by simply pointing out that not even 11 teens die per day from all auto accidents regardless of cause.

I wrote about it again here. I also complained that Richtel had a financial interest in hyping teen texting deaths, and that it was unreasonable to say traffic fatalities were “soaring at a rate not seen in 50 years,” when in fact fatalities were almost at a 50-year low (down more than 60% from 1966, on a per capita basis, and still below the pre-recession levels).

I emailed Richtel, as well as the publisher. I tweeted. All to no avail — until sometime between last September (the last archived copy at the Wayback Machine) and today, when I saw they had finally removed the bogus statistic. Here’s the change:

richtel fixed

The footnote stayed the same, which is funny because it’s not a “statistic” anymore (it never was on the IIHSFF site).

Anyway, because I complained so much it’s important to acknowledge the change.

Meanwhile, while Richtel and his publisher were taking three years to do 10 minutes work to correct an egregious factual error, the meme was still going around. I happened to see it today as I was reading an editorial in the Moscow-Pullman (Idaho) Daily News, in support of our lawsuit against Trump (long story), when I saw this letter:

Letter: Texting while driving is more lethal than school shootings
May 29, 2018

Kudos to the Daily News Editorial Board for having the courage to state (“Our View: Gun reform alone can’t prevent mass killings,” May 23) “it is not the guns killing people, it is the people pulling the trigger …” It sounds like something the NRA would say. And the real problem facing us is ” how to prevent weapons from getting into the wrong hands ” As a longtime NRA member I support all rational steps taken to do exactly that.

Blaming the NRA or gun manufacturers for school shooting deaths is akin to blaming Facebook and/or Apple iPhones and/or Ford Motor Company for teen texting-while-driving deaths, which some reports say cause an average of 11 teen deaths in America every day. It’s not Facebook or the cellphone or the automobile maker that runs that car through the red light or up a tree. It’s the distracted person behind the wheel. Let’s see what kind of reaction we get when we try to separate those young people from their cellphones for their own safety and that of those in the car with them. Mom and dad, have at it.

Texting while driving is vastly more lethal to our teens than school shootings.

Bill Tozer, Moscow

Bogus statistical memes have consequences.

See all the texting posts under this tag.

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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.


Filed under In the news, Me @ work

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:



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 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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