Category Archives: Me @ work

What if you left your kid alone with YouTube


Google Image search for “crazy youtube videos”

YouTube is the educator entertainer that never sleeps. One video leads to the next, literally forever. (YouTube does have a kids channel which is supposed to be a safe space for kids.) They have “YouTube Kids,” which was supposed to help reassure parents. But if your kid is at a random computer and just goes to, or clicks on a link and ends up there, they’re off down Recommendation Alley.

In response to fears that YouTube was promoting bad things to children, unintentionally or not, and thinking about a possible sociology class exercise, I decided to do an exercise where I start from a Disney princess video and then select from one of the top-10 recommended videos on each page to try to get to things that are bad for children. (In the possibly-vain hope that my experiment wouldn’t be contaminated by my own use history, I used an incognito window without logging in to Google.) My goal was Nazi propaganda, and my strategy was to aim for adult stuff, then look out for disturbing, racist, or violent content. As children do.

I gave up after 113 videos, without getting to Nazi stuff. I would love to know — as YouTube surely does — how children really use YouTube when no one’s looking. I know from limited experience they click around a lot — covering a lot of videos in a short time — and they don’t vet their “content” carefully. So this seems like a plausible browsing session. Anyway, still thinking about how to do something like this, and thought I’d share my notes here:

How fast can I get to Nazi stuff from “Disney kids” using videos from the first 10 recommended? (Spoiler, I couldn’t, but still.)

After searching for “Disney kids,” I chose this innocent Disney Princess video, and starting clicking on recommendations.

  1. Kids Makeup Disney Princesses Pretend Play with Cleaning Toys & Real Princess Dresses
  2. Emily Became a Princess-Real Princess Dresses
  3. PRINCESS SCHOOL TEST 🎓 Lilliana Helps Isabella To Cheat! – Princesses In Real Life | Kiddyzuzaa
  4. Kaycee and Rachel in Wonderland # 26
  5. 24 Hours in Box Fort Jail Challenge! 24 Hour Challenge with No LOL Dolls

By #6 I’ve gotten as far as icky

  1. Father & Son PLAY DON’T STEP IN IT! / Avoid The Poo!
  2. Escaping Hello Neighbors Maximum Security Box Fort Prison / Jake and Ty
  3. 9 Weird Ways To Sneak Food Into Class / Summer Pranks!

A reference to a shooter video by #9. This leads into family conflict…

  1. FORTNITE DANCE CHALLENGE !! In Real Life With Ckn Toys
  3. Sister VS Brother Battle!
  4. Katherine is a Barbie!
  5. How To Remove White Marks From Your Baby Alive! Commercial Style
  6. Lalaloopsy school adventure episode 1: bullying!
  7. Sketch Smell Challenge
  9. Older Siblings vs Younger Siblings!! Sisters Trinity and Madison
  10. EXPECTATIONS vs REALITY of Having a Sibling
  12. Amelia and Avelina beach vacation adventure
  15. Is Nicole a Zombie, Forever?
  16. The Girl Who Collects Cockroaches | My Kid’s Obsession
  17. QUEEN BABY: Bath Time
  18. Power Tool Wins and Fails

First real violence by #27. Not that bad, just a skateboard injury, which introduces the people-behaving-badly-in-real-life genre

  1. Skateboarder Crashes into Kid
  2. Lady Yells at Kid on Alpine Slide in Winter Park CO (Fight)
  3. ex-wife acting out in front of kids
  4. Baby Mama manipulating again (Arizona)
  6. CPS murdered my family
  7. CPS Supervisor Calls Parents “White Trash”!!!
  8. Crazy lady at skate park
  9. Crazy lady yells at kids for standing on table
  10. Lady yells at kids
  11. Super mad bus driver and kid trys to escape
  13. Bus Driver Kicks Girl Out of Bus.Miles Away from Home
  14. Mean bus driver
  15. Creepy little girl brings me to bathroom stall and locks the door
  16. My humps remix (Barbie and crazy)
  17. Two girls fighting
  18. Two boys and two girls fighting
  19. This wat happen when a 2nd and 4th grader fight
  20. 3rd Grade fight in school
  21. Bullying 3rd Grade
  23. Little kids fight
  24. 8 vs 10 year old fighting
  25. 8 year old vs 13 year old fighting

Trying to get out of the kids-fighting loop, I chose this one, which led to stuff for parents…

  1. Kid Pukes at Dentist after Getting Mold Removed
  2. 10 year old Isabella shouldn’t know The ‘C’ Word #LyttleFight
  3. Slap Her
  4. Doll test – The effects of racism on children (ENG)
  5. Disturbingly Racist Moments in Cartoons
  6. Top 10 Insanely Racist Moments In Disney Movies That You Totally Forgot About
  7. 10 Dark Theories About Dead Disney Characters
  8. Sausage Party: 10 Important Details You Totally Missed
  9. 15 Moms You Won’t Believe Actually Exist
  10. Most Inappropriate Children Coloring Book Drawings!
  11. Bunk’d Stars ★ Before And After

Somehow this led to freaky or scary images and general danger…

  1. 10 STRONG KIDS That Can Lift More Than You
  2. World’s Strongest Kids Girl
  3. Remember This Viral Photo Of A Nigerian ‘Witch’ You Should See Him Now
  4. 10 SHOCKING Incidents When Kids Left Alone With Pets
  5. 10 Times TOYS Got Kids In TROUBLE With Police Officers
  6. ILLEGAL and BANNED Fidget Spinners
  9. Creepy texts from babysitter.. | TEXT STORY REACTION

No idea why this Trump parody was here but I thought it might lead to more political content. Instead it took me into a video game loop, which I only got out of by going back to bad parenting…

  1. SAVE TRUMP! \ Mr. President
  2. Realistic Minecraft – Highschool Girlfriend ❤
  3. You Can’t Say No To Ella!
  5. What would your kid do if they found a gun?
  6. Kids found home alone
  7. 19 kids found alone in filthy, hot Kentucky home
  8. Baby Buried Alive
  9. Newborn baby found abandoned near Tampa intersection
  10. angry lady yells at kid for no reason…
  11. Man slaps crying baby in it’s mothers arms on Delta airline flight, calls it n-word
  12. Boy Passes After Putting Blue Stain In Carpet. 14 Years Later Mom Floored By Real Meaning

Then we’re back to freak shows, and from there to child brides, poverty, and then – fake poverty…

  1. she was born with an elephant’s trunk, this is what they did to her…
  2. Worst Bug Invasions Ever
  3. Mom Thinks She’s Having Twins, But Drs Quickly Learn She’s Making History With Rare Delivery
  4. Child Marriage in Ethiopia’s Amhara Region HD
  5. Mamoni’s Story: The Child Bride
  6. The Ugly Face of Beauty: Is Child Labour the Foundation for your Makeup? (RT Documentary)
  7. The Poorest of the Poor – On the Edge of Europe
  8. Fake Homeless People CAUGHT On Camera And EXPOSED!

Fake stuff leads to the “what would you do” genre…

  1. White Woman Introduces Asian Fiance To Disapproving Parents | What Would You Do? | WWYD
  2. Christian Discrimination for Praying in Public | What Would You Do? | WWYD
  3. Foster Care Cruelty | What Would You Do? | WWYD | ABC News

And from there back to suffering children.

  1. Foster Care Support – They Come In The Night – With Nothing!
  2. Annie’s Story (Neglect)
  3. Russian Orphans – Master Thesis Documentary
  4. Inside AK Orphanage
  5. Nigeria Beggar Abandons 3 Babies on Street
  6. Hungry Kids In Africa
  7. child survival in Africa | survive a tout prix
  8. AIDS Orphans in South Africa

Don’t know why sassy girl was here, but it got me away from sad orphan stories and back to bad parenting…

  1. Sassy little girl blocks the slide at the zoo
  3. Most Spoiled Kids Compilation 6
  4. Kids Who Are Crying For The Most Ridiculous Reasons
  5. What Would You Do: Mother Uses Harsh Punishments on Son | What Would You Do? | WWYD

Bad parenting is related to sappy family stories, like soldier homecomings, which led to family separations…

  1. Soldiers Coming Home || Emotional Compilation
  2. Military Homecoming – Meeting Baby Elijah
  3. Babies Behind Bars – Part 2
  4. Mom Puts Baby Girl To Bed. Hours Later Hears Screaming & Realizes Hidden Danger In Her Room

Which led to bad parenting again…

  1. Police officer finds pregnant mom and toddler asleep on sidewalk

And finally back to Disney Princesses. Phew!

  1. Moms Dress Like Disney Princesses For Maternity Photos


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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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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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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.


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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):


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.


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

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


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


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


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

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

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

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

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

marriage models.xlsx



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