Author Archives: Philip N. Cohen

Prince Charles and Princess Diana height situation explained

(With media updates)

They were the same height. More or less.

The most incredibly popular tweet of my life was this:

Many people, assuming I was making some kind of argument about sexism, complained that the tweet was a mountain towering over a molehill, that rules of photographic composition, philatelia, ergonomics, or royal succession somehow required the stamp to be composed this way. In response, I composed the new most incredibly popular tweet of my life:

By then it hit the international press, which apparently has had the same decimation of the reportorial workforce that we’ve had here, so they write articles about tweets where the only background information provided is from other tweets in the thread. So we got:

The last one had this awesome graphic:

stampmistake

The Italian service of Huffington Post even produced the definitive video record of the tweet. Anyways.

Actual facts

The actual facts are that we don’t know exactly how tall they were. Like with popular athletes, the biometric data we have about royalty should be considered suspiciously. At the time of their wedding, in July 1981, everyone saw that they were of similar height, and saw the stamp depicting his head above hers. In response, Buckingham Palace put out a statement announcing that he was an inch taller than her. It was reported in the Stamps column of the New York Times on July 26 like this:

stamp2

To me that seems like a Trumpian lie. “You say I was caught lying, but because of this other untruth my original lie is in essence true.” Making a taller person look even taller seems less egregious than reversing the height advantage. But I don’t know for sure.

The funny thing about resurrecting a 36-year-old scandal is it seems that, among those interested, half nod knowingly and say, “That always annoyed me!” and the other half say, “mind blown.” It’s not just memories, of course the milieu has changed; anger at “masculinity so fragile” that it requires trick photographs has replaced the routine acceptance of trick photography in the service of propriety. And of course the legacy of Diana as unhappy wife to unfaithful creep — and virtual saint — has changed the tone.

Anyway, I’m in the category of people who’ve been talking about this for years:

  • I first raised it in 2010, using the picture of the stamp and others as an example of the taller-man norm: “But the rigid adherence to this norm results in a daily, intimate interaction among almost all couples that reinforces the bigger-stronger/smaller-weaker gender dichotomy.”
  • In 2011, on Sociological Images, Lisa Wade said of the photos: “This effort to make Charles appear taller is a social commitment to the idea that men are taller and women shorter. When our own bodies, and our chosen mates, don’t follow this rule, sometimes we’ll go to great lengths to preserve the illusion.”
  • In 2013 I returned to the issue, this time with data showing that U.S. men and women sort themselves into couples that exacerbate the existing difference in average height between them.

height6

Finally, I included the stamp picture and the data analysis in my textbook, The Family, writing:

The taller husband conjures up images of the protective, dominant man (“Let me reach that for you”) with a nurturing, supportive wife (“Can I fix you a sandwich?”). To choose a high-profile example, such an image was apparent in many official photos of Britain’s Prince Charles and Princess Diana. Although Charles was actually 1 inch taller than Diana, he often looked shorter than her in candid pictures. But when they posed for portraits, he usually stood on a box or step, as in the picture for the stamp commemorating their royal wedding (Currie 1981). The idea of women as the weaker sex corresponds to the pattern of male domination in modern society, as symbolized by the muscular male athlete and the taller husband.

The reference there is to a news article that uncritically accepted the official heights reported by the authorities. People like to use Google and Wikipedia to find and debate the “official” heights, and to find photos that show them side by side. There may be no true answer.
candd2

This who line of criticism eventually led me to the issue of actual fantasy, in the form of sexual dimorphism in animation. That’s a whole nother tag.

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Update on SocArXiv and social science without walls

social science without walls

Meanwhile, over at SocArXiv, we’re working on revolutionizing the research process and how we communicate about it in the social sciences. You can follow the exploits of the SocArXiv project on our blog SocOpen. There you can read, most recently:

That’s the update!

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More bad reporting on texting and driving, and new data

The New York Times‘ problem of misrepresenting the relationship between phones and traffic fatalities, which seems to have begun with Matt Richtel, has just gotten worse.

Richtel sells books on the fear of texting and driving (which, of course, is dangerous), and the website for his book still — despite my repeated entreaties, public and private — leads with the bad, false, unsourced Internet meme, that “the texting-while-driving epidemic continues to claim 11 teen lives per day.” (As a reporter, how could you sleep one night with that BS up under your name? Mind boggling.)

Anyway, the new entrant is David Leohnardt. At the heavy risk of jeopardizing future opportunities to publish on the Times op-ed page, I tweeted that his recent column included “one of the dumbest things I’ve ever read in the NYTimes.” Washington Post WonkBlog writer Jeff Guo pointed out Leonardt’s column, which claimed that, with regard to the recent spike in traffic deaths, “The only plausible cause is the texting, calling, watching, and posting that people now do while operating a large piece of machinery.” The column contained not a piece of evidence to support that claim (though there were some awful anecdotes), which is why I said it was dumb.

Which is too bad. But even though the spike in traffic deaths is concerning, reporting should not be wrong.

Early estimates from the National Safety Council (which uses a different method than the Federal NHTSA) show a 6% increase in traffic fatalities for 2016. Leonhardt, working really hard to make that absolutely as alarming as possible, produced this graph, showing percent change in fatalities over successive two year periods going back to 1980:

C6aFWA5U4AEzK8I

Because it’s hard to add up the pluses and minuses in your head, It would be really easy — really really easy — to look at Leonhardt’s chart and think fatalities are higher now than they were in 1980. But rather than pointing out that fatalities per person have fallen by half since 1980, he instead writes, “It’s the first significant rise in a half century,” which would be true except for the significant rise in every single decade of the last half century.

This is a lot like when Richtel described the 2015 rise as, “soaring at a rate not seen in 50 years.” Not that the rate was not seen in 50 years, of course, just that the soaring of the rate hadn’t been (or so the NYT Science Desk told me when I complained).

Adding 6% to the NHTSA numbers for 2015, I get the follow graph, showing the trends in deaths per person in the population, and deaths per mile traveled, as changes since 1970. (The deaths per mile haven’t been released for the whole year yet; click to enlarge.)

PercentWhite

That is a troubling spike, which takes us all the way back to 2009 fatality rates. We should make the roads safer, by using them less and using them more safely. But come on, NYTimes.

Read the whole, completely aggravating series, under the texting tag.

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Fertility trends and the myth of Millennials

The other day I showed trends in employment and marriage rates, and made the argument that the generational term “Millennial” and others are not useful: they are imposed before analyzing data and then trends are shoe-horned into the categories. When you look closely you see that the delineation of “generations” is arbitrary and usually wrong.

Here’s another example: fertility patterns. By the definition of “Millennial” used by Pew and others, the generation is supposed to have begun with those born after 1980. When you look at birth rates, however,  you see a dramatic disruption within that group, possibly triggered by the timing of the 2009 recession in their formative years.

I do this by using the American Community Survey, conducted annually from 2001 to 2015, which asks women if they have had a birth in the previous year. The samples are very large, with all the data points shown including at least 8,000 women and most including more than 60,000.

The figure below shows the birth rates by age for women across six five-year birth cohorts. The dots on each line mark the age at which the midpoint of each cohort reached 2009. The oldest three groups are supposed to be “Generation X.” The three youngest groups shown in yellow, blue, and green — those born 1980-84, 1985-89, and 1990-94 — are all Millennials according to the common myth. But look how their experience differs!

cohort birth rates ACS.xlsx

Most of the fertility effect on the recession was felt at young ages, as women postponed births. The oldest Millennial group was in their late twenties when the recession hit, and it appears their fertility was not dramatically affected. The 1985-89 group clearly took a big hit before rebounding. And the youngest group started their childbearing years under the burden of the economic crisis, and if that curve at 25 holds they will not recover. Within this arbitrarily-constructed “generation” is a great divergence of experience driven by the timing of the great recession within their early childbearing years.

You could collapse these these six arbitrary birth cohorts into two arbitrary “generations,” and you would see some of the difference I describe. I did that for you in the next figure, which is made from the same data. And you could make up some story about the character and personality of Millennials versus previous generations to fit that data, but you would be losing a lot of information to do that.

cohort birth rates ACS.xlsx

Of course, any categories reduce information — even single years of age — so that’s OK. The problem is when you treat the boundaries between categories as meaningful before you look at the data — in the absence of evidence that they are real with regard to the question at hand.

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Two examples of why “Millennials” is wrong

When you make up “generation” labels for arbitrary groups based on year of birth, and start attributing personality traits, behaviors, and experiences to them as if they are an actual group, you add more noise than light to our understanding of social trends.

According to generation-guru Pew Research, “millennials” are born during the years 1981-1997. A Pew essay explaining the generations carefully explains that the divisions are arbitrary, and then proceeds to analyze data according to these divisions as if are already real. (In fact, in the one place the essay talks about differences within generations, with regard to political attitudes, it’s clear that there is no political consistency within them, as they have to differentiate between “early” and “late” members of each “generation.”)

Amazingly, despite countless media reports on these “generations,” especially millennials, in a 2015 Pew survey only 40% of people who are supposed to be millennials could pick the name out of a lineup — that is, asked, “These are some commonly used names for generations. Which of these, if any, do you consider yourself to be?”, and then given the generation names (silent, baby boom, X, millennial), 40% of people born after 1980 picked “millennial.”

“What do they know?” You’re saying. “Millennials.

Two examples

The generational labels we’re currently saddled with create false divisions between groups that aren’t really groups, and then obscure important variation within the groups that are arbitrarily lumped together. Here is just one example: the employment experience of young men around the 2009 recession.

In this figure, I’ve taken three birth cohorts: men born four years apart in 1983, 1987, and 1991 — all “millennials” by the Pew definition. Using data from the 2001-2015 American Community Surveys via IPUMS.org, the figure shows their employment rates by age, with 2009 marked for each, coming at age 26, 22, and 18 respectively.

milemp

Each group took a big hit, but their recoveries look pretty different, with the earlier cohort not recovered as of 2015, while the youngest 1991 group bounced up to surpass the employment rates of the 1987s by age 24. Timing matters. I reckon the year they hit that great recession matters more in their lives than the arbitrary lumping of them all together compared with some other older “generations.”

Next, marriage rates. Here I use the Current Population Survey and analyze the percentage of young adults married by year of birth for people ages 18-29. This is from a regression that controls for year of age and sex, so it can be interpreted as marriage rates for young adults (click to enlarge).

gens-marriage

From the beginning of the Baby Boom generation to those born through 1987 (who turned 29 in 2016, the last year of CPS data), the marriage rate fell from 57% to 21%, or 36 percentage points. Most of that change, 22 points, occurred within the Baby Boom. The marriage experience of the “early” and “late” Baby Boomers is not comparable at all. The subsequent “generations” are also marked by continuously falling marriage rates, with no clear demarcation between the groups. (There is probably some fancy math someone could do to confirm that, with regard to marriage experience, group membership by these arbitrary criteria doesn’t tell you more than any other arbitrary grouping would.)

Anyway, there are lots of fascinating and important ways that birth cohort — or other cohort identifiers — matter in people’s lives. And we could learn more about them if we looked at the data before imposing the categories.

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8 years later: Children of the Deported Wonder, “Who Gets A Family?”

Huh. I published this essay, Children of the Deported Wonder, “Who Gets A Family?”, eight years ago today on Huffington Post. I invite you to draw your own conclusions.


kidswayback

A picture I took of some kids.

Children of the Deported Wonder, “Who Gets A Family?”

Over the 10 years up to 2007, the U.S. deported 108,434 adults whose children were U.S. citizens, according to a Department of Homeland Security report [link updated]. The exact number of citizen children left behind in these deportations is unknown, because no one in the government cared to count them. The homeland security of these citizen children does not seem to have been the paramount concern of the U.S. government. Well, maybe excepting 13 of the removed adults, who were deported for “national security and related grounds.” (Altogether, about half were undocumented immigrants and half were deported for criminal violations.)

Either keeping your parents from being dumped over the border isn’t a right Americans enjoy, or someone in power doesn’t really think these kids are American. Or both.

The New York Times quoted an anti-immigration spokesman as saying, “Should those parents get off the hook just because their kids are put in a difficult position? . . . Children often suffer because of the mistakes of their parents.” As if this is unavoidable.

It is true that children suffer for the mistakes of their parents. They also suffer for the policies of their neighbors’ parents, and for the poverty and discrimination their parents experience. Most children lose out to those whose parents have one advantage or another, but the extent of this intergenerational transfer is something we can affect.

One measure of a society’s meritocracy is the level of advantage – and disadvantage – passed from parents to children. Whatever your own ability and effort, equal opportunity only exists to the extent that your parents’ problems are not your own.

If children get burned by their origins, adults also face unequal opportunities to originate the families they want. Just as deported immigrant workers are denied the right to parent their children, poor parents can’t get Medicaid to cover their infertility treatments – though it might pay for some Viagra. (Even without fertility coverage, economists worry that just providing prenatal care and other services to poor women might increase their tendency to have children. Now that would be a shame.)

Having a family – your family – is not a right of American citizenship, for parents or children. And in a society where intergenerational privilege and disadvantage are deeply entrenched, the denial of that right is a cornerstone of our system of inequality.

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How low is too low for divorce?

I have no idea, but I raised the possibility that there is a too low in this essay for Timeline.

I wrote:

We should ask whether falling divorce rates are always a good thing. Most people getting married would like to think they’ll stay together for the long haul, but what is the right amount of divorce for a society to have?

It seems like an odd question, but divorce really isn’t like crime. Less crime is inarguably good, but we do want some divorces. Otherwise it means people are stuck in bad marriages. If you have no divorce that means even abusive marriages can’t break up. If you have a moderate amount, it means pretty bad marriages can break up but people don’t treat it lightly.

When you put it that way, moderate sounds best. Even as we shouldn’t assume families are always falling apart more than they used to, we should consider the pros and cons of divorce, rather than insisting less is always better.

You can read the whole thing here. In addition to a picture of Donald and Ivana Trump, the piece features my figure:

divtrend

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