Tag Archives: media

Research on teen crashes confirms that reporters selling books on phone risks hype phone risks

Using phones while driving is dangerous and should stop. But the focus on this issue distracts us from other dangers in driving (which have — you’d never believe from the news — declined rapidly in recent decades). And it distracts us from the broader danger of relying on motor vehicle transportation.

I dwell on this subject because it offers lessons beyond its substantive importance (see all the posts under the texting tag). Today’s lesson is about conflicts of interest in the news media.

The AAA Foundation for Traffic Safety published a study of about 1,700 moderate or severe car crashes in which people ages 16-19 were driving. To identify possible causes of the crashes, they used cameras and motion sensors in the cars, and analyzed the seconds before each crash. The headline result probably should have been that 79% of the crashes occurred when teens were driving too fast. But that’s apparently not news, so the AAAFTS and all the news media reporting the story focused on the fact that 59% of the crashes showed distraction as a likely cause.

The report website highlighted the data on distractions, and that’s reasonable. One of their findings is that distractions in their survey account for a greater proportion of accidents than are reported officially — something we’ve assumed but have had trouble establishing empirically. So that’s useful. They used this graphic:


By this accounting, phones were involved in 12% of crashes, second only to interacting with passengers. But this is an artifact of the way the categories are binned. And a lot of smaller categories are left out of the figure, such as eating and drinking (2%), operating vehicle controls (3%), looking at another vehicle (4%), or smoking-related distractions (1%). (Note also that one crash can have multiple related distractions, but they don’t report the overlaps so you can’t do anything about them.)

So I redid the categories. I don’t see why eating and drinking should be a separate category from grooming, or why singing/dancing should be separate from adjusting the radio. So I made a new category called “physically doing something besides driving,” which includes eating or drinking, using an electronic device (besides phone), grooming, reaching for an object, smoking-related activity, operating vehicle controls, and singing/dancing to music. Also, for some reason their figure lists “looking at something outside the vehicle” but only includes “attending to unknown outside vehicle” in that category. I added two other types of distraction to that category, “attending to another vehicle” and “attending to person outside” — bumping up the outside distraction substantially.

Here’s my new version of their figure based on the same data (from table 13 in the report). It’s more comprehensive but uses fewer categories:


Now cellphones are fourth. So that’s a lesson about using arbitrary category collapsing and then ranking the categories. (This happens all the time with occupations, for example, where people say, “The top X occupations…” but the occupations reflect different levels of granularity.)

Anyway, back to cellphones

The New York Times reporter Matt Richtel won a Pulitzer Prize for his reporting on distracted driving. And he published a book — A Deadly Wandering — that tells the tragic story of a driver who killed someone while he was texting. Unfortunately, he is prone to hyping the problem of texting, which his audience is unfortunately prone to fixating on. I previously pointed out that, on the website promoting his book, his publisher uses an extremely wrong statistic, claiming that texting “continues to claim 11 teen lives per day.” He has mentioned this statistic (or its variant, that texting kills more teens than drunk driving) on Twitter, and also in media appearances. I pointed out that this number is more than all the teens killed in motor vehicle accidents, so it’s obviously baloney. I emailed Richtel about this, and he told me he would “get it fixed.” I emailed the publisher. I emailed the Times. I emailed the Diane Rehm show. No one changed anything. Cellphone crashes are like child abuse: people will believe any statistic about how bad it is and attack anyone who’s skeptical.

Of course I don’t want to minimize the problem of distracted driving, and there’s nothing wrong with telling people it’s dangerous. And it’s not my area of expertise. So I’ve only given the issue a few hours. But playing into a public hysteria about a very narrow, behaviorally-driven problem, rather than exposing the systemic problem that it reflects, is not good.

And now that Richtel is selling a book about texting, he’s got a conflict of interest — if he hypes the problem in his NYT stories, he makes more money. So here’s the NYT headline on his story:


And this is his lead paragraph:

Memo to parents: Distracted driving by teenagers is riskier than previously thought, particularly when it comes to multitasking with a cellphone.

Again, it is true the report finds cellphone distraction causes more accidents than police reports have shown — so this is not irrelevant — though, of course, even with the new accounting they still cause orders of magnitude less than Richtel’s own promotional site claims. But mentioning phones in the headline sets the NYT apart from most of the coverage of this report:

  • Washington Post: “AAA: 58 percent of teens involved in traffic crashes are distracted”
  • ABC News: “Distractions a Problem for Teen Drivers, AAA Study Finds”
  • Houston Chronicle: “Distraction a factor in 6 in 10 teen driver crashes”
  • Chicago Tribune: “Distracted driving a key contributor to teen crashes, study shows”

On my first page of Google News searches, only the LA Times also mentioned phones: “Teen drivers distracted by cellphones, talking in most crashes.”

Who cares?

Some people who are tired of me complaining about this think you can’t have too much hype about safe driving, so who cares? But the distraction matters. The evidence that phones are a fundamental cause — a social cause — of accidents and deaths is very weak, although they are certainly the proximate cause in many cases. But we don’t have randomized controlled trials to test the effects of phones. I suspect the people crashing while futzing with their phones are mostly the same people who would be crashing for some other reason if cellphones didn’t exist.

When I look at the video compilation the AAA put out to accompany their report — which mostly shows teens crashing while using their phones — I am struck by what terrible drivers they are. They look down for three seconds and drive straight off the road without noticing. In contrast, I routinely see people driving on the freeway completely absorbed in their phones — driving obnoxiously slowly but using their peripheral vision to keep going straight. They are at grave risk of an accident if something crosses their path or traffic stops, but they’re not veering all over the road. Their slow speed probably mitigates their risk of crashing. I AM NOT RECOMMENDING THIS, I’m just saying: bad drivers cause accidents, and if you give them a phone they’ll use it to cause an accident.

Did you know teen driving fatalities have fallen by more than half in the last decade? (During that time incidentally, teen suicides have risen 45%.) Did you know that, from 1994 to 2011, mobile phone subscriptions increased more than 1200% while the number of traffic fatalities per mile driven fell 36% (and property-damage-only accidents per mile fell 31%)? Don’t count on Matt Richtel to tell you about this.

And yet, of course, thousands of people die in car accidents every year in the U.S. — at rates higher than the vast majority of other rich countries. But as long as people drive, there will be bad drivers. If we really cared, we would replace individual cars with mass transit (or self-driving cars) — putting transport in the hands of computers and professionals. Nothing’s perfect, of course, but preventing car accidents isn’t rocket science, and blaming a systemic problem on the individual behavior of predictably error-prone drivers doesn’t seem likely to help.


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Vox interview on the Moynihan chilling effect

Jenée Desmond-Harris from Vox.com interviewed me about the Moynihan backlash post. The piece is here. In it she links to this blog, but not to the specific post. If you’re looking for that, it’s here.

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On Contexts: Sociology unfound

Over on the Contexts blog, I wrote a follow-up to Justin Wolfers’ piece about economists dominating the news: here it is.

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This word ‘generation,’ I do not think it means what you think it means

The people who make up these things drive me bananas.

NPR launched a new series on “millennials” yesterday, called “New Boom,” with this dramatic declaration: “There are more millennials in America right now than baby boomers — more than 80 million of us.”

The definition NPR gives for this generation is “people born between 1980 and 2000.” And it’s true there are more than 80 million of them. In fact, there are 91 million of them, according to the 2012 American Community Survey data you can get from IPUMS.org.* That’s OK, though, because there are only 76 million Baby Boomers, so the claim checks out.

But what’s a generation?

The Baby Boom was a demographic event. In 1946, after the end of World War II, the crude birth rate — the number of births per 1,000 population — jumped from 20.4 to 24.1, the biggest one-year change recorded in U.S. history. The birth rate didn’t fall back to its previous level until 1965. That’s why the Baby Boom went down in history as 1946 to 1964. Because that’s when it happened.

This figure shows the number of living people by birth year, and the crude birth rate recorded in each year, using the NPR definition of millennials (in red), compared with the baby boom (purple):


Even with population growth I reckon the people born in the years 1946-1964 might outnumber the self-promoting millennials if not for the weight of mortality pulling down the purple bars. But if the young NPR reporters want to brag about outnumbering a generation that is starting to lose its older members to old age (and who are, after all, their parents), then I guess the shoe fits.

The Baby Boom was not a generation. It was a cohort, “a group of people sharing a common demographic experience” (in this case birth during the same period). That demographic event happens to have lasted 18 years, which is unfortunate because that may have contributed to the tendency to declare “generations” of similar lengths.

The Pew Research people, who do lots of interesting work on social change that uses generational concepts, use these slightly different definitions for four generations: Silent Generation, born 1928-1945; the Baby Boom Generation, born 1946-1964; Generation X, born 1965-1980; Millennial Generation, born 1981 and later (Pew says “no chronological endpoint has been set for this group,” which is awkward because if they’re really still going, the oldest are 33 and they have children that are the same generation as themselves**). Ironic, isn’t it, that Pew constructs “Generation X” as the shortest of the four (some generation, a mere 16 years!) before declaring them “America’s neglected ‘middle child.’

Real generations rarely have starting and ending points on a population level. Populations usually just keeping having births every year in smooth patterns of increase or decrease without discrete edges, so generations overlap. Even in families it gets hard to nail down generations once you start moving horizontally; siblings born many years apart are in the same generation, but the cousins get all confused.

Meaningful cohorts, on the other hand, can be defined all over the place, such as: the people who graduated college during the Great Recession, people who introduced the Internet to their parents, and so on. These are not generations.

In 2010, when crisis was really in the air, I was on the NPR show The State of Things in North Carolina, discussing the Baby Boom (no audio online). After attempting to clarify the difference between a generation and a cohort, I offered this dramatic example of a cohort — people born in 1960 specifically:

So if you were born in 1960, graduated college in 1982, and entered the labor force in the middle of an awful recession, then managed to pull some kind of career together, got married and divorced, by the 90s it was time to be downsized already for the first time, you’re 40 in 2000, and it’s time for the dot-com bubble, you’re out of your job again, and here you are ready for your retirement, finally, you’ve been left in your own 401(k), having to put together your own pension, and of course now that’s in the tank and your house isn’t worth anything. So that insecurity and instability is really imprinted this group. We talk about the 60s, and civil rights and antiwar, and great music and everything, but that’s seeming like a long time ago now for people who are looking at retirement.

I don’t know if anyone actually had that experience, but it seems likely.

Anyway, if people really want to keep using these generation labels, and it seems unlikely to stop now given the marketing payoff from naming rights, than that’s the way it goes. But please don’t ask demographers to define them.


* This is a little different from the population estimates the Census Bureau produces, which are coded by age rather than year of birth. I use the ACS data because they report year of birth, and because it’s easier. The differences are very small.

** Thanks to Mo Willow for pointing this out.


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The number one cause of traffic fatalities

Please don’t text while driving.

Note: I have updated this post to reflect a response I received from Matt Richtel.

A data illustration follows the rant.

I don’t yet have a copy of Matt Richtel’s new book, A Deadly Wandering: A Tale of Tragedy and Redemption in the Age of Attention. Based on his Pulitzer-prize winning reporting for the New York Times, however, I’m afraid it’s unlikely to do justice to the complexity of the relationship between mobile phones and motor vehicle accidents. Worse, I fear it distracts attention from the most important cause of traffic fatalities: driving.

A bad sign

The other day Richtel tweeted a link to this old news article that claims texting causes more fatal accidents for teens than alcohol. The article says some researcher estimates “more than 3,000 annual teen deaths from texting,” but there is no reference to a study or any source for the data used to make the estimate. As I previously noted, that’s not plausible.

In fact, only 2,823 teens teens died in motor vehicle accidents in 2012 (only 2,228 of whom were vehicle occupants). So, I get 7.7 teens per day dying in motor vehicle accidents, regardless of the cause. I’m no Pulitzer-prize winning New York Times journalist, but I reckon that makes this giant factoid on Richtel’s website wrong, which doesn’t bode well for the book:


In fact, I suspect the 11-per-day meme comes from Mother Jones (or someone they got it from) doing the math wrong on that Newsday number of 3,000 per year and calling it “nearly a dozen” (3,000 is 8.2 per day). And if you Google around looking for this 11-per day statistic, you find sites like textinganddrivingsafety.com, which, like Richtel does in his website video, attributes the statistic to the “Institute for Highway Safety.” I think they mean the Insurance Institute for Highway Safety, which is the source I used for the 2,823 number above. (The fact that he gets the name wrong suggests he got the statistic second-hand.) IIHS has an extensive page of facts on distracted driving, which doesn’t have any fact like this (they actually express skepticism about inflated claims of cellphone effects).

After I contacted him to complain about that 11-teens-per-day statistic, Richtel pointed out that the page I linked to is run by his publisher, not him, and that he had asked them to “deal with that stat.” I now see that the page includes a footnote that says, “Statistic taken from the Insurance Institute for Highway Safety’s Fatality Facts.” I don’t think that’s true, however, since the “Fatality Facts” page for teenagers still shows 2,228 teens (passengers and drivers) killed in 2012. Richtel added in his email to me:

As I’ve written in previous writings, the cell phone industry also takes your position that fatality rates have fallen. It’s a fair question. Many safety advocates point to air bags, anti-lock brakes and wider roads — billions spent on safety — driving down accident rates (although accidents per miles driven is more complex). These advocates say that accidents would’ve fallen far faster without mobile phones and texting. And they point out that rates have fallen far faster in other countries (deaths per 100,000 drivers) that have tougher laws. In fact, the U.S. rates, they say, have fallen less far than most other countries. Thank you for your thoughtful commentary on this. I think it’s a worthy issue for conversation.

I appreciate his response. Now I’ll read the book before complaining about him any more.

The shocking truth

I generally oppose scare-mongering manipulations of data that take advantage of common ignorance. The people selling mobile-phone panic don’t dwell on the fact that the roads are getting safer and safer, and just let you go on assuming they’re getting more and more dangerous. I reviewed all that here, showing the increase in mobile phone subscriptions relative to the decline in traffic accidents, injuries, and deaths.

That doesn’t mean texting and driving isn’t dangerous. I’m sure it is. Cell phone bans may be a good idea, although the evidence that they save lives is mixed. But the overall situation is surely more complicated than TEXTING-WHILE-DRIVING EPIDEMIC suggests. The whole story doesn’t seem right — how can phones be so dangerous, and growing more and more pervasive, while accidents and injuries fall? At the very least, a powerful part of the explanation is being left out. (I wonder if phones displace other distractions, like eating and putting on makeup; or if some people drive more cautiously while they’re using their phones, to compensate for their distraction; or if distracted phone users were simply the worst drivers already.)

Beyond the general complaint about misleading people and abusing our ignorance, however, the texting scare distracts us (I know, it’s ironic) from the giant problem staring us in the face: our addiction to private vehicles itself costs thousands of lives a year (not including the environmental effects).

To illustrate this, I went through all the trouble of getting data on mobile phone subscriptions by state, to compare with state traffic fatality rates, only to find this: nothing:

cellphones traffic deaths with NEJM.xlsx

What does predict deaths? Driving. This isn’t a joke. Sometimes the obvious answer is obvious because it’s the answer:

cellphones traffic deaths with NEJM.xlsx

If you’re interested, I also put both of these variables in a regression, along with age and sex composition of the states, and the percentage of employed people who drive to work. Only the miles and drive-to-work rates were correlated with vehicle deaths. Mobile phone subscriptions had no effect at all.

Also, pickups?

Failing to find a demographic predictor that accounts for any of the variation after that explained by miles driven, I tried one more thing. I calculated each state’s deviation from the line predicted by miles driven (for example Alaska, where they only drive 6.3 thousand miles per person, is predicted to have 4.5 deaths per 100,000 but they actually have 8.1, putting that state 3.6 points above the line). Taking those numbers and pouring them into the Google correlate tool, I asked what people in those states with higher-than-expected death rates are searching for. And the leading answer is large, American pickup trucks. Among the 100 searches most correlated with this variable, 10 were about Chevy, Dodge, or Ford pickup trucks, like “2008 chevy colorado” (r = .68), shown here:


I could think of several reasons why places where people are into pickup trucks have more than their predicted share of fatal accidents.

So, to sum up: texting while driving is dangerous and getting more common as driving is getting safer, but driving still kills thousands of Americans every year, making it the umbrella social problem under which texting may be one contributing factor.

I used this analogy before, and the parallel isn’t perfect, but the texting panic reminds me of the 1970s “Crying Indian” ad I used to see when I was watching Saturday morning cartoons. The ad famously pivoted from industrial pollution to littering in the climactic final seconds:

Conclusion: Keep your eye on the ball.


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To know poverty proportions, know your terms (Fox News edition)

In a recent interview on Fox & Friends, despite preparing, I found myself not prepared for Tucker Carlson to ask me this:

It’s pretty conclusive that kids who grow up with married parents — biological parents — do way better than kids who don’t. So the fact that the percentage of kids growing up in that environment has been dropping, why shouldn’t we call that a tragedy?

After a little back-and-forth, I came out with this pretty inarticulate statement:

I think we want to think about pros and cons and and challenges that people face in all different arrangements. And part of the point of this report is that we can’t put people in one category and try to come up with a solution. Our poverty problem for example: Only a third of people in poverty now are living in single-mother families. So we have a large problem of poverty in married couple families as well.

My inarticulateness would probably have been even worse if I had noticed that the Fox audience at that moment was being treated to a completely wrong statistic in the caption below our talking heads:


The report I provided to the Fox staff had actually shown that one-third — not two-thirds — of children under 15 live with unmarried parents.

Anyway, my statement, “Only a third of people in poverty now are living in single-mother families,” is pretty much true. On the other hand, the oft-cited Heritage Foundation statement, “Nearly three out of four poor families with children in America are headed by single parents,” is pretty much true, too. How can that be?

To put it as confusingly as possible, the basic issue is that poverty numbers can be reported for different data universes: individuals, families, family households, individuals in families, and families with children. Some families are sub-families — that is, they are in someone else’s household — and some children (if they live in group quarters, or are ages 16-18 and live on their own as neither married nor parents) don’t live in families.

Here are some poverty numbers for 2013 (from various tables here). The rates are just for your information; it’s the numbers in poverty that I refer to below — you can use them to mix and match your own proportions:


Notice that there are 14 million poor people who don’t live in families at all. Some of them have housemates or cohabiting partners that they are sharing income with, but because they’re not technically families that shared income doesn’t count as shared income.

Because, from the 1st and 3rd rows of the table, 15,606/45,318 = .34, my statement that only a third of poor people live in single-mother families was pretty much true. I say “pretty much” because a few of those female-householder-no-husband families aren’t single mothers of children, but rather single women hosting some other family member in their households (such as an older relative).

And because, from rows 12-14, (3,937+607)/6,482 = .70, the Heritage Foundation’s statement that, “Nearly three out of four poor families with children in America are headed by single parents” is pretty much true, too.

So, who’s right?

Well, if you want to talk about the whole poverty problem, it’s fair to say that only a third of it involves people in single-mother families. Maybe by excluding the single fathers from that I’m guilty of shading the number downward to minimize the problem (and I definitely shouldn’t have implied that the rest of the poor people live in married-couple families). I actually did that because the table I get those numbers from (hstpov2) doesn’t report single-man families.

If you want to talk about the problem of children in poverty, then you should use the second panel, which tells you that 57% of children in poverty live with single mothers (8,339/14,659), or if you include single fathers, 65%. That’s what Heritage should do.

The “nearly three out of four” number is true — if you’re OK with 70% as nearly three out of four — but there’s no reason families is the more logical unit of analysis instead of children.

Marriage tracks poverty

Anyway, I was reminded of all this because Brad Wilcox tweeted a link to this editorial from the Tyler Morning Telegraph. The editorial includes the Heritage statistic, and explains why poverty rates haven’t fallen much in the last few years, while unemployment rates have. Quoting Joe Carter of the Acton Institute:

“The findings align with what many family scholars and economists have been predicting: the decline of marriage leads to an increase in poverty. From 2007 to 2011, the American population increased by 10,360,000 while the number of marriages decreased during that same period by 79,000. Over the last few years we’ve seen the same trend: more people, fewer marriages. … The effect of the decline in marriage, coupled with an increase in single parenthood, is that many more children live in poverty than they would if marriage was more common.”

That’s why the headline for the editorial is, “Marriage statistics track with poverty.” To illustrate marriage tracking poverty, I’ve put the two historical trends on the same graph, using this for marriage and this for poverty:

poverty and marriage 1960-2013

As the chart clearly shows (since 1977 at least), when marriage falls, poverty goes up. Also, when marriage falls, poverty goes down. In math-grammar terms, those two equations reduce to: marriage falls; poverty goes up and down.


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Mildly altruistic blog post rooted in the brain

Brain science is super interesting and important, of course. In fact, “the brain” is gaining on “the mind” as a topic of our brain-mind’s fixation (Google ngrams):


I take a tiny share of responsibility for this trend, as during one of my journalism careers I wrote a 1995 news article about “brain-based learning” for a newsletter sent to more than 100,000 K-12 educators.

On the plus side, in my old article I devoted considerable attention to the issue of brain plasticity, or how brains change in response to time and experience. That plasticity perspective was conspicuously absent from Michelle Trudeau’s NPR story this morning about the brains of extreme altruists. The story was based on a paywalled PNAS article which reported that a nonrandom group of 19 anonymous kidney donors had bigger right amygdalas, and heightened emotional response to pictures of faces, than a nonrandom group of 20 controls. The authors conclude that “these findings suggest extraordinary altruism [is] supported by neural mechanisms that underlie social and emotional responsiveness.”

Or, maybe the cumulative experiences of adults who turn out to be extraordinary altruists change their brains. (Or even, maybe the experience of giving a kidney itself affects people’s brains.) It appears that amygdala size changes within people over time, and that it is correlated with the size of people’s social networks. So, the causal sequencing here is something to consider.

What if, as they imply, something about the way people are born makes them more or less likely to be an extraordinary altruist versus a psychopath (a group this researcher previously studied). How much of the real-life variation in altruism might such a genetic or anatomical influence account for? If that proportion is low, then this is a fascinating evolutionary question with little social implication — worth studying, but not worth writing about with headlines like, “Good Deeds May Be Rooted In The Brain.”

The PNAS authors conclude:

It should be emphasized, however, that the mechanisms we have identified are unlikely to represent a complete explanation for altruistic kidney donation, given the extreme rarity of this phenomenon, and given the overlapping distributions we observed for the variables we measured. Acts of extraordinary altruism are likely to reflect a combination of the neurocognitive characteristics identified here, along with other individual- or community-level variables.

That seems like a safe bet, given this distribution of amygdala size across the two groups:


In short, we should consider the possibility, however slight, that altruism also has social causes. Disciplinary culture, I suppose, but I’ve never finished an article with a caution to readers that I may not have completely explained the phenomenon under study.

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