I have written a review of Nicholas Wade’s book, A Troublesome Inheritance: Genes, Race and Human History, for Boston Review. Because there already are a lot of reviews published, I also included discussion of the response to the book. And because I’m not expert in genetics and evolution, I got to do a pile of reading on those subject as well. I hope you’ll have a look: http://www.bostonreview.net/books-ideas/philip-cohen-nicholas-wade-troublesome-inheritance
Brad Wilcox has written up his best case for how marriage protects women and girls from violence. I discussed his initial post earlier, but the blowup has prompted me to provide more general advice for the critical data citizen — reader, writer, and editor — who has to decide what to believe when someone comes at them with a data story.
I have some tips about that at the end, but first this elaborate setup.
The information in this section is true
Consider three stories:
- When Melanie Thernstrom’s toddler, Kieran, first ate cheese, he immediately had a massive allergic attack. His face swelled, his skin turned red and scaly, and he started gasping for breath. They jumped in their car and rushed to the hospital, where doctors were able to save him.
- Chicago mother Tynisha Hilliard had six children in the car when someone opened fire. “Mommy, I’m shot,” said her nine-year-old boy from the back seat. Hilliard immediately sped to the nearest hospital. “My reaction was to save my son. That’s all I can do, save my son,” she said. After emergency surgery for a gunshot wound to the chest, the boy was expected to survive.
- When Dodgers catcher A. J. Ellis’s wife, Cindy, went into labor, they hopped in the car and headed for NYU hospital, normally a 35-minute drive. Despite racing through traffic with a police escort, they didn’t make it in time – the baby was born in the back seat – but they arrived at the hospital moments later, met by an emergency crew that whisked mother and child to care and safety in the hospital.
What do these stories have in common? Children’s lives saved by cars.
Is this part of a wider phenomenon? I know what you’re thinking: The pollution from cars hurts children, the vast resources devoted to infrastructure for cars could be spent instead in ways that help children, the need for gas causes wars all the time, and the individualism promoted by car culture contributes to social isolation instead of community efficacy.
Maybe. But let’s theorize a little. Here are three ways cars might be good for children’s health:
- Kids whose families have cars can get them to doctors in an emergency. Considering that in modern societies a lot of what kills children is various kinds of accidents and medical emergencies, this could be a major advantage.
- Say what you want about individualism, but it’s emerged as a modern character trait in tandem with the cultural shift that brought us the view of children as priceless individuals. Car culture is a major prop of individualism, so it’s reasonable to hypothesize that people who drive individual cars are more totally devoted to their priceless individual children’s well-being (rather than, say, the well-being of children in general).
- Being able to transport oneself at will — any time, any place — may create a sense of self-efficacy, of mastery over one’s environment, which makes people refuse to accept failure (or illness or death), and thus devote themselves more confidently to their survival and the survival of their children.
Don’t take a theoretical word for it, though — let’s go to the data. Here are three small studies.
Cars and children’s health across countries
First we examine the relationship between the number of passenger cars per capita and the rate of child malnutrition in 110 countries (all the countries in the World Bank’s database that have measures of both variables in the last 10 years — mostly poor countries). The largest — India, China, Brazil, and the USA — are highlighted (click to enlarge).
This is a very strong relationship. This single variable, cars per capita, statistically explains no less than 67% of the variation in child malnutrition rates.
But, you liberals object, cars are surely more common in wealthier countries, so this relationship may be spurious. Sure, income and cars are positively correlated (r=.86, in fact). But when I fit a regression model with both per capita income and per capita cars, cars still have a highly significant statistical association with malnutrition (p<.001). (All the regression models are in the appendix at the end.)
Cars and child death rates across US states
Second, we take a closer look within the United States. Here there is a lot less variation in both the number of cars and the condition of children. Still, there is a clear relationship between private cars per person and the death rate of children and teenagers: Children are substantially less likely to die in states with more privately owned passenger cars (click to enlarge).
Again, there is less variation in income between U.S. states than there is between countries of the world. But to make sure this is not just a function of state income, I fit a regression model with cars and a control for median household income. The statistical effect of private cars remains significant at the p<.05 level, confirming it is unlikely to be due to chance.
Car commuting and children’s disabilities within the US
Third, let’s go still further, not just comparing US states but comparing children according to the car-driving habits of their parents within the US. For this I got data on children’s disabilities (four kinds of disability) and the means of transportation to work for their parents using the 2010-2012 American Community Survey, with a sample of more than 700,000 children ages 5-11.
Sure enough, children who live with parents who drive to work are substantially less likely to have disabilities than those who don’t live with a parent who drives to work:
Again, could this be because richer families are more likely to include car-driving parents? The regressions (below) show that, although it is true that children in richer households are less likely to have disabilities, the statistical effect of parents’ commuting method remains highly significant in the model that includes household income.
In summary: Children are less likely to be malnourished if they live in a country with more cars per person; they are less likely to die if they live in a state with more cars per person, and they are less likely to have disabilities if they live with parents who commute to work by car. All of these relationships are statistically significant with controls for income (of the country, state, or family). These are facts.
Compare this analysis to the question of marriage and violence. In their piece for the Washington Post (discussed here), Brad Wilcox and Robin Fretwell Wilson wrote about #YesAllWomen:
This social media outpouring makes it clear that some men pose a real threat to the physical and psychic welfare of women and girls. But obscured in the public conversation about the violence against women is the fact that some other men are more likely to protect women, directly and indirectly, from the threat of male violence: married biological fathers. The bottom line is this: Married women are notably safer than their unmarried peers, and girls raised in a home with their married father are markedly less likely to be abused or assaulted than children living without their own father.
With the facts above I can accurately offer this parallel construction:
Some cars pose a real threat to the health and safety of children. But obscured in the public conversation about auto safety, pollution, and environmental degradation is the fact that some other cars are more likely to protect children, directly and indirectly, from threats to their health and safety: cars driven by their own, responsible, caring parents. The bottom line is this: Children in places with more cars — and in families where parents commute by car — are notably healthier than peers without cars.
At the end of his followup post, Brad concludes:
Of course, none of these studies definitively prove that marriage plays a causal role in protecting women and children. But they are certainly suggestive. What we do know is this: Intact families with married parents are typically safer for women and children. … That’s why the conversation about violence against women and girls … should incorporate the family factor into efforts to reduce the violence facing women and girls.
I am equally confident in my conclusion:
Of course, my brief studies don’t definitively prove that cars plays a causal role in protecting children’s health and safety. But they are certainly suggestive. What we do know is this: Societies and families with cars are typically safer and healthier for children. That’s why the conversation about children’s well-being should incorporate the car factor into efforts to reduce the harms too many children continue to experience.
Both the marriage story and the car story are misleading data manipulations that substitute data volume for analytical power and present results in a way intended to pitch a conclusion rather than tell the truth.
When is a non-causal story “certainly suggestive”? When the person giving you the pitch wants you to believe the conclusion.
Please do not conclude from this that all data stories are equally corrupt, and everyone just picks the version that agrees with their preconception. Not all academics lie or distort their findings to fit their personal, political, or scientific conclusions. I may be more motivated to criticize Brad Wilcox because I disagree with his conclusions (and there may be people I agree with who use bad methods that I haven’t debunked), but that doesn’t mean I’m dishonest in my interpretation and presentation of evidence. Like a real climate scientist debunking climate-change deniers, I am happy that discrediting him is both morally good and scientifically correct (and I think that’s not a coincidence).
There are two main problems with both the cars story and the marriage story. First is selection into the independent variable condition (marriage and car ownership). People end up in these conditions partly because of their values on the dependent variable. For example, women in marriages are less likely to be raped on average because women don’t want to marry men who have raped them, or likely will rape them — the absence of rape causes marriage. In the case of children with disabilities, there is evidence that children’s disabilities increase the odds their parents will divorce (which means at least one of the parents isn’t in the household and so can’t be a car-commuting parent in the ACS data).
The other main problem is omitted variables. Other things cause both family violence and children’s health, and these are not adequately controlled even if researchers tell you they control for them. Controlling for household income (and other easily-measured demographics) does not capture all the benefits and privileges that married (or car-owning) people have and transfer to their children. For tricky questions of selection and omitted variables, we need to get closer to experimental conditions in order to provide causal explanations.
Tips for critical reading
So, based on Wilcox’s car story and my car story, here are practical tips to help you avoid getting hoodwinked by a propagandist with a PhD — or a data journalist looking at a mountain of data and a tight deadline. These are some things to watch out for:
Scatter plot proof
Impressive bivariate relationships; they may be presented with mention of control variables but no mention of adjusted effect size. That’s what I did with my scatter plots above. If you have adjusted results but don’t show them, it’s selling a small net effect with a big unadjusted label. (Wilcox examples here; Mark Regnerus does this, too.)
A classic example is the Obama food stamp meme, but Wilcox had a great example a few years ago when he wanted to show the drop in divorce that resulted from hard times pulling families together during the recession. If you assume divorce is always going up (it fell for decades), this looks like a dramatic change (he called it “the first annual dip since 2005″):
No head-to-head comparison of alternative explanations
This is a lot to ask, but real social scientists take seriously the alternative explanations for what they observe, and try to devise ways to test them against each other. Editors often see this as a low-hanging fruit for removal, because cutting it both shortens the piece and strengthens the argument. In the rape versus marriage story, Wilcox nodded to the alternative explanation that “women in healthy, safe relationships are more likely to select into marriage” — which he called “part of the story” — but he offered nothing to help a reader or editor adjudicate the relative size of that “part” of the story. This connects to the next red flag.
Greater than zero proof
Sometimes just showing that something exists at all is offered as evidence of its importance. That’s why I included three anecdotes about children being saved by private passenger cars — it happened, it’s real. The trick is to identify whether something matters in addition to existing. Here’s a Wilcox example where he showed that a tiny number of people said they didn’t divorce because of the recession; here’s an example in which Nate Cohn at the NYTimes Upshot said that 2% of Hispanics changing their race to White was “evidence consistent with the theory that Hispanics may assimilate as white Americans.” Neither of these provide any comparison to show how important these discoveries were relative to anything else — other reasons people delay divorce? other reasons for race-code changes? — they just exist. This is reasonable if you’re discovering a new subatomic particle, but with social behavior it’s less impressive.
Piles of studies
The reason I presented the car results as the three separate “studies” was to make the point that you can have a lot of studies, but if none of them prove your point it doesn’t matter. For example, in his post Wilcox linked to a series of publications about how children whose parents weren’t married were more likely to be sexually abused, but none of them handle the problem of selection into marriage I described above. Similarly, a generation of research showed that women who have babies as teenagers suffer negative economic consequences, but those effects were all exaggerated because people didn’t take selection into account (women with poor economic prospects are more likely to have babies as teenagers).
Describing one side of inequality as a social good
Let’s say that, in street fights, the person with a gun beats the person with a knife more than 50% of the time. Do we conclude people should have more guns? Some benefits are absolute and have no zero-sum quality to them. (I can’t think of any, but I assume there are some.) Normally, however, we’re talking about relative benefits. The benefits of marriage, or the economic benefits of education, are measured relative to people who aren’t married or schooled.
The typical description of such a pattern is, “This causes a good outcome, we should have more of it.” But we should always consider whether the best thing, socially, might be to reduce the benefit — that is, solve the problems of the people who don’t have the asset in question — rather than try to increase the number of people with the asset.
The benefit of cars that comes from being able to get to the hospital quicker may only be relative to the poor suckers stuck in an ambulance while your personal cars are blocking up Manhattan.
Appendix: Regression results
In the kids’ movie sexual dimorphism saga, we have a new entrant: How to Train Your Dragon 2.
The posts so far include Frozen and Brave (which includes data on real hand size differences), Tangled, and Gnomeo and Juliet. The objections to complaints, and some counter examples, are in this post.
In Dragon, the young hero, Hiccup, and his friend Astrid are about the same size:
So file that under not extreme dimorphism. But there isn’t a lot of romance between them. I wouldn’t have made an entry for the film if not for a few tender moments between Stoick the Vast and his wife, Valka (Hiccup’s parents).
True to form, it is during the tender moments that the greatest sexual dimorphism is displayed. Here are their hands from the scene where their love is (spoiler alert) rekindled (sorry for the image quality – it was dark):
I actually don’t see how her tiny fingers can reach all the way across his hand like that. Ouch! Anyway, the point is the size difference. Please don’t say, “Of course his hands are huge, his name is Stoick the Vast”! It’s fiction. They could have done whatever they wanted. That’s why some of the Vikings have Scottish accents, and there are flying dragons (still not enough magic to get any people of color into the frozen North, though — except the foreign arch villain, Drago Bludvist).
Anyway, here are the previous pictures in the series:
I’m not going to dignify this with a thorough debunking, but here’s a quick note to highlight the evil that walks among us in academic robes.
The post didn’t specifically say what’s in the headline, but in this case I have to give credit to the overreaching headline writer for accurately capturing the basic message of the piece. What Brad wants to do is make people think that without exactly saying it. Erin Gloria Ryan on Jezebel wrote a good alternate headline for it, too: “Violence Against Women Will End When You Sluts Get Married, Says WaPo.”
Their audience is married people who feel superior to women who aren’t married, who want to coerce women into marriage — or cast them out. The friendly side of this is paternalistic shaming, the unfriendly side is violent shaming; both are expressions of patriarchal outlook. Their conclusion:
And, most fundamentally, for the girls and women in their lives, married fathers provide direct protection by watching out for the physical welfare of their wives and daughters, and indirect protection by increasing the odds they live in safe homes and are not exposed to men likely to pose a threat. So, women: if you’re the product of a good marriage, and feel safer as a consequence, lift a glass to dear old dad this Sunday.
I can’t help reading this without hearing a voice that says, “We can do this the easy way, or the hard way.”
The new headline is supposed to be less offensive, I suppose, but it amounts to the same thing. And it’s based on the same correlations in the post. There is still nothing in the post to show that adding marriage to a random relationship would reduce the odds or level of intimate partner violence. So the implication is the same: shame on you.
On Twitter, Marina Adshade pointed out that marriage rates and violence rates have both been falling for several decades. Brad’s response was, “Fair enough. But the question is this: Would they have fallen even more if marriage was stronger?” That’s a question he should probably have asked before writing the piece.
Can you imagine what he would do if he had the opposite result to work with — an increase in violence during a period of decreasing marriage?
We don’t have to imagine, actually, because he and his marriage-promoting compatriots at the National Marriage Project were all over that in the 1990s. To choose one example I have handy, William Galston, who sits on Brad’s board of advisors at NMP, wrote in 1991 in the New Republic (12/2/91) that, “The American family has changed dramatically in the past generation, and it is children who have paid the price.” We needed, he said, to “relegitimate the discussion of the links between family structure and a range of social ills.” Indeed, “theft, violence, and the use of illicit drugs are far more prevalent among teenagers than they were thirty years ago.” Now, as “revolution in the American family” has reached unprecedented levels, crime has fallen for two decades. <Crickets>
As a spoof — but with real data — I illustrated Adshade’s point. Here is the relationship between marriage prevalence and intimate partner violence rates:
That curvilinear statistical relationship explains 84% of the variance in intimate partner violence rates. If you add the linear time trend, the variance explained jumps to 92% and the effects of marriage remain highly significant.
If I were like Brad on the other side of this debate, the news story would read like this:
“We had reason to believe marriage was harmful, on average,” said Prof. Cohen. “But I was surprised by the strength of the relationship, especially the fact that the effect seems to accelerate at higher levels of marriage, as if marriage feeds off itself in a violence loop.” Although further research will be needed to confirm the findings, he added, the statistical association is very strong. “The bottom line is that intimate partner violence is much less common in years when marriage is more rare.”
However, I am not seriously suggesting that the decline in marriage has caused the decline in violence (although reduced exposure of women to men in general may be one factor). In fact, if you add the curvilinear effect of time, the variance explained rises to 95% — and marriage effects disappear. But the fact that violence has dropped so much while marriage has plummeted means Brad has a steeper hill to climb to make his case. It’s not enough to say, maybe violence would have declined even more. This is not one of those random spurious correlations, these are two large social trends affecting whole swaths of the population, and the correlation directly contradicts his theory. When there is a plausible connection, or the trends at least affect the same people, the burden is on the one going beyond the existing evidence to reconcile the hypothesis with the available circumstantial evidence.
But none of this matters to Brad*, or, apparently, Robin Fretwell Wilson. Their conclusion is predetermined. There is nothing that would lead them to conclude that society would not be improved by more marriage. It’s just a case of picking a subject in the news, picking some facts, and repeating their conclusions. And I think it’s appalling.
* If you’re wondering why I seem to be picking on Brad individually, please rest assured it’s nothing personal. If there was any other sociologist who behaved as poorly as he consistently does I would pick on them, too. For endless details, follow the National Marriage Project tag.
If you don’t account for population growth, I don’t get what you’re saying with these employment numbers. I’ll show a simple example, but first a little rundown on Friday’s jobs report.
Here is how CNN Money played the jobs report:
What does it mean, this loss and gain of jobs, returning finally to where we started? Four paragraphs under that happy headline, CNN did points out:
Given population growth over the last four years, the economy still needs more jobs to truly return to a healthy place. How many more? A whopping 7 million, calculates Heidi Shierholz, an economist with the Economic Policy Institute.
So what does “Finally!” mean? The Wall Street Journal ran the headline, “Jobs Return to Peak, but Quality Lags.” On 538 it was, “Women returned to prerecession levels of employment in 2013. Men remain hundreds of thousands of jobs in the hole:”
The Center on Budget and Policy Priorities showed how much better the previous recoveries were:
That’s a good comparison. And CBPP mentioned population growth, too:
…payroll employment has finally topped its level at the start of the recession. Still, with essentially no net job growth since December 2007 but a growing working-age population, many more people today want to work but don’t have a job.
It’s not that no one mentions population growth, it’s that they still lead with the “top line” number. And they all have that horizontal line at the raw number of jobs when the recession started as the benchmark. I don’t know why.
Maybe in some crazy economics world the absolute number of jobs is what really matters for evaluating a recovery, and that explains the fixation on that horizontal line. From a social perspective what matters is the proportion of people with jobs. I could see the logic if you had a finite number of employers that never change, where you could literally count up the jobs at two points in time, and see who added and who subtracted from their payrolls (this is why retail chains report “same-store” trends, so the statistics aren’t confounded by the changing number of stores). But we have zillions of employers, constantly changing and moving around — largely in response to population changes. So that static image seems pointless.
So here are some charts to put the recession and recovery in slightly better perspective. These all use the Bureau of Labor Statistics’ Current Population Survey from March 2003 to March 2013 (from IPUMS), the household survey used to track the labor force. I use ages 15 and older, and combine people in school (up to age 24) with those employed (not how most people do it, but a lot of people went to school, or stayed in school, because of the bad job market, and it doesn’t make sense to count them as not simply not employed). The survey excludes people in institutions, like prisons, and on-base military personnel.
To show the basic issue, here are the changes in the non-institutionalized population, age 15+, along with the number of them employed or in school — showing absolute changes relative to 2008, the peak employment year.
The 15+ population increased almost 12 million from 2008 to 2013. People employed or in school was not yet back to 2008 levels in March 2013. So a basic population adjustment is the least you can ask for (and we get that from the BLS with the employment-population ratio, which as of May was up less than one percent in the last 3.5 years, but it’s not the headline number).
What about age shifts? You don’t expect extreme age composition changes in 5 years, but there are different employment trends at different ages, so those affect how many employed people we are short. Here are the trends in work/school, by age and sex:
This makes it look like the 30-49s that are getting crushed. The 50+ community’s gains, however,are deceptive — their population is increasing. In fact, the population of people 30-49 declined 5% during this decade, while the population 50+ increased almost 30%. The younger people have increased their schooling rates, but their population has also grown. If you look at the employment/school rates, you see that among men, it is the younger groups that have done worst:
Women clearly are doing better (partly because in the 20-29 range they’re going to school more). It is amazing that employment rates didn’t fall at all over age 60. This could be because the population increase in that group is all in Baby Boomers just hitting their sixties, but I reckon it’s also people delaying retirement compensating for unemployment.
Now that we have age-specific work/school rates, and population changes, we can easily calculate how many people in each age group would have to be in work/school to get to the number implied by applying the peak-year 2008 rates to the population in each year. Sorry this one is so ugly: I made the last bar for each group pink to show the bottom line, where each group stands in 2013 relative to 2008:
Worst off are the 20-something men, down more than a million worker/students in 2013. Interestingly, women are only better off in the 20-something and 50+ ranges.
Finally, if you sum these figures you get the total, age-adjusted losses, by sex since 2008, as of March 2013:
And that’s your bottom line. The job/school losses stood at 3.3 million for men and 2.4 million for women as of March 2013.*
Really, there are no huge surprises here. In fact, the total population change is not a bad rough adjustment, especially for the short term. But there are some interesting nuances here. And with all the data and computers we have these days, let’s adjust for age and sex.
*I don’t say that’s how many “jobs” we need, because I don’t think “jobs” exist — are created, destroyed, shipped overseas, etc. I think there are employed people, people getting jobs, losing jobs, etc. I don’t see how a “job” exists absent a worker in it (and no, a listing is not a job until they fill it). So we don’t need to “create jobs” after a recession, what we need to do is “hire people.” Pet peeve.
Hector Cordero-Guzman called my attention to a controversy over Hispanics changing their racial identities. Here is a quick rehash and a few comments. (Spoiler: the New York Times ran a bad story.)
At the Population Association of America, Carolyn Liebler, a sociologist at the University of Minnesota, and James Noon, who works on administrative records at the Census Bureau, presented preliminary results from a comparison of individual race/ethnic responses to the 2000 and 2010 Decennial Censuses. After analyzing millions of individual Census responses, they reported in their abstract that 6% of people changed their race or Hispanic origin classification between 2000 and 2010.
Details of the analysis apparently are not publicly available, but D’Vera Cohn, a writer at the Pew Research Center, reported on their findings, under the headline, “Millions of Americans changed their racial or ethnic identity from one census to the next.” Is this a lot of change? It’s hard to say without a comparison (and without the analysis details). “Millions” does not really mean “a lot,” but it sounds like it does. If the Census race/ethnic identity questions don’t fit people’s self-concept very well then a certain amount of bouncing around is to be expected.
The focus was on Hispanics, whose place in the racial classification scheme is squishy (including immigrants who came at different ages from countries with different racial schemes and ancestral origins, living in different parts of the country with different racial attitudes, some concentrated in dense communities and some dispersed, some economically marginalized and some much more upwardly mobile, etc.). According to D’vera Cohn, 2.5 million Hispanics were “some other race” in 2000 and “white” in 2010, while 1.3 million were “white” in 2000 and “some other race” in 2010.
I might conclude from that that it’s messy and the categories don’t work very well. But it’s also possible that this reflects fluid identities, and people actually change how they see themselves in a systematic way over time. The PAA abstract says “responses and corresponding identities can change over time,” which leaves open the possibility that the change is in measurement in addition to identity, but the hypothesis they suggest are about identity (hypothesizing that women, young people, and people in the West have more complex or less stable identities).
Identity shift is how New York Times Upshot writer Nate Cohn interpreted the D’Vera Cohn report. Under the headline, “More Hispanics Declaring Themselves White,” he converted that bidirectional flow into “net 1.2 million” changing from “some other race” to “white,” and proceeded to run away with the implications. It’s a good example of using any number greater than zero to confirm something you already believe. For example, he wrote:
The data also call into question whether America is destined to become a so-called minority-majority nation, where whites represent a minority of the nation’s population. Those projections assume that Hispanics aren’t white, but if Hispanics ultimately identify as white Americans, then whites will remain the majority for the foreseeable future.
Hm. The “net” flow from “some other race” to “white” is 1.2 million. That is 3% of the 2000 Hispanic population, or 2% of the 2010 population. So even if it’s truly an identity change, does that save the White majority in the long run?
Anyway, as Cordero-Guzman points out in a detailed discussion, referring to a post by Manuel Pastor, the Census questions changed between 2000 and 2010, with Census adding, in bold, “For this census, Hispanic origins are not races” to the form in 2010. Since many Hispanics write “Hispanic” under “some other race,” this probably discouraged them from choosing “some other race” in 2010.
Cordero-Guzman also points out that the context in which the question is asked (and in which the respondents live) is important. For example, 82% of Puerto Ricans on the island use “white” on the American Community Survey, while in New York City only 45% do. Does their identity — in the sense of how they really think of themselves — change when they are in New York, or do they interpret the question differently because they are answering a question in a different social setting? You can’t quantify that difference, probably, but I wouldn’t assume it’s just an identity change.
In a follow-up post, Nate Cohn acknowledges the wording changes — “an important detail” — but returns to the assimilation-upward mobility story. He should have just acknowledged that he jumped to conclusions in the first post and overreached in the race to produce an important, “data-driven” post. (Nate Cohn may have consulted actual experts, but if he did he didn’t include them in the post. It’s just data, I guess.)
The information economy did it
There is a lesson here in the new information economy. Academic conferences used to be less in the public eye. A preliminary analysis, shared with other researchers, then a Pew writer posts on the results, and the Times splashes them all over, all before a paper is even available. I think the Times story is basically wrong — the data as reported are not independent evidence of “assimilation.” (So, the person with the biggest megaphone was the person who was most wrong — surprise!) But the analysis could well be an important piece of research in a larger literature, and I think it’s good to present preliminary research at conferences. You can’t stop reporters from racing to be wrong, but I do think it would be better to distribute the paper publicly when it’s presented.
At Pew Social Trends, Gretchen Livingston has a new report on fathers staying at home with their kids. They define stay at home fathers as any father ages 18-69 living with his children who did not work for pay in the previous year (regardless of marital status or the employment status of others in the household). That produces this trend:
At least for the 1990s and early-2000s recessions, the figure very nicely shows spikes upward of stay-at-home dads during recessions, followed by declines that don’t wipe out the whole gain — we don’t know what will happen in the current decline as men’s employment rates rise.
In Pew’s numbers 21% of the stay at home fathers report their reason for being out of the labor force was caring for their home and family; 23% couldn’t find work, 35% couldn’t work because of health problems, and 22% were in school or retired.
It is reasonable to call a father staying at home with his kids a stay at home father, regardless of his reason. We never needed stay at home mothers to pass some motive-based criteria before we defined them as staying at home. And yet there is a tendency (not evidenced in this report) to read into this a bigger change in gender dynamics than there is. The Census Bureau has for years calculated a much more rigid definition that only applied to married parents of kids under 15: those out of the labor force all year, whose spouse was in the labor force all year, and who specified their reason as taking care of home and family. You can think of this as the hardcore stay at home parents, the ones who do it long term, and have a carework motivation for doing it. When you do it that way, stay at home mothers outnumber stay at home fathers 100-to-1.
I updated a figure from an earlier post for Bryce Covert at Think Progress, who wrote a nice piece with a lot of links on the gender division of labor. This shows the percentage of all married-couple families with kids under 15 who have one of the hard core stay at home parents:
That is a real upward trend for stay at home fathers, but that pattern remains very rare.
(The Census spreadsheet is here)