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Economist Tyler Cowen of Marginal Revolution interviews Stanford economist Raj Chetty and borrows a number of his questions from my appreciative 2015 critique in Taki’s Magazine, “Moneyball for Real Estate,” of the flaws in Chetty’s methodology in his huge and much publicized study of how income mobility over the generations varies by county across the United States.

Chetty is trying to find out what are the local policies or customs that make one county a better place to raise your kids than another county. I think that’s a good topic, but he is still a long ways from finding those kind of subtle answers because his results are still clouded by three big methodological problems. As I concluded two years ago:

In summary, Chetty’s data still suffers from crippling problems with:

- Regression toward the mean (especially among races)
- Temporary booms and busts
- Cost of living differences.

Yet, these should not be impossible challenges for him to overcome in future iterations.

Here’s the transcript of the interview (and the podcast if you like listening rather than reading). Excerpts:

… On drivers of upward mobility

COWEN: Let’s go now to some of your research on mobility, which is maybe, at this moment, what you’re best known for. You can identify counties or parts of the United States where mobility for generations is going to be especially high. To what extent do you think that’s picking up that simply some of those regions end up with resource booms or other good events that is, in a sense, just random? It doesn’t per se have to do with the region? Or do you think we can adjust for that?

CHETTY: Yes. Some of what drives upward mobility, of course, is just having a very vibrant economy. To give you an example, parts of North Dakota, with the natural resource boom there, we see are having very high upward mobility. Of course if you discover natural resources, that’s going to help more people move up the income distribution.

But by and large, that is the exception rather than the key driver of the differences in upward mobility that we find across places. I say that for a couple of reasons. First, even if you hold fixed the rate of growth, the rate of economic growth, you find that some places have much higher rates of upward mobility than others.

To give you an example, Atlanta is a city that’s booming in terms of jobs and economic growth overall. But Atlanta’s one of the places with the lowest levels of upward mobility for kids growing up in low-income families there.

Chetty’s 2013 income mobility map: red=bad

Sorry, but an obvious explanation for why the Atlanta metro area has low upward mobility is the classical statistical phenomenon of regression toward the mean. Atlanta, unlike Chetty’s favorite metro area, Salt Lake City, is heavily black. Atlanta has one of the most prosperous and best educated black communities in America, but blacks in America still regress toward a lower mean income than do whites, so it’s almost statistically inevitable that Chetty would find that Atlanta has lower upward income mobility than Salt Lake City.

The second thing you see is these rates of upward mobility, to the extent we have data, they tend to be quite persistent overtime. It’s not like the places that have high upward mobility in one decade, suddenly a very low upward mobility in the next decade. It’s a pretty persistent phenomenon.

A striking example of that is states in the middle of the US, like Iowa for example, which historical data going back to work by Claudia Goldin and Larry Katz has always looked like a place with very good outcomes for kids in lower-income families. And what’s amazing about that is, Iowa suffers from a brain drain phenomenon where the most successful people often end up leaving the state, going to Chicago, going to New York, to get higher-paying jobs. Yet generation after generation, Iowa seems to produce very good outcomes for low-income families. So that again suggests it’s not about natural resources or temporary booms. It’s something more persistent.

Chetty’s single best county in America for blue collar kids’ upward income mobility is Sioux County, Iowa, where iSteve commenter The Last Real Calvinist is from. Somewhat like Mormon Salt Lake City, Sioux County is famously Dutch and socially conservative. Of course, like all the top 25 counties on Chetty’s list, it’s also extremely white.

But there is some evidence from Chetty’s research that social conservatism is good for blue collar kids’ future earnings. Unfortunately, he needs to adjust for the three big problems I identified above to test his hunch.

CHETTY: Yes. Where did that come from? Why does Iowa have good public schools?

COWEN: Right.

CHETTY: One of the strong correlates we find is that places that are more integrated across socioeconomic groups, that have lower segregation, tend to have better outcomes for kids. And that kind of thing in a rural area — you can see why that occurs and why it might lead to better outcomes.

Obviously, Chetty is being silly here. Sioux County, Iowa and his other 24 top counties are not at all what normal people would call “integrated.” They are extremely white.

Instead, he’s using “integrated” as a euphemism for “heavily white and/or Asian,” and “segregated” as a euphemism for “heavily non-Asian minority.” Liberal sociologist Philip N. Cohen pointed out:

Philip N. Cohen pointed out how Chetty’s 2014 paper tries to euphemize the role of blackness behind related factors like de facto “segregation:” in a 2014 blog post entitled “Where Is Race in the Chetty et al Mobility Paper?

Instead, they drop percent Black for racial segregation. I have no idea why, especially considering … [I]n these normalized correlations, fraction Black has a stronger relationship to mobility than racial segregation or economic segregation! In fact, it’s just about the strongest relationship on the whole long table (except for single mothers, with which it is of course highly correlated).

Back to the interview:

If you live in a big city, it’s very easy to self-segregate in various ways. You live in a gated community, you send your kids to a private school. You essentially don’t interact with people from different socioeconomic classes. If you live in a small town in Iowa, pretty much there’s one place your kids are going to go to school. There’s one set of activities that you can all participate in. And that is likely to lead to more integration.

But living in a small corn-farming town in Iowa will not lead to more racial integration.

COWEN: And you think that’s causal rather than just restating the same fact about the quality of the place?

CHETTY: We don’t have definitive evidence on this, and we’re working on trying to establish clear evidence. But our sense is that integration — actual contact with people from other backgrounds — is a strong predictor and likely a causal determinant of kids’ long-term outcomes. I suspect that that’s one major factor in what’s going on.

This is basically Charles Murray’s Coming Apart view that growing up in Newton, Iowa (home to Maytag) was a healthy social environment because the children of Maytag execs played with the children of local tradesmen. These days, however, Maytag executives mostly live in an upscale suburb of Des Moines and reverse commute 35 miles to Newton. But this doesn’t have much at all to do with racial integration.

It would be perfectly reasonable for Chetty to maintain a stance of agnosticism toward the ultimate causes of the national race gaps in mean income. He could admit that “Until whatever it is that causes the races in America to have different average incomes stops happening, it’s almost statistically inevitable that my methodology will show lower income mobility in heavily non-Asian minority counties than in counties that are heavily white and/or Asian.”

But he doesn’t want to admit that, perhaps because there isn’t much interest in America in how we can influence, say, whites to be more like the whites in Sioux County, Iowa and less like the whites in McDowell County, West Virginia.*

All the excitement and money and prestige is in Closing the Race Gap. Chetty encourages people to assume that his study will find out how to close The Gap between blacks and whites, even though his study is almost hopeless at finding that (because even though he has your IRS returns and mine, he doesn’t have the race of taxpayers due to the IRS not collecting it and the IRS anonymizing the data before giving it to Chetty).

* McDowell County, WV has been notorious since JFK’s famous visit for having the poorest, most backward, most self-destructive white hillbillies in America. And yet, even McDowell Co. does fairly well in Chetty’s measures of upward mobility: such is the power of Regression Toward the Mean. McDowell County comes in at the 46th percentile in Chetty’s rankings, just slightly below the average county in America.

It would be perfectly reasonable for Chetty to adjust for regression toward the racial mean in some fashion so he could look for more subtle drivers of income mobility.

If that’s not feasible, Chetty could simply end up doing what Charles Murray did in Coming Apart and in much of The Bell Curve: just compare highly white counties to other highly white counties. There might still be something interesting to find, although I suspect his white vs. white results would tend to echo David Hackett Fischer’s Albion’s Seed.

Overall, there’s a fair amount of evidence that Chetty’s study really is on to something, which is why I want him to clean up its three big remaining methodological problems of Regression Toward the Mean, Temporary Booms and Busts, and Cost of Living Differences.

If Chetty would take those problems seriously and fix them, he might actually get some results in what he’s been hoping to find out about government policies and social norms that make a difference, pro or con, for the next generation.

Commenter The Last Real Calvinist, who is from heavily Calvinist Sioux County, Iowa, Chetty’s #1 Best County in America explains the trick Chetty is pulling on audiences:

The Last Real Calvinist

I read the whole interview; it’s pretty remarkable stuff.

I think your take (and Steve’s) on this is right; Chetty is referring on one hand, initially, strictly to socio-economic — i.e. class — integration in places such as Sioux County. And he’s right; I went to school with the children of doctors and lawyers and bankers as well as those of farmers and manual workers.

And then he’s saying that this kind of integration is promoted by limitations on options, e.g., presumably, living in a boring, social-capital-scarce setting leads to better outcomes:

If you live in a small town in Iowa, pretty much there’s one place your kids are going to go to school. There’s one set of activities that you can all participate in. And that is likely to lead to more integration.

But then he conflates this class-based integration with — presumably, although he doesn’t come right out and say it — racial/cultural integration:

But our sense is that integration — actual contact with people from other backgrounds — is a strong predictor and likely a causal determinant of kids’ long-term outcomes.

At the least, he’s leaving the door open here to those who would like to interpret his findings in a way that fits The Narrative. That is, successful outcomes are the product not of Sioux County’s cultural homogeneity, but rather of its ‘diversity’.

Nice trick.

Surely there are no other identifiable factors that could possibly be leading to the good outcomes for Sioux County kids.

It’s also interesting to juxtapose this class-integration success narrative with Chetty’s own experience in his ‘outstanding college prep school’ as the son of an economist and a medical specialist. How intrepid he must have been to overcome his limited chances to integrate with the proletariat! I wonder how his school managed to compensate for its no doubt shocking deficit in magic class integration opportunities?

Or perhaps I’m assuming too much, and his school instead carefully assembled an alchemically-potent mixture comprising the correct proportions of children from all classes, no doubt mimicking the class breakdown in Sioux County’s schools?

By the way, in my Taki’s Magazine article on Chetty, I speculated:

Fertility is actually a promising avenue for Chetty to pursue in the future. As we’ll see below, his income calculations are stricken with problems, but he appears to have the data to estimate the answers to questions such as: where should you move if you want your child to present you with a legitimate grandchild by the time you are, say, 70? That is the kind of thing you aren’t supposed to discuss in public these days, but I’d be surprised if Mr. and Mrs. Chetty don’t worry about it.

It turns out my speculation was largely on the money:

On geography and gender

COWEN: Yes. Have you thought much about within this country, geographic differences in gender inequality? …

CHETTY: Yeah, that’s a very interesting question. We find sharp differences in outcomes by gender across areas for various reasons. Let me give you a couple of examples. One, we find that areas with more concentrated poverty — take the city of Baltimore, for example — we find very poor outcomes for boys in particular, relative to girls, and we think that that has to do with crime, and getting involved in gangs, and so forth — things that girls are less likely to do.

As a result, growing up in a place like Baltimore turns out to be extremely detrimental for boys. We estimate that you lose something like 30 percent of your earnings relative to if you’ve grown up in an average place in America. Whereas for girls, it’s slightly negative but not nearly as bad. There are a set of urban ghettos, places with concentrated poverty, that tend to have particularly negative outcomes for boys.

There are also other phenomena that are more subtle, related to things like marriage patterns. Relating this back to personal experience, I remember when working on these issues and thinking about our decision to move from Harvard to Stanford. At the time, we actually were expecting our first child, a daughter. And I noticed in our data that, for kids in affluent families in the Bay Area, daughters tend to have very low household earnings. And I found that kind of curious and we spent some time trying to dig into why what was, partly given my personal interest in the issue.

COWEN: So, your own moving decision was influenced by this research.

CHETTY: [laughs] In some ways.

COWEN: Yeah.

CHETTY: What you find is an interesting explanation, which is, if you look at individual earnings rather than household income, girls growing up here in the Bay Area do extremely well. However, when you look at household income, they don’t do so well, and that’s because they’re much, much less likely to be married than if they grew up somewhere else.


CHETTY: So if you’re in your mid-30s, only something like a quarter or less of girls growing up in the Bay Area are married, and we show in our paper that every extra year you spend growing up in the Bay Area, you’re less likely to get married. I remember telling my wife, “I don’t think we need to worry. Our daughter will be fine in terms of earnings. It’s just that she might not be married if we move to California.”

COWEN: So, you’ve lowered your expectations for grandchildren?

CHETTY: Yes. [laughs]

I’d be interested to know what Mrs. Chetty thinks about not having grandchildren.

• Category: Economics • Tags: Chetty, Inequality, Political Correctness 
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We can ask this question about life expectancy first for people in the bottom quarter of the income distribution and then for people in the top quarter of affluence.

According to Stanford economist Raj Chetty’s paper, the poor live longest where there is massive economic inequality, lots and lots of cops, and unaffordable housing: e.g., New York City. Health care access doesn’t much matter to the poor’s life expectancy. Social conservatism and social capital doesn’t matter either. In other words, the poor appear to do best in some kind of plutocratic Giuliani-ville. Ayn Rand would feel vindicated (if she cared about the poor).

(I don’t actually believe this is true in terms of policy advice: I think Chetty’s result may well be an artifact of churn of his populations: healthy young poor immigrants move to ultra-expensive cities like NYC until they are used up, at which point they leave for some place cheaper.)

In contrast, the top quarter of income Americans live longest in economically more equal and socially conservative places, with broad health care access, fewer immigrants, and non-supercharged economies: kind of like Denmark.

From Raj Chetty’s new paper based on your confidential tax returns, The Association Between Income and Life Expectancy in the United States, 2001-2014, here are the correlations between life expectancies for people in the bottom 25% of national income on their 1040s and various characteristics of their “commuting zones” (e.g. extra-large metro areas). The highest life expectancy metro for people in the bottom quarter of income is New York, followed by Santa Barbara, San Jose (Silicon Valley), Miami, Los Angeles, San Diego, and San Francisco. In other words, the poor live longest in super expensive cities with lots of rich people, lots of economic inequality, and lots of cops.

The shortest life expectancy metros for bottom quartile individuals are Gary, Las Vegas, Oklahoma City, Indianapolis, Tulsa, and Detroit.

For example, unsurprisingly, there’s a strong negative correlation between the % of residents who smoke and the life expectancy of lower income residents. (Keep in mind, though, that these are not individual-level correlations. Chetty has individual-level data from 1040s on income and whether or not the individual died in the last year. He doesn’t have data on whether the individual taxpayer smokes, is obese, exercises, or what not. So, he’s correlating individual level data on income and age at death with local averages, such as smoking.)

Screenshot 2016-04-11 16.53.07

In summary, local customs regarding health behaviors (smoking, obesity, exercise) are very important for the poor’s life expectancy. Measures of access to health care don’t correlate well with life expectancy.

Income inequality and income segregation by neighborhood are modestly good for the life expectancy of poor people.

Prosperity and population growth aren’t very important.

The unimportance of the % Black Adults figure is an artifact of Chetty presenting to us race/ethnicity adjusted figures. Blacks have shorter life expectancies than, say, Asians, but Chetty has already adjusted life expectancy in each metro area for its racial makeup. (But there are racial interaction effects that he’s missing, which drive some of his outliers.)

Black life expectancy, fortunately, has been improving since NWA broke up, with fewer black on black homicides and fewer deaths from AIDS. Asian life expectancy is expectedly high, while Mexican life expectancy is unexpectedly high. White and American Indian life expectancy has been doing poorly in this century, especially working class and/or Scots-Irish whites.

The Other Factors section at the bottom give away what’s going on in driving local areas’ life expectancies among bottom quartile income individuals. Places with high median home values, which correlate with having lots of college graduates, attract lots of immigrants who come to work hard for the relatively high wages available to people willing to sacrifice living space or short commutes for some period of years. These “sojourners” tend to be healthy and live a long time. Like the man said, if they can make it there they can make it anywhere.

If they can’t make it in an ultra-expensive city, such as because they are in poor health, they tend to leave for some place cheaper. Less expensive cities tend to fill up with people either shed from expensive cities or daunted from even trying.

That’s one reason the Charles Murray / Robert D. Putnam community virtues don’t matter much in this graph: poor people appear to live longer if there is a lot of dynamic churn in the economy, which disrupts community social capital and makes life more economically unequal.

But, we don’t know if that’s a genuine treatment effect or if it’s just an artifact of churn in the population: if somebody moves from Mexico to New York City and sleeps in a bunk bed while hustling at two busboy jobs, they’re probably in vigorous health. But if their health breaks down in NYC and they move to relatively short-lived San Antonio for an easier life, where they die early there, how does Chetty count that?

In contrast, for people in the upper quarter of the national income distribution, the contributory factors for longer life expectancies (other than smoking, obesity, and exercise work) work quite differently.

The longest lived people in the top quarter of income (already adjusted for race) are found in the region around tee-totaling Salt Lake City (no surprise), hardy Portland in Maine, Spokane, Santa Barbara, Denver, Minneapolis, and quite Dutch Grand Rapids: the more conservative part of a Stuff White People Like list of places to live.

For the well-to-do, the worst life expectancy cities are Las Vegas, which you’ll be Leaving a half year earlier than anywhere else, Gary, Honolulu, Brownsville, El Paso, Bakersfield, Miami, Lakeland in FL, and Los Angeles: very Stuff White People Don’t Like.

Screenshot 2016-04-11 17.26.08

So, looking at correlations, a culture of non-smoking, non-obese, and exercise helps the affluent as well as the poor.

After that however, things diverge: Health care access measures matter more for affluent than the poor, paradoxically.

The well to do benefit health-wise from Murray-Putnam socially conservative social capital and greater economic equality. The well to do don’t last long in boom towns. Having a lot of immigrants around isn’t good for the upper quarter, but having a lot of college graduates around is good.

In other words, white people tend to do best in the more unfashionable SWPL places like Salt Lake City and the other Portland. Perhaps that shouldn’t be surprising.

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The New York Times’ “Upshot” section has a long-running arrangement with economist Raj Chetty (who recently moved from Harvard to Stanford) to publicize his research on a vast trove of otherwise confidential IRS 1040 data without emphasizing the politically incorrect implications of his research.

Chetty has now posted a new paper on life expectancies by income across the country. The NYT reports on it:

The Rich Live Longer Everywhere. For the Poor, Geography Matters.


For poor Americans, the place they call home can be a matter of life or death.

The poor in some cities — big ones like New York and Los Angeles, and also quite a few smaller ones like Birmingham, Ala. — live nearly as long as their middle-class neighbors or have seen rising life expectancy in the 21st century. But in some other parts of the country, adults with the lowest incomes die on average as young as people in much poorer nations like Rwanda, and their life spans are getting shorter.

In those differences, documented in sweeping new research, lies an optimistic message: The right mix of steps to improve habits and public health could help people live longer, regardless of how much money they make.

One conclusion from this work, published on Monday in The Journal of the American Medical Association, is that the gap in life spans between rich and poor widened from 2001 to 2014. The top 1 percent in income among American men live 15 years longer than the poorest 1 percent; for women, the gap is 10 years. These rich Americans have gained three years of longevity just in this century. They live longer almost without regard to where they live. Poor Americans had very little gain as a whole, with big differences among different places. …

“You want to think about this problem at a more local level than you might have before,” said Raj Chetty, a Stanford economist who is the study’s lead author.

“You don’t want to just think about why things are going badly for the poor in America. You want to think specifically about why they’re going poorly in Tulsa and Detroit,” he said, naming two cities with the lowest levels of life expectancy among low-income residents. …

It may be good to know that poor Americans are living a lot longer in some places than in others, but it would be better to know — in terms of specific policy prescriptions — how the places with better results are doing it.

Unfortunately, Chetty doesn’t actually know much about different parts of the country, and he seldom demonstrates much insight into how his methodology interacts with local conditions. Another big problem is that Chetty’s social engineering ambitions drive him toward over-implying that local policies drive geographic differences, when it’s pretty obvious that selection factors, such as race, are more important.

Chetty’s new paper is pretty interesting, particularly in how it rejects conventional liberal wisdom. Chetty writes:

Correlational analysis of the differences in life expectancy across geographic areas did not provide strong support for 4 leading explanations for socioeconomic differences in longevity: differences in access to medical care (as measured by health insurance coverage and proxies for the quality and quantity of primary care), environmental differences (as measured by residential segregation), adverse effects of inequality (as measured by Gini indices), and labor market conditions (as measured by unemployment rates). Rather, most of the variation in life expectancy across areas was related to differences in health behaviors, including smoking, obesity, and exercise. Individuals in the lowest income quartile have more healthful behaviors and live longer in areas with more immigrants, higher home prices, and more college graduates. …

Theories positing that differences in mortality are driven by the physical environment (eg, exposure to air pollution or a lack of access to healthy food) suggest that the gap in life expectancy between rich and poor individuals should be larger in more residentially segregated cities. Empirically, in areas where rich and poor individuals are more residentially segregated, differences in life expectancy between individuals in the top and bottom income quartile were smaller (r = −0.23, P = .09). Individuals in the bottom income quartile who lived in more segregated commuting zones had higher levels of life expectancy (r = 0.26, P = .04). …

As I pointed out last year in my long critical analysis of Chetty’s work, Moneyball for Real Estate, looking at the extremes of Chetty’s rankings are a good way to figure out for yourself what’s going on.

In the latest, places with long life expectancy for poor people tend to be extremely high cost of living cities, with New York #1 and Santa Barbara #2. Here’s his top ten “commuting zones” for long life expectancy among people who tell the IRS on their 1040s that they are in the bottom 25% of income.

Screenshot 2016-04-11 02.41.37

Why do low income people have long life expectancies in extremely unwelcoming cities like New York?

The NYT flails about trying to explain the pattern:

A common thread among many of the places with a smaller longevity gap was population density, with wealthy cities leading the way. New York has a high rate of social spending for low-income residents and has been aggressive in regulating trans fats and smoking.

But, as a frequent (and highly observant) visitor explained about New York in 1978:

New York and coastal California shed poor people who aren’t tough, tough, tough, tough, tough. (Plus, these look like a list of cities where a lot of income is off the books for tax purposes.)

In contrast, the top life expectancy cities for people in the top quarter of the income range look like Moynihan’s Law of the Canadian Border again, a bunch of cool weather and/or outdoor paradise cities:

Screenshot 2016-04-11 03.05.18

And I wouldn’t be surprised if these were pretty honest towns where there is less income tax evasion.

The lowest life expectancy for the affluent is Las Vegas, which isn’t surprising. Way back in the 1970s, George Gilder liked to point out how different Utah and Nevada were in lifestyle.

Los Angeles has long life expectancy for its (mostly Mexican or Asian immigrant) poor, and short life expectancy for its affluent.

Angus Deaton, the latest Nobel Economics laureate, who kicked off the awareness of the White Death last fall, offers a technical critique here.

Then there are changes in life expectancy by “commuting zone” from 2001 to 2014:

Mr. Cutler, the Harvard economist, argues that the new research should serve as a jumping-off point.

“Why is it that Birmingham has done well but Tulsa has done poorly?” he said.

Probably little in terms of policy. The differences between Birmingham in Alabama and Tulsa and Oklahoma probably have more to do with overall racial trends in lifespans. The 21st Century has been good for the life expectancy of blacks, who aren’t murdering each other or killing each other with AIDS as much as they were 25 years ago. And the life expectancy of Mexicans has long been phenomenally high for their incomes and obesity levels. These days, Mexicans don’t shoot heroin, as Sam Quinones documents in Dreamland, they sell it.

In contrast, this has been a bad century for the life expectancies of working class whites, especially Scots-Irish, and American Indians. Birmingham has lots of blacks, while metro Tulsa has lots of Scots-Irish and as many American Indians as blacks.

The IRS gives Chetty 1040 data that has been (hopefully) anonymized, with no race data. Chetty attempts to adjust for the racial makeup of the region.

But I suspect he doesn’t have a good handle on the interaction effects of racial percentages and income.

In ultra-expensive Santa Barbara, for example, people in the bottom quarter of the national reported income distribution are probably either Latino service workers who are likely to leave for some place cheaper (such as Mexico) if they suffer a health setback, waitresses who aren’t reporting all their tips, or trust funders who are most in danger of death from being eaten by a Great White Shark while surfing.

• Tags: Chetty, White Death 
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An influential book on 21st Century nature versus nurture arguments was 1998′s The Nurture Assumption by Judith Rich Harris (with a foreword by Steven Pinker). Harris downplayed the idea that parenting has much impact on their children. For example, children grow up with the accents of their playground peers, not their parents. (Of course, parents play a role in who their children’s playground peers are, as we’ve seen with all the hysteria among Manhattan and Brooklyn parents over getting their scions to ace the IQ test to get into the $35,000 tuition elite kindergartens.)

My review of Harris’s book in National Review 18 years ago took a somewhat different approach:

To show that peers outweigh parents, she repeatedly cites Darwinian linguist Pinker’s work on how young immigrant kids automatically develop the accents of their playmates, not their parents. True, but there’s more to life than language. Not until p. 191 does she admit — in a footnote — that immigrant parents do pass down home-based aspects of their culture like cuisine, since kids don’t learn to cook from their friends. (How about attitudes toward housekeeping, charity, courtesy, wife-beating, and child-rearing itself?) Not until p. 330 does she recall something else where peers don’t much matter: religion! Worse, she never notices what Thomas Sowell has voluminously documented in his accounts of ethnic economic specialization. It’s parents and relatives who pass on both specific occupations (e.g., Italians and marble-cutting or Cambodians and donut-making) and general attitudes toward hard work, thrift, and entrepreneurship.

Nor can peers account for social change among young children, such as the current switch from football to soccer, since preteen peer groups are intensely conservative. (Some playground games have been passed down since Roman times). Even more so, the trend toward having little girls play soccer and other cootie-infested boys sports did not, rest assured, originate among peer groups of little girls. That was primarily their dads’ idea, especially sports-crazed dads without sons.

It’s almost as if life is pretty complicated. So instead of arguing about Nature Versus Nurture in the generalized abstract, a more productive research agenda would be too look for different topics where either nature or nurture are more important.

For example, there’s decent evidence that young people tend to imprint on the type of landscape they’re living in around puberty and thus that parent decision has a lifetime effect on where they feel most at home. So, where you choose to live with your children can have a sizable influence on where they will choose to live decades later.

You can see this in Raj Chetty’s data that says the best place to grow up in the U.S. for working class kids is in the North Central (e.g., South Dakota). Some of this is a permanent cultural difference, but some of this is regional economic cycles — the cold parts of the Great Plains have done well in this century due to global demand for resources and technological advances, such as the Great Plains. But another aspect is that the place is so cold and bleak looking to people who didn’t grow up there — an East Coast character in a Jay McInerney novel says that the problem with the Midwest is that there’s nothing to see and nothing to keep you from seeing it — that there hasn’t been much in-flow of workers from outside the region, especially not from South of the Border. This allowed local blue collar workers to prosper from the long regional boom (which is likely now ending).

So, in Chetty’s data, the parents who chose to raise their kids in the North Central in the 1990s can look back with satisfaction because they helped their kids imprint on the place, which has helped them prosper in the 2010s.

In contrast, lots of people moved to North Carolina, a state that’s more broadly attractive in looks — not too cold, not too hot, lots of trees, some elevation changes — a place that’s not too severe looking for whatever you imprinted upon. And, in Chetty’s study, their kids got hammered. In part due to regional economic cycles (wood and furniture went out of demand with the collapse of the Housing Bubble), and in part due to a big influx of people from the rest of America and from Latin America.

Of course, what parents really want to know is what to do that will give their children permanent advantages. Chetty sells his study to the media on the idea that he’s about to discover Permanent Truths, because that’s what Science is, like, you know, gravitational waves. And indeed sometimes he comes up with findings that really are long term, like the conservative Dutch culture of Sioux County, Iowa is really better for kids than the culture of the Pine Ridge Sioux Indian reservation.

But much of what he’s discovered is that blue collar parents should try to have your kids imprint on parts of the country that will be more prosperous in 15 years than they are now.

Good luck with that.

• Tags: Chetty 
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Economist Raj Chetty, now at Stanford, has a new paper out based on his giant data trove of IRS 1040 returns. The IRS gave him anonymized access to track the life histories of a whole bunch of kids born in 1980-82 via their parents’ tax returns in 1996-2000 and their own tax returns in 2011-2012.

I’ve been keeping up a running commentary on the strengths and weakness of his analyses of this amazing data source since 2013. Last year, in a Taki’s Magazine article (“Moneyball for Real Estate“) focusing on Chetty’s county-level results, I noted gender gaps hurting boys in some counties. I wrote:

A close reading of the new 2015 paper by Chetty and Nathaniel Hendren, “The Impacts of Neighborhoods on Intergenerational Mobility: Childhood Exposure Effects and County-Level Estimates,” reveals much that is plausible. For example, the effect of local culture, such as gangs, can be different on boys and girls. Chetty and Hendren write:

This suggests that there are pockets of places across the U.S., like Baltimore MD, Pima AZ [Tucson], Wayne County (Detroit) MI, Fresno CA, Hillsborough FL [Tampa], and New Haven CT, which seem to produce especially poor outcomes for boys.

New Haven County is a fine place to live if you have daughters and you are a Tiger Mother professor at Yale Law School, but it’s a terrible place to move to if you have poor black sons. Chetty has no data on what percentage of boys who were moved to Baltimore, Detroit, or New Haven weren’t earning much in 2011-12 because they were in jail, but it’s obviously a considerable risk.

In contrast, Tucson, Fresno, and Tampa were all home construction boomtowns that got wiped out by the bursting of the Housing Bubble in 2008, a memorable cataclysm whose effects on his data Chetty doesn’t seem to have pondered.

Conversely, girls whose parents moved them when they were teens in the 1990s to now booming and low crime Manhattan are likely to pay a penalty in terms of lower family income in 2011-2012 because they are less likely to be married than if they had been moved to Salt Lake City.

Chetty has now followed up with a new paper looking at some of these gender gaps:

Raj Chetty
Nathaniel Hendren
Frina Lin
Jeremy Majerovitz
Benjamin Scuderi
Working Paper 21936

January 2016

We show that differences in childhood environments play an important role in shaping gender gaps in adulthood by documenting three facts using population tax records for children born in the 1980s. First, gender gaps in employment rates, earnings, and college attendance vary substantially across the parental income distribution. Notably, the traditional gender gap in employment rates is reversed for children growing up in poor families: boys in families in the bottom quintile of the income distribution are less likely to work than girls. Second, these gender gaps vary substantially across counties and commuting zones in which children grow up. The degree of variation in outcomes across places is largest for boys growing up in poor, single-parent families. Third, the spatial variation in gender gaps is highly correlated with proxies for neighborhood disadvantage. Low-income boys who grow up in high-poverty, high-minority areas work significantly less than girls. These areas also have higher rates of crime, suggesting that boys growing up in concentrated poverty substitute from formal employment to crime. Together, these findings demonstrate that gender gaps in adulthood have roots in childhood, perhaps because childhood disadvantage is especially harmful for boys.

Among children of the poorest families of the 1990s, girls are more likely to grow up to be employed at age 30 than are boys.

Here are the top ten and bottom ten “commuting zones” for more 30ish men than 30ish women working:

Screenshot 2016-02-03 17.13.17

Salt Lake City metro has lots of white people with 1950s social values where dad works and mom stays home raising the three kids. The next metros tend to be ones with Hispanics and conservative whites. The bottom ten metros, where more women than men work, all have high black percentages.

As usual, Chetty’s latest map turns out to be another one of Where the Blacks Are:

Screenshot 2016-02-03 17.16.41

When looking at the concentrations of red on the map, it’s hard not to say the word “Washington DC.” Just as JFK said D.C. combines northern charm and southern efficiency, Washington DC combines northern welfare and southern lackadaisicalness.

Chetty’s maps of social problems are so dominated by Where the Blacks Are that West Virginia, the worst white state in the country on many measures, comes out once again looking pretty good on his latest map.

Chetty doesn’t have individual family-level data on race, so he has to guesstimate the effects of race from the percentage of the geographical area that is black.

And even with that blunt of an instrument, it turns out the percent black in the locale is the dominant factor in this reverse gender gap of working women and idle men:

Screenshot 2016-02-03 17.19.23

The single factor that is off by itself in terms of correlation with this Reverse Gender Gap is “Frac. Black Population.”

This shouldn’t be a surprise: after all, in black Africa, feminist organizations do the opposite of what feminist organizations do in the rest of the world: complain that men let women do too much of the work.

Chetty’s data is downloadable here.

Here’s a 538 article with some nice graphs of Chetty’s latest data.

I can be brusque with Chetty in analyzing his analyses, but that’s because he has been handed an unprecedented data set of your tax returns. So we all owe it to ourselves to publicly discuss what he’s doing right (which is considerable) and what he should do to improve his work in the future.

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