Tag Archives: race

On Asian-American earnings

In a previous post I showed that generalizations about Asian-American incomes often are misleading, as some groups have above-average incomes and some have below-average incomes (also, divorce rates) and that inequality within Asian-American groups was large as well. In this post I briefly expand that to show breakdowns in individual earnings by gender and national-origin group.

The point is basically the same: This category is usually not useful for economic statistics, and should usually be dropped for data on specific groups when possible.

Today’s news

What’s new is a Pew report by Eileen Patten showing trends in race and gender wage gaps. The report isn’t focused on Asian-American earnings, but they stand out in their charts. This led Charles Murray, who is fixated on what he believes is the genetic origin of Asian cognitive superiority, to tweet sarcastically, “Oppose Asian male privilege!” Here is one of Pew’s charts:

pewraceearn

The figure, using the Current Population Survey (CPS), shows Asian men earning about 14.5% more per hour than White men, and Asian women earning 11% more than White women. This is not wrong, exactly, but it’s not good information either, as I’ll argue below.

First a note on data

The CPS data is better for some labor force questions (including wages) than the American Community Survey, which is much larger. However, it’s too small a sample to get into detail on Asian subgroups (notice the Pew report doesn’t mention American Indians, an even smaller group). To do that I will need to activate the ACS, which is better for race/ethnic detail.

As a reminder, this is the “race” question on the 2014 American Community Survey, which I use for this post:

acsrace2014

There is no “Asian” or “Pacific Islander” box to check. So what do you do if you are thinking, “I’m Asian, what do I check?” The question is premised on that assumption that is not what you’re thinking. Instead, you choose from a list of national origins, which the Census Bureau then combines to make “Asian” (the first 7 boxes) and “Pacific Islander” (the last 3) categories. And you can check as many as you like, which is good because there’s a lot of intermarriage among Asians, and between Asians and other groups (mostly Whites). This is a lot like the Hispanic origin question, which also lists national origins — except that question is prefaced by the unifying phrase, “Is Person 1 of Hispanic, Latino, or Spanish origin?” before listing the options, each beginning with “Yes”, as in “Yes, Cuban.”

Although changes have not been announced, it is likely that future questions will combine the race and Hispanic-origin questions, and also preface the Asian categories with the umbrella term. This may mark the progress of getting Asian immigrants to internalize the American racial classification system, so that descendants from groups that in some cases have centuries-old cultural differentiation start to identify and label themselves as from the same racial group (who would have put Pakistanis and Japanese in the same “race” group 100 years ago?). It’s hard to make this progress, naturally, when so many people from these groups are immigrants — in my sample below, for example, 75% of the full-time, year-round workers are foreign-born.

Earnings

The problem with the earnings chart Pew posted, and which Charles Murray loved, is that it lumps all the different Asian-origin groups together. That is not crazy but it’s not really good. Of course every group has diversity within it, so any category masks differences, but in my opinion this Asian grouping is worse in that regard than most. If someone argued that all these groups see themselves as united under a common identity that would push me in the direction of dropping this complaint. In any event, the diversity is interesting even if you don’t object to the Pew/Census grouping.

Here are two breakouts. The first is immigration. As I noted, 75% of the full-time, year-round workers (excluding self-employed people, like Pew does) with an Asian/Pacific Islander (Asian for short) racial identification are foreign born. That ranges from less than 4% for Hawaiians, to around 20% for the White+Asian multiple-race people, to more than 90% for Asian Indian men. It turns out that the wage advantage is mostly concentrated among these immigrants. Here is a replication of the Pew chart using the ACS data (a little different because I had to use FTFY workers), using the same colors. On the left is their chart, on the right is the same data limited to US-born workers.

api1

Among the US-born workers the Asian male advantage is reduced from 14.5% to 4.2% (the women’s advantage is not much changed; as in Pew’s chart, Hispanics are a mutually exclusive category.) There are some very high-earning Asian immigrants, especially Indians. Here are the breakdowns, by gender, comparing each of the larger Asian-American groups to Whites:

api2

Seven groups of men and nine groups of women have hourly earnings higher than Whites’, while nine groups of men and seven groups have women have lower earnings. In fact, among Laotians, Hawaiians, and Hmong, even the men earn less than White women. (Note, in my old post, I showed that Asian household incomes are not as high as they look when they are compared instead with those of their local peers, because they are concentrated in expensive metropolitan markets.)

Sometimes when I have a situation like this I just drop the relatively small, complex group, which leads some people to accuse me of trying to skew results. (For example, I might show a chart that has Blacks in the worst position, even though American Indians have it even worse.)

But generalization has consequences, so we should use it judiciously. In most cases “Asian” doesn’t work well. It may make more sense to group people by regions, such as East-, South-, and Southeast Asia, and/or according to immigrant status.

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Tell me why it’s not racist to oppose Black Oscar categories

cr

Good comedy is like sociology only better. Today’s edition: Race and gender.

In Chris Rock’s monologue at the Oscars, he said this:

Hey, if you want Black nominees every year, you need to just have Black categories. That’s what you need. You need to have Black categories.

You already do it with men and women. Think about it: There’s no real reason for there to be a man and a woman category in acting.

C’mon. There’s no reason. It’s not track and field.

You don’t have to separate ’em. You know, Robert De Niro’s never said, “I better slow this acting down, so Meryl Streep can catch up.”

No, not at all, man. If you want Black people every year at the Oscars, just have Black categories. Like Best Black Friend.

If you say, “Where does it end?”, then tell me why you don’t oppose the gender categories. Tell me why it’s not racist to leave the acting gender categories unquestioned but oppose race categories. Not making that argument, of course, just asking the question.

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Looks like racist Southern Whites like Trump

We’ve been given lots of reasons people support Trump, like authoritarian attitudes and “the legitimate anger that many Americans feel about the course that the country has taken.” But it’s also racism, I’m pretty sure. Or at least racists.

It’s hard to measure racism – or even define exactly what we mean by racism. One way to approach it is racial salience, which is closely related to racism, and we can measure that with the proportion of the local population that is Black. (I reviewed a bunch of this research here.)

It’s also usually hard to get measures of just White behavior, which makes a Southern Republican primary perfect — they’re basically all White. This is also good because we’re trying to figure out what’s driving the Trump thing. In South Carolina, it looks like it was driven by the presence of Blacks and people born in South Carolina, and less urban populations.

In counties with less than 40% of the population born out of South Carolina — 33 of the 46 countries — there is a strong positive relationship between Trump vote share and population proportion Black. Here is the plot, with the high local-born counties in red:

Those red dots are the classic percent-Black racism pattern. Whites are more racist where there are more Blacks. Interesting in the case of Trump, because most of his overt racism is directed at immigrants and Muslims. But the regular anti-Black racists have been very apparent in his crowds, and in his endorsements.

I looked at the out-of-state share because of the outliers. That’s Horry County in the top left (site of Myrtle Beach) and Richland County at the bottom right (site of Columbia), two places with less of an old-school (i.e., rural slave plantation) feel. The rural thing is important, too, as larger counties voted less for Trump.

I’d love to see someone do this for the Super Tuesday states. The other variables you could use would be slave populations in 1850, or post-bellum lynchings.

Here it is in regression terms, with the 46 counties (ask me for the data and code):

trump-sc-black-gop-reg

Objection addendum:

This is just a descriptive analysis. And I probably only presented it because it fits so well with my prior assumptions – so that’s good or bad on my part, I guess. That said, I did it and posted it so I own it.

Someone on Facebook objected that I am obscuring the positive slope in the high out-of-state counties. In fact, if you exclude Horry County, you see a positive slope in those counties as well. As you would if you included all the counties together. That black line is not good because it’s so skewed by the outlier. However, the regression didn’t use the categorical breakdown of out-of-state population. There you can see the positive effect of proportion Black in all counties, but at high levels of out-of-state the model says proportion Black would turn negative – which is because of the Horry County outlier.

In fact, if you just drop Horry County, the interaction and out-of-state effects are no longer significant, it’s just proportion Black and population size! So, you could rephrase the conclusion as: more Trump votes where there is more Black population, and in smaller-population counties, and ignore the out-of-state thing. But I don’t know enough about South Carolina to know whether it’s justifiable to exclude Horry County that way. It’s one of the most populous counties (6% of the total).

Here is the chart with all the counties, dropping the out-of-state distinction. Still a nice positive relationship and a good racism story, but weaker:

trump-sc-black-gop2

And just to show how extreme the effect of the outlier is, here are just the high out-of-state counties, with and without Horry:

horry

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When the map says race but all you can talk about is fatherhood

Raj Chetty and colleagues have a new paper showing that “childhood environments play an important role in shaping gender gaps in adulthood.” Essentially, boys from poor or or single parents are doing worse. Also, this gender difference is greater in Black and poor places.

The tricky thing with this data, and I don’t blame Chetty et al. for this, although I would like them to say more about it, is that they don’t know the race of the children. The data are from tax records, which allow you to know the income and marital status of the parents, but not the race. But they know where they grew up. So if they have a strong effect of the racial composition of the county kids grow up in, but they don’t know the race of the kids, you have to figure a big part of that is race of the kids — and by “you” I mean someone who knows anything about America.

So here’s their map of the gender difference in employment rates associated with having poor parents:

chetmap

To help make the point, here is their list of local areas at the top and bottom of the map:

chettab

I hope that is enough to make the point for the demographically literate reader.

I credit them in this paper for at least using percent Black as a variable, which they oddly omitted from a previous analysis. This allows the careful reader to see that this is the most important local-area variable — which makes perfect sense because it is doing the work of the individual data, which doesn’t include race.

racechettyeffects

Wow!

It’s important that these examples are all about employment rates. We know that the penalty for being a Black man is especially large for employment, partly because of the direct effects of mass incarceration, but also because of discrimination, some of which is directly related to incarceration and the rest of which may be affected by its aura. This is not something we measure well. Our employment reporting system does not include prison records. Prisoners are excluded from the Current Population Survey, but then included when they are released. So they show up as jobless (mostly) men.

Whenever you see something about how race affects poor men, you have to think hard about what incarceration is doing there — we can’t just rely on the data in front of us and assume it’s telling the whole story, when we know there is a massive influence not captured in the data.

This is exactly what marriage promoters delight in doing. I give just one example, a blog post by the Brookings Institution’s Richard Reeves, which — amazingly, astoundingly, remarkably, disappointingly, not surprisingly — discusses the effect of growing up poor and “less-educated” in Baltimore (Baltimore!) without once mentioning race or incarceration. Instead, he goes right to this:

Wanted: Fathers

Of course, there is much more to being a man than money: in fact, to define masculinity in breadwinning terms alone is a fatal move. As Barack Obama said on Fathers’ Day seven years ago, fathers are “teachers and coaches. They are mentors and role models.” But as he also said, “too many fathers are missing—missing from too many lives and too many homes.” In its poorest neighborhoods, America faces a fathering deficit, one that will make it even harder for the boys of today to make it as men in the new world.

Fatherhood is important. You could investigate a fathering deficit, but if you really cared about it you want to look at in the context of well-known, massive causes of harm to Black boys in America, chief among them racism and mass incarceration.

 

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I overspoke myself on Twitter

Possibly not the only time.

A blog called Random Critical Analysis (RCA) has posted, “On Philip Cohen’s knee-jerk response to Chetty’s “causal mobility” data and its association with single-motherhood.” I now must admit that I overspoke myself on Twitter.

But I think the blog post I wrote holds up OK. I complained in the post that the now-famous Chetty et al. analysis of intergenerational mobility had mishandled race, leading to people like David Leonhardt (and rightward from there) to conclude that the big story of hampered social mobility is family structure. It’s part of the overall pattern of polite society embracing the issue of economic inequality but also using that as a foil to avoid the issue of race inequality.

Brad Wilcox has seized on the Chetty analysis, repeating ad nauseum the quote that single parenthood is the “single strongest correlate of upward mobility.” My beef was, and is, that the analysis that was based on — which used the rate of single parenthood at the labor market level to predict intergenerational mobility — did not control for the racial composition of the labor market. That’s an obvious problem when your map of mobility looks like this:

mobilitymap

When your analysis is ecological, that is, based only on aggregate characteristics, you have to be very cautious about drawing conclusions. It’s especially dicey in the Chetty case because the basic data, from tax, returns, includes family structure (because of parents’ marital status) but not race (which doesn’t go on your tax form). And that’s even more dicey because we know that at the individual level single parenthood is definitely not the “single strongest correlate of upward mobility.” I’ve been writing about this for years (follow the single-mother tag), but this figure from 2012 sums it up nicely (details in the old post):

You just have to keep that in perspective when you jump to an aggregate-level analysis. The difference between averages in Atlanta versus Salt Lake City — important as it is — is never going to be as big as the difference between a rich family and a poor family. Social parents’ class matters much more for determining children’s social class than does family structure.

Anyway, RCA is reworking my very simple analysis showing the effect of single motherhood rates was reduced by two-thirds when a single control for racial composition (percent Black) was added. That’s making the obvious point that, because single parenthood and percent Black in the local area are so strongly correlated, if you don’t take percent Black into account it looks like single parenthood has a huge, independent effect — which incorporates the effects of racism or other community factors associated with historical race composition. The new RCA post goes much further in the analysis, and concludes:

It ought to be pretty clear by now single-motherhood is capturing something quite powerful and that, contrary to Cohen’s strong assertions, it is not well explained by race.  If anything, single-motherhood mediates the black association much better than the reverse.

I’m not persuaded by the conclusion; you can evaluate it yourself. But the premise of the RCA post is actually not my blog post, but my tweets. As time went by I apparently became frustrated at the continued repetitions of the single mother thing by people who were ignoring my very clever post, and with the carelessness that distance allows I overstated my own claim, so I tweeted this,

The table and the highlighting are mine. What I should have paid attention to was my own next sentence after the underlined part: “That’s not an analysis, it’s just an argument for keeping percent Black in the more complex models.” I didn’t do a serious analysis — I just did enough to prove the point that racial composition should be in the model. Without that, you shouldn’t run around saying single parenthood is the most important factor. (RCA also believes I shouldn’t have said in the post that “Percent Black statistically explains the relationship between single motherhood and intergenerational immobility.”  I think “explains” is defensible, in that the effect is no longer statistically distinguishable from zero at the conventional level, but it’s clearly not the same as proving there is no effect, so I’ll take the criticism, too.)

I actually first did the little analysis in an earlier post, debunking a univariate analysis by Scott Winship and Donald Schneider. In that case I concluded: “This [my analysis] is not a rigorous examination of the cause of intergenerational immobility. It is just debunking one bivariate story that is too easily picked up by the forces of bad.” That seems about right.

Anyway, in conclusion, it was incorrect based on what I did for me to tweet, “the single mother effect in Chetty is all in the % Black effect.” I should just say single parenthood hasn’t been proven to matter as much as its partisans say it has. Even if it’s less effective in a tweet. This is a common frustration, that it takes more work to debunk something than to bunk it in the first place. But that’s not a good excuse.

Finally, I’m grateful that what I write matters enough that someone would go to the trouble of testing my claims to hold me accountable.

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Quick correction on that 90-percent-of-faculty-are-White thing

The other day I saw a number of anti-racist people tweeting that “nearly 90% of full-time professors are White.” As I have previously complained when 90% of the full professors at my then-school (UNC) were White, I was interested to follow up. Unfortunately, that popular tweet turns out to be a stretched description of a simple error.

The facts are in this Education Department report from May, which was reported at the time by The Ed Advocate, and suddenly started going around the other day for unknown reasons. The “nearly 90%” is the Ed Advocate’s description of 84%, which is the percentage White among full-time full professors, which the original report in one place accidentally describes as just full-time professors. Among all full-time instructional faculty, in fact, 79% are White. So the headline, “Study: Nearly 90 Percent of Full-time Professors Are White,” was a conflation of two errors. It presumably became popular because it put a number to a real problem lots of people are aware of and looking for ways to highlight.

Here is the original chart:

whitefac

The problem of White over-representation among college faculty is not that apparent in this national 79% statistic. Consider, for example, that among all full-time, full-year workers age 40 and older (my made-up benchmark), 71% are non-Hispanic White. Among those with a Masters degree or higher, 77% are White. So faculty, nationally and at all levels, don’t look that different from the pool from which they’re drawn.

The 84% full professor statistic reflects the greater White representation as you move up the academic hierarchy. And that’s not just a question of waiting for younger cohorts with more non-White faculty to age into the professoriate. Because the pipeline isn’t working that well, especially for Black faculty. Which brings me back to my old UNC complaint, which focused mostly on Back under-representation. In 2010 I noted that the North Carolina population was 22% Black, while the UNC faculty was 4.7% Black. But full professors at UNC were just 2.4% Black, while the assistant professors were 7.5% Black. Is that the pipeline working? Well, only 4.5% of the recent faculty hires were Black.

I went back to check on things. As of the 2014 report (they’re all here), the update is that UNC has stopped reporting the numbers by rank, so now all they say is that 5.2% of all faculty are Black, and they don’t report the makeup of recent hires. So take from that what you will.

And what about further up the pipeline? I previously shared numbers showing a drop in Black representation among entering freshmen at the University of Michigan, from 10% to 5% over the 2000s. The trend at UNC is in the same direction:

unc black studentsOf course we always need to be cautious about numbers that support what we already know or believe. Some people will respond to this by saying, “but the point remains.” Right, but if the number is irrelevant to the point, there’s no need to use the number. Plenty of people can say, “In all my undergraduate years, I never had a Black professor,” or some other highly relevant observation.*

On the other hand, others of us need to disabuse ourselves of the notion that progress on under-representation is just happening out there because everyone thinks it should and it’s just a matter of time. That common assumption allows defensive administrators to do write thinks like this caption (from UNC’s 2011-2012 report):

unc1112

This is misleading: There was a big increase in Hispanic students (North Carolina has a growing Hispanic population) and Asian students, and marked drops in Black and American Indian students. But “overall, steady increase” is an easy narrative to sell.

If they scaled that chart from 0 to 12 and dropped Whites, “overall, steady increase” would look like this:

uncscaled

* I think I had three great Black professors at Michigan: Walter Allen, Robin D. G. Kelley, and Cecilia Green, each of whom changed my life forever. Sorry if I’m forgetting someone.

Related posts:

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