Mystery solved? Why “women in their 20s” earn more

When pundits like David Brooks get sucked into the factoid-warp of Hanna Rosin (The End of Men) and Liza Mundy (The Richer Sex), they are always floored by the idea that young women earn more than young men. To them this represents the future. And woe to any woman trying to convince a jury she’s being discriminated against while these books are in the headlines. Brooks spelled it out real simple: “Women in their 20s outearn men in their 20s.”

That’s easily shown to be wrong (still holding my breath for the correction). But the more detailed factoid, the one you get in the long-soundbite version of the end-of-history, is that “median full-time wages for single childless women ages 22-30 exceeds those of single childless men in the same age group,” as reported in USA Today, for example. That was calculated by Reach Advisors using the American Community Survey.*

Making broad conclusions based on weird data slices is bad practice. And this is a great case study in why.

Who are those full-time working, not-married and childfree 20-somethings in metro areas? I ran that filter over the 2010 ACS data available from IPUMS, and this jumped out:

OK, so for whatever reason, notice that this group includes a disproportionate share of White women and Latino men. That turns out to be pivotal, since these particular Latino men have very low earnings. Check the earnings by race/ethnicity and gender:

So that’s it. The overall $1,000 advantage for women (seen in the bars on the far right) is the result of these particular Latino men’s low earnings. The high earnings of these White women are important, of course, they’re just not higher than White men’s. If you just look at Whites or Blacks there is no advantage for women.

I am all for getting into the problem of Latino men’s (and women’s) low average earnings. But that’s not where this story has been going. More than anything this is just shoddy statistical cherry-picking.

Hey media mega-conglomerates: give that meme a rest!

* Reach Advisors also limited the analysis to metro areas, so I did that as well. I don’t get as big an advantage for “women” as that reported in that 2010 USA Today article, which said it was based on 2008 data (they got an 8% gap, I get 3%). I don’t care to figure out exactly the source of the differences (and Reach hasn’t published their code).

Quick book review: The Price of Inequality

The Price of Inequality: How Today’s Divided Society Endangers Our Future, by Joseph E. Stiglitz (W. W. Norton, 2012)

My economics training as a sociologist — with a background in American Culture studies — has been spotty and roundabout. I got a healthy dose of Marxist economics in college, and then some feminist economics, a little human capital theory and some dated econometrics in grad school and since.

All that made reading made it interesting, and also frustrating, to read The Price of Inequality, by Joseph Stiglitz – a winner of the Nobel Prize for economics and an “insanely great economist,” according to Paul Krugman.

On the plus side, I am glad to see someone within mainstream economic theory freely discussing all the ways that common assumptions simply do not predominate in the modern economic scene. Especially helpful in this category is his discussion of how “rents” accumulate vast resources at the upper end of the income distribution, with perverse effects on economic development and politics alike. At the very top — in the finance sector especially, but also in energy and big manufacturing — there is nothing like free-market competition. And the beneficiaries of those distortions are the most powerful players in the economy and political system.

It is refreshing to see this concentration of wealth described as waste and distortion, as their vast profits provide little gain to anyone else. In fact, dumping vast wealth on the 1% creates a drag on the macroeconomy while fueling the historic run-up in economic inequality. This is all very timely and takes you right through the financial crisis up to early 2012.

So if you want to understand from an economic perspective how “the market” in America isn’t the way it’s supposed to be, this book may be for you.

Top 1% income shares, including capital gains, for the U.S. and Sweden. From the World Top Incomes Database.

The other good thing about the book for many readers will be its cogent and comprehensive economic rationale for the liberal reforms that many of you probably supported already. Stiglitz makes the case that a suite of reforms – an agenda Rachel Maddow, Elizabeth Warren and Robert Reich probably agree on – would, by (directly or indirectly) increasing taxes (or reducing subsidies) on the wealthy and redistributing wealth downward, reduce the federal debt, increase economic growth, and reduce economic inequality all at the same time.

Round numbers: if the richest 1% earn about 20% of all income, then taxing them another 10% would generate government revenue equivalent to 2% of GDP. (And it wouldn’t hurt anything, since they just hoard or waste their extra cash anyway rather than “creating jobs” with it, and they’re so greedy they wouldn’t be discouraged by the disincentive effect of higher taxes.) That’s an amount of money that could actually be useful for poor people.

The frustration I feel reading the book is more amorphous. I think there have to be better ways of describing this whole system than using the language of mainstream economics, which ends up painting a picture of an entire system that does not work according to the rules as imagined. Concepts like power, social class, social networks, elites and reification do not figure heavily in this story. In fact, Stiglitz’s apparent ignorance of sociology is sometimes funny as in this passage:

Social sciences like economics differ from the hard sciences in that beliefs affect reality: beliefs about how atoms behave don’t affect how Adams actually behave, but beliefs about how the economic system functions affect how it actually functions. George Soros, the great financier, has referred to this phenomenon has “reflexivity,” and his understanding of it may have contributed to his success.

I guess after what people like me have made of econometrics it’s only fair that economists would attribute the idea of reflexivity to Soros. (The discussion of reflexivity in Anthony Giddens’s book The Consequences of Modernity is very approachable.)

Anyway, the book is easy to read and informative, and has lots of footnotes and references.

Do Asians in the U.S. have high incomes?

The Pew Research Center last week released a lengthy research report on Asians in the U.S., titled “The Rise of Asian Americans.” It combines information from the Census and government sources with the results of Pew’s own national survey of attitudes and opinions.

The report has lots of good information, but there are some thorny problems here. I’ll describe a few problems, then offer one data exercise to help clarify. This gets technical and it’s long, so I will give you the substantive conclusion at the top:

1. Because Asians are a diverse category made up of groups with very different profiles, and their household composition and geographic distribution vary by national origin group, generalizations are often unhelpful.
2. Among the 10 largest Asian groups, five (Japanese, Indian, Chinese, Filipino, Korean) are above average in income and five (Vietnamese, Pakistani, Laotian, Cambodian, Hmong) are below. But all 10 Asian groups are doing better compared to the national average than they are compared to the average incomes in the places they live — they are richer nationally than they are locally.
3. The amount of income inequality within Asian groups varies as well. Pakistanis,  Chinese, Koreans and Indians have the highest levels of inequality, while Filipinos and Laotians have low levels of inequality.

Details follow.

But first: Who is Asian? On the Census questionnaire, Asian is not exactly a category – rather, the category is created from all the responses of people who specify Asian national origins in the race question. To refresh, this is the question:

So “Asian” is all the people who specify Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese or “Other Asian.” (The right-hand column is for Pacific Islanders.) Yes, in the U.S., Hispanic/Latino national origins are “ethnicities,” but Asian national origins are “races.” Go figure.

That lack of a common definition is compounded by two factors: First, there is so much diversity among Asians that the using a single category is as challenging statistically as it is politically. And second, Asians – as the Pew report shows – have a high rate of intermarriage with Whites, as well as (among some groups) across Asian national-origin lines. As a result, some Asian groups have high rates of “multiple-race identification” — especially those whose immigration was generations ago.

The controversy over the Pew report is summarized in this Color Lines story and this response from the Asian American / Pacific Islander Policy Research Consortium. The gist of it is that the report was too rosy in its description of Asian advantages and too homogenizing in its treatment of Asian diversity – as a result repeating the “divisive trope” of the “model minority.” Here’s part of the summary from the New York Times:

Drawing on Census Bureau and other government data as well as telephone surveys from Jan. 3 to March 27 of more than 3,500 people of Asian descent, the 214-page study found that Asians are the highest-earning and best-educated racial group in the country.

Among Asians 25 or older, 49 percent hold a college degree, compared with 28 percent of all people in that age range in the United States. Median annual household income among Asians is $66,000 versus $49,800 among the general population.

In the survey, Asians are also distinguished by their emphasis on traditional family mores. About 54 percent of the respondents, compared with 34 percent of all adults in the country, said having a successful marriage was one of the most important goals in life; another was being a good parent, according to 67 percent of Asian adults, compared with about half of all adults in the general population.

Asians also place greater importance on career and material success, the study reported, values reflected in child-rearing styles. About 62 percent of Asians in the United States believe that most American parents do not put enough pressure on their children to do well in school.

Did Pew homogenize or glorify too much? I don’t know. Here’s a graph from the report, which shows that Asian groups differ, but they all have higher-than-average household incomes:

The Color Lines story quotes Deepa Iyer, head of the National Council of Asian Pacific Americans and executive director of South Asian Americans Leading Together:

The danger in framing the study the way Pew did, and the way the media picked up on it, is that folks who are in the general public and institutional stakeholders and policy makers might get the impression that they don’t necessarily need to dig deep into our communities to understand any sort of disparities that exist.

The problem of homogenizing Asians is longstanding in American sociology. In most data analyses, the Asian sample is small to begin with, so they are often collapsed into one category (which I’ve done) or dropped from the story (which I’ve also done, angering some readers). Here is a typical passage, from a 2001 article by Leslie McCall:

That didn’t stop her (or lots of other people) from extensively analyzing Asians as a combined group, and offering speculation on her results.

There are other examples. In my experience, Jen’nan Read and I broke out six Asian groups for a study of women’s employment with the 2000 Decennial Census data — which reinforced my conviction that disaggregating is best. (This 2010 Census report gives some detail on more than 20 national-origin groups.)

Some new numbers

Anyway, I’ve got four specific issues to address with Pew’s comparison of household incomes (some of which they acknowledge in the report): a) Household composition differs between groups (more or fewer kids, grandparents); b) Asians disproportionately live in parts of the U.S. with high costs of living (like Hawaii and California, and urban areas generally); c) different members of a household might have different “race” identities (so, a Korean man married to a Chinese woman might define their child is either or both); and d), levels of inequality differ between groups, so central tendency comparisons don’t capture the whole story.

In this exercise I address these problems. I adjust for household size and composition, count individuals’ own “race” rather than imposing a single identity on the household, compare incomes to the average in the local metropolitan area as well as the national average, and compare levels of within-group inequality.

All in one blog post! Someone might want to work this up into a real paper (and maybe someone else already has? The last time I really read about this was more than 10 years ago.) So I’m just offering this approach as a suggestion, and making my code available if anyone wants to pursue it (see below).

I use the 2006-2010 combined American Community Survey, from IPUMS, for maximum recent sample size. This is about 15 million people, and the Asian samples range from about 160,000 Chinese to 7,500 Laotians. I identify individuals according to their individual “race.”

I calculate their incomes as per capita household income, adjusted for economies of scale. To do that, I count adults as 1 person, kids under 18 as .7 of a person, and divide the total household income by that count to the power of .65 for economies of scale (see here for details). Then I take the natural log of all that to pull in the right tail of the distribution (so the mean isn’t pulled up by the ~1%). When I’m done, everyone in the household has the same income, and the distribution is pretty normal. Nice!

To see what this does: The mean household income for individuals in the country in 2006-2010 is $79,174, and the natural log of the composition-and-scale adjusted per capita income is 10.26 (see figure), which works out to $28,439. In comparison, the logged incomes for Asians range from 10.6 (~$40,000) for Indians and Japanese, down to 9.7 (~$16,000) for Hmong.

To deal with the issue of living in expensive areas, I take the mean of that logged income in each metropolitan area, and compare each person’s own per capita income to that. So a score of 0 means you have the average income in your area — more than 0 means richer than average, less than zero is poorer.

There is not one correct answer about how to do this: Having an average income in a rich area still means you can buy more stuff on Amazon than someone with a lower absolute income. But it might also mean having a smaller house, or not being considered rich by your neighbors. On the third hand, if a rich family moves to a rich area, we shouldn’t feel sorry for them for not being above average in their neighborhood. For your consideration, I show the incomes compared with the national average and with the local metro mean, for the 10 largest Asian groups (click for higher resolution):

To interpret the figure, you can see that Japanese and Indians are about 0.36 higher in log dollars than the national average but only 0.26 higher than their metro-area averages. On the downside, Hmong individuals have adjusted per capita incomes of 0.58 less than the national average, but 0.63 less than their local average.

Higher-than-average-income Japanese, Indians, Filipinos and Chinese are about 73% of the total; Koreans are about average, and the lower-than-average groups are 17% of the total. By this method, then, a big majority of Asians in the U.S. belong to above-local-average income groups, but a substantial fraction are well below average. And they are all doing worse relative to their metro area neighbors than they are to the national average.

Notice how it’s different from the Pew figure. In that, Vietnamese households had higher incomes than Koreans, and both were above the national average. Here Koreans are doing substantially better, mostly as a result of the household size adjustments. Also, the smaller groups I show – the ones Pew did not detail in that figure – are the poorer ones. And they are also doing worse locally relative to their national position.

Finally, consider the inequality within groups. Without doing a full-blown analysis of this, I can show the importance of the question with a simple box-and-whisker plot. This shows the distribution of income — adjusted as described above for household composition and size — for each group, including non-Asians for comparison.

The graph shows a lot of information in a small space:

• The line through the middle of each box is the median, or mid point, of each income distribution.
• The blue + sign is the mean. The further the mean is above the median, the more rich people there are pulling the mean up.
• The top and bottom of the boxes are the 75th and 25th percentiles. The further apart they are, the greater the income gap between top and bottom.

(The top whiskers, which can be used to show the highest point in each distribution, aren’t shown here, because they’re so far away it would make the graph unreadable.)

As I mentioned at the top, the graph shows that Pakistanis and Chinese, and to a lesser extent Koreans and Indians, have high levels of inequality — their + signs are far from their median lines, and their 75/25 spreads are large. On the other hand, Filipinos, Laotians and Hmong have much narrower spreads.

Practically speaking, all this means that some groups are misrepresented by measures of the overall status of “Asians,” especially the smaller, poorer groups. And further, that generalizing will represent some groups worse than others because of their internal diversity. For example, the average Chinese American is quite a bit richer than the average non-Asian American, but the poorest 25% of Chinese are not much better off than the poorest 25% of the population at large.

Like I said, just an idea, with a few examples.

Take it away

Feel free to do it more, and/or better, yourself. Here’s my SAS code. Please credit me if it works, but don’t blame me if it’s wrong. This has not been peer-reviewed – it’s rough work product. Send any corrections written on the back of a $20-bill. (Everyone else: You can stop reading now!)

Just get these variables from IPUMS:

SERIAL
 METAREA
 HHINCOME
 PERWT
 AGE
 RACED

And then do this to them:

/* exclude households with no income */
if hhincome>0;
/* this codes folks into this scheme, with Asians from richest to poorest:
0="Not Asian"
1= "Japanese" 
2= "Indian" 
3= "Filipino" 
4= "Chinese" 
5= "Korean" 
6= "Vietnam" 
7= "Pakistani"
8= "Laotian"
9= "Cambodian"
10= "Hmong"
11= "OtherA"
12= "twoplusA" 
*/
/* these codes refer to RACED, the detailed race variable on IPUMS */
/* Count asians as those who are asian alone, multiple asian, asian and white, asian and PI, or white-asian-PI */
asian=0;
if raced in (400 410 420 811 861 911) then asian=4;
if raced in (610 814) then asian=2;
if raced in (600 813 864 865 914) then asian=3;
if raced in (640 816) then asian=6;
if raced in (620 815) then asian=5;
if raced in (500 812) then asian=1;
if raced in (660) then asian=9;
if raced in (661) then asian=10;
if raced in (662) then asian=8;
if raced in (669) then asian=7;
if raced in ( 663 664 665 666 667 668 670 671 672 810 817 818 860 867 868 910 915) then asian = 11;
if raced in ( 673 674 675 676 677 678 679 819 869) then asian = 12;
/* so the variable labels display in output */
format
 METAREA METAREA_f.
 ASIAN asian.
;
/* add the decimal to the weight variable */
format PERWT 11.2;
run;
/* this counts up the number of kids and adults in each household */
proc sort data=temp; by serial; run;
data hh;
set temp (keep=serial age);
by serial;
if first.serial then do;
kids=0;
adults=0;
end;
retain kids adults;
if age le 18 then do; kids=kids+1; end;
if age gt 18 then do; adults=adults+1; end;
keep serial kids adults;
if last.serial;
run;
proc sort data=hh; by serial; run;
/* this merges in those people counts, and then calculates the household income variable */
data people;
merge temp hh; by serial;
equiv = hhincome/((adults+(.7*kids))**.65);
lnequiv = log(hhincome/((adults+(.7*kids))**.65));
run;
/* this outputs the mean logged household equivalent income for each metro area (with non-metro folks as 0 */
proc means noprint data=people;
var lnequiv;
class metarea;
weight perwt;
output out=msa mean=msaequiv;
run;
proc sort data=msa; by metarea; run;
proc sort data=people; by metarea; run;
/* this merges in the metro area variable and calculates the income-difference variable */
data merged;
merge people (in=a) msa;
by metarea;
if a;
relhhinc = lnequiv-msaequiv;
run;
/* Distribution of the logged income variable */
proc univariate data=merged; var lnequiv; run;
proc univariate data=merged; var lnequiv; class asian; run;
/* Boxplots */
proc sort data=merged; by asian; run;
title 'Income distributions, household composition- and scale-adjusted';
proc boxplot data=merged;
 plot equiv*asian / clipfactor = 1.5 grid;
where asian le 10;
run;
title;
/* National income means */
proc means mean data=merged;
var lnequiv;
weight perwt;
run;
/* National asian income means by group */
proc means mean missing data=merged;
var lnequiv; class asian; weight perwt;
run;
/* Relative income for each Asian group, for metro people only */
proc means mean;
var relhhinc; class asian; weight perwt;
where asian >0 and metarea>0;
run;

That giant gobbling sound (is the 1% eating more and more of the cookies)

The Congressional Budget Office has a new report on trends in the income distribution. The big news is the 1%’s blitzkrieg assault on equality.

But it’s not just another rehash of Census numbers. Two adjustments they made seem especially good. First, they used a tricky matching method to combine Current Population Survey numbers (which do better at benefits and low-income households) combined with Internal Revenue Service data (which is better for high-end data). Second, they adjusted for household size and composition, and calculated distributions before and after taxes and transfers, and among different kinds of income.

The headline is the changing share of after-tax-and-transfer household income. Every group except the top 1% had a smaller share of income in 2007 than they did in 1979, or just an equal share in the case of the 81st-99th percentile group. That means the top quintile’s whole gain came in the top 1%.

That is very important. A source of outrage for the hundreds of thousands of Facebook users posting, commenting, or Liking Occupy Wall St. and its related pages.

It would be misleading, however, to view the chart as showing that incomes fell for the other groups. Income growth has been very skewed toward the top, but it is by no means confined to the top 1%. Here is my graph showing the income cutoffs for each quintile, and for the top slices separately. These are the bottom cutoffs in 1979 and 2007 (in inflation-adjusted dollars), with the percentage change in the backgrounded bars.

(Note there is no cutoff for the bottom quintile — the price of entry for that group is always $0).

Two thoughts about this.

1. Even if there were no 1%, if the graph only included the green bars, there would be plenty of increasing inequality for what might then be called “the 80%” to protest. The 81st-99th folks may be lucky to have the popular anger directed at the grotesque opulence of the sliver above them. (I’m not diminishing the 1%’s income gains, but as Matt Taibbi pointed out yesterday, the object of opposition is not just their income, but their influence.)

2. If you look at the families and networks of the top 1%, how many of them have relatives, friends, and even co-”workers” who are only in the top 10%? Would a self-respecting 1% family be appalled if their son married someone from a stable 5%-er family?

What I’m wondering is whether the 1% folks are merely a statistical convenience rather than a socially cohesive group (class?). That’s an empirical question that national income distributions can’t necessarily answer.

The CBO report is here, a summary is here, and the blog post version is here.

Little income distribution graph

From the department of unhelpful statistics today I read this:

“Recent estimates indicate that at the current rate it will take more than 800 years for the bottom billion of the world population to achieve 10% of global income.”

Seems like a shockingly slow rate of progress, since anything that takes 800 years is basically not happening. But the problem is with the juxtaposition of a big number (billion) with a small fraction (10%). A billion people isn’t that big a fraction of the population anymore. Actually, if we could ever get to that level of world inequality it would be great.

Since the bottom billion of the world is about 14% of the 7 billion people in the world, getting them 10% of the global income would be a very low level of inequality — they’d only be 4% away from a perfectly egalitarian world. In the United States now, for example, the bottom 14% of families only get about 3% of the income.

Incidentally, here’s that family distribution:

What it’s all worth, in work-life cash

A Census Bureau research report estimates lifetime earnings by education, race/ethnicity and gender.

The report, by the Bureau’s Tiffany Julian and Robert Kominski, uses national data from the American Community Survey to create “synthetic work-life estimates” of earnings.

The method takes earnings information from one time period — in this case the years 2006-2008 combined (before the recession) — and calculates how much money people would make if they lived through their whole work lives (40 years, from age 25 to 64) during that period. Demographers use the same method to estimate life expectancy. It’s a way of using the most current period to project an image of the future in today’s shape. It’s a better look at the future, for most purposes, than looking back at the lives of people who are wrapping it up today.

Here is a figure they made, using earnings from people working full-time and year round:

That is for people working full-time and year-round at their jobs. That is not reasonable, of course, if people take time out of the labor force, or out of full-time work. So this understates the earnings gaps, especially by gender, since women take more time out of the labor force than men, on average.

They also reported the projected lifetime earnings for all workers — including those working only part-time or part of the year. The figure above showed a ratio of 4.7-to-1 from top to bottom, whereas the all-worker data has a ratio of 5.6-to-1 from White male professional-degree holders to Latina high school graduates.

I turned their all-worker table into this graph with men and women color coded:

This is not a real prediction, just a projection of the present into the future. But the scale is good for the imagination — the gap from top to bottom is 3.65 million dollars in 2008 terms.

Note that in addition to employer discrimination, these gaps reflects the full range of influences on people’s earnings, including sorting into occupations, part-time work, lost tenure and experience from time out of the labor force, and regional variation (which is one reason Asian workers show up high – many live in expensive cities like San Francisco and Honolulu).

Is it a “marriage problem”?

A self-described liberal (Andrew Cherlin) and conservative (W. Bradford Wilcox) pair of academics have produced a “policy brief”* for the Brookings Institution entitled, The Marginalization of Marriage in Middle America.

There’s no new information or analysis in the report, so I won’t dwell on it. But I’d like to use it to point out a logical problem with pro-marriage social science in general. Here’s an excerpt from the introduction, with my comment following:

This policy brief reviews the deepening marginalization of marriage and the growing instability of family life among moderately-educated Americans: those who hold high school degrees but not four-year college degrees and who constitute 51 percent of the young adult population (aged twenty-five to thirty-four). … [b]oth of us agree that children are more likely to thrive when they reside in stable, two-parent homes. … Thus, we conclude by offering six policy ideas, some economic, some cultural, and some legal, designed to strengthen marriage and family life among moderately-educated Americans. … To be sure, not every married family is a healthy one that benefits children. Yet, on average, the institution of marriage conveys important benefits to adults and children. … The fact is that children born and raised in intact, married homes typically enjoy higher quality relationships with their parents, are more likely to steer clear of trouble with the law, to graduate from high school and college, to be gainfully employed as adults, and to enjoy stable marriages of their own in adulthood. Women and men who get and stay married are more likely to accrue substantial financial assets and to enjoy good physical and mental health. In fact, married men enjoy a wage premium compared to their single peers that may exceed 10 percent. At the collective level, the retreat from marriage has played a noteworthy role in fueling the growth in family income inequality and child poverty that has beset the nation since the 1970s. For all these reasons, then, the institution of marriage has been an important pillar of the American Dream, and the erosion of marriage in Middle America is one reason the dream is increasingly out of reach for men, women, and children from moderately-educated homes.

It’s obvious empirically that adults and children in married-couple families, on average, are doing better on many measures than those not in such families. The logical problem is when people conclude from this pattern that the obvious response is to “strengthen marriage and family life.” But, why not try to reduce that disparity instead?

This is the logical equivalent of the Republican mantra that “We don’t have a revenue problem in Washington; we have a spending problem.” That’s only true if you’re doing one-handed math. And the same holds for marriage.

Yes, there is less marriage, and many people are less well off without it. Does that mean we have a “marriage” problem, or a family inequality problem? Is there any other way to help people develop high quality relationships with their parents, complete more education, get better jobs, accrue financial assets and maintain good physical and mental health?

In the categorical math of inequality, you can try (with little chance of success in this case) to reduce the number of people in the disadvantaged category (non-married families), or you can try to reduce the size of the disparity between the two categories.

*I’m not sure, but I think a “policy brief” is a blog post about policy matters, produced on the PDF letterhead of a foundation. Not that there’s anything wrong with that. As far as I can tell, this one is a non-peer-reviewed essay which handles sourcing like this: “the findings detailed in this policy brief come from a new report by Wilcox, When Marriage Disappears: The New Middle America.” As I’ve pointed out (here andhere), Wilcox’s reports at the National Marriage Project are also non-peer-reviewed essays with a lot of substantially misleading and erroneous content.

 

 

 

 

 

 

 

 

 

 

 

 

Birthweight and infant mortality inequality

Birthweight drives the Black-White gap.

Here’s a look at birthweight patterns and their effects on the difference in infant mortality rates between Black and White children.

A new report from the Centers for Disease Control, based on 2007 data, shows the distribution of birthweights and mortality outcomes by the race/ethnicity of mothers. Here is a story in three figures.

1. The infant mortality rate gap is large

In the figures below I focus on White (non-Hispanic), Black (non-Hispanic), and Hispanic. Since White and Hispanic infants have such similar rates, the issue I’m most concerned with is the Black-White gap.

2. Infant mortality rates are drastically affected by birthweight. But at each birthweight the race/ethnic gap is small.

The Black mortality rates are higher among the high-birthweight infants, but there are very few deaths out there (note the log scale, which is necessary to even see those gaps).

3. Black mothers are much more likely to have very-lowbirthweight infants.

Again, because of the log scale, you can see the gaps clearly even though there are very few births at the very low end. Still, 1.8% of Black women’s infants are born below 1,000 grams, where a large portion of infants don’t survive.

So what explains the higher infant mortality rates among Black women’s infants? The overwhelming issue is birthweight. If they had the same mortality rates at each birthweight, I calculate, the gap would close by 10%. But if they had the same birthweight distributions, the gap would close by 88%.

In previous posts, I reported that women who experienced childhood hardships are more likely to have low-birthweight babies. And I described the weathering hypothesis, which suggests delaying first births only improves outcomes for infants if their mothers’ health is not already deteriorating in their 20s, as it more often is with Black women. With this evidence, it is clear that the major problem driving the infant-mortality gap is not care of newborn infants itself, but rather the long-term health of Black women.

Income inequality in mental illness

In South Korea, rising inequality, mental illness — and inequality in mental illness.

South Korea has a high and rising suicide rate, which doubled from 1997 to 2008, becoming the worse of any country in the Organization for Economic Cooperation and Development (OECD). During that time, income inequality also increased.

A new paper in the journal World Psychiatry (the source of those figures) shows that the concentration of mental illness among poorer people in South Korea also increased during that period. That is, the income inequality in mental illness grew worse. Using a large survey of self-reported mental health and income, the authors, Jihyung Hong and colleagues, calculated he distribution of illness along the income distribution, like a Lorenz curve and its related Gini coefficient.

I have rescaled their numbers, so that zero equals equality (same rates of illness at all income levels) and 1 equals complete inequality (all illness among the poor), and plotted the trends here:

South Korea had a major economic crisis at the end of the 1990s, the shocks from which reverberated in many social aspects of the society. For example, that high rate of economic inequality in suicide attempts in 1998 took place in a year that saw a big jump in suicide attempts nationwide.

Inequality has not increase continuously for all three measures during this period, but they are all substantially higher in 2007 than they were 10 years earlier — and they all show considerable economic inequality in major mental illness.

Depressing inequality

What is down must feel down.

The latest Morbidity and Mortality Weekly Report from the CDC falls under the category of when-it-rains-it-pours. Depression, according to two different measures, follows the pattern of at least four major indicators of inequality: gender, race/ethnicity, education, and employment status.

Source: My graph from the report.

One could argue the depression contributes to employment problems, and educational failure as well. So on those the causality may run both directions. But that’s not the case with gender and race/ethnicity.

For a handy debunking of the idea that people in dire straights should just buck up, I recommend Bright-sided: How Positive Thinking is Undermining America, by Barbara Ehrenreich.