Tag Archives: income

The War on Poverty at 50: Swimming against the tide

I have written a brief report for the Council on Contemporary Families, released today, for the 50th anniversary of the War on Poverty declaration by Lyndon Johnson: Was the War on Poverty a Failure? Or Are Anti-Poverty Efforts Swimming Simply Against a Stronger Tide?

The figures include this one, showing changes in earnings by gender and education over the past two decades:

fig5-earnings

In between figures and statistics, key points:

  • The suite of social welfare programs introduced or expanded in that era moved millions of people out of poverty and improved the lives of millions more who remained income-poor.
  • In recent years, however, poverty has been rising once again.
  • Focusing on children, our most vulnerable citizens, highlights both the strengths and the limits of our current anti-poverty programs.
  • The high rates of child poverty in America highlight a basic feature about the U.S. system, and its principal vulnerability: ours remains predominantly a market-based system of care.
  • And the multiplication of low-wage jobs that has come with widening inequality is a formidable obstacle to reducing poverty today.
  • Despite frequent claims to the contrary, that government can play a key role in reducing poverty.

The report is paired with an excellent piece by Kristi Williams: Promoting Marriage among Single Mothers: An Ineffective Weapon in the War on Poverty? Her bullet points are:

  • The rapid rise in nonmarital fertility is arguably the most significant demographic trend of the past two decades.
  • How can we improve the lives of the growing numbers of unmarried mothers and their children? So far, a dominant approach has been to encourage their mothers to marry.
  • The flaw in this argument is the assumption that all marriages are equally beneficial.
  • Our recent research adds to the growing body of evidence that promoting marriage is not the answer to the problems facing single mothers and their children.
  • A more promising approach is to focus on reducing unintended or mistimed births.
  • If the goal of marriage promotion efforts was truly to lower poverty rates and improve the well-being of unmarried parents and their children, then it is time to take a different approach toward this goal.

Kudos and thanks to the Council on Contemporary Families (of which I’m a board member) for putting this together, especially Stephanie Coontz and Virginia Rutter, who did the work of coordinating, editing, and distributing the reports.

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Race, class, and gender in one chart (just kidding)

You can’t do it (or much of anything) in one chart.

A colleague reports that a college student wrote to her:

The wage gap itself shows white women earn more than more than black men; that is to say my race is a greater determinant of wage than is my gender.

Her link was to this pretty confused post by Derek Thompson, which I’m not going to get into except to show this figure he made:

thompson-earnsThis shows earnings, not taking into account education. Later he shows earnings by education, not taking into account gender. Not wrong, but you can see the confusion it caused for the student quoted above. If she finishes college, she will be in a group whose earnings hierarchy is more by gender than by race, as I show in this figure I made from 2011 American Community Survey data from IPUMS:

earns-race-gen

This shows that Black men and White women — full-time, year-round, 25-54 — have the same median earnings if you don’t take into account education. Within each education group, however, Black men earn more. Who gets to be in the full-time, year-round population (instead of dead, incarcerated, unemployed or underemployed), of course, is a big issue. I can’t show that in one chart.

 

 

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Gender Gap Statistic Gets it from All Sides

I was very happy to write this post for the Gender & Society blog, where it first appeared.

The “gender gap” has gotten a lot of attention this fall. This hard-working statistic is as often abused and attacked by antifeminists as it is misused and misunderstood by those sympathetic to feminism. But it is good for one thing: information.

The statistic is released each year with the U.S. Census Bureau’s income and poverty report. This year they reported 2012 annual earnings as recorded in the March 2013 Current Population Survey (CPS): the median earnings of full-time, year-round working women ($37,791) was 76.5% of men’s ($49,398). That is the source of (accurate) statements such as, “Women still earned only 77 cents for every dollar that men earned in 2012.”

In the category of reading too much into a single number, I put this data brieffrom the Institute for Women’s Policy Research, which helpfully informed us that, “Most Women Working Today Will Not See Equal Pay during their Working Lives.” Here is the chart:

Cohen_image1

Of course, real life projections are not usually made by simply extending a trend with a straight line. The future is not that easy to foresee. If you did want to fit a line to that trend, however, the bad news is that it’s not a straight line that fits, but a third-order polynomial (which improves the measure of fit from .90 to .98). And projected this way, the trend will never reach equality:

Cohen_image2

Fortunately, curvy lines are often no better at predicting the future than straight ones.

Flog that stat

Some defenders of equal pay for women misstate the statistic, as President Bill Clinton did when he said:

“How would you like to show up for work every day, but only get to take home three out of every four paychecks? … if you get paid 75 percent for the same kind of work, it’s as if you were only picking up three paychecks, instead of four, in four pay periods. The average woman has to work, therefore, an extra 17 weeks a year to earn what a similarly-qualified man in the same kind of job makes.”

The mistake here is that he said “same kind of work” and “similarly-qualified man.” That led to the screaming headline on the American Enterprise Institute website, “Still Hyping the Phony Pay Gap.”

But he also went on to say:

“Yes, some of this can be explained — by differences in education, experience and occupation. But even after you make all those adjustments, there is still a very significant gap.”

So he belatedly acknowledged the complexities, and that second statement is true.

Oh, and that exchange occurred in 2000. How far we’ve come.

When Clinton, ever a repository for handy statistics, essentially repeated his statement on September 29 of this year, he played right into the screaming headlines of today’s anti-feminists, including Hanna Rosin, who declared, “I feel the need to set the record straight” in a piece she titled, “The Gender Gap Lie.” Kay Hymowitz also has written extensively to debunk the gender gap, arguing that it mostly results from women’s choices – the educations and occupations they choose, the hours they choose, the “mommy track” they prefer. (Naturally, sociologists are very interested in that construction of “choice.”)

There is no single number that can tell us the true state of gender inequality. But if you had to pick one, this one is pretty good. That’s because it combines factors that affect employment levels, work experience, occupational distributions, and pay discrimination – to give a sense of the place of the typical worker. As long as that number is not zero, there is a gender inequality problem to discuss, whether it results from socialization, family demands, educational sorting and tracking, hiring and promotion discrimination, or pay discrimination – and the details depend on further scrutiny.

Take your pick

We could use a different gender gap. The next figure shows some gender gaps for earnings among full-time, full-year workers in the 2011 American Community Survey (ACS). I’ve cut the sample to compare men and women by education, long-hours status (50+ hours), parenthood (no co-residential children) and marital status (never married). As you can see, the gaps range from a low of 65% for women with an MA degree and no children all the way up to 93% for never-married professional degree or PhD holders with no kids. Generally, the 50-hour limit doesn’t help, but marriage and children make a big difference.Cohen_image3

Another way of restricting the data to consider real-world gaps is shown in the next figure. Here, from the same data, I’ve taken full-time, full-year workers who have a bachelor’s degree and no further education, and sorted them by college major. So these gaps account for educational specialization, and reflect – in addition to any hiring and pay discrimination – occupational sorting within those categories, as well as other educational processes such as university prestige and school performance. The gaps range from 69% for transportation science majors all the way up to 94% for architecture majors.

Cohen_image4

Finally, we might look more closely at occupations. In this figure, again from the 2011 ACS, I have sorted 484 detailed occupational categories according to the median earnings wage gap within them, for full-time, year-round workers. The y-axis shows the cumulative percentage of women who work at or below each level as you move from less equal occupations on the left to more equal ones on the right. I’ve labeled the 25th, 50th and 75th percentile, showing, for example, that half of women work in occupations with a wage gap of 83% or worse.Cohen_image5

Although this figure shows inequality within occupations, it is occupational segregation itself, which extends the gender division of labor into the labor market, that lies behind much of the gender gap – representing the culmination of historical and contemporary processes of allocating people to tasks.

In summary, the wage gap clearly is smaller in some situations than others – smaller for workers without children, especially if they’re never married, smaller for some college majors and in some occupations. Each of these comparisons tells us something different. (More complete statistical analysesthat control for several factors at once create counterfactuals that don’t actually exist, but that do help us isolate important dynamics behind the gender gap.)

We mustn’t read into these numbers more than they can tell us. None of the numbers I’ve shown can discern occupational choice from employer discrimination, for example; or the cumulative effects of time out of the labor force versus discrimination in previous jobs. But the gender gap numbers are measures of inequality. And as long as we are accurate and responsible in our use of these numbers, they are useful sources of information.

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More married mothers earn more than their husbands

For this Washington Post article by Brigid Schulte, I did some calculations that allowed her to add, “In a trend accelerated by the recent recession” to the first sentence. When you line up the numbers this way — percentage of married mothers with children present who have higher incomes their husbands — there was a steep acceleration:

pew-post-trendSource: My calculation from Census and American Community Survey data from IPUMS.

My explanation for this was:

“The decade of the 2000s witnessed the most rapid change in the percentage of married mothers earning more than their husbands of any decade since 1960,” said Philip Cohen, a University of Maryland sociologist who studies gender and family trends. “This reflects the larger job losses experienced by men at the beginning of the Great Recession. Also, some women decided to work more hours or seek better jobs in response to their husbands’ job loss, potential loss or declining wages.”

The trend was reported by Pew Research in this report, titled “Breadwinner Moms,” which wrote:

A record 40% of all households with children under the age of 18 include mothers who are either the sole or primary source of income for the family, according to a new Pew Research Center analysis of data from the U.S. Census Bureau. The share was just 11% in 1960.

I just did the married mothers earning more, and added the data from the years between 2000 and 2011, to show the recession-period acceleration of the higher-earning mothers. Pew added polling numbers about attitudes toward women’s income, and Schulte added exemplary interviews.

Breadwinning

A wife who earns $1 more than her husband for one year is not the “breadwinner” of the family. That’s not what made “traditional” men the breadwinners of their families — that image is of a long-term pattern in which the husband/father earns all or almost all of the money, which implies a more entrenched economic domination.

However, she can be the “primary breadwinner,” a post-1970s concept acknowledging the rise of secondary breadwinners (usually women) in families. Here is the Google ngrams trend showing appearances of “primary breadwinner” as a fraction of all uses of “breadwinner” since 1920 (click to enlarge):

breadwinner-ngrams

I have previously complained about lumping single mothers together with higher-earning wives to construct an image of “female breadwinners.” That’s partly because the $1-more-for-one-year problem, and partly that I object to using the single-mother trend to inflate descriptions of women’s advancement.

Anyway, it’s hard to capture the trends without overdoing it, but Brigid Schulte and the Pew authors (Wendy Wang, Kim Parker and Paul Taylor) did a nice job.

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Gender wage gap, 2012 edition

Gender inequality stagnation continues apace.

The Bureau of Labor Statistics has released the wage report for 2012, which shows women’s earnings relative to men’s falling back to the 2005 level. The gender breakdown is available here (the content at that link changes when new data come out), and the historical series from 1979 to 2011 is available here.

The usually-reported number is the median weekly earnings of full-time wage and salary workers. These are the gender ratios (women’s earnings divided by men’s):

gender earnings gap 2012

 

Follow the gender inequality tag for updates and previous posts.

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More Women Are Doctors and Lawyers Than Ever—but Progress Is Stalling

Originally posted on The Atlantic.

In the Wall Street Journal last week, Josh Mitchell reported that “Women account for a third of the nation’s lawyers and doctors, a major shift from a generation ago.” The report was triggered by anew analysis of occupations from the Census Bureau, which showed women increased their share of doctor and lawyer by four percent and six percent, respectively, from a decade earlier.

These professional advances mark “very significant progress,” according to feminist economist Heidi Hartmann, and I don’t disagree. Still, when I spoke to Mitchell I suggested he consider a glass-half-empty perspective, which somehow ended up on the cutting-room floor.

My question is, will progress continue? It doesn’t look good. I happen to be a demographer, but you don’t need to be one to see that progress for women in these fields is stalling.

First, look at the degrees earned. This figure uses statistics from the Department of Education and breaks the gender trend in law and medical degrees up by decades. Both trends show slowing progress—a smaller increase in women’s representation each decade—and both peaked (for now) at just under 50 percent female.

cohen_doctorlawyer.png

If half of new doctors and lawyers are women, eventually it should be possible to have professions that are gender-balanced. But don’t hold your breath.

I looked at today’s doctors and lawyers using the 2008-2010 American Community Survey (you can get the data here). Here is the representation of women among full-time and year-round working doctors and lawyers by age. Half of the youngest doctors and lawyers are women, while only one in eight of the oldest are. So as they all age, equal representation should be on the way.

cohen_doctorlawyer2.png

But women are much more likely to drop out of these professions (and others). Among early-career professionals—people ages 25 to 44—who list their most recent jobs as doctor or lawyer, you can see that women are much more likely to be out of the labor force:

cohen_doctorlawyer3.png

With the kind of dropout rates that produce these disparities, we would need much more than 50 percent female in the graduating classes to reach equal representation in these professions.

In Mitchell’s report, the economist Claudia Goldin, who has recently investigated women’s success as pharmacists, argues that the corporatization of medicine has helped women by introducing the concept of work-family balance, and reducing the gender earnings gap—all changes that helped women in pharmacies as well. But I don’t see the evidence that such practices have yet changed the medical industry enough to reduce the gender differences in drop-out rates. And the research evidence shows that explicit diversity policies—with teeth—often are necessary to break the logjam.

And Mitchell’s story did not mention any efforts to reduce the segregation of men and women—especially in medicine—into different specialties. That segregation is a big part of what drives the earnings gap among doctors and lawyers. Here are the median earnings by age for doctors and lawyers, from the same source:

cohen_doctorlawyer4.png

At the peak of that curve—ages 45 to 50—female doctors are earning just 62 percent of men’s median earnings. As they make their decisions about whether to enter the field, and how to specialize, and how to handle their family demands and opportunities, these disparities in representation and rewards come into play. The decisions men and women in these professions make should never be seen as free choices unconstrained or unaffected by the institutional environment.

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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).

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Fact-checking David Brooks, citing Hanna Rosin edition

I’m getting hundreds of clicks today from someone who posted a comment on David Brooks’ column (a sliver of a fraction of a percent of his readers). So here’s a little detail.

He wrote:

Men still dominate the tippy-top of the corporate ladder because many women take time off to raise children, but women lead or are gaining nearly everywhere else. Women in their 20s outearn men in their 20s.

The first sentence is too slippery to fact-check. But I think it would be more accurate to say, and less slippery: “men dominate at all levels of the corporate ladder, and women having children is only part of the reason why.”

But anyways, the second sentence is a specific empirical claim, and it is not true.

If Brooks’s intern — or (shudder) a fact-checker at a newspaper — had typed this into Google they would know: “bls earnings by age and sex”. As of today, that search leads to this as the second link:

This is not an obscure source. Finding it hardly counts as research. If you opened it, you would see the median earnings for men and women, by age, race and ethnicity, for full-time workers. If you copied a couple of those columns into your spreadsheet and asked it for the ratio of the medians, this is what you would see: Men earn more at every age.

I know the history of this factoid meme, which is detailed here and here. What he is quoting is probably a Hanna Rosin (or Liza Mundy) misquote of an analysis which compared 22-30-year-old, never-married, childless, metro-area residents a few years ago.

But there is being succinct and punchy, and there is being wrong. Shortening “22-30-year-old, never-married, childless, metro-area residents” to “women in their 20s” is wrong.

Common, major media conglomerates: get the facts!

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End-of-Men-Richer-Sex reality check, 40 years of pants edition

In 2010, 28% of wives were earning more than their husbands. And wives were 8-times as likely as their husbands to have no earnings.

I still don’t have my copies of The End of Men, by Hanna Rosin, or The Richer Sex, by Liza Mundy. But I’ve read enough of their excerpts to plan out some quick data checks.

Both Rosin and Mundy say women are rapidly becoming primary earners, breadwinners, pants-wearers, etc., in their families. It is absolutely true that the trend is in that direction. Similarly, the Earth is heading toward being devoured by the Sun, but the details are still to be worked out. As Rosin wrote in her Atlantic article:

In feminist circles, these social, political, and economic changes are always cast as a slow, arduous form of catch-up in a continuing struggle for female equality.

Which is right. So, where are we now, really, and what is the pace of change?

For the question of relative income within married-couple families, which is only one part of this picture – and an increasingly selective one – I got some Census data for 1970 to 2010 from IPUMS.

I selected married couples (heterogamous, throughout this post) in which the wife was in the age range 25-54, with couple income greater than $0. I added husbands’ and wives’ incomes, and calculated the percentage of the total coming from the wife. The results show and increase from 7% to 28% of couples in which the wife earns more than the husband:

(Thanks to the NYTimes Magazine for the triumphant wife image).

Please note this is not the percentage of working wives who earn more. That would be higher — Mundy calls it 38% in 2009 — but it wouldn’t describe the state of all women, which is what you need for a global gender trend claim. This is the percentage of all wives who earn more, which is what you need to describe the state of married couples.

But this 51% cutoff is frustratingly arbitrary. No serious study of power and inequality would rest everything on one such point. Earning 51% of the couple’s earnings doesn’t make one “the breadwinner,” and doesn’t determine who “wears the pants.”

Looking at the whole distribution gives much more information. Here it is, at 10-year intervals:

These are the points that jump out at me from this graph:

  • Couples in which the wife earns 0% of the income have fallen from 46% to 19%, but they are still 8-times as common as the reverse — couples where the wife earns 100%.
  • There have been very big proportionate increases in the frequency of wives earning more — such as a tripling among those who earn 50-59% of the total, and a quadrupling among those in which the wife earns it all.
  • But the most common wife-earning-more scenario is the one in which she earns just over half the total. Looking more closely (details in a later post) shows that these are mostly in the middle-income ranges. The poorest and the richest families are most often the ones in which the wife earns 0%.

Maybe it’s just the feminist in me that brings out the stickler in these posts, but I don’t think this shows us to be very far along on the road to female-dominance.

Previous posts in this series…

  • Discussed The Richer Sex excerpt in Time (finding that, in fact, the richer sex is still men).
  • Discussed that statistical meme about young women earning more than young men (finding it a misleading data manipulation), and showed that the pattern is stable and 20 years old.
  • Debunked the common claim that “40% of American women” are “the breadwinners” in their families.
  • Debunked the description of stay-at-home dads as the “new normal,” including correcting a few errors from Rosin’s TED Talk.
  • Showed how rare the families are that Rosin profiled in her excerpt from The End of Men.

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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;

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