Yo, how big is that yogurt bucket?

People don’t know how much they’re eating.

A recent experiment found that people eat more when the container is larger, even when the portion size is not. They gave Belgian college students a container of M&Ms and parked them in front of a TV, with some cover story. The students were randomly assigned to three groups, medium-portion/small-container, medium-portion/large-container, and large-portion/large-container. These were the results: The ones who got the large container ate more, whether it was full or not (the difference between the two wasn’t significant). These kinds of experiments continuously suggest that distractions, distortions and other apparently irrelevant information and events routinely have large effects on people’s eating practices (here’s an extensive review). One infamous study showed that even people served 14-day-stale popcorn at the movies ate 34% more when it was served in a large container. In an earlier popcorn study, researchers found that people given large containers not only ate more, but were less able to report how much they ate. They concluded:

When a food is eaten from a large container, it appears easy to lose track of how much one eats. Even if the food were to taste relatively unfavorable, eating it from a large container may cause one to overeat because they lose track of how much they have consumed.

About that yogurt tub All this occurred to me when I visited one of our many local Frozenyo franchise outlets. It’s a self-serve frozen yogurt place where you pay one price by weight no matter what you put in your bucket. The trick that impressed me is the bucket — there is only one size, and it’s very large. But you can’t judge how big it is because there’s nothing to compare it with — no sizes or prices on the wall, no mini cup for kids — just one stack of identical buckets. So the person who posted this picture on Yelp probably thought she had a reasonable size serving, since the thing is barely half full:

There are three possible ways to judge your self-served serving size. You can go by the tub (“I filled it half way”), you can go by the person next to you (“sheesh!”), or you can look at the cartoon penguins on the wall:

How much is the penguin eating? I took home one of the buckets, and measured the volume of water it holds: 18 ounces. In comparison, a standard kid-sized serving bowl, the kind some people use to give their kids ice cream at home, holds 12 ounces:

An innocent child used to half a bowl of ice cream — in the bowl on the left — might be pretty steamed if you served her this:

According to the serving size information on the back wall of Frozenyo, I think that’s about 1.5 servings, or 150 calories of the nonfat variety, before toppings. The penguin’s overflowing bowl is 5.0 servings. With no toppings that’s 500 calories. If you pile it with M&Ms, sprinkles, hot fudge, Captain Crunch, coconut topping and fresh kiwis, who knows. It’s not really that many calories to consume — the same number as a single slice of banana bread at Starbucks.

But the point is you don’t know how much you’re eating. One Yelp reviewer cautioned that you can get a stomach ache after eating at Frozenyo, because “your eyes are bigger than your stomach.” I think it’s because the dump-truck sized delivery vehicle you eat it out of is bigger than your stomach.

But most reviewers love it for the individual control over serving size and toppings, and the reasonable price ($.39 per ounce by weight, or $5-$6 for a typical load).* I think it’s a winning business model, with low labor costs, because all you need is one person to pour the mix into the machines and another to weigh the tubs and swipe credit cards. According to the company’s ambitious map, there are still 46 states with “territory available.”

If I were them, I would increase the bucket size by 5% per year. I doubt anyone would notice.

* Paging George Ritzer: it’s the irrationality of rationality.

Is sameness the kiss of marital demise?

Does marriage thrive on the mystery? To hear religious marriage experts tell it (at least on the covers of their books), marriage is a mystery, the solving of which might just mean its demise. So just keep asking yourself, “What am I doing in this marriage?” Historically, the reason so many marriages survived arguably was because divorce wasn’t an option. So the survival of marriage was due to its external environment not its internal dynamics. Now that the padlock has been blown off the door, people need reasons to stay together, if they want to. Is it the mystery that holds them together? In the debate over marriage rights and homogamous (“same-sex”) marriage, one odd issue has been the relative stability of different forms of marriage. I say “odd” because there is no logical connection between relationship stability and civil rights, but it’s all wrapped up in the sideshow over what kind of marriage is “good” for children. It turns out there is a whole right-wing Christian theory about gay and lesbian relationship stability, which I had naively never heard of. This came to my attention recently when Carlos Maza, a marriage rights activist, went undercover at an “It Takes A Family To Raise A Village” conference put on by the National Organization for Marriage. One of his audio recordings reportedly featured Jenet Erickson, a conservative Christian teaching at Brigham Young University. She offered the students at her session this theory (transcribed by me), which she said she got second hand from Brad Wilcox:

I remember talking about this with Brad Wilcox and he said, he was talking about gay marriage being accepted. And I’ll just make this side comment — we do not have enough research to say it is any kind of law, but I want to be thinking about it, keep it in your mind, because there’s something about same-gender relationships also being unstable — they seem to be less stable just inherently. And he would say … when we look at the Scandinavian countries that accepted gay marriage some years ago … after people could get married as same gender couples after that period of time, when they measured how long a lesbian relationship — and you should know those are most likely to marry, are women, right? — how long those relationships last and the average was 18 months [someone whistles]. And so he just commented that there seems to be something — this is the way he would say it — that women don’t want to hang out, they’re not interesting enough, “They’re too much like me!” He would say, women bonding with women — it’s like there’s something about the difference between genders that allows for stability inherently, because there’s enough difference: “I’m going to stay in this to figure you out. It may take an entire lifetime to get into your brain,” right? He had an interesting thought that I thought was a fascinating idea, that there seems to be something inherently unstable about same gender relationships. We’ve known that about gay relationships with men, but it’s also true of lesbian relationships. And why, why is it this heterosexual dynamic, inherently seems to potentially lead to greater stability. He would say, you’ve got to figure this person out.

(I couldn’t find an email address for Erickson to run this by her. But Wilcox, via email, said it was not an accurate representation of his views.) In the recent scandal over Mark Regnerus’s study, this came up as well, due to his argument that children of any parent who ever had a same-sex relationship were worse off as young adults than those of forever-married-bio-parents. In his self-defense piece, apparently forthcoming in the journal Social Science Research, he returns to the issue of relationship stability. Regnerus writes, about a “study of Norwegian and Swedish same-sex marriages” (referring to this one in Demography) that, “The study authors estimate that in Sweden, 30% of female marriages are likely to end in divorce within 6 years of formation, compared with 20% for male marriages and 13% for heterosexual ones.” That sounds pretty high. He omits two important sentences from the same paragraph of the paper, however:

• “In Norway, 13% of partnerships of men and 21% of female partnerships are likely to end in divorce within six years from partnership registration.” (Selectively not mentioning the lower rates in Norway. I don’t know where Erickson got that 18-months figure.)
• “These levels are higher than the corresponding 13% of heterosexual marriages that end in divorce within five years in Sweden, but not high when compared with divorce levels in the United States.” (Selectively not putting the divorce rates in context.)

My not-yet-peer-reviewed lifetable estimate of divorce rates in the U.S. puts the 6-year risk at 19%, so I’d say those Swedish homogamous-divorce rates are a little higher, at least for the first six years. (Before death does them part, I reckon 49% of U.S. marriages are headed for divorce. With rates that high, you have to wonder if there is something inherently unstable about heterogamous relationships.) The authors of that Demography article note that lesbian marriages in Scandinavia feature a high degree of similarity between spouses in terms of demographics such as age, nationality, education and income. This is “usually assumed to enhance marital stability.”

However, some aspects of homogamy, especially in terms of economic characteristics, may be related to less-clear power structures in a couple. This situation may be conducive to a high level of dynamism in the relationship, but perhaps not to the kind of inertia that is related to marital stability.

It gets confusing trying to parse the speculation on the role of sameness and difference here. In this interpretation, sameness creates dynamism and undermines stability. On the other hand, differences associated with unequal power structures produce stability and inertia, which is good. For marriage. Is that good? In general, the research on marriage has indeed shown that difference is not good for the odds of not divorcing:

• When one spouse goes to church more often that the other, it bodes ill for the marriage’s survival.
• Ethnic and religious intermarriage in the Netherlands increases the odds of divorce.
• Difference between spouses in age or education increases the odds of divorce.
• Racial/ethnic intermarried couples are more likely to divorce (although there is debate about whether intermarriage has an independent effect) and recent research finds higher divorce rates among specific ethnicity/gender combinations.

But is gender difference different? Do couples inherently need to be similar on ethnicity, age, education, religion and income but different on gender — if they want the relationship to last?

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.

Work-family links for male bosses

From Reeve Vanneman comes a tip about an interesting piece of research: researchers asked if men in positions of authority were more likely to make sexist judgments if they were themselves in “traditional” marriages involving stay-at-home wives.

In this article, we examine a heretofore neglected pocket of resistance to the gender revolution in the workplace: married male employees who have stay-at-home wives. We develop and empirically test the theoretical argument suggesting that such organizational members, compared to male employees in modern marriages, are more likely to exhibit attitudes, beliefs, and behaviors that are harmful to women in the workplace.

First, in an analysis of the General Social Survey, they found that men with stay-at-home wives had more negative opinions toward working women and women’s employment. Second, using the GSS linked to the National Organizations Survey, they found that men were less likely to see their female dominated workplaces as “running smoothly” if they had stay-at-home wives.

Then they conducted an experiment using several hundred married, male college students with managerial jobs. The men were asked to rate the “organizational attractiveness” of a fictional organization where they might work; and some of the men were additionally told that women were well represented on the organization’s board of directors. As expected, men in “traditional” marriages were less likely to find the egalitarian organization attractive as a potential workplace.

Finally, researchers recruited another few hundred male managers from an accounting association. These men were asked to make a recommendation about employing a person whose resume they reviewed; half received a resume with a man’s name and half reviewed an identical resume from a woman. The results also matched their expectations, with managers in “traditional” marriages being less likely to recommend the female applicant.

Is it all really Carmela’s fault?

The write-up of the study in Forbes took the nonsensical, but not surprising, approach of finding a way to blame women:

But new research … adds another layer to the debate over gender discrimination at work, and another (possibly just as important) person to blame: your boss’s stay-at-home wife.

Really?

Anyway, the authors speculate that they have uncovered “a pocket of resistance to the gender revolution,” and that seems reasonable. It is no surprise that the gendered nature of relationships at home and at work would be related in this way.

I don’t see much evidence here that the relationship is causal, however, such that a stay-at-home wife causes a manager to make more sexist decisions. The researchers use controls for common demographic characteristics, but not much that can account for the personalities and experiences that would produce sexist men. That is, “men may be self-selecting simultaneously into traditional marriage structures and non-egalitarian attitudes and behaviors towards women in the workplace.”

But the paper does suggest that those of us who study the gendered decisions of people in positions of authority would do well to keep looking for ways to get at additional qualities beyond their gender.

I found a complete draft of the paper here.

Incarceration’s contribution to infant mortality

A recent study in the journal Social Problems by sociologist Chistopher Wildeman shows that America’s practice of mass incarceration may be exacerbating both infant mortality in general and stubborn racial inequality in infant mortality in particular.

Drawing on recent literature by himself and others, Wildeman spells out the case for incarceration’s negative effect on family economies, including: lost earnings and financial contributions from fathers, the expensive burden of maintaining the relationship with an incarcerated parent, and the lost value of the incarcerated parent’s unpaid labor. All of those costs may take a toll on mothers’ health, which is the primary cause of infant mortality.

In addition, family members of incarcerated parents may contract infectious diseases, experience significant stress, and lose support networks — all taking an additional health toll.

Sure enough, his analysis of data from the Pregnancy Risk Assessment Monitoring System confirms that children born into families in which a parent has been incarcerated are more likely to die in the first year of life. The association may not be causal, but it holds with a lot of important control variables.

Does this increase racial inequality? Probably, because parental incarceration is so concentrated among Black families, as Wildeman and Bruce Western reported previously (my graph of their numbers):

To make the connection to racial inequality explicit, Wildeman moves to compare states over time, on the suspicion that incarceration could increase infant mortality rates, and racial inequality in infant mortality rates. That could be because concentrated incarceration undermines community support and income, people with felony records often are disenfranchised (so the political system can ignore their needs), and the costs of incarceration crowd out more beneficial spending that could improve community health.

The results of a lot of fancy statistical models comparing states show that:

the imprisonment rate is positively and significantly associated with the total infant mortality rate, the black infant mortality rate, and the black-white gap in the infant mortality rate.

It’s an impressive article on an important subject, one that thankfully is attracting more attention from good scholars.

I previously reported on Wildeman’s work on how the drug war affect families, here.

The disparate lives of fifth graders

A new study of about 5,000 fifth-grade students in the three public school districts shows wide disparities by race/ethnicity in a number of important health practices and outcome measures. The study, published in the New England Journal of Medicine, showed unadjusted disparities and then attempted to account for them statistically with common control variables, such as family socioeconomic status and school characteristics.

Here is a breakdown of some of the indicators (my graph):

On all but alcohol consumption (remember these are fifth graders), the white students showed advantages over Black and Latino students. In the subsequent analysis, the authors showed what amount of the disparity was accounted for by the different control variables. Here is their graph illustrating the findings:

It shows, for example, that about 10 points out of the 20-point difference between Latinos and Whites on the frequency of reporting fair or poor health is accounted for by their control variables. For Black children, about four points out of the eight point difference is accounted for. (These gaps would likely be larger if private school students were included.)

Determining the causal story behind these disparities is interesting and important, however it is most important to realize that at the descriptive level these represent major disparities in the lived experience of young children who are blameless.

It is interesting to note that some of these practices and outcomes speak to parenting practices, which has been the subject of a growing literature in recent years. However, after Annette Lareau reported that parenting practices in her study differed more by social class than they did by race, class has been the focus of much of this research. For example, although I did not see it, a study by Jessica McCrory Calarco at Indiana University, presented at the annual meeting of the American Sociological Association last week, looks very interesting. She used observation and interviews and found stark differences between middle-class and working-class parent-child interactions. From the press release:

Working-class parents, she found, coached their children on how to avoid problems, often through finding a solution on their own and by being polite and deferential to authority figures. Middle-class parents, on the other hand, were more likely to encourage their kids to ask questions or ask for help.

Single mothers’ hardships

From the article, “Effects of unemployment and underemployment on material hardship in single-mother families,” in Children and Youth Services Review, comes this list of hardships recorded by single mothers on the Survey of Income and Program Participation from the mid-2000s.

For context, you can situate the mid-2000s on this trend-mashup I made:

Sources: Employment from Table FG5 here ; TANF caseloads from these reports; poverty from Census, here.

Employment down (after rising in the 1990s), poverty up, TANF non-responsive; lots of financial, health, food and housing hardship.

 

 

LGBT teens made homeless

From the Williams Institute at UCLA, a report for the No Family For You file: “Serving Our Youth: Findings from a National Survey of Services Providers Working with Lesbian, Gay, Bisexual and Transgender Youth Who Are Homeless or At Risk of Becoming Homeless.”

The report is cautious in its write-up, which is appropriate, because a survey of service providers only gets you a view through one window into the problem of homeless youth who are LGBT. But in terms of orders of magnitude, I think it’s fair to conclude that LGBT youth make up a very disproportionate share of homeless youth, and that rejection by their families is the leading precursor to their homelessness.

Here is the relevant figure, based on the responses of service providers:

It’s a good reminder that families are only a source of care and support for those who are cared for and supported by their families.

As this report hit the wires last week, ThinkProgress generated one of those graphic Facebook memes, which looked like this:

I wouldn’t use this survey of agencies – representing an unknown proportion of all agencies serving an unknown proportion of all homeless people – to try to nail down a number like “40% of homeless youth are LGBT.” (One question: what about homeless youth who are with their homeless families?) Anyway, let’s just agree it’s a serious problem and they are probably very over-represented in the homeless population.

Work-family decisions, in person

Here’s an interesting new study on work-family decisions around the time of childbirth.

Medora Barnes has written, “Having a First Versus a Second Child: Comparing Women’s Maternity Leave Choices and Concerns,” in Journal of Family Issues. It’s a nice research design, with 16 school teachers interviewed — half having a first child, half having a second — before and after they have the baby, interviewed with and without their partners.

Here’s one nugget:

Nate: The day care is more her decision. I would say it was mainly Jenn who makes those decisions. Ultimately when it came down to making the final decision, we discussed it. But she took more of the lead on finding things out, especially with the first [child]. The second time around, she did the leg work and then—that one might have been more equal, but ultimately it was her decision on where they were going to go.

Jennifer: Yeah, the first time Nate had no part in it. The second time, I think he did more because I said to him, “You need to help me with this!” I was torn . . . and he was kind of like, “Whatever you think is right.” I got annoyed and I said, “I’m asking you. I want your help with this! What do you think?” I was like, “They’re your kids too!  What do you really think?” Because I didn’t want it to just be choosing [a day care] based on which person was cheaper or whatever.

Nate: Whatever.  [There is a pause, and then we all laugh at his clear dismissal of the issue]

And on the issue of being pressured to take more time off work:

Oh yeah! I remember having a conversation with Matthew’s sister. She said, “What! Oh! Only taking six weeks? Blah, blah, blah.” And I was thinking, “I am not going to put us in debt so that I can stay home for six more weeks!” I’m just not going to do it. It’s ridiculous. The baby’s not going to remember if I was there or not. You know? She’ll be fine! (Jill, elementary special education teacher, second-time mother)

Lots of good material for discussing women’s and couple’s decision-making about work-family issues (based on research, not stereotypical cartoons).

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;