Tag Archives: census

Changing Hispanic racial identity, or not

Hector Cordero-Guzman called my attention to a controversy over Hispanics changing their racial identities. Here is a quick rehash and a few comments. (Spoiler: the New York Times ran a bad story.)

At the Population Association of America, Carolyn Liebler, a sociologist at the University of Minnesota, and James Noon, who works on administrative records at the Census Bureau, presented preliminary results from a comparison of individual race/ethnic responses to the 2000 and 2010 Decennial Censuses. After analyzing millions of individual Census responses, they reported in their abstract that 6% of people changed their race or Hispanic origin classification between 2000 and 2010.

Details of the analysis apparently are not publicly available, but D’Vera Cohn, a writer at the Pew Research Center, reported on their findings, under the headline, “Millions of Americans changed their racial or ethnic identity from one census to the next.” Is this a lot of change? It’s hard to say without a comparison (and without the analysis details). “Millions” does not really mean “a lot,” but it sounds like it does. If the Census race/ethnic identity questions don’t fit people’s self-concept very well then a certain amount of bouncing around is to be expected.

The focus was on Hispanics, whose place in the racial classification scheme is squishy (including immigrants who came at different ages from countries with different racial schemes and ancestral origins, living in different parts of the country with different racial attitudes, some concentrated in dense communities and some dispersed, some economically marginalized and some much more upwardly mobile, etc.). According to D’vera Cohn, 2.5 million Hispanics were “some other race” in 2000 and “white” in 2010, while 1.3 million were “white” in 2000 and “some other race” in 2010.

I might conclude from that that it’s messy and the categories don’t work very well. But it’s also possible that this reflects fluid identities, and people actually change how they see themselves in a systematic way over time. The PAA abstract says “responses and corresponding identities can change over time,” which leaves open the possibility that the change is in measurement in addition to identity, but the hypothesis they suggest are about identity (hypothesizing that women, young people, and people in the West have more complex or less stable identities).

Identity shift is how New York Times Upshot writer Nate Cohn interpreted the D’Vera Cohn report. Under the headline, “More Hispanics Declaring Themselves White,” he converted that bidirectional flow into “net 1.2 million” changing from “some other race” to “white,” and proceeded to run away with the implications. It’s a good example of using any number greater than zero to confirm something you already believe. For example, he wrote:

The data also call into question whether America is destined to become a so-called minority-majority nation, where whites represent a minority of the nation’s population. Those projections assume that Hispanics aren’t white, but if Hispanics ultimately identify as white Americans, then whites will remain the majority for the foreseeable future.

Hm. The “net” flow from “some other race” to “white” is 1.2 million. That is 3% of the 2000 Hispanic population, or 2% of the 2010 population. So even if it’s truly an identity change, does that save the White majority in the long run?

Anyway, as Cordero-Guzman points out in a detailed discussion, referring to a post by Manuel Pastor, the Census questions changed between 2000 and 2010, with Census adding, in bold, “For this census, Hispanic origins are not races” to the form in 2010. Since many Hispanics write “Hispanic” under “some other race,” this probably discouraged them from choosing “some other race” in 2010.

Cordero-Guzman also points out that the context in which the question is asked (and in which the respondents live) is important. For example, 82% of Puerto Ricans on the island use “white” on the American Community Survey, while in New York City only 45% do. Does their identity — in the sense of how they really think of themselves — change when they are in New York, or do they interpret the question differently because they are answering a question in a different social setting? You can’t quantify that difference, probably, but I wouldn’t assume it’s just an identity change.

In a follow-up post, Nate Cohn acknowledges the wording changes — “an important detail” — but returns to the assimilation-upward mobility story. He should have just acknowledged that he jumped to conclusions in the first post and overreached in the race to produce an important, “data-driven” post. (Nate Cohn may have consulted actual experts, but if he did he didn’t include them in the post. It’s just data, I guess.)

The information economy did it

There is a lesson here in the new information economy. Academic conferences used to be less in the public eye. A preliminary analysis, shared with other researchers, then a Pew writer posts on the results, and the Times splashes them all over, all before a paper is even available. I think the Times story is basically wrong — the data as reported are not independent evidence of “assimilation.” (So, the person with the biggest megaphone was the person who was most wrong — surprise!) But the analysis could well be an important piece of research in a larger literature, and I think it’s good to present preliminary research at conferences. You can’t stop reporters from racing to be wrong, but I do think it would be better to distribute the paper publicly when it’s presented.

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Sexual minority counts

One of the big happenings at the Population Association of American (PAA) conference, just completed, was news of progress toward collecting better data on sexual diversity.

Photo by Philip Cohen from Flickr Creative Commons

Photo of PAA 2014 by Philip Cohen from Flickr Creative Commons

Call it weakness if you like, but in this area I am prone to viewing modernity as a march of progress from a dark past toward a half-full glass of bright future, with popular politics driving widening notions of human rights, motivating legal reforms, compelling the adoption of state bureaucracies to progressive social reality, and gradually incorporating us into a new world order more or less of our own creation.

That last part – about the bureaucracies incorporating the public – might not be the most complicated, but it is still pretty thorny. (And from here till the next subhead it gets technical.)

Good news bad news

The good news is that we have great new data collections coming along. Virginia Cain from the National Center for Health Statistics reported on their new sexual orientation question for the National Health Interview Survey, the largest federal health survey (the paper doesn’t seem to be available yet). This is already yielding important data on health disparities for sexual minorities, which is vital for policy responses to inequality.

Tim Vizard from the UK Office of National Statistics also reported on his agency’s new sexual identity question, which has been tested for several years on a few hundred thousand people each year. The latest numbers show 1.5% of adults self-identifying as lesbian, gay, or bisexual. They get these low numbers because they ask a very simple, narrow question, only on sexual identity rather than sexual attraction or sexual behavior (see other studies for the range of estimates).* Importantly, less than 4% of the UK respondents are refusing to answer, and the question is not affecting overall response rates – two big fears in the statistical agencies that appear to be receding with these and other results. Here’s how they ask it (semi-confidentially, so that in theory a husband and wife taking the survey together could both tell the interviewer they’re gay without either knowing what the other said):

lgbt1

The other good news is that the U.S. Census Bureau is making great strides (which I first praised here), on several tracks. First, they are working on the same-sex married couple data from the American Community Survey (ACS). At present they only release aggregate estimates of same-sex couples, differentiating between those that are married versus cohabiting (explained here).

A big reason we don’t have more data is the bad news: In another paper (just an abstract is posted, but you can ask the authors for a copy), Census analysts Daphne Lofquist and Jamie Lewis reported on their investigation into possible errors in the same-sex couple data the ACS has collected.

The background is that in a 2011 paper (linked here) Census analysts showed that a lot of seemingly same-sex couples were actually different-sex couples in which someone’s sex was miscoded.** If even a tiny percentage of different-sex couples make a mistake on the form – say, 1-in-1000 – then you would roughly double the number of same-sex couples. And they do. The paper used name-gender associations to reveal that, for example, in Texas 29% of supposedly male-male couples had one partner with a name that was used by women 95% of the time in that state – probably women accidentally marked as male.

But that 95% cutoff is a conservative estimate of the error. In the new analysis Lofquist and Lewis went further and checked same-sex couples against their Social Security records to see what sex they had recorded there. The result was shocking: 72.5% of the same-sex couples had a member whose sex didn’t match the Social Security record. Yes, some people change their sex/gender, and some people’s Social Security Records are wrong, but not that many. The much more likely culprit is simply a tiny number of straight people mismarking the sex box (there are some other technical possibilities, too).

The great thing about just asking people their marital status and sex is that you can count gay and lesbian couples without changing anything about the form (such as asking about sexual identity or orientation). That’s what all the people want who think I’m backward for worrying about couple-sex gender terminology. “C’mon!” they say, “Why do you have to label marriage as homogamous or heterogamous – just call it marriage!” Maybe someday, but at the moment that approach is producing an accuracy-crushing level of noise in the same-sex couple data.

Fortunately, Census is also moving forward with other improvements to fix this. The most important change is probably to the basic relationship question, which will soon look something like this, with couples labeled “opposite-sex” or “same-sex,” and the gender-neutral “spouse” added beside “husband/wife.” This will allow Census to check those couples that are reported as married to see if their same/opposite relationship identification matches what they reported for their sexes:

lgbt2

If we end up with a question like that, which seems most likely (the Census testing and development is quite far along), then we should be able to much more reliably identify same-sex couples (both married and cohabiting).

We’ll get used to this

That proposed new relationship question has 17 categories. That’s a long way from these six, in 1960 (the whole series of Census forms is here):

1960relationships

That goes to show you that family diversity is a state of collective mind as well as a structural reality. Building bureaucratic bins into which we pour data describing the various aspects of our lives is one of the defining elements of modern life. Eventually, I am pretty sure people will become disciplined by the new bureaucratic reality, and identities will calcify around checkboxes. That’s life under the modern state. (Even most haters, once they realize the data is being collected, will want to answer the questions accurately so they don’t get counted as gay – although, just as a few people refuse to answer race questions, there will be holdouts.)

* Identifying transgender people is much more complicated and difficult. The number of required questions and categories increases as the size of the groups in question grows smaller. This is feasible for smaller, more targeted surveys, but not in the immediate cards for the big ones (see Gary Gates’s presentation at PAA for more on this).

** I’m pretty sure Gary Gates was the first person to identify this problem, but can’t remember which paper it was in.

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What’s in a ratio? Teen birth and marriage edition

Even in our post-apocalypse world, births and marriages are still related, somehow.

Some teenage women get married, and some have babies. Are they the same women? First the relationship between the two across states, then a puzzle.

In the years 2008-2012 combined, 2.5 percent of women ages 15-19 per year had a baby, and 1 percent got married. That is, they were reported in the American Community Survey (IPUMS) to have given birth, or gotten married, in the 12 months before they were surveyed. Here’s the relationship between those two rates across states:

teenbirthmarriage1The teen birth rate  ranges from a low of 1.2 percent in New Hampshire to 4.4 percent in New Mexico. The teen marriage rate ranges from .13 percent in Vermont to 2.3 percent in Idaho.

But how much of these weddings are “shotgun weddings” — those where the marriage takes place after the pregnancy begins? And how many of these births are “gungo-ho marriages” — those where the pregnancy follows immediately after the marriage? (OK, I made that term up.) The ACS, which is wonderful for having these questions, is somewhat maddening in not nailing down the timing more precisely. “In the past 12 months” is all you get.

Here is the relationship between two ratios. The x-axis is percentage of teens who got married who also had a birth (birth/marriage). On the y-axis is the percent of teens who had a birth who also got married (marriage/birth).

teenbirthmarriageIf you can figure out how to interpret these numbers, and the difference between them within states, please post your answer in the comments.

 

 

 

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Divorce recession drop rebound, with the 2012 rate

Note: Technical addendum added.

The Census Bureau’s American Community Survey is the best annual national data source for marital events. The 2012 data came out recently, and I don’t believe anyone else has published a divorce rate for 2012. The refined divorce rate – the number of divorces per 1,000 married people – was 19.0 in 2012. Here is the trend since the ACS starting counting divorces:

divrat08-12

What does this mean? It’s a shame the ACS didn’t start counting marital events till 2008, because it means we can’t put that year’s high rate in context. Was it (a) a spike up, suggesting divorce was a leading indicator for the recession; (b) part of a consistent decline, suggesting the the years since have been a pretty substantial increase from the historical trend; or, (c) a data anomaly.*

To put this in the context of the larger trend doesn’t really help answer the question, since we switched from vital records to a national survey, and had a decade with no national statistics in between:

divrate40-12

So, it’s a mystery. My preferred interpretation is still that the recession caused a decline in divorces because disgruntled people were tied up in other crises, couldn’t sell their houses, or couldn’t afford to move out, followed by a rebound of accumulated divorces to our current level.

I published a working paper suggesting this [now forthcoming in Population Research and Policy Review], in which I use 2008 predictors of divorce and estimate that 4% fewer divorces occurred through 2011 compared to what would have been expected had the determinants of divorce not changed in the subsequent years.

My blog series on divorce includes previous reports on rates, and attempts to predict divorce rates using Google searches.

Technical addendum

To replicate my rates for 2012, you start here at the FactFinder, then get the number of married people by sex (ACS Table B12001) and the number of people who got divorced in the 12 months before the survey (ACS Table S1251) — you can enter the table numbers into the search box. There is a slight problem with this, however. Some people who say they got divorced in the past 12 months also say they are currently married (presumably remarried already). Those people are counted twice in the denominator of the FactFinder-based divorce rate — once as divorced people and once as currently married. If you download the public-use file and count those people only once in the denominator, the divorce rate rises by .02 per 1,000 (or 2 people per 100,000) — but this would not change the figures above at the level of precision reported. However, the public-use files produce slightly different estimates than the FactFinder files anyway, because the latter are based on the Census Bureau’s complete file not a subsample, so I use those even though they produce this tiny under-estimate of the divorce rate.

Secondly, what about the difference in divorce rates between men and women? This is a survey, not a vital records count, and there is no way to verify with the now-missing spouses whether they also consider themselves divorced. Maybe they weren’t legally married, or they didn’t really get legally divorced. So there are several possibilities: (a) lots of lesbian divorces, which is unlikely given the small number of lesbian marriages (but note we don’t know the sex of the spouse who is no longer in the household so we can’t distinguish homogamous from heterogamous divorces); (b) women are more likely to describe a breakup as a divorce for reasons unknown; (c) something funky with the survey weights (unweighted divorce rates from the public-use file also show the disparity, but it’s 20% smaller), or; (d) something funky with the sampling.

Who knows! If you are reading this and considering a new career — or a new direction in your existing career — consider becoming a family demographer and helping us figure it out.

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Two notes on the poverty report

Two quick notes on the new Census poverty report, which mostly showed poverty and income levels flat from the previous year.

Note 1. The share of poor people who live in single-mother families has declined. It’s now 34%, down from a peak of 39% in 1996. That is, 34% of poor people live in single-mother families. It’s been between 34% and 39% for 27 years. I point that out because it’s important to realize the rise of single mothers (see Note #2 below) is not driving poverty rates. I don’t know if it’s significant, but the poverty rate for single-mother families fell from 34.2% to 33.9%, while the overall rate was steady at 15.0%. Here’s the first chart:

povertybyhouseholdtypeNote 2: There was an unusual blip down in the percentage of all (civilian non-institutionalized) people living in single-mother families. And a continued increase in people living in no family (which includes unmarried cohabitors if they have no kids). In the absence of a rise in marriage, I’m guessing this decline in the single-mother family population (a drop of half a million) is related to the recession-driven decline in fertility.

singlemomunrelated

Both charts are from Current Population Survey data, as reported in the hispov2 table (link to spreadsheet file).

 

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Why I don’t defend the sex-versus-gender distinction

Or, the sex/gender distinction which is not one?

sexgendermaze

(This post includes research from my excellent graduate assistant, Lucia Lykke.)

Recently I was corrected by another sociologist: “Phil – ‘female’ and ‘male’ refer to one’s sex, not gender.”

Feminists — including feminist sociologists — have made important progress by drawing the conceptual distinction between sex and gender, with sex the biological and gender the social categories. From this, maybe, we could recognize that gendered behavior was not simply an expression of sex categories — related to the term “sex roles” — but a socially-constructed set of practices layered on top of a crude biological base.

Lucia informs me we can date this to Simone de Beauvoir in The Second Sex. In 1949 she wrote:

It would appear, then, that every female human being is not necessarily a woman; to be so considered she must share in that mysterious and threatened reality known as femininity.

Later, she added, “One is not born, but rather becomes, a woman.” And this is what Judith Butler put down as the root of the gender/sex distinction, calling it “the distinguished contribution of Simone de Beauvoir’s formulation”:

The distinction between sex and gender has been crucial to the long-standing feminist effort to debunk the claim that anatomy is destiny… At its limit, then, the sex/gender distinction implies a radical heteronomy of natural bodies and constructed genders with the consequence that ‘being’ female and ‘being’ a woman are two very different sort of being.

In their famous article, “Doing Gender,” West and Zimmerman report making the sex/gender distinction in their sociology classes starting in the late 1960s. I’m guessing this really started to catch on among sociologists in the 1970s, based on this ngram of “social construction of gender” and “social construction of sex” as percentages of all uses of “social construction” in American English:

socialconstructionofgender

The spread of this distinction in the popular understanding — and I don’t know how far it has spread — seems to be credited to sociologists, maybe because people learn it in an introductory sociology course. As of today, Wikipedia says this under Introduction to Sex/Gender:

Sociologists make a distinction between gender and sex. Gender is the perceived or projected component of human sexuality while sex is the biological or genetic component. Why do sociologists differentiate between gender and sex? Differentiating gender from sex allows social scientists to study influences on sexuality without confusing the social and psychological aspects with the biological and genetic aspects. As discussed below, gender is a social construction. If a social scientist were to continually talk about the social construction of sex, which biologists understand to be a genetic trait, this could lead to confusion.

Lots of people devote energy to defending the sex-versus-gender distinction, but I’m not one of them. It’s that dichotomy, nature versus culture. I got turned on to turning off this distinction by Catharine MacKinnon, whose book Toward a Feminist Theory of the State I have used to teach social theory as well as gender. In her introduction, she wrote (p. xiii):

Much has been made of the supposed distinction between sex and gender. Sex is thought to be the more biological, gender the more social; the relation of each to sexuality varies. I see sexuality as fundamental to gender and as fundamentally social. Biology becomes the social meaning of biology within the system of sex inequality much as race becomes ethnicity within a system of racial inequality. Both are social and political in a system that does not rest independently on biological differences in any respect. In this light, the sex/gender distinction looks like a nature/culture distinction in the sense criticized by Sherry Ortner in ‘Is Female to Male as Nature Is to Culture?’ I use sex and gender relatively interchangeably.

From another perspective, Joan Fujimura argued for mixing more social into that biological scheme:

My investigation is an argument for broadening our social imaginaries—our definitions and understandings—of the material, the natural. A critical sociomaterial view of sex integrates sociocultural and historical investigations of the production of the material (e.g., the complexities and variations of sex physiologies and genetics) with diverse social imaginaries about sex and bodies proposed by feminists, queer theorists, intersexuals, and others. In this approach, we study and juxtapose the actions and interactions of social activist groups, social theorists, biologists, bodies, and genes in order to understand the collective, contentious, contradictory, and interactive crafting of sex in humans.

… [D]emonstrations of the sociomaterial production of sex, the Möbius strip production of sex, are useful for maintaining our awareness that natural categories are also social categories. Further, even as our current language of analysis maintains the division between the natural and the social, the point of a critical sociomaterial approach is to move in the direction of a language where there is no division, where we are always conscious that the natural and the social are not separated.

For example, we need to think of the categories male and female not as representing stable, fundamental differences but as already and always social categories. They form a set of concepts, a set of social categories of difference to be deployed for particular purposes. Ergo, what counts as male and female must be evaluated in their context of use. The categories male and female, like the categories men and women, may be useful for organizing particular kinds of social investigation or action, but they may also inhibit actions.

In that West and Zimmerman article, you may remember, they argue that “since about 1975 … we learned that the relationship between biological and cultural processes was far more complex — and reflexive — than we previously had supposed.” To help smooth the relationship between sex and gender, they use “sex category,” which “stands as a proxy” for sex but actually is created by identificatory displays, which in turn lead to gender. As I see it, the sex category concept makes the story about the social construction of sex as well as gender. For example, their use of the bathroom “equipment” discussion from Goffman’s 1977 essay is also about the social process of hardening sex, not just gender.

The U.S. Census Bureau says, “For the purpose of Census Bureau surveys and the decennial census, sex refers to a person’s biological sex,” and their form asks, “What is Person X’s Sex: Male/Female.”

But that explanation is not on the form, and there’s no (longer) policing of people filling it out — like race, it’s based on self-identification. (Everything on the form is self-identification, but some things are edited out, like married people under age 15.) So for any reason anyone can choose either “male” or “female.” What they can’t do is write in an alternative (there is no space for a write-in) or leave it blank (it will be made up for you if you do).

So its words are asking for something “biological,” but people are social animals, and they check the box they want. I think its eliciting sex category identification, which is socially produced, which is gender.

This all means that, to me, it would be OK if the form said, “Gender: Male/Female” (and that’s not a recommendation for how forms should be made, which is beyond my expertise, or an argument for how anyone should fill it out). I’m just not sure the benefits of defending the theoretical sex/gender distinction outweigh the costs of treating biological sex as outside the realm of the social.

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That number you want, it is not precise (women’s labor force edition)

Everyone wants a number. You want to know if the number is different from last year, or 100 years ago. Numbers are great. But the number you’re using is usually a statistic, a number calculated from a sample drawn from a population. You want a good number, you need a good sample. And a big one. And that’s going to cost you.

Who didn’t love the news recently that single British men ages 18-25 change their bedsheets only four times a year? Really? Really. How does anyone know this? Ergoflex, a memory-foam mattress distributor. At least UPI had the decency to report, “No survey details were provided,” although somehow Time found out the sample size was 2,004 (men and women, all ages). Rubbish, I reckon, or bonkers, or whatever. No one can resist a number; methods details don’t make it into the tweet version of the press release.

Here’s a more answerable question: What is the labor force participation rate for married, college graduate women with children, ages 25-54 in the United States? I’d say 76.1% — plus or minus a percentage point — based on the gold standard for labor force data collection, the Current Population Survey, easily analyzable these days for free with the IPUMS online tool.That’s from a sample of 60,000 households with a 90+% response rate, at a cost of umpteen million taxpayer dollars (well spent).

Here’s the trend in that number from 1990 to 2012, with 95% confidence intervals, based on the sample size, as calculated by IPUMS:

cps-error-bars

As more women have gotten college degrees, and the CPS sample has been enlarged, the sample size for this trend has grown and the error bars have shrunk, from a spread of almost 3 points to just less than 2. Still, there are only 8,265 of these women in the sample.

Only! Hold that up to a Gallup or Pew poll and compare confidence intervals when they start dividing and subdividing their samples. (Nothing against them — they give us the information we need to know how much variance there is in the estimates they put out, and then most people [+/- 51%] ignore it.)

There aren’t many one-year changes in this trend that are statistically significant at conventional levels. Of course, with this sample size you could say with confidence the labor force participation rate was higher in the late 1990s than the early 1990s (but check the survey redesign in 1994…), and higher again in the late 2000s than in the early 2000s. But were 2007 and 2002 sample flukes? And if so, what about 2012?

What about if you want a slightly smaller subgroup, say, Black married, college graduate women with children, ages 25-54. That’s a reasonable question. Here’s the trend (note the y-axis scale changed):

cps-error-bars-black

Now the sample size is a couple hundred and the confidence intervals are more than 6 points wide; there isn’t a pair of years in the trend that doesn’t have overlapping confidence intervals. And look at 2007 and 2012 — Black women are blipping in the opposite direction from the larger group in each of those years. Yes, if you put the whole Black trend in the blender with a time trend you have a significant decline of about a fifth of a point per year on average (and a sliver of this change is because of the increasing tendency of college graduates to be in grad school and not working — there are 13 of them in 2012, dragging down the participation rate by 0.6%). But don’t hang a lot on one year.

So, my advice for doing simple description:

  • Eyes on the prize: who cares what the exact number is? Is it a lot or little, going up or going down, higher or lower than some other group? That’s usually what matters.
  • Stick to data with reported methods
  • Know the size of your subsamples, try to get confidence intervals
  • Don’t fixate on (or report) small changes or differences (don’t use that second decimal place if the margin of error is 6%)
  • For trends, pool data from multiple years, or report moving averages
  • Spend tax money on surveys, not war

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