Seth Stephens-Davidowitz, a PhD student in economics at Harvard, has analyzed Google searches for racially offensive terms across metro areas, and tested for a “racial animus” effect on the vote for Obama in 2008.* The results are pretty strong:

 The baseline proxy that I use is the percentage of an area’s total Google searches from 2004-2007 that included the word “nigger” or “niggers.” … A one standard deviation increase in an area’s racially charged search is associated with a 1.5 percentage point decrease in Barack Obama’s vote share, controlling for John Kerry’s vote share. The results imply that, relative to the area with the lowest racial animus, racial animus cost Obama between 3 to 5 percentage points of the national popular vote. … The statistical significance and large magnitude are robust to numerous controls including local unemployment rates; home state candidate preference; Census division fixed effects; changes in House voting over the same period; prior trends in Presidential voting; and a variety of demographics controls.

This is a creative way to measure racism — not perfect, but nothing is. And he did a fair amount of experimenting and tinkering with the measures to make sure it wasn’t fluky. Very nice.

Racism at the population level

Another thing that jumped out at me in the paper, however, was the finding among the control variables that racist searches are more common in markets with higher proportions of Black residents. This raises a potentially difficult issue with the whole Google-search method, since we don’t know who is doing the searching. Does his finding suggest that Blacks are doing racist searches? I don’t think so. I previously looked at state-level correlations between race/ethnic composition and search terms, and it looks to me like the most correlated search terms are indeed being performed by those groups. For example, Americans Indians live in states where people Google “Indian Health Service” and Blacks live in states where people Google stuff about historically Black colleges and universities (and Whites apparently Google AC/DC songs).

But at lower levels of correlation, I would expect the presence of one group to affect the search behavior of others. An obvious example is how Southern states mostly vote Republican in national elections — more Blacks equals more conservative voting, even though the great majority of Black voters vote Democratic. The higher rates of conservatism among Whites in those places outweighs the presence of Democratic-voting Blacks. (The effect on Whites was discovered before Blacks could vote in the South, but remains true.)

We also know from way back that inequality between Blacks and Whites is greater where Blacks are more highly represented in the population, and there’s good evidence at least some of this is due to increased racism by Whites. I’ve found this for earnings for both men and women, for middle and working class workers; and, with Matt Huffman, for occupational segregation and access to managerial positions. Only some of that research has actually measured racial attitudes, however. Google gives us a chance to look from a different angle — at the private behavior, not expressed attitudes, of populations.

Removing West Virginia, which is an extreme outlier on the jokes variable (more than 3 standard deviations from the mean), the correlation between searches for “nigger jokes” and Black population percentage is .48. Here’s the scatter plot (the non-Southern states have the pink centers).

And here’s the regression numbers for the relationship:

That positive relationship, tapering off, fits the long-standing pattern, as seen for example in this 1998 paper, which tested the percent-Black on common attitude measures in the General Social Survey (the figure estimates are net of a variety of controls):

All adding to the accumulating evidence for search behavior as a valuable research tool.

* Thanks to a tip from Rachel Lovell.

** Some searches seem even better for this purpose, such as “funny nigger jokes,” but fortunately there isn’t enough searching for that to get state-level frequencies, according to Google.

The centerpiece of this campaign, I believe, is American exceptionalism versus the radicalism of Saul Alinsky [chants of "USA! USA!"] … We are going to argue American exceptionalism, the American Declaration of Independence, the American Constitution, the American federalist papers; the founding fathers of America are the source from which we draw our understanding of America. He draws his from Saul Alinsky, radical left-wingers and people who don’t like the classical America.

Here he is in a December debate on Fox:

Who is Saul Alinsky? And what kind of first name is “Saul,” anyway? Jewish. And who Googles “alinsky”? I don’t know for sure, but I do know that whoever they are, they live in the same states as people who Google “obama citizenship” at a correlation of .73:

Some other searches that have the highest correlation  with “Alinsky” across states (all .68 or better):

• charles krauthammer
• conservative blogs
• drudge
• drudgereport.com
• fairness doctrine
• greta van susteren
• health care bill text
• hr 1388
• mccain for president
• national firearms act
• natural born citizen
• presidential order
• trilateral commission

My guess is that hardly anyone in Gingrich’s intended audience knows who Saul Alinsky is. But I think he’s giving them enough information to know what kind of person he is. In December he put it this way:

…if you look at his background, he’s really a lot more Saul Alinsky and radicalism than he is anything to do with the traditional American models.

FYI, this morning someone removed the description of Alinsky as Jewish from the opening line of his Wikipedia entry, which was added in December.

The authors, Nathanael Lauster and Frank Tester, point out that the definition of “crowded” living conditions in the U.S. has been downsized from 2 people per room in 1940 to 1 person per room now. And in Canada the current common definition is based on the age, gender and relationship of the household members, such that children over age 4, for example, can only share rooms with siblings of the same gender without being “crowded.”

In such a way, a household comprised of an adult couple with two sons, aged 4 and 6, would require housing with a minimum of two bedrooms to avoid being considered overcrowded. Change the 6 year old son to a daughter, or age the 6 year old son to 19, and a minimum of three bedrooms would be required.

It’s nice to have a standard of crowded housing, since at some point crowded conditions have negative effects on health. But once a standard is set, well-meaning government programs may end up “disciplining” cultural minority groups, the authors argue.

They cite as evidence the Inuit communities in Arctic Canada, who were semi-nomadic until the 1950s, and lived in “extended family-based hunting camps.” Part of the overall process of cultural dispossession involved getting them to move into modern housing. The fear of overcrowding required special attention from authorities, who stressed the desirability of each family having its own house. The result was an education program designed to change cultural standards of appropriate household sharing and gender propriety that were quite foreign.

The article has a figure used as part of this program, from 1966:

This two-bedroom house is suitable for up to two couples with one baby each, but if any children are 12 or older then it’s only big enough for one family. And the couple can only have 2 children if they are the same gender or one is under 12.

I’m sure many readers are more familiar with this Canadian history — or similar situations from other parts of the world — but this story jumped out at me because it involves official definitions of families, cultural domination/discipline, and real problems of health and well-being. This relates to the “no family” theme insofar as it represents a contest over family self-definition.

The first chapter is posted free, and it includes a good introduction to the statistical and conceptual issues that arise when trying to understand patterns of mobility across generations. It includes a discussion of heritability, genetics, IQ and the like, which is quite approachable to the reader who is ready to think about decomposing correlations.

One good example regarding genetic heritability of traits that determine income: race in South Africa, which is almost entirely inherited (since there’s very little interracial marriage) and has a huge effect on income, but the effect of which is still social/environmental, not “natural.”

Anyway, I like this “twin peaks” figure, which shows the relationship between parent and child family income decile:

Probability of offspring attaining given income decile, by parents’ income deciles, United States. Based on total family income for black and white participants in the Panel Study of Income Dynamics who were born between 1942 and 1972, and their parents. The income of the children was measured when they were aged 26 or older, and was averaged over all such years for which it was observed. The number of years of income data ranged from 1 to 29 with an average of 11.5; the median year of observation was 1991. Parents’ income was averaged over all observed years in which the child lived with the parents. The number of years of income data ranged from 1 to 27 with an average of 11.9; the median year of observation was 1974. The simple age adjusted correlation of parents’ and children’s incomes in the data set represented in the figure is 0.42.

So, 30% of children from the top decile stay there (point D), 32% of children from the bottom decile stay there (C), while the odds of making it from the top to the bottom, or vice versa, are both less than 2% (A and B).

There is a nice symmetry to the figure, but it’s important to know that what’s happening up and down the distribution is highly varied, according to the analyses in the book. For example, at the top there is a lot of transmitted wealth. At the bottom there are a lot of health crises and premature deaths, including from violence. And the bottom is much stickier for Black children than for Whites.

Big gaps (a) are only useful for creating a big impression. Long-standing gaps (b) are undermined by the scale of change in recent decades. And a new study does a very nice job weakening type-C support.

In “Debunking Myths about Gender and Mathematics Performance,” in the Notices of the American Mathematical Society, Jonathan Kane and Janet Mert study variation both between and within countries to test a variety of hypotheses about the sources male math advantage. They look at the distribution and variance in scores, the association with single-gender schooling, religious context and, most importantly, broader patterns of gender inequality. The main message I get is that gender ability in math differs so much across social contexts that any conclusion about “natural” ability is untenable. Also, gender equality is good.

Here’s my favorite figure from the paper, showing the distribution of eighth-grade scores for boys and girls in three countries:

In the Czech Republic there is no difference in either the means or the distributions for boys versus girls, and the average ability is high. Bahrain shows a much greater variance for boys versus girls — which is sometimes used to explain why to many top achievers are men — but women’s average is higher. Finally, in Tunisia the girls have a higher variance but a lower mean. Where’s the natural ability story?

An important consideration in all of these patterns is the role of selective dropouts. That is a potential problem with any school-based test, but also shows the problem with using any test of school-based knowledge to understand underlying “natural” ability (including SATs). Unless you can test populations with no schooling, or identical schooling experiences, you can’t resolve this.

In the meantime, the great social variability shows us that context matters, and since that’s something we can definitely address, there is no reason to get hung up on the biological stuff — at least as far as policy and practice are concerned.

Here’s a previous post from me on how teacher interactions affect gender patterns of learning, and another writeup on the new study from ScienceBlogs.

This updates my mancession/hecovery series, which last appeared here.

Using the population survey numbers (not the payroll numbers IWPR uses), I’ve also updated my series on Black-White women’s trends:

Here is the text of the interview that Norton Sociology‘s Andrea Lam did with me for their Featured Sociologist column. I’ve added some links to relevant posts and articles for background or followup, and some pictures. Thanks to Karl Bakeman for setting it up (follow his great sociology feed here).

Q: In a number of your articles, you discuss gender and race inequality as it occurs in the workplace. What are your thoughts on how social and cultural factors encourage these inequalities to be reproduced and/or enforced? Have you observed any recent trends that might indicate improvement in these areas?

One of the incredible features of modern society is how certain kinds of work are so strongly associated with certain kinds of people. In fact, the very categories of people (such as man and woman) and of jobs (janitor / housecleaner) are partly reproduced by these associations. Modern workplaces don’t have formal, 100% exclusive rules about who does what work, but the patterns are very strong — and they are always implicated in inequality, because I know of no systematic division of labor without inequality in its rewards as well.

As ubiquitous as these divisions are, however, how exactly they are reproduced is not completely clear. Sociologists remain divided, for example, over how the different actions and ideas of men and women on the “supply” (employee) and “demand” (employer) sides influence the segregation of men and women into different jobs. In terms of recent trends, we are witnessing gradual changes in some areas toward integrating jobs by gender and race/ethnicity, but the progress is slow. One of the things we try to do is look at the rates of change in different places, or in different kinds of work, to understand what drives those changes. Is it law and policy, politics and social movements, globalization, technology? These are some of the pressing research questions now.

Q: You recently co-authored with Claudia Geist an article on contemporary gendered divisions of housework (“Headed Toward Equality? Housework Change in Comparative Perspective”, in Journal of Marriage and Family 73, August 2011). What methods did you use in gathering the data for this study? What did your findings indicate about the future of gender inequality within the family?

One of the great advances in data collection has been the development of big comparative surveys. We used one such survey – the International Social Survey Program — which has gathered information about how couples divide housework in about a dozen countries since the 1990s (and it now includes more than 30 countries). From previous research (some of it with former students Jeanne Batalova and Makiko Fuwa), we know that there are big differences in the how couples negotiate over unpaid housework, with very different patterns across nations. Claudia and I wanted to know whether countries with less equal divisions of labor between men and women were catching up or falling behind in the global trend toward more equality — and that is what we found, although the pattern is not that strong, so “convergence” is a long way off at this rate.

Q: You are a strong proponent of using the terms “homogamy” and “heterogamy” in discussions of unions such as marriage and cohabitation, rather than “same-sex” and “opposite-sex” or “different-sex,” respectively. Could you talk a bit about your decision to use these terms and why you feel that they should be adopted?

Classification of categories — and their names — is a core function of science, and the decisions we make toward that end matter a lot. With family types, there is a long history of classification using Greek and Latin terms — such as “monogamy” and “polygamy” for the number of spouses people (men, originally) have, “hypergamy” for marriage between people of unequal statuses, “exogamy” for marrying outside the group, and so on.

I became concerned about our language for same-gender marriage when I noticed a double-standard in which people talked about “marriage” (unmodified) when they were referring to couples including a man and a woman, but “same-sex marriage” when they were talking about gay or lesbian couples. When pressed, people usually say “opposite-sex,” which I think reinforces the dichotomization of men and women in harmful ways.

Anyway, in trying to decide what to do about this, I looked at “homosexual” and “heterosexual,” and realized “hetero” means “different” rather than opposite — which is good. And if you look back at the history of the terms “homogamy” and “heterogamy,” you see they have come to be used for similarity and difference within couples (such as ethnicity or education level) in which the partners are already presumed to be of different genders. But in the 19th century “homogamy” was used for same-sex reproduction among plants. So I am pretty sure that if marriage rights had been extended to gay and lesbian couples 150 years ago, social scientists would have called them “homogamous.”

My main goal is to promote serious scientific consideration of our categories and terms for families and relationships. If we end up with “homogamy” and “heterogamy” I think that would be progress. That’s the argument I made in the article “Homogamy Unmodified” in the Journal of Family Theory and Review this year.

Q: On your blog, Family Inequality (www.familyinequality.com), you analyze a wide variety of articles and data that discuss the complex relationships between families and inequality. How do you select what articles to analyze?

I am very suggestible. And one of the ways I try to integrate things that I am learning myself is to replay them verbally and represent them visually — to myself, my students, and now, thanks to the blog, anyone who will listen. So must of what I write is a description of something I have learned — even if it’s just new evidence for something I already understood (or thought I understood). The great thing about a blog — although it’s sad, too — is that it’s so ephemeral. I don’t have to worry about being comprehensive and covering everything, since it’s just a small stream feeding the river of information (and other things) that everyone sees.

Q: On Family Inequality, you occasionally make use of Google Correlate (correlate.googlelabs.com), which “uses web search activity data to find queries with a similar pattern to a target data series”. What are your thoughts on the relationship between sociology and Internet search data?

What I like about looking at search patterns is it’s a representation of what people really do, not what they say they do or think. I think it offers great opportunities to measure behavior in something like real time. Google realized this when they came out with the flu tracker — which uses searches for flu symptoms as an indicator of infection rates. I think we could do the same thing, for example, with fertility rates. It takes many months to get real birth data counted up and released by the government. But Google knows how many people searched for “pregnancy workout” and “baby shower gifts” yesterday.

Beyond such practical uses, though, I am always looking for ways to see and understand regularities in social behavior. For example, how is it possible that out of the 2 million girls born last year, my prediction for how many would be named Mary was only off by 22? I didn’t do anything fancy, just tracked the trend in the number of Marys over time. Most people don’t like to think of themselves as so predictable, but the relationship between predictability and individuality is one of the sweet spots of sociology.

Q: Are there other sociologists, living or dead, whom you particularly admire? If so, what about them or their work do you find inspiring?

I am very inspired by a group of feminist social scientists who got their PhDs around the 1970s. I wrote a blog post describing a few of them, including Judith Stacey, Harriet Presser, Ruth Sidel, Phyllis Andors and Janet Salaff (whose death inspired the post) — it’s not an exhaustive list. They emerged at a time when feminism had a very up-for-grabs gestalt, in which anything seemed possible, and it shows in their work. Their coming of age was a historical breakthrough for women and for sociology, and their work has taught me a lot.

The Bureau of Justice Statistics has released Correctional Population in the United States, 2010, which updates my standard figures. First, the total trend toward unfreedom in the population — from less than 2 million in 1980 to more than 7 million 30 years later:

And second, to understand the disparate impact of this change on Black men in young adulthood primarily — and secondarily, Latino men — here are the rates of incarceration for men by age and race/ethnicity (Blacks here exclude Latinos; Asians and American Indians are not included in the statistics):

Just to make sure you read the scale right, that incarceration rate for Black men in their early 30s is 9,892 per 100,000, or 9.9%, or one-in-ten — more than five-times the rate for White men.

I come at this largely from its effects on families. In a nutshell: The overall trend is largely a consequence of how the U.S. has waged its drug war over this period; these policies fit into a web of practices that deny families to millions of people in the U.S. (only a minority of whom have been convicted of crimes), including by simply removing men from communities and increasing the number of single-parent families.

All that said, you may notice the little decline at the end of that long upward trend in the first figure. In fact, for the first time since 1980, there has been a decline in the incarcerated population for two years running. There has been a long-term decline in crime, but I don’t know whether that is more important than the budget crises facing so many states, or the diminished lust for locking people up. In New York, for example, seven incarceration facilities were closed in the last year, after the number of prisoners dropped about one-fifth in the past decade:

The inmate decline followed a 25 percent statewide drop in crime over the past decade and revisions in sentencing laws that allowed earlier releases and alternative programs for nonviolent drug offenders. The number of prisoners in medium-security prisons declined almost 20 percent from 2001 to 2010 while those in minimum-security facilities dropped 57 percent.

The numbers on the charts are still off the charts, meanwhile — and remember these are just those in the system now. Many more people (and their families) live lives permanently hampered by criminal records and the experience of imprisonment.

In the Washington Post: Plummeting birthrates in Brazil

The Washington Post reports on Brazil’s fall from more than 6 to less then 2 children per woman in the past 50 years:

It’s a good case study for fertility transitions, featuring a combination of common economic and cultural suspects in accelerated sequence.

In the NY Times: Peggy Orenstein on the ideal of gender-free toys

Rather then seek a gender-free ideal, she argues, consider how children’s environments exacerbate or mitigate the differences between them:

At issue, then, is not nature or nurture but how nurture becomes nature: the environment in which children play and grow can encourage a range of aptitudes or foreclose them. So blithely indulging — let alone exploiting — stereotypically gendered play patterns may have a more negative long-term impact on kids’ potential than parents imagine. And promoting, without forcing, cross-sex friendships as well as a breadth of play styles may be more beneficial. There is even evidence that children who have opposite-sex friendships during their early years have healthier romantic relationships as teenagers.

In Slate: Mara Hvistendahl on C-sections in China

The tradition of natural childbirth was continued by the training of nurses and midwives during the early years of Chinese socialism. Now, the one-child policy combines with the medicalization of childbirth – and the attendant profit motive – to tip the scales toward C-sections. She writes:

For modern expectant women, by contrast, the combination of the one-child policy and feverish economic development has yielded an environment in which they—and the in-laws and husbands who have so much riding on a single birth—fear any potential misstep.

In The Chronicle of Higher Education: Andrea Doucet on scholar-bloggers

As an established scholar who has taken to blogging, she confronts the difference between slow-and-deep versus fast-and-thin, how it affects her reading as well as her writing, and her self image as a scholar. She is “convinced that blogging can and should be part of scholarly life,” but it comes with risks:

At its best, a blog post can move and inspire in what seems like the blink of an eye. The combination of brevity, focused vision, and engaging language creates a storytelling style that could make a scholar green with envy. But blogs also generally call for a form of reading that verges on consumption.

On CNN.com: Kris Marsh on the Black middle class

Kris – a friend and colleague – argues that the Black middle class is being transformed by the growing presence of single adults without children, the “Love Jones Cohort.” Taking this group seriously undermines the narrative of the “failure” of marriage in Black America.

I propose we embrace the reality of a changing black middle class and start taking a serious look at how the Love Jones Cohort is changing the face of black America, changing how we think about middle class, and changing our understanding of being black in America.

Ideally, we need Google to give up the real data, warts and all, for real research. For example, this disturbing bit of text has appeared on their FAQ page:

In December 2011, we added support for time series correlations on a number of new countries. As part of this change, we reduced our sample size for US states and US time series to match that of the other countries. While this does not have much of an effect on popular queries, it may cause a noticeable increase in variance for queries with lower volumes.

In fact, some of the searches I’ve reported on in the past no longer produce the same results, apparently because of this change. Since they have apparently reduced the sample size, I favor the results I reported, but this is not the kind of thing we’d do if we ran the zoo. If anyone knows Google, you should try to work this out with them, get some grant money, and really wow us.

Anyway.

Here’s where the blog has been:

• Shotgun pistol grip, meet tofu marinade (Merry Christmas!): How “traditional Christmas dinner” and “vegetarian Christmas” searches delineate states.
• MaddowColbert vs. FoxLimbaugh, Google edition: How searches for a range of oddities, from fennel salad and home abortion methods, follow red/blue-state patterns and illustrate the Internet’s political echo chambers.
• What do commuters Google?: Searches that follow the pattern of state average commuting times. Think, “change careers.”
• Google searches foretold Census report of divorce increase?: Google time trends supported the idea of pent up demand for divorces as a result of the recession (reported in this post). Census data show an uptick in divorce for 2010. Discuss.
• Google index of poor mothers’ pain: How time trends for food stamps, Wal-Mart jobs, and “help for single mothers” searches all spike together — along with “help with rent,” “iud side effects,” and other indicators of poor women’s struggles.
• Warning: What do smokers Google?: Nine disease- or symptom-related searches that are highly correlated with state-level smoking rates. Yikes.
• Is fertility ready to rebound?: Searches for terms like “pregnancy growth” and “pregnancy tips” point toward a post-recession rebound in fertility rates.
• Promoting breastfeeding here and there: The high correlation between state breastfeeding report card scores and searches for the Center for Science in the Public Interest newsletters.
• Stuff White people Google: Race/ethnicity, age and education distributions across states all show correlations with searches for related concepts (like “Regina Belle” for Black population, and “fosomax” for population age 65+.
• Divorce, handguns, Obama, Top Chef, Tea Party: Looking at correlations between real data — like state divorce rates and votes for Obama — and Google searches.
• Marriage drop now, divorce drop later?: An anemic seasonal spike in wedding-related searches for 2010. Maybe that’s good news for the current crop of marriages, though.
• Unemployment’s greenjobs porn Google mind?: Tracking searches that follow weekly unemployment claims, including loan modifications, green jobs, and porn.

Wow, that’s a lot of posts. (It reminds of when Maine’s recycling law led Stephen King to start stockpiling beer cans instead of throwing them out, and the size of the pile illustrated the depth of his alcoholism.) Maybe I should cool it for a while.

The story is here, with an audio link.

NPR addendum:

Here are the foods that appear in the top-100 correlations with searches for “npr” across states:

• couscous
• no knead
• cooks illustrated
• knead bread
• no knead bread
• flourless
• flourless chocolate
• creme fraiche
• curried
• chard recipes
• polenta
• wheat allergy
• caster sugar
• bittersweet chocolate