Tag Archives: music

Here it is, your moment of White

A couple years ago, in a post called “Stuff White People Google,” I showed which Google search patterns were most highly correlated with the representation of different race/ethnic groups in the Census. That was a much better post than this.

This is a moment-of-White followup.

Here are Whites, by county, from this tool:

PercentWhite

Here are the searches for “back in black,” from Google Correlate:

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Google searches for “back in black”

And here is the correlation between searches for “back in black” and searches for “kitten pictures,” by state:

backinblackkittens

The scales are normed to a mean of 0 and standard deviation of 1 by Google, I think. I made the graph in Stata with this command (which I’m putting here because I always forget this syntax):

gr twoway scatter backinblack kittenpictures, mlabel(state) mlabposition(0) msymbol(i)

Random question

So, if it is Whites doing the searching for “back in black” and “kitten pictures,” is it possible that the searches are going on in the same households with some kind of gender division?

acdcfans

Don’t let that selectively-chosen picture fool you. According to the Alexa web traffic site, visitors to acdc.com skew only slightly male. And Facebook tells me I can reach a mostly- but not overwhelmingly-male mix of 3 million women versus 4 million men if I target people with an interest in AC/DC for an ad. (However, if people Googling AC/DC are looking for guitar tabs, maybe it’s the intersection of guitar and AC/DC as interests that matter.)

On the other hand, cuteoverload.com, which is loaded with kitten pictures, skews strongly female, and Facebook tells me that “cat pictures” as an interest will attract women more than men at a ratio of 4-to-1 (much more skewed  than the general interest in cats: 1.5-to-1).

Anyway, this might not be the best case. I wonder what other examples there might be of a specific group (e.g., Whites) being divided between men who have a uniquely strong interest in something (AC/DC) and women who have a uniquely strong interest in something else (kitten pictures), with low overlap between the genders. That would be neat – intersectionality seen in Google search patterns.

So

Anyway, it’s time for another year of graduate student admissions. If you or someone you know like playing with data and making graphs, pursuing hunches about social patterns (more or less important than the ones here), and reading and writing a lot, maybe you or your friend should be in next year’s pile of applications.

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Marriage is going down, so what does Kanye West have to do with it?

The marriage rate has fallen almost continuously for more than half a century, from a sky-high 90 per 1,000 unmarried women in 1950 (meaning almost 1 in 10 single women got married that year) to a bare 31 per 1,000 in 2011. Splashdown appears imminent.

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Sources: 1940-1960; 1970-2011.

Social scientists understand that there is a combination of demographic, economic, policy, and cultural factors involved. These include the aging population, men’s declining fortunes, the incarceration of millions of poor men, the rise of secular ideology and the sexual revolution.

Often, however, cultural influence is left to what you might call residual interpretation. Proving that culture affects demographic trends is difficult. Instead, people consider how demographic, economic and policy factors play their roles, and then attribute what’s left of the trend to culture.

Recently, the National Center for Family and Marriage Research at Bowling Green University reported the marriage rate for each state and D.C., ranging from 61 marriages per 1,000 unmarried women in Utah down to 19 per 1,000 in Washington, D.C. and 20 in Rhode Island. To explain the pattern using normal demographic practices, I gathered some other data about states from the Census Bureau: The percent of the population over 65, percent female, percent with a BA or higher education, population density, per capita income and race/ethnic composition. With that information – using a regression – I can guess the marriage rate to within 3.1 points on average. This is what the regression looks like, showing what happens when I start with age and sex composition, add income and education, and then add race/ethnicity:

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In statistical terms (R2), my simple model explains 73 percent of the variation in marriage rates, which is pretty good. Before I would use the marriage rate as an indicator of something like “culture,” then, I would say most of what’s going on reflects larger demographic and economic patterns that we more or less understand. The differences that remain, however, still might be the result of cultural, religious, or attitudinal factors that are harder to assess. (I stress this is not about low Black marriage rates: note the population percentage Black has no effect once the other factors are controlled.)

Culture, meet big data

What about big data, the billions of bits of information people leave strewn around wherever they go? Marketers and government spying agencies make most of the headlines, but social scientists, too, are scraping up millions of words and turning them into analyzable numbers, so they can tell you things like:

One of the easiest sources to use for this kind of thing is the Google Correlate tool, which finds the search terms whose frequency most closely follows a specified pattern. I entered the marriage rate for each state, shown on the map on the left, with darker green indicating higher marriage rates. Google Correlate tells me which searches track this variation: which searches are most popular in Utah, least popular in D.C., and so on. (I actually trimmed the Utah rate to it wouldn’t be such an outlier, from 61 down to 57, just above the next highest). It turns out the most correlated search is for “rolls recipe,” which is correlated with the marriage rate at .85 on a scale of -1 to 1.

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But since my interest is in the decline of marriage, I multiplied the marriage rate by -1 and tried again (so now darker green indicates a lower marriage rate). The answer, overwhelmingly: Kanye West. (Experts at finding any website anywhere will know that he’s a never-married proud father-to-be with co-parent Kim Kardashian.)

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That correlation between the inverse of the 2011 marriage rate and “kanye west my beautiful dark twisted fantasy” (his last album) is .81. Further, Google produces the top 100 most correlated searches, and of those, no fewer than 28 were about Kanye West (such as “kanye west new album,” “devil in a new dress lyrics” and “air yeezys”). Another 16 were other hip-hop searches, including some about Jay Z and Lil Wayne. Other apparent themes include mafia-related entertainment (“sopranos episode,” “pacino movies,” “corleone”) Sex and the City, and shopping at Marshalls.

Does this tell us more than the simple demographic analysis I did above? When I put the top Kanye search into my model, it has the strongest effect, and the variance explained jumps to 81 percent. The model now can predict the marriage rate to within 2.5 points on average.  It’s a very good predictor, and it’s not just reflecting simple demographics like age, gender and race. Whether Kanye is in the analysis or not, Black population percentage has no effect on this prediction. Here is the regression, with new parts in red:

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Explanations

So, I dredged all the search data in the world for something correlated with marriage rates, and found something. But what does it mean? Two cautionary stories are revealing. Forecasting guru Nate Silver has a good description of how noise looks like signal. For example, with the tens of thousands of economic statistics available to build a forecasting model, finding a pattern after the fact is deceptively easy. But it usually doesn’t work for predicting future economic trends.

Another caution comes from genomic studies. In a study of, say, cancer genetics, statisticians may conduct millions of tests for the association between any genetic variant and the occurrence of cancer. With the typical definition of “statistical significance” – which tolerates a 5 percent random chance of being wrong – that means they’d find hundreds of thousands of bogus “significant” associations. So good scientists set their significance threshold for such studies much tighter, more like.00005 percent than 5 percent. That way they are sure to only blow the whistle on genes if the chances of being wrong are vanishingly small.

So, this is a suggestive game of Big-Data Craps, not real research. It’s meant to provoke a little. I hope we’ll think creatively about new kinds of data we can use. Also, I want to generate ideas about cultural explanations for demographic trends. It should be at least as useful as some pundit simply declaring, for example, that gay marriage is killing real marriage. (“As the cause of gay marriage has pressed forward,” wrote Ross Douthat, “the social link between marriage and childbearing has indeed weakened faster than before.” That theory has about as much going for it as one linking the decline of marriage to the rise of high fructose corn syrup or the explosion of red cards in World Cup soccer.)

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Kanye’s fantasy

With those caveats, here are three possible explanations for the finding:

  1. Google, by trawling through millions of search term patterns, has come up with a random bit of noise that just happened to catch my attention. There’s nothing there, really.
  2. The hip-hop Google search is capturing a more finely-grained demographic pattern than I did with my simple Census numbers. So what matters for marriage is not just things like the percentage female, education levels and racial composition of the population, but the presence of particular combinations of these demographic groups. Hip hop’s audience is notoriously difficult to define — it’s featured on top-five radio stations in markets such as San Francisco and Los Angeles as well as Detroit and Atlanta — but it’s certainly not as simple as age, gender, and race
  3. Hip-hop actually is weakening marriage in America. People who listen to Kanye West and other hip-hop music are taken in by the music’s consumerist individualism and shun marriage, with its staid image of tradition, conformity and restraint. As a result, they are less likely to get married than the people Googling “rolls recipe.”

I lean toward explanation #2. Explanation #3 might have something to it. As the philosopher xkcd wrote, “correlation does not imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there.’” But I wouldn’t draw that conclusion without a lot more evidence, including doing some comparisons to other cultural factors, like other kinds of music or religious patterns. Since I have no expertise in hip hop (post 1989), I would be glad to hear from people who know about it for realz.

Addendum: Here’s a scattergram showing the correlations between some of the variables in the regression. In each cell there’s a dot for every state plus DC. The Kanye variable is scaled (by Google) to have a mean of 0 and standard deviation of 1 (click to enlarge).

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Hit song, hit name

In the comments of my Atlantic post about the name Mary, and in a number of personal emails, people have suggested pop culture triggers for changes in name direction – like the turnaround in Emma and its eventual peak at #1 in 2002. In that year, Emma jumped 9 places, from 13th to 4th among U.S. girls. There are other examples, but I prefer a few that date back to a golden era of pop songs with women’s names in the titles.

Some hits in the 1960s did not produce name bounces (Sharona, Sherry). But in the 1970s three #1 songs produced name bounces: Maggie (from “Maggie May,” by Rod Stewart in 1971), Brandy (Looking Glass, 1972), and Angie (Rolling Stones, 1973). I added a personal favorite, Rhiannon (Fleetwood Mac, 1975).

song-names

Each of these tells a good story.

  • Rhiannon and Brandy both debuted in the top 1,000 names the year they made the charts, with Brandy making a credible run for the top before collapsing in the 1990s. Rhiannon hung on as a niche name until the 2000s.
  • Maggie changed direction the year of the Rod Stewart hit, reversing a long slide.
  • Angie, which gained popularity in the 1950s (Angie Dickinson?) seemed to get a boost from the Stones song, but it was not long lasting (it is a breakup song, after all). I don’t know why Angie came back in the 1990s (1994 movie with Geena Davis?).

My series of posts on names is here.

The names database is here.

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Where did we go wrong, or did we? (love and sex edition)

For ever – or at least for 70 years – make love was many times more common than have sex, at least in the Google Ngrams database of millions of books in American English. And, then — well, you can guess what happened then:

The results are the same with “making” and “having” (you can play with the search here).

Why? What happened? Could it be “the culture”? Zooming in on the period since 1950, preliminary evidence is mixed:

I’m open to hypotheses.

 

 

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