Tag Archives: google

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:

backinblack

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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What’s been queered?

How much has the term, and concept, of queer penetrated the discourse of sexuality, politics and identity?

In the overall use of the terms queer sexualityqueer politics, and queer identity, according to the Google ngrams database of American English usage, queer politics occurs most often, and queer sexuality is last.

queer-useSource: Google ngrams.

On the other hand, as a fraction of references to politics, identity, and sexuality respectively — what you could call the relative penetration of queer — the order is different: queer sexuality has most successfully entered the discourse on sexuality, with queer politics and queer identity quite behind in their relative niches:

queer-penetration

Source: Google ngrams.

(In all of these I used both capitalized and un-capitalized versions. Follow the links to modify the codes yourself.)

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Family Inequality marriage forecast contest

Enter to win: How many people will get married last year?

marriage-forecase-cartoon

An outfit called Demographic Intelligence, which I’ve written about before, got USA Today to do a story on their new U.S. Wedding Forecast™.

Although there’s no sign of him on the website anymore, DI was founded by W. Bradford Wilcox, according to the Wayback Machine‘s archive. Now it is reportedly run by Samuel Sturgeon, who did a little work for the Heritage Foundation while working on his PhD on welfare and abortion policy with David Eggebeen at Penn State (who joined Mark Regnerus in a Supreme Court brief opposing marriage equality).

Anyway, the wedding forecast is available for sale only, and the formula is a secret (the ™ is for “trust me”). But they leaked some details to USA Today.

The company projects a 4% increase in the number of weddings since 2009, reaching 2.168 million this year; 2.189 million in 2014. Depending on the economic recovery, the report projects a continuing increase to 2.208 million in 2015. … From 2007 to 2009, the number of marriages each year fell from 2.197 million to 2.080 million. The report estimates that more than 175,000 weddings have been postponed or foregone since the recession began.

OK, so the projection for 2013 is 2.168 million. The story doesn’t say what DI forecasts for 2012 — which has already happened, although the official number hasn’t been released. But we should be cautious before buying wedding futures, because, according to USA Today: “This is the company’s first foray into wedding forecasts.”

Do it yourself™

I’ve made a forecast, but like DI-LLC™, I’m sealing it till the end of the contest. But I’ll give you a few data points so you can enter your predicted number of marriages in the U.S. in 2012. The person whose prediction, posted in the comments, is closest to the actual number reported on this page will win a free Family Inequality t-shirt, if I ever get around to making them. In the event of a tie, the prediction posted earliest wins.

Here is the trend from 2000 to 2011. Observe: long-run decline, recession-spike down, then rebound.

marriage-trend

So, is the rebound just a little catching up from delayed marriage, or what? That’s the question. DI says 2,168,000 by 2013. Go marriage!

The USA Today story reports that DI’s forecast is “based on a variety of measures, including unemployment and consumer confidence.” I got some of that for you. I also added the number of women in the US ages 20-39 (who account for about 75% of marriages); these children of the Baby Boomers are a producing a little population bulge which could bring more marriages even at falling rates.

In what could be bad news for the DI forecast, however, I also checked the Google search trends for “wedding invitations,” “bridal shower,” and “wedding gifts.” These are the trends, shown in 3-week moving averages, with each normed so that 100% was the most popular week (the originals are here). See the big rebound continuing in 2012? Me neither. Click to enlarge:

google-marriage-trendsI annualized those numbers for each year 2004 to 2013, with a seasonal adjustment for the first 24 weeks of the year (don’t ask).

But are the Google numbers good for prediction? I used them to predict another down year for marriage in 2011. That wasn’t born out by the vital statistics numbers, which rebounded (as shown in the chart above). On the other hand, the numbers from the American Community Survey showed the decline continuing in to 2011, as reported by Pew. On the third hand, ACS shows continued decline in marriage rates (the difference is in the number of marriage-aged people). We don’t know enough about the difference between ACS and vital statistics to interpret this yet. Uncharted waters.

So, here are your numbers, with everything up to 2013 except the outcome: the number of marriages. Feel free to use these or anything else you like. Or just guess. Remember, Demographic Intelligence boasts of 99% accuracy, but except for 2009 you would have been at 97.5% or better just guessing no change — so you’re bound to be close. The contest is for 2012, but 2013 forecasts are welcome, too — better early than never. Click to enlarge:

marriage-forecast-data

To make it easier, I’ve uploaded the spreadsheet, with sources, here.

In marketing terminology, these variables are very hot leads. Here are the correlations between each variable and the number of marriages, for the years 2004-2011:

marriage-correlations

For other posts about prediction, see:

Good luck!

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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.

k1

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:

k2

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.

k3

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.)

k4b

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:

k4

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.)

k5

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).

k6

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What are we becoming a nation of now?

In Jonathan Haidt’s TED talk, “How common threats can make common political ground,” he mentions an influential New York Times article about how people with college degrees are more likely to get and stay married compared with those without college degrees.

At about 15:20 in the talk, Haidt says: “We are becoming a nation of just two classes.”

And I got to thinking about that phrase, “become a nation of…” It puts the reader at the moment of a transition from an assumed past to a specified future. A Google Books search reveals that we have become a nation of many things over the years:

1805: Becoming a nation of free men.

1815: becoming a nation of drunkards.

1822: becoming a nation of castes.

1840: becoming a nation of bull-dogs.

1856: becoming a nation of music lovers in the legitimate sense of the term.

1905: becoming a nation of dreamers, and then, in the next sentence, becoming a nation of money lovers and materialists.

1905: becoming a nation of physicians or even of lawyers.

1944:  fast becoming a nation of neurotics.

1953: becoming a nation of coffee drinkers instead of one of tea drinkers, like England.

1969: becoming a nation of two societies— one white and one black— separate and unequal. (from this awesome issue of Ebony:)

ebony1969

1977: becoming a nation of the elderly.

1985: Becoming a Nation of Readers.

1987: becoming a NATION OF ILLITERATES.

1988: becoming a nation of hamburger stands, and, in the same sentence, becoming a nation of management consultants, doctors, software designers, and international bankers.

1989: Becoming a Nation of Burger Flippers?

2008: becoming a nation of joiners.

2008: becoming a nation of orthorexics (people with an unhealthy obsession with healthy eating)

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Do people working work in working families?

It’s not that “working families” don’t exist, it’s just the way most people use this term it doesn’t mean anything.

Search Google images for “working families,” and you’ll find images like this:

4f4a9a28-ff28-4bc7-88e5-f0df4522b2dbAnd that’s pretty much the way the term is used: every family is a working family.

To hear the White House talk, you have to wonder whether there are people who aren’t in families. I’ve complained about this before, Obama’s tendency to say things like, “This reform is good for families; it’s good for businesses; it’s good for the entire economy.” As if “families” covers all people.

Specifically, if you Google search the White House website‘s press office directory, which is where the speeches live, like this, you get 457 results, such as this transcript of remarks by Michelle Obama at a “Corporate Voices for Working Families” event. The equivalent search for “working people” yields a paltry 108 hits (many of them Obama speeches at campaign events, which include false-positives, like him making the ridiculous claim that Americans are the “hardest working people on Earth.”) If you search the entire Googleverse for “working families” you get about 318 million hits, versus just just 7 million for “working people” (less than the 10 million that turns up for “Kardashians,” whatever that means.)

You would never know that 33 million Americans live alone – comprising 27% of all households. And 50 million people, or one out of every 6 people, lives in what the Census Bureau defines as a “non-family household,” or a household in which the householder has no relatives (some of those people may be cohabitors, however). The rise of this phenomenon was ably described by Eric Klinenberg in Going Solo: The Extraordinary Rise and Surprising Appeal of Living Alone.

This is partly a complaint about cheap rhetoric, but it’s also about the assumption that families are primary social units when it comes to things like policy and economics, and about the false universality of “middle class” (which is made up of “working families”) in reference to anyone (in a family with anyone) with a job.

Here’s one visualization, from a Google ngrams search of millions of books. The blue line is use of the phrase “working people” as a fraction of references to “people,” while the red line is use of the phrase “working families” as a fraction of references to “families.” It shows, I think, that “working” is coming to define families, not people.

CaptureThis isn’t all bad. Families matter, and part of the attention to “working families” (or Families That Work) is driven by important problems of work-family conflict, unequal care work burdens, and so on. But ultimately these are problems because they affect people (some of whom are in families). When we treat families as the primary unit of analysis, we mask the divisions within families – the conflicts of interest and exploitation, the violence and abuse, and the ephemeral nature of many family relationships and commitments – and we contribute to the marginalization of people who aren’t in, or don’t have, families.  And those members of the No Family community need our attention, too.

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What do Jews Google on Christmas?

I don’t know. But I do know what Google searches are geographically correlated with searches for “Hanukkah,” and it includes “Chinese food.”

Here are the maps:

hanukkahgoogle

The map looks close to the Jewish population map from the 2010 U.S. Religion Census (a private outfit).

jewishmap

Here are the top-100 most Hanukkah-like searches, in the obvious categories: Religious/cultural, Howard Stern, Food, Travel and People.

jewishsearches

Merry Christmas.

 

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