Cross-posted at Family Inequality.

A blue-collar worker gets laid off. Maybe he or she reads an article in the Boston Globe with an image of a wind farm, like this. So he or she types “wind energy jobs” into Google, and ends up at one of many websites promoting wind energy jobs, with an upbeat graph like this:

Was this a common pattern during the recession, during which Obama has promoted the idea of moving workers into the wind energy sector? Honestly, I never would have thought of that question if not for the results of today’s poking around.

Action in context

New kinds of data have the potential to open up vast new territory in the study of patterned individual behavior. Finding and understanding aggregate patterns in micro-level behavior is more feasible than before. My prior poking around has included tracking the relentless decline of the name Mary given to children born in the U.S., the search patterns associated with having a baby across millions of Google users leading up to the recession, or patterns of divorces across states according to their unemployment levels.

In each of these situations, individual behavior assumes a social form that emerges when the data are aggregated and analyzed in relation to other patterns or time periods. And in each case it appears that separate individuals are responding similarly to larger forces — allowing us to understand those forces in new ways.

In today’s exercise I plugged the weekly number of initial claims for unemployment into the Google Correlate tool, and asked it for the 100 search term trends that were most closely correlated with the unemployment trend since 2007.* On the list were “wind energy jobs” and “green jobs.” Beyond those, it was pretty easy to group the 100 search terms into categories: 38 of them were searches for songs and lyrics (especially MGMT lyrics), 17 were Internet/technology related (such as “roadrunner webmail login”). I have no explanation for those.

But the last large group was clearly recession-related: those about loan modifications (such as “loan modification,” “loan mod,” or “mortgage hardship.”) All of these were very highly correlated with the initial unemployment claims trend (.93 or higher on a scale of -1.0 to 1.0). Here they are, plotted by week since the start of 2007.

The Google search volumes are relative (on the right axis), so we don’t know how many people were doing these searches, only that they were doing it in the same weeks that unemployment claims occurred.

A final, small group of terms were related to porn. Maybe there are just so many porn search terms that something is correlated with any trend. But the search terms “snake tube,” “uncoached” and “coomclips” track initial unemployment claims very well, with correlations over .94. Here they are together:

Maybe some brave Sociological Images reader will explain why these particular terms might follow the unemployment trend. (It could just be that they were new sites that became popular and then tapered off in 2008-2009.)

What’s the point?

It’s not news to people interested in sociology that individual, intimate behavior follows common patterns, which are related to cultural forces. What’s interesting to me here is that capacity to find patterns we couldn’t before. For example, does losing a job lead to more porn consumption? Are those porn searchers different from the people typing in “green jobs”? I’m hoping that other people will dig further and turn these tools to productive uses.

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* To avoid big seasonal spikes unrelated to unemployment, I used the seasonally-adjusted unemployment claims, which basically tamp down the big jump in layoffs after Christmas and when school gets out each summer.