Too Short for a Blog Post, Too Long for a Tweet XCIII

Here's two excerpts from a book I recently read, "Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are," by Seth Stephens-Davidowitz:

During the general election, there were clues that the electorate might be a favorable one for Trump. Black Americans told polls they would turn out in large numbers to oppose Trump. But Google searches for information on voting in heavily black areas were way down. On election day, Clinton would be hurt by low black turnout. 

There were even signs that supposedly undecided voters were going Trump’s way. Gabriel and I found that there were more searches for “Trump Clinton” than “Clinton Trump” in key states in the Midwest that Clinton was expected to win. Indeed, Trump owed his election to the fact that he sharply outperformed his polls there. 

But the major clue, I would argue, that Trump might prove a successful candidate—in the primaries, to begin with—was all that secret racism that my Obama study had uncovered. The Google searches revealed a darkness and hatred among a meaningful number of Americans that pundits, for many years, missed. Search data revealed that we lived in a very different society from the one academics and journalists, relying on polls, thought that we lived in. It revealed a nasty, scary, and widespread rage that was waiting for a candidate to give voice to it. 

People frequently lie—to themselves and to others. In 2008, Americans told surveys that they no longer cared about race. Eight years later, they elected as president Donald J. Trump, a man who retweeted a false claim that black people are responsible for the majority of murders of white Americans, defended his supporters for roughing up a Black Lives Matters protester at one of his rallies, and hesitated in repudiating support from a former leader of the Ku Klux Klan. The same hidden racism that hurt Barack Obama helped Donald Trump. 

Early in the primaries, Nate Silver famously claimed that there was virtually no chance that Trump would win. As the primaries progressed and it became increasingly clear that Trump had widespread support, Silver decided to look at the data to see if he could understand what was going on. How could Trump possibly be doing so well? 

Silver noticed that the areas where Trump performed best made for an odd map. Trump performed well in parts of the Northeast and industrial Midwest, as well as the South. He performed notably worse out West. Silver looked for variables to try to explain this map. Was it unemployment? Was it religion? Was it gun ownership? Was it rates of immigration? Was it opposition to Obama? 

Silver found that the single factor that best correlated with Donald Trump’s support in the Republican primaries was that measure I had discovered four years earlier. Areas that supported Trump in the largest numbers were those that made the most Google searches for “ni**er.”

Why do some parts of the country appear to be so much better at churning out America’s movers and shakers? I closely examined the top counties. It turns out that nearly all of them fit into one of two categories. 
First, and this surprised me, many of these counties contained a sizable college town. Just about every time I saw the name of a county that I had not heard of near the top of the list, like Washtenaw, Michigan, I found out that it was dominated by a classic college town, in this case Ann Arbor. The counties graced by Madison, Wisconsin; Athens, Georgia; Columbia, Missouri; Berkeley, California; Chapel Hill, North Carolina; Gainesville, Florida; Lexington, Kentucky; and Ithaca, New York, are all in the top 3 percent. 
Why is this? Some of it is may well be due to the gene pool: sons and daughters of professors and graduate students tend to be smart (a trait that, in the game of big success, can be mighty useful). And, indeed, having more college graduates in an area is a strong predictor of the success of the people born there. 
But there is most likely something more going on: early exposure to innovation. One of the fields where college towns are most successful in producing top dogs is music. A kid in a college town will be exposed to unique concerts, unusual radio stations, and even independent record stores. 
And this isn’t limited to the arts. College towns also incubate more than their expected share of notable businesspeople. Maybe early exposure to cutting-edge art and ideas helps them, too. 
The success of college towns does not just cross regions. It crosses race. African-Americans were noticeably underrepresented on Wikipedia in nonathletic fields, especially business and science. This undoubtedly has a lot to do with discrimination. But one small county, where the 1950 population was 84 percent black, produced notable baby boomers at a rate near those of the highest counties. Of fewer than 13,000 boomers born in Macon County, Alabama, fifteen made it to Wikipedia—or one in 852. Every single one of them is black. Fourteen of them were from the town of Tuskegee, home of Tuskegee University, a historically black college founded by Booker T. Washington. The list included judges, writers, and scientists. In fact, a black child born in Tuskegee had the same probability of becoming a notable in a field outside of sports as a white child born in some of the highest-scoring, majority-white college towns.

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