• Illness as indicator

    THE first piece of news Americans woke up to on November 9th was that Donald Trump had been elected president. The second was that he owed his victory to a massive swing towards Republicans by white voters without college degrees across the north of the country, who delivered him the rustbelt states of Michigan, Wisconsin and Pennsylvania—all by one percentage point or less. Pundits had scoffed at Mr Trump’s plan to transform the Wall Street-friendly Republicans into a “workers’ party”, and flip the long-Democratic industrial Midwest: Hillary Clinton had led virtually every poll in these states, mostly by comfortable margins. But it was the plutocratic Donald who enjoyed the last laugh.

    #indicateur #maladie #santé #visualisation #graphique #élection #USA #Etats-Unis

    • In the aftermath of the stunning result, statistical analysts homed in on blue-collar whites as never before. Although pre-election polls showed Mr Trump with a 30-percentage-point advantage among whites without a college degree, exit polls revealed he actually won them by almost 40 points. Unsurprisingly, the single best predictor identified so far of the change from 2012 to 2016 in the share of each county’s eligible voters that voted Republican—in other words, the swing from Mitt Romney to Mr Trump—is the percentage of potential voters who are non-college whites. The impact of this bloc was so large that on November 15th Patrick Ruffini, a well-known pollster, offered a “challenge for data nerds” on Twitter: “Find the variable that can beat % of non-college whites in the electorate as a predictor of county swing to Trump.

      With no shortage of nerds, The Economist has taken Mr Ruffini up on his challenge. Although we could not find a single factor whose explanatory power was greater than that of non-college whites, we did identify a group of them that did so collectively: an index of public-health statistics. The Institute for Health Metrics and Evaluation at the University of Washington has compiled county-level data on life expectancy and the prevalence of obesity, diabetes, heavy drinking and regular physical activity (or lack thereof). Together, these variables explain 43% of Mr Trump’s gains over Mr Romney, just edging out the 41% accounted for by the share of non-college whites (see chart).

      The two categories significantly overlap: counties with a large proportion of whites without a degree also tend to fare poorly when it comes to public health. However, even after controlling for race, education, age, sex, income, marital status, immigration and employment, these figures remain highly statistically significant. Holding all other factors constant—including the share of non-college whites—the better physical shape a county’s residents are in, the worse Mr Trump did relative to Mr Romney.

      #multicolinéarité !