Sunday, November 27, 2016

DAMN, THOSE RUSSIAN HACKERS ARE GOOD! Nate Silver took a look the Jill Stein challenge of election results in Wisconsin, Michigan, and Pennsylvania. Interestingly enough, although the rationale for the recount is the difference in voting between paper and electronic ballots, Michigan only used paper:
Without a recount, all we can do for now is look for any meaningful difference in the three states named in the New York article between votes in counties that used paper ballots and votes in ones that used machines. That quickly crossed Michigan off the list: The entire state uses paper ballots, which are read by optical scanners.2 So we couldn’t compare results by type of voting in that state. Instead, we checked the six other states with a margin between Clinton and Trump of less than 10 percentage points that use a mix of paper and machine voting: Arizona, Florida, North Carolina, Ohio, Texas and Virginia.

For each county in those states, we looked at Clinton’s vote share and whether it was associated with the type of voting system the county used, based on voting-system data compiled by a nonprofit electoral-reform group called Verified Voting and 2016 vote data from Dave Leip’s U.S. Election Atlas and ABC News.3 It doesn’t make much sense, though, to just look at raw vote counts and how they differed, because we know there are many factors that affect how a county voted, both in those states and everywhere else around the country. So we separated out two of the main factors that we know drove differences in voting results: the share of each county’s population age 25 and older with a college degree, and the share of the county that is non-white.4

We found no apparent correlation5 between voting method and outcome in six of the eight states, and a thin possible link between voting method and results in Wisconsin and Texas. However, the two states showed opposite results: The use of any machine voting in a county was associated with a 5.6-percentage-point reduction in Democratic two-party vote share in Wisconsin but a 2.7-point increase in Texas, both of which were statistically significant.6 Even if we focus only on Wisconsin, the effect disappears when we weight our results by population. More than 75 percent of Wisconsin’s population lives in the 23 most populous counties, which don’t appear to show any evidence for an effect driven by voting systems.7 To have effectively manipulated the statewide vote total, hackers probably would have needed to target some of these larger counties. When we included all counties but weighted the regression by the number of people living in each county, the statistical significance of the opposite effects in Wisconsin and Texas both evaporated.8

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