Data for the regression is available here
The structure of the regression is roughly as follows:
- The number of votes for Hillary is distributed
binomial
in the average propensity to vote for her in that county and the number of votes cast in total - The average propensity to vote for her is distributed
beta
in a reparameterization of the normalalpha
andbeta
parameters - see this link - The reparameterization is as follows:
alpha = mu * phi
beta = (1-mu) * phi
mu
is expected value of the beta distribution and can be interpreted as the average vote share for HRCphi
is a polarization parameter (low values imply low density of the beta distribution in the center of its support, high values the opposite), and can be interpreted as the polarization of the county
mu
is a linear function of covariates with a logistic link functionphi
is a linear function of covariates with an exponential link function- the coefficients for
mu
andphi
are estimated from the data - the following are the predictors (all at the county level):
- the proportion of residents that are white
- the median age
- the prevalance of diabetes (a proxy for overall health levels)
- the median income
- the proportion of residents that have a high school diploma
- the uninsurance rate
- the unemployment rate
- the rate of violent crime