Two new methodological pieces that will be of interest to students of election administration just came out in Political Analysis, (which is edited by my VTP-co-conspirator, Mike Alvarez).
(Warning to my non-academic followers: serious math is involved in these papers.)
The first, by Kosuke Imai and Kabir Khanna, is entitled “Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records.” In a nutshell, there are a lot of times when we need to know the race of registered voters, but we don’t have race as a data field in the voter file. (This is true in all but a handful of states.) Some people have dealt with this problem by relying on proprietary modeling techniques, such as that employed by Catalist, and others have simply used Census Bureau lists that classify last names by (likely) ethnicity. Imai and Khanna have developed a technique, based on Bayes’s rule, to combine a variety of information, ranging from the surname list to geocoded information, to produce an improved method for modelling a voter’s ethnicity. The technique is tested using the Florida voter file, which has race already coded, to make “ground truth” comparisons.
The second, by Gabriel Cepaluni and F. Daniel Hidalgo, is entitled “Compulsory Voting Can Increase Political Inequality: Evidence from Brazil.” This article will definitely be relevant for those interested in proposals to institute mandatory voting in the US. Brazil is the largest country in the world with mandatory voting, which makes this case of particular interest. Cepaluni and Hidalgo show that the causal effect of making voting mandatory is to increase SES disparities in turnout. The reason is that the non-monetary penalties for non-voting primarily affect voters with higher incomes.