American Government and Politics

The Divided (But Not More Predictable) Electorate: A Machine Learning Analysis of Voting in American Presidential Elections

Seo-young Silvia Kim American University
,
Jan Zilinsky New York University

Abstract

Partisan sorting by social groups is believed to increase affective polarization and decrease group-level leverage in representation. Mounting evidence suggests that social groups are increasingly polarized in voting behavior, but how reliable are demographic labels as predictors of vote choice? We test for demographic sorting, using public opinion surveys between 1952--2020 and applying tree-based machine learning models to calculate out-of-sample predictions of presidential voting decisions. We calculate predictions based on voters' demographics and then gradually incorporate more information to test whether the electorate is becoming more predictable. Demographics alone typically can predict 63.5% of vote choices correctly. But contrary to the sorting hypothesis's implications, demographics have not grown more predictive over time, while partisanship has. Additional information about voters, such as issue positions or candidate perceptions, continue to be necessary for obtaining out-of-sample error rates of 5% or less. However, their added value decreases as partisanship's predictive power grows.

Content

Thumbnail image of Election_Surveys_and_Machine_Learning_SK_JZ.pdf
cloud_download

Supplementary material

Thumbnail image of Election_Surveys_and_Machine_Learning_SI.pdf
cloud_download
Supporting Information
Comparisons of logit-based results and tree-based classification models.

Comments

Log in or register with APSA to comment open_in_new
Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy open_in_new – please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here open_in_new .
This site is protected by reCAPTCHA and the Google Privacy Policy open_in_new and Terms of Service open_in_new apply.