Electoral Accountability and Selection with Personalized Information Aggregation

TL;DR

This paper models how voters with limited attention and partisan biases interpret incumbent performance data, revealing complex effects of polarization and information correlation on electoral accountability and voter welfare.

Contribution

It introduces a model of personalized information aggregation by voters, highlighting how partisan biases and signal correlation influence electoral accountability and selection.

Findings

Partisan biases hinder voters' ability to accurately assess incumbents.

Partisan disagreement can enhance the pivotal role of centrist voters.

Correlating signals appropriately improves electoral accountability.

Abstract

We study a model of electoral accountability and selection whereby heterogeneous voters aggregate incumbent politician's performance data into personalized signals through paying limited attention. Extreme voters' signals exhibit an own-party bias, which hampers their ability to discern the good and bad performances of the incumbent. While this effect alone would undermine electoral accountability and selection, there is a countervailing effect stemming from partisan disagreement, which makes the centrist voter more likely to be pivotal. In case the latter's unbiased signal is very informative about the incumbent's performance, the combined effect on electoral accountability and selection can actually be a positive one. For this reason, factors that carry a negative connotation in every political discourse -- such as increasing mass polarization and shrinking attention span -- have ambiguous accountability and selection effects in general. Correlating voters' signals, if done appropriately, unambiguously improves electoral accountability and selection and, hence, voter welfare.