Working Paper: NBER ID: w24888
Authors: Eric Arias; Horacio Larreguy; John Marshall; Pablo Querubín
Abstract: Effective policy-making requires that voters avoid electing malfeasant politicians. However, as our simple learning model emphasizing voters’ prior beliefs and updating highlights, informing voters of incumbent malfeasance may not entail sanctioning. Specifically, electoral punishment of incumbents revealed to be malfeasant is rare where voters already believed them to be malfeasant, while information’s effect on turnout is non-linear in the magnitude of revealed malfeasance. These Bayesian predictions are supported by a field experiment informing Mexican voters about malfeasant mayoral spending before municipal elections. Given voters’ low expectations and initial uncertainty, as well as politician responses, relatively severe malfeasance revelations increased incumbent vote share on average. Consistent with voter learning, rewards were lower among voters with lower malfeasance priors, among voters with more precise prior beliefs, when audits revealed greater malfeasance, and among voters updating less favorably. Furthermore, both low and high malfeasance revelations increased turnout, while less surprising information reduced turnout.
Keywords: malfeasance; voter behavior; electoral accountability; field experiment; Mexico
JEL Codes: D72; D73
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Voters' prior beliefs (D72) | Vote share for the incumbent party (D72) |
| Revealing information about malfeasance (K42) | Vote share for the incumbent party (D72) |
| Extreme cases of malfeasance (K42) | Voter turnout (K16) |
| Moderate revelations of malfeasance (D73) | Voter turnout (K16) |
| Revealing malfeasance (K42) | Uncertainty among risk-averse voters (D81) |