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.