Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Effective policy-making requires that voters avoid electing malfeasant politicians. However, informing voters of incumbent malfeasance in corrupt contexts may not reduce incumbent support. As our simple learning model shows, electoral sanctioning is limited where voters already believed incumbents to be malfeasant, while information’s effect on turnout is non-monotonic in the magnitude of reported malfeasance. We conducted a field experiment in Mexico that informed voters about malfeasant mayoral spending before municipal elections, to test whether these Bayesian predictions apply in a developing context where many voters are poorly informed. Consistent with voter learning, the intervention increased incumbent vote share where voters possessed unfavorable prior beliefs and when audit reports caused voters to favorably update their posterior beliefs about the incumbent’s malfeasance. Furthermore, we find that low and, especially, high malfeasance revelations increased turnout, while less surprising information reduced turnout. These results suggest that improved governance requires greater transparency and citizen expectations.