Priors Rule: When Do Malfeasance Revelations Help Or Hurt Incumbent Parties?
基于贝叶斯学习模型，在墨西哥实地实验发现，选民对现任的腐败先验信念影响信息效果：不利先验下揭露反而增加现任得票，且低度或高度渎职揭露提高投票率，中度则降低。
Abstract 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.