NBER Working Papers · 2026 · Ricardo Alonso、Monica Martinez-Bravo、Gerard Padró I Miquel、Carlos Sanz、Silvia Vannutelli
中文摘要
责任归属的清晰性是政治问责的一个基本要素。我们构建了一个在归因模糊情况下进行贝叶斯更新的理性模型，并利用一项原创调查检验其预测。我们表明，受访者的党派立场、对公共医疗质量的评价，以及他们对哪一级政府应对医疗负责的信念之间，存在模型所预测的相关关系：评价良好的选民将责任归于其偏好政党执政的政府层级，而评价不佳的选民则将责任归咎于其不喜欢的政党执政的层级。这些带有党派色彩的归功与归责模式，常被解读为动机性推理或党派偏见的证据，但实际上可能源于归因模糊情形下的理性贝叶斯更新。当地区政府与中央政府由同一政党执政时，此类党派模式并不存在。在一项向受试者告知官方医疗质量信息的调查实验中，受试者按照模型预测的、带有党派色彩的方向更新了信念。模型与实证结果表明，当归因存在模糊性时，党派先验信念极难被撼动。
Abstract
Clarity of responsibility is an essential element of political accountability. We develop a rational model of Bayesian updating in the presence of ambiguous attribution and we test its predictions using an original survey. We show that respondents’ partisanship, assessment of public healthcare quality, and beliefs over which layer of government is responsible for healthcare are correlated as predicted: good-assessment voters attribute responsibility to the layer governed by their preferred party, while bad-assessment voters blame the layer governed by the party they dislike. These partisan patterns of credit and blame, often interpreted as evidence of motivated reasoning or partisan bias, can thus arise from rational Bayesian updating under attribution ambiguity. No such partisan patterns exist where the same party is in charge of regional and central government. A survey experiment in which we inform subjects of the official quality of healthcare has them update in the predicted, partisan, direction. Model and empirical results show that partisan priors are extremely hard to dislodge when attribution is ambiguous.