Original
Media Coverage Shifts and Policy Overreactions: Evidence on Government Serial Processing and Information Saturation
Abstract
ABSTRACT We investigate the causal link between societal signals and policy changes through novel data and a unique empirical setting. Using a corpus with all the opinion columns published in 2017 in the nine major Mexican newspapers (the signals) and data on the universe of individual expenditure programs (the policies), we provide quantitative evidence about government overreactions (in terms of overspending) to differentiated media coverage across various policy topics. First, we test formal models of serial and parallel information processing that relate to the debate between punctuated equilibrium and incrementalism. We find empirical support for the serial processing hypothesis. Second, we frame a natural experiment exploiting two earthquakes that took place in September of the same year. By leveraging the disparity in coverage of opinion columns across both earthquakes, we employ a difference‐in‐differences design and find evidence of the causal relationship between coverage shifts and policy overreactions. Our study sheds new light on a central topic in the study of policy changes and agenda‐setting, and provides quantitative evidence difficult to obtain under conventional empirical frameworks.
中文
媒体报道变化与政策过度反应：关于政府序列处理与信息饱和的证据
摘要
摘要 我们通过新颖数据和独特实证情境，考察社会信号与政策变化之间的因果联系。利用2017年墨西哥九大报纸发表的所有评论专栏构成的语料库（即信号），以及个人支出项目全样本数据（即政策），我们提供了关于政府对不同政策议题上差异化媒体报道作出过度反应（表现为过度支出）的定量证据。首先，我们检验与间断均衡和渐进主义之争相关的序列信息处理与并行信息处理形式模型。我们发现经验证据支持序列处理假说。其次，我们构建一个自然实验，利用同年9月发生的两次地震。通过利用两次地震在评论专栏报道上的差异，我们采用双重差分设计，发现媒体报道变化与政策过度反应之间因果关系的证据。我们的研究为政策变化与议程设置研究中的一个核心议题提供了新的启示，并提供了在传统实证框架下难以获得的定量证据。
关键词
媒体报道变化、政策过度反应、政府序列处理、信息饱和、议程设置、间断均衡、渐进主义、双重差分