Multi-Level Policy Responsiveness to Public Opinion

民主制度是否为普通公民影响公共政策提供了一个有意义的角色？答案在多大程度上取决于政治制度、政府对相关政策的责任分担以及具体问题？这些问题的答案对于清楚地了解民主的表现和制度设计的关键问题至关重要，这些问题可以使民主更好地发挥作用。该项目为这两个问题提供了实质性的答案，同时开发了解决这些问题的创新工具。因此，这个项目的智力价值有两个组成部分。首先，它揭示了为什么一级政府制定的政策并不总是被下级政府忠实地执行。这既提供了关于民主机构运作的基本知识，也提供了可用于设计更有效率和更有效的公共政策的实用知识。该项目智力价值的第二个要素在于，政治学“政策响应”领域的进步依赖于越来越复杂的方法来估计小区域（如美国的州和地方）或小时间段的民意，因此它受益于应用统计学领域的进步，即“小区域估计”。该项目通过扩展当前的最佳实践（多层回归与后分层，或“MRP”），以一种创新的替代估计程序，将小面积估计的挑战视为缺失数据问题，为小面积估计的科学做出了贡献。该项目还做出了一些更广泛的贡献。最广泛地说，该项目扩大了对民主制度运作的理解，并产生了学者、政策制定者和公民都感兴趣的发现。此外，作为该项目的小区域估计工作的最终产品，其项目创建并广泛提供了多个政策领域的州，县和更大的市政当局的当前民意估计数据集。这将对许多领域的学术、政府和私营部门的研究人员有用。

Do democratic systems provide a meaningful role for ordinary citizens to influence public policy? To what extent does the answer depend on political institutions, shared responsibility among governments for the policy in question, and the particular issue? Answers to these questions are essential to gaining a clear understanding of democratic performance and the critical issues of institutional design that can make democracies function better. This project provides substantive answers to both questions at the same time that it develops innovative tools to address the questions. The intellectual merit of this project thus has two components. First, it sheds light on why policies written at one level of government are not always faithfully implemented by lower levels of government. This provides both basic knowledge about the functioning of democratic institutions and practical knowledge that can be used to design more efficient and effective public policies. The second element of the project's intellectual merit rests on the fact that progress in the "policy responsiveness" field of political science has depended on increasingly sophisticated methodologies for estimating public opinion for small areas (such as states and localities in the US) or small time periods, so that it has benefitted from advances in the area of applied statistics known as "small area estimation." This project contributes to the science of small area estimation by extending the current best practice (Multilevel regression with post-stratification, or "MRP") with an innovative alternative estimation procedure that treats the challenge of small area estimation as a missing data problem. The project also makes several broader contributions. Most broadly, the project expands understanding of the workings of democratic institutions and yields findings of interest to scholars, policy makers, and citizens alike. In addition, as an end product of the project's small area estimation efforts, its project creates and makes widely available a dataset of current estimates of public opinion for states, counties and larger municipalities for multiple policy domains. This will be useful for academic, government and private sector researchers in many fields.