Strategic Redistricting and Government Interventions

不公正划分选区是对国会和州立法区边界的战略性重新划分。在美国的大多数州（39个州），是州立法者自己绘制新的选举地图。除了产生地理上不紧凑或不团结的“奇形怪状”的地区外，不公正划分选区似乎还通过各种渠道影响政策制定（实际政策的意识形态倾向和公共资源的分配）。该项目声称，投票率的地理差异是理解选区划分做法的关键;这些差异可以用有资格投票的人口比例的差异来解释（例如，由于年龄或移民身份），投票倾向，或登记和投票的容易程度。调查人员将回答具体的，与政策相关的问题：是否有我们的理论有助于理解和检测的实践中观察到的gerrymandering模式？对政策和政府资源分配有何影响？这个研究项目有三个组成部分。首先，该项目将开发一个战略性选区重划的理论模型，其中选民不仅在党派方面存在差异，而且在投票率方面也存在差异。现有的模型和措施的gerrymandering集中在不同的党派在个人之间的差异，忽略了异质性的其他方面。然而，在实践中，投票率的差异是巨大的，不能忽视。事实上，现有的不公正划分选区理论无法解释实践中观察到的一些模式。从模型中，我们将从选区划分方的角度来描述最优地图，并确定各方应该绘制的地图的明确可测试的含义。我们的理论可以帮助解释在实践中观察到的一些令人困惑的重新划分模式。该项目的第二部分将测试这些影响使用的建议地图，由双方的立法者为同一个国家在2020年重新划分周期在美国最后，在第三个项目，我们专注于政策的影响和政府资源分配的不平等。不公正划分选区如何影响政策的选择或公共资源在空间和人口中的分配？如果不公正划分选区影响资源分配，那么未来资源分配方面的选择如何影响现任者的选举地图设计？作者将用他们的理论指导经验测试和政策建议的gerrymandering，这是一个非常普遍的兴趣话题。这个奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的知识价值和更广泛的影响审查标准。

Gerrymandering is the strategic redrawing of the boundaries of congressional and state legislative districts. In most US states (39 states), it is the state lawmakers themselves who draw the new electoral map. Beyond producing “bizarrely-shaped” districts that are not geographically compact or cohesive, gerrymandering appears to impact policy-making (both the ideological slant of actual policies and the allocation of public resources) through a variety of channels. This project claims that geographic disparities in turnout are key to understanding gerrymandering practices; and these disparities may be explained by differences in the share of the population eligible to vote (e.g., due to age or immigration status), the propensity to vote, or the easiness of registration and voting. The investigators will answer specific, policy-relevant, questions: Are there patterns of gerrymandering observed in practice that our theory helps understand and detect? What are the implications for policies and governmental resource allocation? There are three components to this research project. First, the project will develop a theoretical model of strategic redistricting in which voters differ not only in partisanship, but also in turnout rates. Existing models and measures of gerrymandering focus on difference in partisanship across individuals, ignoring other dimensions of heterogeneity. However, in practice, differences in turnout rates are substantial and cannot be ignored. Indeed, existing theories of gerrymandering cannot explain some patterns observed in practice. From the model, we will characterize the optimal map from the gerrymandering party's viewpoint and identify clear testable implications for the map that each party should draw. Our theory could help explain some puzzling redistricting patterns observed in practice. The second part of the project will test these implications using the proposed maps by legislators of both parties for the same state in the 2020 redistricting cycle in the U.S. Finally, in the third project, we focus on policy implications and the inequality in the allocation of governmental resources. How does gerrymandering affect the choice of policies or allocation of public resources across the space and the population? If gerrymandering affects the allocation of resources, how do future choices in terms of resource allocation affect the incumbent’s electoral map design? The authors will use their theory to guide empirical tests and policy recommendations on gerrymandering, which is a topic of great general interest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.