Neighborhood Demand Estimation and Ex-Ante Policy Evaluation

政府住房政策，例如住房选择券计划或授权区计划，可以改变社区的相对价格，从而改变家庭对社区的需求。尽管了解这些邻里需求模式的重要性，现有的文献还没有估计邻里需求系统，旨在捕捉现实的替代模式和评估的住房政策。该项目旨在了解政府住房政策如何影响社区需求，平衡社区人口组成和价格水平，并最终影响家庭的福祉。研究人员将开发经验上易于处理的模型，对社区的替代性进行最小的事前限制。使用关于位置选择和信用信息的详细面板数据，调查人员将估计模型，并进一步对现有和尚未颁布的政府住房计划进行项目评估。该项目开发和估计经验上易于处理的模型，允许不受限制的替代模式，跨社区和特定社区的需求弹性的社区的本地需求。需求模型通过设定大量可观察的家庭类型来实现灵活性，对每种类型的流量间接效用在社区之间的差异限制最小。使用NYFRB/Equifax消费者信贷小组从1999年到现在的5%美国人口的位置选择的面板数据，调查人员通过最大似然估计模型的结构参数，利用Hotz和米勒（1993）以及Arcidiacono和米勒（2011）的条件选择概率反演技术构建似然函数，而不必重复求解完整的动态模型。估计模型有利于各种政策评估方法，包括回归分析，处理估计的邻里需求弹性作为解释变量;部分均衡反事实政策实验，研究各种补贴对家庭的位置选择的影响;和均衡反事实政策实验，研究各种住房政策对邻里租金水平和邻里人口构成的影响。具体而言，研究人员使用估计模型研究以下政策问题：（1）在哪些地点，基于地点的补贴-授权区，企业区等-的发生率可能会落在以前的区域居民？(2)大规模推广第8条式的住房券会导致更多的混合收入社区处于均衡状态吗？(3)什么样的住房券目标规则最有可能促使家庭选择对孩子的学业成绩有积极贡献的社区？

Government housing policies, such as the Housing Choice Voucher program or the Empowerment Zone program, can change the relative prices of neighborhoods, thereby altering households' demand for neighborhoods. Despite the importance of understanding these neighborhood demand patterns, the existing literature has not estimated neighborhood demand systems intended to capture realistic substitution patterns and evaluate the housing policies. This project seeks to understand how government housing policies would affect demand for neighborhoods, equilibrium neighborhood demographic compositions and price-levels, and ultimately households' well-being. The investigators will develop empirically tractable models of local demand for neighborhoods which place minimal ex ante restrictions on the substitutability of neighborhoods. Using detailed panel data on location choices and credit information, the investigators will estimate the models and further perform program evaluations of existing and yet-to-be enacted government housing programs. This project develops and estimates empirically tractable models of local demand for neighborhoods that allow for unrestricted patterns of substitution across neighborhoods and elasticities of demand for particular neighborhoods. The demand model achieves flexibility by positing a large number of observable types of households, with minimal restrictions on how each type's flow indirect utility differs across neighborhoods. Using panel data on the location choices from 1999 to present for 5% of the U.S. population from the NYFRB/Equifax Consumer Credit Panel, the investigators estimate the model's structural parameters by maximum likelihood, exploiting the conditional-choice-probability inversion techniques of Hotz and Miller (1993) and Arcidiacono and Miller (2011) to construct likelihood functions without having to repeatedly solve the full dynamic model. The estimated model facilitates a variety of policy evaluation methodologies, including regression analyses that treat the estimated neighborhood demand elasticities as explanatory variables; partial equilibrium counterfactual policy experiments that study the impact of various subsidies on households' location choices; and equilibrium counterfactual policy experiments that study the impact of various housing policies on neighborhood rent levels and neighborhood demographic compositions. Specifically, the investigators use the estimated model to study the following policy questions: (1) In which locations is the incidence of place-based subsidies -- Empowerment Zones, Enterprise Zones, etc. -- likely to fall on prior zone residents?; (2) Would a large expansion to Section 8-style housing vouchers lead to more mixed-income neighborhoods in equilibrium?; (3) What targeting rules for housing vouchers are most likely to induce households to choose neighborhoods that contribute positively to their children's academic achievement?