标题： 非参数识别空间处理效应边界：来自银行分支机构合并的证据 显示英文标题

标题： Nonparametric Identification of Spatial Treatment Effect Boundaries: Evidence from Bank Branch Consolidation

摘要： 我开发了一个非参数框架，用于识别处理效应的空间边界，而无需施加参数函数形式的限制。 该方法采用数据驱动带宽选择的局部线性回归，灵活估计空间衰减模式并检测处理效应边界。 蒙特卡洛模拟表明，非参数方法表现出较低的偏差，并在不存在边界时正确识别出边界的缺失，这与可能强加虚假空间模式的参数方法不同。 我将这个框架应用于2015年至2020年间的银行分支机构开设情况，将5,743家新分支机构与14,209个普查区内的590万份抵押贷款申请相匹配。 分析结果显示，分支机构的距离显著影响贷款申请量（每10英里下降8.5%），但不影响批准率，这与分支机构通过本地存在刺激需求而信贷决策保持集中的情况一致。 在数字转型时代（2010年至2023年）考察分支机构的生存情况，我发现其与地区收入之间存在非单调关系：尽管有传统观念认为高收入地区应更稳定，但这些地区反而经历了更多关闭。 这种反直觉的模式反映了在过度银行化的富裕城市地区对冗余分支机构的战略性整合，而不是对贫困社区的歧视。 在控制分支机构密度、城市化和竞争因素后，直接收入效应显著减弱，分支机构密度成为生存的主要决定因素。 这些发现表明，为了检测参数模型可能忽略的复杂空间模式，需要使用灵活的非参数方法，并通过揭示空间整合决策背后的组织复杂性，挑战了关于银行荒漠的简单叙述。 显示英文摘要

摘要： I develop a nonparametric framework for identifying spatial boundaries of treatment effects without imposing parametric functional form restrictions. The method employs local linear regression with data-driven bandwidth selection to flexibly estimate spatial decay patterns and detect treatment effect boundaries. Monte Carlo simulations demonstrate that the nonparametric approach exhibits lower bias and correctly identifies the absence of boundaries when none exist, unlike parametric methods that may impose spurious spatial patterns. I apply this framework to bank branch openings during 2015--2020, matching 5,743 new branches to 5.9 million mortgage applications across 14,209 census tracts. The analysis reveals that branch proximity significantly affects loan application volume (8.5\% decline per 10 miles) but not approval rates, consistent with branches stimulating demand through local presence while credit decisions remain centralized. Examining branch survival during the digital transformation era (2010--2023), I find a non-monotonic relationship with area income: high-income areas experience more closures despite conventional wisdom. This counterintuitive pattern reflects strategic consolidation of redundant branches in over-banked wealthy urban areas rather than discrimination against poor neighborhoods. Controlling for branch density, urbanization, and competition, the direct income effect diminishes substantially, with branch density emerging as the primary determinant of survival. These findings demonstrate the necessity of flexible nonparametric methods for detecting complex spatial patterns that parametric models would miss, and challenge simplistic narratives about banking deserts by revealing the organizational complexity underlying spatial consolidation decisions.