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.

翻译：本文提出了一种非参数框架，用于识别处理效应的空间边界，无需施加参数化函数形式约束。该方法采用数据驱动带宽选择的局部线性回归，以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，与可能强加虚假空间模式的参数化方法不同，非参数方法展现出更低的偏差，并能在不存在边界时正确识别无边界状态。我将此框架应用于2015-2020年间的银行网点开设数据，将5,743个新网点与覆盖14,209个人口普查区的590万份抵押贷款申请进行匹配。分析显示，网点邻近度显著影响贷款申请数量（每10英里下降8.5%），但不影响批准率，这与网点通过本地存在刺激需求而信贷决策保持集中化的机制一致。通过考察数字化转型时代（2010-2023年）的网点存续情况，我发现其与区域收入存在非单调关系：与传统认知相反，高收入区域经历了更多网点关闭。这一反直觉模式反映了在过度银行化的富裕城市区域对冗余网点的战略性整合，而非对贫困社区的歧视。在控制网点密度、城市化水平和竞争程度后，收入的直接效应大幅减弱，网点密度成为存续的主要决定因素。这些发现证明了采用灵活非参数方法检测复杂空间模式的必要性——这些模式可能被参数化模型忽略，同时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"的简单化叙事。