Nonparametric Identification of Spatial Treatment Effect Boundaries: Evidence from Bank Branch Consolidation
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空间处理效应边界的非参数识别：银行网点整合的证据
- 研究问题
- 如何在不预设空间衰减函数的情况下识别处理效应边界，并分析银行网点距离与贷款申请、审批及网点存续的关系？
- 方法与思路
- 作者使用局部线性回归和数据驱动带宽估计空间衰减与边界，开展蒙特卡洛比较，并分别分析网点开设和网点存续数据。
- 材料陈述的贡献
- 摘要称方法降低模拟偏差并识别无边界情形；应用报告距离主要关联申请量，网点存续的收入关系在控制密度等因素后减弱。
- 阅读关注点
- 应核对空间边界的识别条件、网点开设与整合样本的关系，以及选址内生性、空间相关和存续回归能否支持机制解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明外生变化或完整因果识别设计，不能据相关模式确认需求或整合机制。
原文摘要
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.