arXiv 15 Oct 2025 · Econometrics
arXiv:2510.13148 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kikuchi, T (2024) A unified framework for spatial and temporal treatment effect boundaries: Theory and identification self | 1.000 | 9 | 3 | 100% |
| 2 | Kikuchi, T (2024) Nonparametric Identification and Estimation of Spatial Treatment Effect Boundaries: Evidence from 42 Million Pollution Observati… self | 1.000 | 5 | 3 | 100% |
| 3 | Fan, J., & Gijbels, I (1996) Local Polynomial Modelling and Its Applications | 0.941 | 6 | 3 | 83% |
| 4 | Fuster, A., Plosser, M., Schnabl, P., & Vickery, J (2019) The role of technology in mortgage lending | 0.928 | 4 | 3 | 100% |
| 5 | Nguyen, H.-L. Q (2019) Are credit markets still local? Evidence from bank branch closings | 0.928 | 4 | 3 | 100% |
| 6 | Kikuchi, T (2024) Spatial and temporal boundaries in difference-in-differences: A framework from Navier-Stokes equation self | 0.874 | 5 | 2 | 100% |
| 7 | Kikuchi, T (2024) Stochastic boundaries in spatial general equilibrium: A diffusion-based approach to causal inference with spillover effects self | 0.874 | 5 | 2 | 100% |
| 8 | Muller, N. Z., & Machado, R. M (2011) External costs of power plants from the U.S.: Air pollution, human health, and land disturbance | 0.693 | 5 | 1 | 100% |
| 9 | Butts, K (2023) Machine learning methods for spatial treatment effects | 0.644 | 4 | 1 | 100% |
| 10 | Agarwal, S., Benmelech, E., Bergman, N., & Seru, A (2012) Did the Community Reinvestment Act (CRA) lead to risky lending? | 0.644 | 2 | 2 | 100% |
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