arXiv 14 Oct 2025 · Econometrics
arXiv:2510.12289 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a nonparametric framework for identifying and estimating spatial boundaries of treatment effects in settings with geographic spillovers. While atmospheric dispersion theory predicts exponential decay of pollution under idealized assumptions, these assumptions -- steady winds, homogeneous atmospheres, flat terrain -- are systematically violated in practice. I establish nonparametric identification of spatial boundaries under weak smoothness and monotonicity conditions, propose a kernel-based estimator with data-driven bandwidth selection, and derive asymptotic theory for inference. Using 42 million satellite observations of NO$_2$ concentrations near coal plants (2019-2021), I find that nonparametric kernel regression reduces prediction errors by 1.0 percentage point on average compared to parametric exponential decay assumptions, with largest improvements at policy-relevant distances: 2.8 percentage points at 10 km (near-source impacts) and 3.7 percentage points at 100 km (long-range transport). Parametric methods systematically underestimate near-source concentrations while overestimating long-range decay. The COVID-19 pandemic provides a natural experiment validating the framework's temporal sensitivity: NO$_2$ concentrations dropped 4.6% in 2020, then recovered 5.7% in 2021. These results demonstrate that flexible, data-driven spatial methods substantially outperform restrictive parametric assumptions in environmental policy applications.
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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 | Müller, U. K. and Watson, M. W (2022) Spatial correlation robust inference | 1.000 | 12 | 4 | 100% |
| 2 | Kikuchi, T (2024) Spatial and temporal boundaries in difference-in-differences: A framework from Navier-Stokes equation self | 1.000 | 9 | 3 | 100% |
| 3 | Kikuchi, T (2024) A unified framework for spatial and temporal treatment effect boundaries: Theory and identification self | 1.000 | 8 | 3 | 100% |
| 4 | Kikuchi, T (2024) Stochastic boundaries in spatial general equilibrium: A diffusion-based approach to causal inference with spillover effects self | 1.000 | 7 | 3 | 100% |
| 5 | Müller, U. K. and Watson, M. W (2024) Spatial unit roots and spurious regression | 1.000 | 6 | 3 | 100% |
| 6 | Fan, J. and Gijbels, I (1996) Local Polynomial Modelling and Its Applications | 0.894 | 7 | 3 | 71% |
| 7 | Butts, K. and Gardner, J (2023) Difference-in-differences with spatial spillovers | 0.511 | 2 | 1 | 100% |
| 8 | Deryugina, T., Heutel, G., Miller, N. H., Molitor, D., and Reif, J (2019) The mortality and medical costs of air pollution: Evidence from changes in wind direction | 0.511 | 2 | 1 | 100% |
| 9 | Fan, J. and Gijbels, I (1992) Variable bandwidth and local linear regression smoothers | 0.511 | 2 | 1 | 100% |
| 10 | Jones, M. C., Marron, J. S., and Sheather, S. J (1996) A brief survey of bandwidth selection for density estimation | 0.511 | 2 | 1 | 100% |
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