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JUE Insight: Difference-in-differences with geocoded microdata

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  • Butts, Kyle

Abstract

I formalize a commonly-used estimator for the effects of spatially-targeted treatment with geocoded microdata. This estimator compares units immediately next to treatment to units slightly further away. I introduce intuitive identifying assumptions for the average treatment effect among affected units and illustrate problems when these assumptions fail. I propose a new method that allows for nonparametric estimation following methods introduced in Cattaneo et al.(2019b) that allows estimation without requiring knowledge of exactly how far treatment effects are experienced. Since treatment effects can change with distance, the proposed estimator improves estimation by estimating a treatment effect curve.

Suggested Citation

  • Butts, Kyle, 2023. "JUE Insight: Difference-in-differences with geocoded microdata," Journal of Urban Economics, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:juecon:v:133:y:2023:i:c:s0094119022000705
    DOI: 10.1016/j.jue.2022.103493
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    References listed on IDEAS

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    3. Rodríguez-Puello, Gabriel & Rickardsson, Jonna, 2024. "Spatial Diffusion of Economic Shocks in the Labor Market: Evidence from a Mining Boom and Bust," OSF Preprints tzmf2, Center for Open Science.

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    More about this item

    Keywords

    Spatial econometrics; Spillover effects; Difference-in-differences; Nonparametric estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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