arXiv 19 Oct 2021 · Econometrics · 1 citations (OpenAlex)
arXiv:2110.10192 · PDF · DOI · OpenAlex · Extracted main text
This paper formalizes a common approach for estimating effects of treatment at a specific location using geocoded microdata. This estimator compares units immediately next to treatment (an inner-ring) to units just slightly further away (an outer-ring). I introduce intuitive assumptions needed to identify the average treatment effect among the affected units and illustrates pitfalls that occur when these assumptions fail. Since one of these assumptions requires knowledge of exactly how far treatment effects are experienced, I propose a new method that relaxes this assumption and allows for nonparametric estimation using partitioning-based least squares developed in Cattaneo et. al. (2019). Since treatment effects typically decay/change over distance, this estimator improves analysis by estimating a treatment effect curve as a function of distance from treatment. This is contrast to the traditional method which, at best, identifies the average effect of treatment. To illustrate the advantages of this method, I show that Linden and Rockoff (2008) under estimate the effects of increased crime risk on home values closest to the treatment and overestimate how far the effects extend by selecting a treatment ring that is too wide.
appendix boundary found by appendix_command · 79% of the source is main text. Read the extracted text to check this.
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 | Cattaneo, Crump, Farrell, and Feng (2019) On Binscatter | 0.950 | 7 | 4 | 86% |
| 2 | Linden and Rockoff (2008) Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws | 0.941 | 6 | 3 | 83% |
| 3 | Cattaneo, Farrell, and Feng (2019) Large Sample Properties of Partitioning-Based Series Estimators | 0.843 | 4 | 4 | 75% |
| 4 | Sullivan (2017) The True Cost of Air Pollution: Evidence from the Housing Market | 0.737 | 3 | 2 | 100% |
| 5 | Currie, Davis, Greenstone, and Walker (2015) Environmental Health Risks and Housing Values: Evidence from 1,600 Toxic Plant Openings and Closings | 0.644 | 2 | 2 | 100% |
| 6 | Marcus (2021) Going Beneath the Surface: Petroleum Pollution, Regulation, and Health | 0.644 | 2 | 2 | 100% |
| 7 | Clarke (2017) Estimating Difference-in-Differences in the Presence of Spillovers | 0.585 | 3 | 1 | 100% |
| 8 | Butts (2021) Difference-in-Differences Estimation with Spatial Spillovers self | 0.511 | 2 | 1 | 100% |
| 9 | Gerardi, Rosenblatt, Willen, and Yao (2015) Foreclosure externalities: New evidence | 0.511 | 2 | 1 | 100% |
| 10 | Alexander, Currie, and Schnell (2019) Check up before you check out: Retail clinics and emergency room use | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Difference-in-Differences with Spatial Spillovers | 1.000 | 5 | 3 |