Haotian Zhong, Wei Li, Marlon G. Boarnet
arXiv 2 Nov 2019 · Econometrics · publishedEnvironment and Planning B Urban Analytics and City Science (2020) · 20 citations (OpenAlex)
arXiv:1911.00667 · PDF · DOI · OpenAlex
The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such studies by pairing observations across two dimensions: longitudinal and cross-sectional. Researchers mimic randomized controlled trials (RCTs) and match observations in both dimensions, to find synthetic control groups that are similar to the treatment group and to match subjects synthetically across before-treatment and after-treatment time periods. We call this a two-dimensional propensity score matching (2DPSM). This method demonstrates superior performance for estimating treatment effects based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the impact of transportation infrastructure on travel behavior.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Pseudo Panel Difference-in-Differences (DiD) Analysis of Online Shopping Behavior in the Puget Sound Regional Council (PSRC) Region | 0.405 | 1 | 1 |