| Summary: | The student, Alexander Stevens, submitted this Dissertation for approval on 2019-07-08 at 10:48. This Dissertation was approved for publication on 2019-07-08 at 15:43. DSpace SAF Submission Ingestion Package generated from Vireo submission #14190 on 2019-11-26 at 12:51:30 Who Benefits from the Cleanup of Superfund Landfill Sites? Evidence from New York State uses a difference-in-difference approach to estimate the average treatment effects of different groups that benefit from Superfund landfill cleanup. The benefits of Superfund landfill cleanup go to those with higher value homes and those with well water. These projects are partially financed with state and local property taxes, which are flat taxes within New York municipalities (New York State Department of Taxation and Finance, 2018). This chapter provides evidence that the cleanup of these sites is regressive. Also, there is an opportunity for land value capture, as states and local governments can tax houses with these identifiable characteristics more than other households to recuperate the costs of cleanup. Estimating Heterogeneous Treatment Effects in the Housing Market with Machine Learning Techniques uses an empirical Monte Carlo experiment to compare the performance of machine learning and standard econometric methods in estimating both average treatment effects and geographically heterogeneous treatment effects. This chapter finds that Double Machine Learning (DML) performs similarly to standard parametric methods in full randomization but shows large performance gains when there is unobserved selection into treatment. For geographically heterogeneous treatment effects, this chapter finds that Conditionally Parametric regressions (CPAR) have the best performance when treatment is randomized. However, when there is unobserved selection into treatment machine learning methods outperform CPAR. This chapter also finds that ensembles of various methods can outperform individual methods. This chapter finds that machine learning methods perform ...
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