• Medientyp: E-Book
  • Titel: Heterogeneity in the Effect of Federal Spending on Local Crime : Evidence from Causal Forests
  • Beteiligte: Hoffman, Ian [VerfasserIn]; Mast, Evan [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [S.l.]: SSRN, [2019]
  • Umfang: 1 Online-Ressource (47 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3059137
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 2019 erstellt
  • Beschreibung: Federal place-based policy could improve efficiency if it targets areas with large amenity or agglomeration externalities. We begin by showing that positive shocks to federal spending in a county and their associated economic stimulus substantially decrease crime, an important amenity. We then employ two machine learning algorithms---causal trees and causal forests---to conduct a data-driven search for heterogeneity in this effect. The effect is larger in below-median income counties, and the difference is economically and statistically significant. This heterogeneity likely improves the efficiency of the many place-based policies that target such areas
  • Zugangsstatus: Freier Zugang