Heterogeneity in the Effect of Federal Spending on Local Crime: Evidence from Causal Forests

47 Pages Posted: 25 Oct 2017 Last revised: 11 Jan 2019

See all articles by Ian Hoffman

Ian Hoffman

Cornerstone Research - Washington Office; Stanford University - Department of Economics

Evan Mast

Upjohn Institute

Date Written: January 2019

Abstract

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.

Keywords: place-based policies; amenities; machine learning; crime

JEL Classification: R1, H2, R23

Suggested Citation

Hoffman, Ian and Mast, Evan, Heterogeneity in the Effect of Federal Spending on Local Crime: Evidence from Causal Forests (January 2019). Available at SSRN: https://ssrn.com/abstract=3059137 or http://dx.doi.org/10.2139/ssrn.3059137

Ian Hoffman

Cornerstone Research - Washington Office

2001 K St NW
Suite 800, North Tower
Washington, DC Washington DC 20006
United States

Stanford University - Department of Economics ( email )

Landau Economics Building
579 Serra Mall
STANFORD, CA 94305-6072
United States

Evan Mast (Contact Author)

Upjohn Institute ( email )

300 South Westnedge Avenue
Kalamazoo, MI 49007-4686
United States

HOME PAGE: http://https://sites.google.com/site/evanemast/home

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