Title

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

Authors

Ian Hoffman, Cornerstone ResearchFollow
Evan Mast, W.E. Upjohn Institute for Employment ResearchFollow

Upjohn Author ORCID Identifier

https://orcid.org/0000-0002-0774-0664

Publication Date

6-9-2020

Source

Regional Science and Urban Economics 78: 103463

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.

DOI

10.1016/j.regsciurbeco.2019.103463

Publisher

Elsevier-ScienceDirect

Subject Areas

ECONOMIC DEVELOPMENT