Heterogeneity in the effect of federal spending on local crime: Evidence from causal forests
Upjohn Author ORCID Identifier
Regional Science and Urban Economics 78(September 2019)
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.
Hoffman, Ian and Evan Mast. 2019. "Heterogeneity in the effect of federal spending on local crime: Evidence from causal forests." Regional Science and Urban Economics 79(September). https://doi.org/10.1016/j.regsciurbeco.2019.103463