Heterogeneity in the effect of federal spending on local crime: Evidence from causal forests

Publication date: Available online 13 August 2019Source: Regional Science and Urban EconomicsAuthor(s): Ian Hoffman, Evan MastAbstractFederal 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.
Source: Regional Science and Urban Economics - Category: Science Source Type: research