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

Ian Hoffman and Evan Mast

Regional Science and Urban Economics, 2019, vol. 78, issue C

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 (search for similar items in EconPapers)
JEL-codes: H2 R1 R23 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:78:y:2019:i:c:s0166046219300122

DOI: 10.1016/j.regsciurbeco.2019.103463

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