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on Law and Economics |
| By: | Gianmarco Daniele; Marco De Simoni; Domenico J. Marchetti; Giovanna Marcolongo; Paolo Pinotti |
| Abstract: | We show that credit constraints significantly increase the risk that firms are infiltrated by organized crime, defined as the covert involvement of criminal organizations in corporate decision-making. Using confidential data on criminal investigations, credit ratings, and loan histories for the universe of Italian firms, we find that a downgrade to substandard credit status reduces credit availability by 30% over five years and increases the probability of infiltration by 5%, relative to comparable firms. A local randomization design comparing firms just above and below the downgrade threshold confirms this result. The effect is pervasive across sectors and regions, but particularly strong in real estate, where the probability of infiltration rises by 10% following a downgrade. Infiltrated firms also display higher survival rates than other downgraded firms, despite similar declines in employment and revenues. These findings suggest that organized crime can serve as a financial backstop – sustaining non-viable businesses and potentially redirecting their strategies to serve criminal interests. |
| Keywords: | Organized crime, Firms, Bank Credit |
| JEL: | G32 K42 L25 O17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp25255 |
| By: | Derek A. Christopher; Mark Duggan; Olivia H. Martin |
| Abstract: | What does homeless shelter achieve? We leverage administrative records of homeless services in Los Angeles County to construct a novel dataset of daily, site-level counts of shelter beds and occupants from 2014 to 2019. We pair this with daily, block-level crime incident data and daily, hospital-level data on ER visits to assess the relationship between shelter and area crime and health. We exploit variation from shocks to shelter availability from Los Angeles County's winter shelters program to study the effects of providing temporary shelter. We find that reducing unsheltered homelessness significantly reduces crime and ER visits for psychiatric conditions. We conclude with evidence that entering shelter also reduces short-run mortality but find no evidence that temporary shelter reduces future homelessness more than street outreach or other non-shelter services. Our findings suggest that shelter functions as a public good with high social benefits. When agents charged with provision of homeless services are evaluated on their ability to reduce overall homelessness, they are unlikely to internalize these benefits and may under provide shelter. |
| JEL: | H41 H51 H53 H75 I38 K42 R28 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34376 |
| By: | Matthew Brown; Mark Brown; Ryan Macdonald |
| Abstract: | This article presents an exploratory analysis of the relationship between the population, firm counts and average property crime from 2017 to 2020 across the Toronto census metropolitan area (CMA). It combines datasets from different domains—crime, business counts and population data—using 500 m by 500 m spatial grids to explore their relationships. At this scale, residential and business land use can be at least partially separated, allowing the independent association between residential populations, business counts and crime to be measured and mapped across the Toronto CMA. This analysis provides a picture of the spatial pattern of crimes across the CMA, explores and validate the data by establishing expected baseline relationships, and points towards areas for more in-depth analysis to determine the relationship between crime and business outcomes. After accounting for the population of grid squares, a positive association between business counts and crime was found, consistent with previous work. Furthermore, after considering population and firm counts, statistically significant spatial clusters of high (and low) crime rates were found. This work therefore sets the foundation for future analysis that would examine how variations in crime rates across space and time affect business outcomes (e.g., firm profitability and exit). |
| Keywords: | property crime, firms, businesses, spatial crime patterns, geospatial analysis, crime hotspots |
| JEL: | J23 M21 |
| Date: | 2024–11–27 |
| URL: | https://d.repec.org/n?u=RePEc:stc:stcp8e:202401100001e |