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The social costs of crime over trust: An approach with machine learning

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  • Angelo Cozzubo

    (University of Chicago)

Abstract

In Peru, 55% of the population considers insecurity as the country's main problem. The present study seeks to contribute to the understanding of the social costs of crime in Peru by measuring the impact of patrimonial crime on trust in public institutions, using victimization surveys and censuses of police stations and municipalities and using the newly implemented machine-learning techniques in Stata combined with propensity score matching. Results: reduction of 3 percentage points (pp.) in the probability of trusting in the police and Serenazgo in the short term and 2 pp. in judicial power in the long term. Female victims would lose more confidence in Serenazgo and the Public Ministry. Robustness in the presence of unobservables, different pairings, and falsification tests, which would suggest potential causal character.

Suggested Citation

  • Angelo Cozzubo, 2020. "The social costs of crime over trust: An approach with machine learning," 2020 Stata Conference 27, Stata Users Group.
    • Handle: RePEc:boc:scon20:27
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    File URL: http://fmwww.bc.edu/repec/scon2020/us20_Cozzubo.pdf
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