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Crime and punishment: the economic burden of impunity

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  • M. B. Gordon
  • J. R. Iglesias
  • V. Semeshenko
  • J. P. Nadal

Abstract

Crime is an economically important activity, sometimes called the industry of crime. It may represent a mechanism of wealth distribution but also a social and economic charge because of the cost of the law enforcement system. Sometimes it may be less costly for the society to allow for some level of criminality. A drawback of such policy may lead to a high increase of criminal activity that may become hard to reduce. We investigate the level of law enforcement required to keep crime within acceptable limits and show that a sharp phase transition is observed as a function of the probability of punishment. We also analyze the growth of the economy, the inequality in the wealth distribution (the Gini coefficient) and other relevant quantities under different scenarios of criminal activity and probability of apprehension.
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  • M. B. Gordon & J. R. Iglesias & V. Semeshenko & J. P. Nadal, 2009. "Crime and punishment: the economic burden of impunity," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 68(1), pages 133-144, March.
    • Handle: RePEc:spr:eurphb:v:68:y:2009:i:1:p:133-144
    DOI: 10.1140/epjb/e2009-00066-x
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    1. Matz Dahlberg & Magnus Gustavsson, 2008. "Inequality and Crime: Separating the Effects of Permanent and Transitory Income," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(2), pages 129-153, April.
    2. Mirta B. Gordon & Jean-Pierre Nadal & Denis Phan & Viktoriya Semeshenko, 2007. "Discrete Choices under Social Influence: Generic Properties," Working Papers halshs-00135405, HAL.
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    Cited by:

    1. Alves, Luiz G.A. & Ribeiro, Haroldo V. & Mendes, Renio S., 2013. "Scaling laws in the dynamics of crime growth rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2672-2679.
    2. Luiz G A Alves & Haroldo V Ribeiro & Ervin K Lenzi & Renio S Mendes, 2013. "Distance to the Scaling Law: A Useful Approach for Unveiling Relationships between Crime and Urban Metrics," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    3. Alves, Luiz G.A. & Ribeiro, Haroldo V. & Rodrigues, Francisco A., 2018. "Crime prediction through urban metrics and statistical learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 435-443.
    4. Raimundo, Silvia Martorano & Yang, Hyun Mo & Rubio, Felipe Alves & Greenhalgh, David & Massad, Eduardo, 2023. "Modeling criminal careers of different levels of offence," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    5. Abbas, Syed & Tripathi, Jai Prakash & Neha, A.A., 2017. "Dynamical analysis of a model of social behavior: Criminal vs non-criminal population," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 121-129.
    6. Gordon, Mirta B. & Laguna, M.F. & Gonçalves, S. & Iglesias, J.R., 2017. "Adoption of innovations with contrarian agents and repentance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 192-205.
    7. Iglesias, J.R. & Semeshenko, V. & Schneider, E.M. & Gordon, M.B., 2012. "Crime and punishment: Does it pay to punish?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 3942-3950.

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