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Assessing substitution and complementary effects amongst crime typologies

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  • Detotto, Claudio
  • Pulina, Manuela

Abstract

This paper aims at assessing how offenders allocate their effort amongst several crime typologies. Specifically, complementary and substitution effects are tested amongst number of recorded crimes. Furthermore, the extent to which crime is detrimental for economic growth is also tested. The case study is Italy and the time span under analysis is from 1981:1 up to 2004:4. A Vector Autoregressive Correction Mechanism (VECM) is employed after having assessed the integration and cointegration status of the variables under investigation. The main findings are that a bi-directional complementary effect exists between drug related crimes and receiving, whereas a bi-directional substitution effect is detected between robberies, extortions and kidnapping and homicides and falsity, respectively. Furthermore, economic growth produces a positive effect on the growth of homicides, receiving and drug related crimes; while, the growth in robberies, extortion and kidnapping and falsity have a crowding-out effect on economic growth.

Suggested Citation

  • Detotto, Claudio & Pulina, Manuela, 2010. "Assessing substitution and complementary effects amongst crime typologies," MPRA Paper 20046, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:20046
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    References listed on IDEAS

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    1. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54, National Bureau of Economic Research, Inc.
    2. Anthony Garratt & Kevin Lee & M. Hashem Pesaran & Yongcheol Shin, 2003. "A Long run structural macroeconometric model of the UK," Economic Journal, Royal Economic Society, vol. 113(487), pages 412-455, April.
    3. Claudio Detotto & Marco Vannini, 2010. "Counting the cost of crime in Italy," Global Crime, Taylor & Francis Journals, vol. 11(4), pages 421-435, November.
    4. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    5. Enders, Walter & Sandler, Todd, 1993. "The Effectiveness of Antiterrorism Policies: A Vector-Autoregression-Intervention Analysis," American Political Science Review, Cambridge University Press, vol. 87(4), pages 829-844, December.
    6. Holtmann, A G & Yap, L, 1978. "Does Punishment Pay?," Public Finance = Finances publiques, , vol. 33(1-2), pages 90-97.
    7. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    8. Shyh-Wei Chen, 2009. "Investigating causality among unemployment, income and crime in Taiwan: evidence from the bounds test approach," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 7(1), pages 115-125.
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    Cited by:

    1. Diana L Carreon-Guzman & Jorge Garza-Rodriguez & David R Garza-Turrubiates & Ricardo A Gonzalez-Camargo & Eugenio Lozano-Castillo, 2015. "The effects of crime on the Mexican economy: a vector error correction model," Economics Bulletin, AccessEcon, vol. 35(2), pages 959-967.
    2. Pérez-Moreno, Salvador & Bárcena-Martín, Elena & Ritzen, Jo, 2017. "Institutional diversity in the Euro area : Any evidence of convergence?," MERIT Working Papers 2017-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    3. Tomas Brabenec & Josef Montag, 2016. "Criminals and the Price System: Evidence from Czech Metal Thieves," CERGE-EI Working Papers wp558, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. Detotto, Claudio & McCannon, Bryan C. & Vannini, Marco, 2015. "Evidence of marginal deterrence: Kidnapping and murder in Italy," International Review of Law and Economics, Elsevier, vol. 41(C), pages 63-67.
    5. C. Detotto & B. Mccannon & M. Vannini, 2013. "A Note on Marginal Deterrence: Evidence," Working Paper CRENoS 201310, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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    More about this item

    Keywords

    Crime; substitution and complementary effect; economic growth; crowding-out effect;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • K14 - Law and Economics - - Basic Areas of Law - - - Criminal Law

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