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The Effect of Adult Criminals’ Spillovers On the Likelihood of Youths Becoming Criminals

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Author Info

  • Carlos Medina

    ()

  • Jorge Andrés Tamayo

    ()

  • Christian Posso

    ()

Abstract

We use a unique data set at the individual level to estimate an empirical model explaining the probability of young individuals to become criminals as a function of the presence of adult criminals in their neighborhoods, an a complete set of control variables, including census sector fixed effects. We use the census of criminals captured in Medellín between 2000 and 2010 to construct our peer’s variables, and find a strong and robust positive effect of the presence of adult criminal neighbors on the probability of becoming criminal. The result is robust across different specifications of the presence of criminals, and with respect to the probability of committing different types of crimes, even controlling for contextual and group effects. Both modeling peer effects as the sum of friends’ efforts and modeling them as deviations from the means, affect the likelihood to become criminal, although with differential importance by type of crime.

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Bibliographic Info

Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 755.

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Length: 28
Date of creation: Feb 2013
Date of revision:
Handle: RePEc:bdr:borrec:755

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Keywords: Crime; Social Interactions. Classification JEL: C31; K40; K42;

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References

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  1. Rajiv Sethi & Rohini Somanathan, 2001. "Inequality and Segregation," Microeconomics 0108005, EconWPA.
  2. Edward L. Glaeser & Bruce I. Sacerdote & Jose A. Scheinkman, 2003. "The Social Multiplier," Journal of the European Economic Association, MIT Press, vol. 1(2-3), pages 345-353, 04/05.
  3. Carlos Medina & Jorge Andrés Tamayo Author-Email jtamayca@banrep.gov.co, . "An Assessment of How Urban Crime and Victimization Affects Life Satisfaction," Borradores de Economia 640, Banco de la Republica de Colombia.
  4. Lung-fei Lee & Xiaodong Liu & Xu Lin, 2010. "Specification and estimation of social interaction models with network structures," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 145-176, 07.
  5. O'Flaherty, Brendan & Sethi, Rajiv, 2007. "Crime and segregation," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 391-405.
  6. Roland Benabou, 1991. "Workings of a City: Location, Education, and Production," NBER Technical Working Papers 0113, National Bureau of Economic Research, Inc.
  7. Alejandro Gaviria & Carlos Medina & Jorge Tamayo, 2010. "Assessing the Link between Adolescent Fertility and Urban Crime," BORRADORES DE ECONOMIA 006860, BANCO DE LA REPÚBLICA.
  8. Heinemann, Alessandra & Verner, Dorte, 2006. "Crime and violence in development : a literature review of Latin America and the Caribbean," Policy Research Working Paper Series 4041, The World Bank.
  9. Falk, Armin & Fischbacher, Urs, 2002. ""Crime" in the lab-detecting social interaction," European Economic Review, Elsevier, vol. 46(4-5), pages 859-869, May.
  10. O'Flaherty, Brendan & Sethi, Rajiv, 2010. "Homicide in black and white," Journal of Urban Economics, Elsevier, vol. 68(3), pages 215-230, November.
  11. Carlos Medina & Christian Posso & Jorge Andrés Tamayo, 2011. "Costos de la violencia urbana y políticas públicas: algunas lecciones de Medellín," BORRADORES DE ECONOMIA 009076, BANCO DE LA REPÚBLICA.
  12. Lee, Lung-fei, 2007. "Identification and estimation of econometric models with group interactions, contextual factors and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 333-374, October.
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Blog mentions

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  1. Criminal path-dependence (Medellin)
    by UDADISI in UDADISI on 2013-03-06 03:00:00

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