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Ernesto Aguayo Téllez Sandra Edith Medellín Mendoza

Abstract

This paper studies the impact that the characteristics of the environment have on crime using neighborhood aggregate data of the Monterrey Metropolitan Area for the year 2010. Data spatial autocorrelation is corroborated, i.e. neighborhoods with high crime rates have a positive impact on the crime rates of its surrounding neighborhoods. Once it was controlled through the bias caused by spatial autocorrelation and data censoring, it is evidenced that the likelihood of being a crime victim and the probability of becoming an offender is positively related to variables such as unemployment, the percentage of young men and the existence of schools, hospitals or markets in the neighborhood. 

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Abstract

This paper studies the impact that the characteristics of the environment have on crime using neighborhood aggregate data of the Monterrey Metropolitan Area for the year 2010. Data spatial autocorrelation is corroborated, i.e. neighborhoods with high crime rates have a positive impact on the crime rates of its surrounding neighborhoods. Once it was controlled through the bias caused by spatial autocorrelation and data censoring, it is evidenced that the likelihood of being a crime victim and the probability of becoming an offender is positively related to variables such as unemployment, the percentage of young men and the existence of schools, hospitals or markets in the neighborhood. 

Article Details

Keywords
Crime; Spatial Autocorrelation; The Neighborhood Effects
How to Cite
AGUAYO TÉLLEZ, Ernesto; MEDELLÍN MENDOZA, Sandra Edith. Spatial Dependence of Crime in Monterrey, Mexico. Ecos de Economía: A Latin American Journal of Applied Economics, [S.l.], v. 18, n. 38, p. 63-92, june 2014. ISSN 2462-8107. Available at: <http://publicaciones.eafit.edu.co/index.php/ecos-economia/article/view/2514>. Date accessed: 26 apr. 2018.
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