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A hierarchical SLX model application to violent crime in Mexico

Author

Listed:
  • Donald J. Lacombe

    (Texas Tech University)

  • Miguel Flores

    (ITESM)

Abstract

In this paper, we develop a Bayesian Hierarchical Spatial Lag of X (SLX) spatial econometric model to examine the relationship between contextual factors associated with violence levels in Mexico over the period 2005–2010, a period in which violence in the country reached record levels. We provide local spillover estimates of relevant covariates at both levels of the hierarchy, i.e., the municipal and state levels in Mexico. We also provide a methodology to compare relevant models regarding the proper specification of the spatial weight matrix in these models.

Suggested Citation

  • Donald J. Lacombe & Miguel Flores, 2017. "A hierarchical SLX model application to violent crime in Mexico," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 119-134, January.
    • Handle: RePEc:spr:anresc:v:58:y:2017:i:1:d:10.1007_s00168-016-0788-z
    DOI: 10.1007/s00168-016-0788-z
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    References listed on IDEAS

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    1. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    2. Miguel Flores & Eduardo Rodriguez-Oreggia, 2014. "Spillover Effects on Homicides across Mexican Municipalities: A Spatial Regime Model Approach," The Review of Regional Studies, Southern Regional Science Association, vol. 44(3), pages 241-262, Winter.
    3. Richard B. Freeman, 1996. "Why Do So Many Young American Men Commit Crimes and What Might We Do about It?," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 25-42, Winter.
    4. Melissa Dell, 2015. "Trafficking Networks and the Mexican Drug War," American Economic Review, American Economic Association, vol. 105(6), pages 1738-1779, June.
    5. Lance Lochner & Enrico Moretti, 2004. "The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports," American Economic Review, American Economic Association, vol. 94(1), pages 155-189, March.
    6. Oeindrila Dube, Omar Garcia-Ponce, and Kevin Thom, 2014. "From Maize to Haze: Agricultural Shocks and the Growth of the Mexican Drug Sector - Working Paper 355," Working Papers 355, Center for Global Development.
    7. James P. LeSage, 2014. "What Regional Scientists Need to Know about Spatial Econometrics," The Review of Regional Studies, Southern Regional Science Association, vol. 44(1), pages 13-32, Spring.
    8. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    9. Fajnzylber, Pablo & Lederman, Daniel & Loayza, Norman, 2002. "Inequality and Violent Crime," Journal of Law and Economics, University of Chicago Press, vol. 45(1), pages 1-40, April.
    10. Dube, Arindrajit & Dube, Oeindrila & Garcã A-Ponce, Omar, 2013. "Cross-Border Spillover: U.S. Gun Laws and Violence in Mexico," American Political Science Review, Cambridge University Press, vol. 107(3), pages 397-417, August.
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    Cited by:

    1. Joshua C. Hall & Donald J. Lacombe & Amir Neto & James Young, 2022. "Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 360-373, April.
    2. Zimmerman, Brianne R. & Collins, Alan R. & Lacombe, Don, 2017. "Analyzing the Spatial Distribution of NRCS Conservation Programs in West Virginia," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258378, Agricultural and Applied Economics Association.
    3. Joshua C. Hall & Donald J. Lacombe & Shree B. Pokharel, 2020. "State Exit Exams and Graduation Rates: A Hierarchical SLX Modelling Approach," The Review of Regional Studies, Southern Regional Science Association, vol. 50(2), pages 189-206.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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