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Crime in Colombia: more law enforcement or more justice?

Crime, Law and Social Change volume 68, pages 233–249 (2017)Cite this article

Abstract

This study considers the effect that judicial and police efficiency exercised on crime in 25 of the 33 political-administrative divisions in Colombia during the period 2000–2011. Specifically, the study seeks to determine whether the reduction of crime was the result of increases in the cost of crime as a result of the strengthening of the country’s security forces, especially the National Police, or instead was due to the greater efficiency of the penal system resulting from a structural change stemming from Act 906 of 2004. To view this we propose a model of dynamic panel data that not only includes the individual and temporal effects of the variables of interest, but also allows us to understand the inertia in criminal behavior. The results indicate an inverse relationship between the number of crimes and the greater efficiency of the police and judicial action, which is consistent with the evidence reported in other international work. Robustness checks confirmed the validity of the findings.

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Notes

  1. It is noteworthy that literature has considered additional causes that have an impact on the deterrence of crime: informal institutions, civic norms, and building social capital (see [5, 6]). This approach assumes that civic norms may attach guilt and shame to criminal behavior, thus increasing its opportunity cost. Besides, associated networks may increase returns to noncriminal activities and raise detection probabilities. Despite the relevance of this analysis, the interest of this paper is to study the effects of two approaches mentioned above, which are directly related to police and justice efficiency.

  2. Arbeláez [7] states that in 2002, there was “[...] a daily average of 79 murders, eight kidnappings, six terrorist attacks; 14% of municipalities without police presence; one of three mayors exiled from his jurisdiction by threats from guerrilla groups, which had a presence in 30 of the 32 departments of Colombia, or paramilitary forces, present in 27 of them.”

  3. Article 250, Colombian Political Constitution. The change was introduced by the Constitution of 1991 and therefore it is not an essential part in this research, it is mentioned only as illustratively.

  4. The analysis presented in this paper is related to a period where the Defense and Democratic Security Policy (DSP) was prevailing. Thus, the main objective of police and justice was in reducing high-impact crimes such as murder, kidnapping, extortion, and theft. Some alternative crimes like corruption, fraud, money laundering and other “white collar” crimes, are not taken into account in this investigation. Probably, the deterrent effect of social control (informal institutions, civic norms) can be much clearer in those cases.

  5. Deterrence refers to the reduction of crime that occurs due to police presence, and incapacitation is the reduction of crime after catching criminals and therefore the emergence of minor criminals engaging in criminal activity.

  6. No individual data are available for eight departments because in these branches, there is no attorney’s office. These departments include Amazonas, Arauca, Casanare, Guainía, Guaviare, Vaupes, Vichada and San Andrés, Providencia and Santa Catalina.

  7. The last two are introduced as control variables used in the econometric specification because their behavior is guided more by characteristics of the internal armed conflict (public safety) than by crimes commonly associated with citizen safety, which is the subject of police action more than of military forces.

  8. The rate of police operation, iop j , t , results from the weighted sum of apprehension for the five different types of crime committed. The variable iop j , t considers the same 11 crimes as delj , t but groups together two types of homicides and six types of robberies, resulting in five major types: homicide, theft, terrorism, extortion and kidnapping. The relative weight of apprehensions associated with each type of crime are given by a standard indicator from the rate of police efficiency, indicatori , j , t, here understood as the number of captures for crimes committed for each offense type, in each year and department. Thus,

    $$ {\mathrm{i}\mathrm{op}}_{\mathrm{j},\mathrm{t}}=\frac{\sum_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{Apprehensions}}_{\mathrm{i},\mathrm{j},\mathrm{t}}\ast {\mathrm{Indicator}}_{\mathrm{i},\mathrm{j},\mathrm{t}}\right)}{\sum_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Apprehensions}}_{\mathrm{i},\mathrm{j},\mathrm{t}}} $$

    where apprehensionsi , j , t is the number of seizures associated with criminal type i in department j in year t, and the average weight of seizures is defined as

    $$ {\mathrm{Indicator}}_{\mathrm{i},\mathrm{j},\mathrm{t}}=\frac{{\mathrm{EfficiencyRate}}_{\mathrm{i},\mathrm{j},\mathrm{t}}-{\mathrm{Min}}_{\mathrm{i},\mathrm{j}}\left({\mathrm{EfficiencyRate}}_{\mathrm{i},\mathrm{j},\mathrm{t}}\right)}{{\mathrm{Max}}_{\mathrm{i},\mathrm{j}}\left({\mathrm{EfficiencyRate}}_{\mathrm{i},\mathrm{j},\mathrm{t}}\right)-{\mathrm{Min}}_{\mathrm{i},\mathrm{j}}\left({\mathrm{EfficiencyRate}}_{\mathrm{i},\mathrm{j},\mathrm{t},}\right)} $$

    This, by construction, shows a measure whose range is between 0 and 1, where 0 represents a combination of no seizures and the worst relative efficiency rate for all crimes and 1 the best efficiency rate for each offense. The rate of police efficiency is defined as

    $$ {\mathrm{EfficiencyRate}}_{i, j, t}=\frac{Seizures_{i, j, t}}{Crimes_{i, j, t}} $$

    which may be greater than 1 when the number of seizures is greater than the number of crimes committed, either because there are more arrests or because seizures are made for crimes committed in prior periods.

  9. The index of judicial efficiency is defined as

    $$ {iej}_{j, t}=\frac{EvacuatedP_{j, t}}{NewP_{j, t}+{PriorP}_{j, t}} $$

    where EvacuatedP j , t refers to the processes that leave the system in the department j and year t, NewP j , t indicates the processes that entered the system in the same department and period t, and PriorP j , t refers accumulated processes by the system until t in j department.

  10. When calculating the total crime rate directly from 100,000 people in the population over the whole territory, and not as an average among political and administrative units, it is possible to identify a slight reduction between 2000 and 2011. Indeed, in that scenario the crime rate fell from 26.73 to 26.43 However, this reduction is relatively insignificant considering the length of the period.

  11. The scales of the maps are identical, and the quintiles represent the joint distribution of the 50 data points per crime committed in both 2000 and 2011.

  12. The political-administrative units with a value in iop below 0.5 increased from 12 in 2000 to 17 in 2011. However, the change was positive in 13 units, among which Risaralda, Cauca, Norte de Santander, Santander, Atlántico and Chocó stand out. The situation was different in departments such as Guajira, Valle del Cauca, Meta, Magdalena, Huila and Cesar, where the indicator deteriorated.

  13. The index of judicial efficiency was below 0.57 in 60% of the political-administrative units that were studied in 2011, whereas in 2000, only one of the units was below that level. Also, the indicator was worse in 2011 than in 2000 in all units except Caldas and the Atlantic departments. The worst judicial performance departments were Huila, Meta and Cesar.

  14. As mentioned above, in this paper we take into account only crimes affecting democratic and public security (as were called by the policing strategy implemented during the period 2002–2010), but not those “white collar crimes” related to other criminal actions such as corruption and fraud. Therefore, even though the impact informal institutions may have in reducing crime is recognized, this is not the approach taken into account in this analysis.

References

  1. Becker, G. S. (1968). Crime and punishment: An economic approach. In The Economic Dimensions of Crime (pp. 13–68). Palgrave Macmillan UK. London, UK.

  2. Ehrlich, I. (1974). Participation in illegitimate activities: an econoomic analysis. National Bureau of Economic Research, 68–134.

  3. Levitt, S., & Miles, T. (2006). Economic contributions to the understanding of crime. Annual Review of Law and Social Science.

  4. Ministerio de Defensa Nacional. (2010). Logros de la política de consolidación de la seguridad democrática - PCSD (1st Ed.). Bogotá, Colombia.

  5. Lederman, D., Loayza, N., & Menéndez, A. (2000). Violent crime: Does social capital matter? University of Chicago Press, 50(3), 509–539.

    Google Scholar 

  6. Buonanno, P., Montolio, D., & Vanin, P. (2009). Does social capital reduce crime? The journal of law and economics 2009, 52(1), 145–170.

    Google Scholar 

  7. Arbeláez, A. (2012). La Política de Seguridad Democrática en Colombia: Análisis de su discurso, políticas y resultados durante la administración de Uribe. Washington, DC: NDU.

    Google Scholar 

  8. Buscaglia, E., & Dakolias, M. (1999). An analysis of the causes of corruption in the judiciary. Legal and judicial reform unit, Legal Department, The World Bank.

  9. Waldmann, P. (2007). Is there a culture of violence in Colombia? Terrorism and Political Violence, 19(4), 593–609.

    Article  Google Scholar 

  10. Pecaut, D., & González, L. (1997). Presente, pasado y futuro de la violencia en Colombia. Desarrollo Económico, 36(144), 891–930 Retrieved from http://www.jstor.org.ezproxy.eafit.edu.co/stable/3467131.

    Article  Google Scholar 

  11. García Sánchez, I., Rodríguez Domínguez, L., & Parra Domínguez, J. (2015). Relationship between police efficiency and crime rate: a worldwide approach. European Journal of Law and Economics, 39(1), 203–223.

  12. Gius, M. (1999). The economics of the criminal behavior of young adults. American Journal of Economics and Sociology, 58(4), 947–957.

  13. McCormick, R. E., & Tollison, R. D. (1984). Crime on the Court. Journal of Political Economy, 92(2), 223–235.

  14. Kessler, D., & Levitt, S. (1998). Using sentence enhancements to distinguish between deterrence and incapacitation. NBER Working Paper Series.

  15. Levitt, S. (1995a). Why do increased arrest rates appear to reduce crime: deterrence, incapacitation, or measurement error? NBER Working Paper Series.

    Book  Google Scholar 

  16. Di Tella, R., & Schargrodsky, E. (2004). Do police reduce crime? Estimates using the allocation of police forces after a terrorist attack. The American Economic Review, 94(1), 115–133.

  17. Thanassoulis, E. (1995). Assessing police forces in England and Wales using data envelopment analysis. European Journal of Operational Research, 87(3), 641–657.

  18. Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

  19. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290.

  20. García-Sánchez, I. M., Rodríguez-Domínguez, L., & Domínguez, J. P. (2013). Evaluation of the efficacy and effectiveness of the Spanish security forces. European Journal of Law and Economics, 36(1), 57–75.

  21. Deyneli, F. (2012). Analysis of relationship between efficiency of justice services and salaries of judges with two-stage DEA method. European Journal of Law and Economics, 34(3), 477–493.

  22. Pedraja, F., & Salinas, J. (1996). An assessment of the efficiency of Spanish courts using DEA. Applied Economics, 28(11), 1391–1403.

  23. Tulkens, H. (1993). On FDH efficiency analysis: some methodological issues and applications to retail banking, courts, and urban transit. JPA, 4, 183–211.

    Google Scholar 

  24. Yeung, L., & Azevedo, P. (2009). Measuring efficiency of Brazilian courts with data envelopment analysis (DEA). IMA Journal of Management Mathematics.

    Google Scholar 

  25. Rivera, J., Núñez, J., & Villavicencio, X. (2004). Crimen y disuasión: evidencia desde un modelo de ecuaciones simultáneas para las regiones de Chile. El trimestre económico, 811–846.

  26. Durlauf, S., & Nagin, D. (2011). Imprisonment and crime. Criminology, 10(1), 13–54.

  27. Corman, H., & Mocan, N. (2002). Carrots. NBER Working Paper Series: Sticks and Broken Windows.

    Google Scholar 

  28. Lott, J. (2010). More guns, Less crime. Chicago: University Of Chicago Press.

    Book  Google Scholar 

  29. Ayres, I., & Donohue III, J. J. (2002). Shooting down the more guns, less crime hypothesis (No. w9336). National Bureau of Economic Research. Cambridge, Massachusetts.

  30. Duggan, M. (2000). More Guns, More Crime. National Bureau of Economic Research: Working Paper.

    Book  Google Scholar 

  31. Levitt, S. (2004). Understanding why crime fell in the 1990s: four factors that explain the declain and six that do not. Journal of Economic Perspectives, 18(1), 163–190.

  32. Soares, R. R., & Naritomi, J. (2010). Understanding high crime rates in Latin America: The role of social and policy factors. In The economics of crime: Lessons for and from Latin America (pp. 19–55). University of Chicago Press. Chicago, Illinois.

  33. Buonanno, P. (2003). The socioeconomic determinants of crime: a review of literature. Working Paper Series: Department of Economics, Univeristy of Milan-Bicocca.

    Google Scholar 

  34. Freeman, R. (1991). Crime and the employment of disadvantaged youths. NBER Working Paper Series.

    Book  Google Scholar 

  35. Uprimmy, R., Esther, D., & Parra, J. (2013). Addicted to punishment: the disproportionality of drug laws in Latin America. Colectivo de Estudios de Drogas y Derecho.

    Google Scholar 

  36. Sánchez, F., Espinosa, S., & Rivas, S. (2003). ¿Garrote o Zanahoria? Factores Asocioados a la Disminución de la Violencia Homicida y el Crimen en Bogotá, 1993–2002. CEDE.

  37. Arellano, A., & Olympia, B. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.

  38. Blundell, R., & Bond, S. (2000). GMM estimation with persistent panel data: an application to production functions. Economic Review, 19(3), 321–340.

  39. Toumi, F. (2001). Drogas ilegales, economía y sociedad en los Andes. In Centro de Estudios y Observatorio de Drogas y Delito-CEODD-Facultad de Economía. del Rosario: Universidad.

    Google Scholar 

  40. Walters, G. D. (1992). Foundations of Criminal Science: The development of knowledge (Vol. 1). Greenwood Publishing Group.

  41. Betancourt, D., & Martha Luz García Bustos García B. (1994). Contrabandistas, marimberos y mafiosos: historia social de la mafia colombiana, 1965–1992. Bogota: TM Editores.

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Author information

Authors and Affiliations

  1. Department of Economics, School of Economics and Finance, Universidad EAFIT, Carrera 49 # 7 sur 50, Medellín-, Colombia

    Catalina Gómez, Hermilson Velásquez, Andrés Julián Rendón & Santiago Bohórquez

Authors
  1. Catalina Gómez
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  2. Hermilson Velásquez
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  3. Andrés Julián Rendón
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  4. Santiago Bohórquez
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Corresponding author

Correspondence to Catalina Gómez.

Appendix 1: Statistical tests

Appendix 1: Statistical tests

The results of statistical tests that validate the robustness of the model are presented in the table. Among these are unit root tests and the Sargan and Hansen tests to eliminate possible endogeneity problems.

Test

Characteristic

Statistical value

P value

Conclusion

Pesaran-Shin

Unit Root

-21.086

0.018

Some panels are stationary

AR(1)

Autocorrelation

-2.41

0.016

First lag correlation

AR(2)

Autocorrelation

-1.39

0.166

No second lag correlation

AR(3)

Autocorrelation

1.18

0.237

No third lag correlation

Sargan

Instrument validation

216.47

0.347

Instruments are valid

Hansen

Instrument validation

18.7

1

Instruments are valid

Hansen in levels.

Instrument validation in levels

-4.36

1

At levels, the instruments are valid

Hansen subgroup

Instrument validation in levels

14.56

0.951

The main instruments are valid

The Arellano-Bond test considering three laps ensures no autocorrelation between errors and lagged exogenous variables.

The Sargan and Hansen tests help ensure the validity of the instruments used in the estimated individual and overall models.

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Gómez, C., Velásquez, H., Rendón, A.J. et al. Crime in Colombia: more law enforcement or more justice?. Crime Law Soc Change 68, 233–249 (2017). https://doi.org/10.1007/s10611-017-9682-6

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