11. oktober 2015new directions in welfare 2011 congress is the fight against mexican drug cartels...

19. April 2023New Directions in Welfare 2011 Congress

Is the fight against Mexican drug cartels beneficial to publicsecurity?

Nils-Hendrik KLANNUniversity of Göttingen & University of Heidelberg


Motivation

“We have 18 months and if we do not produce a tangible success

that is recognizable to the Mexican people, it will be difficult to

sustain this confrontation into the next administration.”Gerónimo Gutiérrez, Deputy Secretary for Domestic Security

(Diplomatic cable from 2009; Wikileaks)


Motivation

The ‘War on Drugs’ has become a global phenomenon as

governments in many countries seek to fight the activities of

international drug cartels.

The growing sophistication of drug gangs as well as their ever-

increasing affinity to violence against opponents pose a direct

challenge to the authority of governments.

In many countries, society is caught in the middle between

opposing forces in an increasingly brutal conflict between gangs

and security forces.


Motivation

Acting both as a producer and transport hub for drugs, Mexico has

become the center stage for an extremely violent conflict between

gangs competing to deliver drugs to the US market.

Since 2006, the Mexican government has significantly stepped up

its initiative against drug cartels, relying on police as well as 35,000

soldiers to fight Mexico’s drug cartels.

With regards to tackling the activities of Mexico’s drug cartels, the

government’s anti-drug initiative is often called into question.


Motivation

On the other hand, no empirical analysis exists to date focusing on

the broader implications of Mexico’s drug war for society.

Objective of this paper

Investigate the effect of Mexico’s anti-drug initiative on the

prevalence of non-drug offences such as property crime, assault,

rape and murder at the district level.


Outline

1. Introduction

2. Literature Overview

3. Research Outline

4. Empirical Results

5. Conclusion


1. Introduction

With the ‘War on Drugs’ going on for several years and its effect on

cartel activities dubious at best, which effects will this initiative have

on the Mexican society?

This paper focuses on the prevalence of non-drug related offences

in the approximately 2500 municipal districts in the time period from

1998 until 2008 to assess whether or not the intensity of anti-drug

efforts impacts on the prevalence of non-drug crime (NDC).



Prior research on the effect of drug enforcement on other forms of

crime focus provide contrasting predictions:

Theory 1 Intensified drug enforcement increases non-drug crime

Welfare of society is reduced as the anti-drug initiative implies

negative externalities in the form of rising crime rates.

Relies on seminal contribution by Becker (1986) and Ehrlich (1973)

predicting the likelihood of criminal activity in the light of expected

gains and costs:



Several sources such as Sollars, Benson et al. (1994)

Benson, Kim et al. (1994), Benson, Rasmussen et al. (1998)

Benson, Leburn et al. (2001) analyze the implications of a marked

concentration of police forces to battle drug offenders in 67 Florida

counties around the 80s and 90s on non-drug crime.

Also, Resignato (2000) and Shepard and Blackley (2005) conduct

a similar investigation focusing on New York State.

Key finding the concentration of finite law enforcement resources on one predominant type of crime

increases all other types of crime (crowding out effect)



An alternative explanation of this positive relationship is discussed

in Miron (1999) and with an empirical analysis in Miron (2001).

Here, violence is a systemic feature of black markets, in which

participants are unable to resort to legal institutions such as

the police or courts to resolve disputes or enforce their property

rights. This process is exacerbated as enforcement intensifies.

Key finding Intensifying enforcement efforts increases the potential for violent turf wars.

Classic example: gang crime in the ambit of alcohol prohibition in

the United States. (e.g., Asbridge and Weerasinghe (2009))



Theory 2 Intensified drug enforcement reduces non-drug crime

Contrasting opinion see the potential for complementarity between

the objectives to fight DC and NDC offenders.

In an ideal case, stepping up drug enforcement not only reduces

drug offences but beyond that yields additional gains as the

measure leads to a simultaneous reduction of non-drug offences

e.g., robbery, assault and kidnapping.



Analyzing US crime and incarceration data for the time period of

1983 until 1996 Kuziemko and Levitt (2004) find evidence that

stricter punishment of drug offences yields a twofold effect:

1. longer incarceration of drug offenders impacts negatively

on the duration served by NDC felons BUT counter to the

predictions of the aforementioned Becker framework has

no proliferating effect on NDC crime:

2. significant negative relationship between the incarceration of drug offenders and non-drug crime:



Key finding Incarcerating drug offenders indirectly helps to reduce non-drug crime as

drug offenders dedicate a significant share of their activities towards offences such as

assault and murder.

In line with this, Levitt and Venkatesh (2000) which investigate the

daily routine and financials of a Chicago drug gang finds that about

one fourth of a gang member’s time is dedicated to violent crime:

Shipley (1989) finds similar trends analyzing offences committed by

incarcerated drug offenders:

April 19, 2023New Directions in Welfare 2011 Congress


Summary of the two strands of literature

Incarcerating drug traffickers

NDC increase

NDC decrease

Limited Police resources

Crowding out effects

positive secondary

effects


3. Research Outline and Methodology



Analysis of district-level data from approx. 2500 municipal districts,

spread out over 32 federal states over the time period of

1998-2008; running the following empirical model:

Dependent variable

NDC log of non-drug crime incidents in a district (5 indicators)Source: Statistical Yearbooks from 32 federal states; National Statistics Institute



Independent Variables

DE share of drug arrests over all DENS district population per km2

arrests in a district Source: Authors calculation based on surface data from

Source: Statistical Yearbooks from 32 states; the Mexican Geographic Service

Mexican National Statistics Institute (INEGI)

POP log district population URATE district unemployment rateSource: Natl. Institute for Federalism and Municipal Development Source: Secretary for Employment and Social Security (STPS)

HIGHW Highway Dummy CDET deterrence arrests/offences Source: Author’s calculation Source: Authors calculation based on Statistical Yearbooks from

32 federal states; National Statistics Institute

Source: Secretary for Employment and Social Security



Offences implemented as depended variable include

- Robbery

- Assault

- Rape

- Murder

- Gang murder*

*Data on gang murder is provided by the Mexican Interior Ministry from the year

2006 onwards. Victims were categorized as gang victims based on the

circumstances of their death, e.g., the use of large caliber weapons, signs of torture


4 Empirical Results

Regression Framework

Ordinary Least Squares (baseline)

FE (district fixed effects)

Negative Binomial (count data)

GMM (endogeneity)

Robustness checks carried out in the course of regressions:

Exclusion of each federal state and year



[Pooled OLS] Total offences in logRobbery Assault Murder Rape Gang Killings

drug enforcement -0.615*** -1.465*** 0.022 -0.304** 0.996**(-7.60) (-19.13) (0.29) (-2.45) (2.43)

log of population 1.234*** 1.064*** 0.881*** 0.888*** 0.704***(95.57) (89.96) (69.45) (51.06) (11.46)

log of density -0.032*** -0.006 -0.089*** -0.035*** -0.238***(-3.88) (-0.86) (-11.70) (-3.56) (-6.75)

unemployment rate 10.548*** 5.168*** -3.830*** -0.900* 5.048***(22.53) (12.30) (-8.83) (-1.77) (2.59)

highway in district 0.357*** 0.246*** -0.023 0.005 -0.174(11.55) (8.60) (-0.77) (0.13) (-1.35)

robbery | arrests/offences -2.021***(-29.28)

assault | arrests/offences -1.866***(-32.67)

murder | arrests/offences -1.802*** -0.290(-33.30) (-1.07)

rape | arrests/offences -2.020***(-28.78)

constant -8.672*** -6.846*** -5.584*** -6.238*** -5.719***(-66.82) (-55.76) (-46.47) (-34.30) (-9.80)

R2 0.834 0.844 0.722 0.778 0.291N 5097 4976 3876 2330 545* p<0.10, ** p<0.05, *** p<0.01



[Fixed Effects] Total offences in logRobbery Assault Murder Rape Gang Killings

drug enforcement -0.474*** -0.544*** -0.324*** -0.396** 0.632(-7.16) (-8.04) (-4.42) (-2.37) (1.13)

log of population 0.088** 0.173*** 0.032 0.135*** 0.125(2.06) (3.82) (0.81) (2.99) (0.97)

unemployment rate 0.291 -2.481*** -2.234*** -0.759 28.767***(0.47) (-3.45) (-3.03) (-0.72) (5.38)





constant -8.672*** -6.846*** -5.584*** -6.238*** -5.719***(-66.82) (-55.76) (-46.47) (-34.30) (-9.80)

R2 0.346 0.220 0.308 0.384 0.533N 4878 4714 3663 2091 353* p<0.10, ** p<0.05, *** p<0.01All regressions with time and district fixed effects; robust t-values in bracketsStandard errors in OLS Fixed Effects regressions adjusted for clustering across districts


4. Empirical Results[Negative Binomial] Total offences

Robbery Assault Murder Rape Gang Killings

drug enforcement -0.481*** -0.613*** -0.268*** -0.787*** 1.624***(-8.91) (-10.45) (-3.80) (-5.18) (5.36)

log of population 0.184*** 0.141*** 0.005 0.012 0.600***(9.47) (7.59) (0.21) (0.44) (11.99)

unemployment rate 0.058 -3.097*** -2.946*** 0.660 2.879(0.14) (-6.64) (-4.66) (0.85) (1.22)

highway in district -0.168** -0.461*** -0.279** -0.046 -0.119(-2.23) (-5.59) (-2.27) (-0.30) (-0.71)





N 4878.000 4714.000 3663.000 2091.000 1160.000* p<0.10, ** p<0.05, *** p<0.01



.

[GMM] Total offencesRobbery Assault Murder Rape Gang Killings

lagged dependent 0.327*** 0.291*** 0.208*** 0.216*** 0.837***(5.43) (6.45) (4.60) (3.53) (3.67)

drug enforcement -0.306*** -0.848*** 0.025 -0.222 0.805(-3.36) (-9.69) (0.31) (-1.50) (1.19)

log of population 0.796*** 0.742*** 0.590*** 0.638*** 0.202*(10.20) (13.88) (15.20) (10.60) (1.80)

unemployment rate 4.856*** 2.194*** -3.166*** -1.270* 2.361(5.86) (3.43) (-4.14) (-1.80) (0.84)

highway in district 0.257*** 0.166*** 0.021 0.017 -0.159(4.18) (3.05) (0.35) (0.28) (-0.64)





constant -5.224*** -4.519*** -3.600*** -4.208*** -1.914(-8.88) (-11.28) (-11.57) (-8.23) (-1.35)

R2N 3380 3236 2670 1622 195Hansen J 13.882 17.279 11.085 13.337 0.278(p-value) 0.178 0.068 0.351 0.148 0.598Arellano-Bond test for AR1 in 1st differences -6.351 -7.494 -7.957 -4.212(p-value) 0.000 0.000 0.000 0.000

Arellano-Bond test for AR2 in 1st differences -1.047 -0.574 -0.868 -0.32(p-value) 0.295 0.566 0.385 0.749

Number of districts 670 685 572 471 176Number of instruments 27 27 27 26 10* p<0.10, ** p<0.05, *** p<0.01Two-step System GMM with time fixed effects and Windmeijer finite sample correction


5. Conclusion

Regarding the effect of drug enforcement in general:

A greater share of arrests among all arrests seems to reduce the

Prevalence of most NDC offences at the district level.

Some indication exists regarding the expected positive relationship

between drug enforcement and gang murder – results should be

Interpreted with caution however, given the short time span of the

Data.


5. Conclusion

Regarding the control variables:

In line with expectations, population yields a positive effect on the

number of offences.

Unemployment has a significant effect on crime - however no

singular relationship can be derived from the results.

Deterrence measures show the expected negative effect on all

types of crime except gang murder.


5. Conclusion

Thank you

11. oktober 2015new directions in welfare 2011 congress is the fight against mexican drug cartels...

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