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Is the fight against Mexican drug cartels beneficial to public security? Nils-Hendrik KLANN University 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 - PowerPoint PPT PresentationTRANSCRIPT
20. 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
20. April 2023New Directions in Welfare 2011 Congress
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)
20. April 2023New Directions in Welfare 2011 Congress
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.
20. April 2023New Directions in Welfare 2011 Congress
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.
20. April 2023New Directions in Welfare 2011 Congress
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.
20. April 2023New Directions in Welfare 2011 Congress
Outline
1. Introduction
2. Literature Overview
3. Research Outline
4. Empirical Results
5. Conclusion
20. April 2023New Directions in Welfare 2011 Congress
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).
20. April 2023New Directions in Welfare 2011 Congress
2. Literature Overview
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:
20. April 2023New Directions in Welfare 2011 Congress
2. Literature Overview
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)
20. April 2023New Directions in Welfare 2011 Congress
2. Literature Overview
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))
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2. Literature Overview
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.
20. April 2023New Directions in Welfare 2011 Congress
2. Literature Overview
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:
20. April 2023New Directions in Welfare 2011 Congress
2. Literature Overview
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 20, 2023New Directions in Welfare 2011 Congress
2. Literature Overview
Summary of the two strands of literature
Incarcerating drug traffickers
NDC increase
NDC decrease
Limited Police resources
Crowding out effects
positive secondary
effects
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3. Research Outline and Methodology
20. April 2023New Directions in Welfare 2011 Congress
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
20. April 2023New Directions in Welfare 2011 Congress
3. Research Outline and Methodology
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
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3. Research Outline and Methodology
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
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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
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4. Empirical Results
[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
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4. Empirical Results
[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)
robbery | arrests/offences -1.744***(-24.52)
assault | arrests/offences -1.326***(-19.24)
murder | arrests/offences -1.428*** -0.566(-21.22) (-1.48)
rape | arrests/offences -1.420***(-17.29)
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
20. April 2023New Directions in Welfare 2011 Congress
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)
robbery | arrests/offences -1.850***(-33.73)
assault | arrests/offences -1.362***(-28.24)
murder | arrests/offences -1.452*** -0.272(-29.46) (-1.01)
rape | arrests/offences -1.520***(-20.56)
N 4878.000 4714.000 3663.000 2091.000 1160.000* p<0.10, ** p<0.05, *** p<0.01
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4. Empirical Results
.
[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)
robbery | arrests/offences -1.568***(-14.89)
assault | arrests/offences -1.503***(-16.92)
murder | arrests/offences -1.244*** -0.770(-17.12) (-1.19)
rape | arrests/offences -1.725***(-16.45)
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
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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.
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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.
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5. Conclusion
Thank you