Has the Mexican government’s policy of removing drug-trafficking organization (DTO) leaders reduced or increased violence? In the first 4 years of the Calderón administration, over 34,000 drug-related murders were committed. In response, the Mexican government captured or killed 25 DTO leaders. This study analyzes changes in violence (drug-related murders) that followed those leadership removals.
The analysis consists of cross-sectional time-series negative binomial modeling of 49 months of murder counts in 32 Mexican states (including the federal district).
Leadership removals are generally followed by increases in drug-related murders. A DTO’s home state experiences more subsequent violence than the state where the leader was removed. Killing leaders is associated with more violence than capturing them. However, removing leaders for whom a $30m peso bounty was offered is associated with a smaller increase than other removals.
DTO leadership removals in Mexico were associated with an estimated 415 additional deaths during the first 4 years of the Calderón administration. Reforming Mexican law enforcement and improving career prospects for young men are more promising counter-narcotics strategies. Further research is needed to analyze how the rank of leaders mediates the effect of their removal.
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This claim is made by experts quoted in Olson et al. (2010), Currie (2011) and elsewhere. It was also the position taken in a conversation that the author had with Mark Wells (Director of the Office of Americas Programs for the State Department’s Bureau of International Narcotics and Law Enforcement) and Ambassador Patrick Duddy, who served throughout Latin America during his career.
La Familia is one possible exception to the non-ideological argument. See Williams (2012) for a thorough discussion of this point.
All of the DTO leaders in the sample were male, so the gendered pronoun is used—advisedly—throughout this section and the remainder of the paper
An objection could be raised here that rational leaders might anticipate their own demise and leave the group voluntarily upon news of an attempted removal. Alternately, they could be pushed out of the group by their own members. However, neither voluntary nor internally initiated removals were observed in the sample. The absence of the former is likely due to the high level of profits involved in drug trafficking, some of which can be used to buy off corrupt officials and finance greater security. The absence of the latter form of removal is not theoretically necessary, and cases of internal removals may become public in the future. Thanks to an anonymous reviewer for raising this point.
See section “ Research Design, Data, and Key Definitions” for a discussion of how DTOs’ base states were identified and coded in the data set.
The documents cited throughout this paper were obtained in one or more of the following three ways: through Freedom of Information Act (FOIA) requests; as part of the Wikileaks releases; or as another release of hacked documents. Wherever possible, hacked documents (including Wikileaks cables) were requested through FOIA and confirmed by multiple versions and sources. Thanks to Glen Courtney at US Customs and Border Patrol and other agency contacts throughout the Department of Homeland Security for their prompt responses to requests and phone calls.
Reducing this number as low as four or increasing it as high as eight, roughly the plausible range for the time period considered here, could introduce biased estimates but should not affect their sign.
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Stratfor, “Mexico Security Memo”, May 17, 2010; Beittel, J.S. 2009. “Mexico’s Drug Related Violence”. Congressional Research Service. For visualizations of DTO-controlled areas, see http://www.npr.org/templates/story/story.php?storyId=126890893, http://www.nytimes.com/interactive/2009/03/22/us/BORDER.html?ref=us, and http://www.economist.com/blogs/graphicdetail/2012/11/mexican-drug-war. Most DTOs’ primary areas of influence in 2010 were limited to single states, although the Guzmán cartel was more expansive as was the Gulf-Los Zetas DTO before their split.
The data can be accessed at (and downloaded from) http://www.guardian.co.uk/news/datablog/2011/jan/14/mexico-drug-war-murders-map.
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Thanks to the anonymous reviewers for their input on modeling strategies and checks of robustness.
Thanks to Marvin McNeese Jr. for providing this data, obtained from Nicole Ramos of the Trans-Border Institute.
According to Reforma vigilante groups are now active in 13 states (http://www.reforma.com/estados/articulo/691/1380952/). For an overview on vigilante groups and other non-government responses to violence in Mexico see this summary http://www.insightcrime.org/news-briefs/growth-of-mexican-vigilante-groups-cause-concern by Miriam Wells.
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Thanks to Cassy Dorff, Jim Granato, Ryan Kennedy, Sandra Ley, Scott de Marchi, Shahryar Minhas, David Skarbek, Lydia Tiede, Guillermo Trejo, seminar participants at the University of Houston and Duke University, and the anonymous reviewers for their comments on earlier versions of this paper. All remaining errors and opinions are the author’s alone.
Appendix: Robustness Checks
Appendix: Robustness Checks
Supplementary material in this section includes alternative model specifications and distributional assumptions, subsetting the data, and checking for reverse causality. Results from all of these robustness checks support the findings presented in the main body of the text.
Table 5 displays model estimates corresponding to those in Table 3, extending the lag structure in all four models to 6 months. Removal in a DTO’s base state (Model 1) has a positive effect on murders in the all six lags, further supporting the argument that removing leaders is followed by additional violence. In the state in which a removal occurred, the coefficient of interest loses its significance the as early as the second month after the removal. Estimates of increased violence for killing leaders also endure for the full 6-month period. The removal of leaders for whom a $30m peso bounty was offered are followed by less violence than the average removal.
Although the dependent variable is an overdispersed count—and thus most appropriately modeled by a negative binomial distribution—some readers may be interested in a set of Poisson models with the same covariates. Table 6 displays the results of using Poisson models with the same fixed effects structure as the negative binomial models in Table 3. Poisson estimation did not change the direction of the results but did yield more precise estimates. However, the model fit statistics (AIC and log-likelihood) indicate that this model is less appropriate for the data at hand.
Omitting Cases of Low-Level Leadership
Inferring information about DTO leaders from news reports and government documents runs the risk of over- or under-counting removals. The government of Mexico may have an incentive to exaggerate the significance of the leaders they have removed in order to gain confidence from the public. During data collection the leaders were assigned to one of three tiers (discussed in section “Research Design, Data, and Key Definitions”). Tiers one and two contain the highest level leaders who exercised control over the entire organization or a significant portion thereof (such as a plaza). Tier three leaders had more operationally-focused roles, such as controlling trafficking routes or logistics.
How do the estimates change if we exclude this lowest tier of leaders? Table 7 displays negative binomial estimates for the same models as in the main text, but with cases of tier three leadership removals omitted. Although the point estimates have changed slightly, the direction and relative magnitudes of all covariates remains the same as before. This suggests that including third-tier leaders in the data did not undermine the results. Analyzing removal of different types of leadership is an intriguing topic for future research.
Checking for Reverse Causality
We have seen so far that leadership removals are followed by increased levels of murders. By lagging leadership removal by 1 month we avoided simultaneity bias, but there is still the potential that causation is running in the reverse direction. Leadership removal could be more likely in states with higher levels of violence. To assess whether this is the case, Table 8 presents two logistic regressions with removals from Models 1 and 2 above as the dependent variables. Total murders are used to predict the probability of a removal. The cross-sectional and temporal fixed effects structure is the same as in the models above. The lag structure extends to 6 months to allow for the time it takes to organize a removal mission. No coefficients in the model are significantly different from zero, suggesting that levels of violence do not help to predict where or when removals will occur.
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Dickenson, M. The Impact of Leadership Removal on Mexican Drug Trafficking Organizations. J Quant Criminol 30, 651–676 (2014). https://doi.org/10.1007/s10940-014-9218-5
- Drug trafficking