# Understanding support for Mano Dura strategies: Lessons from Brazil and Colombia

• Published:

## Abstract

There are many scholarly works focusing on organized crime and violence in Latin America. Scholars have shown empirically that tough on crime strategies have had collateral damages and have not been effective. By conducting logistic regression models using individual country survey data, this work seeks to analyze why tough on crime policies remain popular in some Latin American countries despite the decades of research criticizing these strategies. This article explores the cases of Colombia and Brazil, which have long histories of gang activity, organized crime, and violence. These countries also have elected presidential candidates who campaigned on iron fist strategies.

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## Notes

1. Mano dura strategies generally refer to hardline policies. In this article, we are focusing on the need to increase the penalties for crimes.

2. The “balloon effect” is an analogy used by drug policy analysts to illustrate the process by which narcotics production is displaced from one region to another as an attempt to evade eradication and interdiction efforts.

3. The data can be found here: https://www.vanderbilt.edu/lapop/core-surveys.php

4. For more, see: “AmericasBarometer, 2018/2019: Technical Information, https://www.vanderbilt.edu/lapop/ab2018/AmericasBarometer_2018-19_Technical_Report_W_102919.pdf

5. The recode of the variable is as follows: recode aoj22new (1/3 = 0) (4/7 = 1), gen(tough). There are consequences of collapsing a 7-point Likert Scale into a dummy variable. There is no theoretical or empirical foundation for where to put the number four. We ran the model both ways, but we decided to put it in the second category.

6. The recodes are as follows: recode q2 (16/28 = 1) (29/39 = 2) (40/50 = 3) (51/61 = 4) (62/72 = 5) (73/86 = 6), gen (age); recode q2 (18/28 = 1) (29/39 = 2) (40/50 = 3) (51/61 = 4) (62/72 = 5) (73/88 = 6), gen (age).

7. Some scholars like to transform this variable to a 100 point-scale to make it easier to read. However, we have decided not to transform the scale so that we do not distort the variable and lose explanatory power. We have consulted with several scholars about this who have agreed with our assessment.

8. LAPOP indicates the following command for setting the data: svyset upm [pw = wt], strata(estratopri). The svy command reports linearized coefficients and standard errors.

9. We ran the model using household income in the last two years and personal monthly income. The variable personal monthly income was statistically significant, but we dropped this variable because it had 883 observations in our sample. The different measures of income are something that we will explore in future studies.

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## Acknowledgements

We would like to thank two anonymous reviewers for their excellent comments, which greatly helped improve this article. In addition, we want to thank the following people for their statistical consultations over the past year: Barnett Koven, John Polga-Hecimovich, Alexa Bonacquisti, Kimberly Dasch-Yee, Jennifer DeCicco, Stacy McDonald, Jane Whittington, and Jan Buzydlowski. Some of these colleagues have reviewed different regression models, and we thank them for the many conversations about statistical modeling. A special thanks to José Miguel Cruz of Florida International University for his help and support. We have asked him many methodological questions over the past few years, and he has always been an outstanding colleague and friend.

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Correspondence to Sebastián Cutrona.

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Rosen, J.D., Cutrona, S. Understanding support for Mano Dura strategies: Lessons from Brazil and Colombia. Trends Organ Crim 24, 324–342 (2021). https://doi.org/10.1007/s12117-020-09396-6