Mexican Drug Activity, Economic Development, and Unemployment in a Rational Choice Framework

By David J. Masucci
2013, Vol. 5 No. 09 | pg. 2/4 |

The scope of the research sample encompasses cities in northern Mexico with a significant “Big Four” drug cartel presence, to include the Monterrey Metropolitan Region in Nuevo Leon, Ciudad Juarez in Chihuahua, Nogales in Sonora, and Nuevo Laredo in Coahuila. According to Manwaring, all four cities have seen a precipitous increase in cartel-related violence over the past two decades and have served as primary drug corridors into the United States. In addition, each city has gone through a period of accelerated urbanization, with large numbers of rural occupants migrating to the cities to take advantage of economic opportunities (2007). According to the 2010 Mexican Census, Monterrey has a population of approximately 1,130,960 people, Ciudad Juarez has a population of 1,321,004, Nogales has a population of 212,533, and Nuevo Laredo has a population of 373,725 (INEGI-M, 2012). Four Southern U.S. cities were also chosen based on their relative population size, proximity to the border, and history as nexus points for drug traffickers. The selected cities include Phoenix, Arizona (population: 1,445,632), Tucson, Arizona (population: 520,116), El Paso, Texas (population: 649,121), and Laredo, Texas (population: 236,091) (US Census Bureau, 2012).

Consistent with Widner et al.’s collection methods, GDP per capita, official unemployment rates, annual homicide totals, and drug arrests for Monterrey, Ciudad Juarez, Nogales, and Nuevo Laredo were provided by the Instituto Nacional De Estadistica Y Geografia Mexicana (INEGI-M) or the Mexican National Institute of Statistics and Geography. Much like the U.S. Census Bureau, the INEGI releases annual reports that track national, state and municipal socioeconomic indicators. As the country’s primary information repository, the INEGI also provides criminal justice statistics, to include reported crimes and arrests by different law enforcement agencies.

Unlike Mexico, statistical information concerning the four US border cities was gathered from a number of different official sources. Municipal drug arrest data was gathered from the Bureau of Justice Statistics, while municipal homicide statistics was collected utilizing the Federal Bureau of Investigation’s annual Uniform Crime Report (UCR). Per capita GDP was found at the Department of Commerce’s Bureau of Economic Analysis and unemployment figures were obtained from the Department of Labor’s Bureau of Labor Statistics. As a time series study, independent and dependent variable data was collected to capture the period from 2000 to 2010. Raw data for the independent and dependent variables can be found in the Appendix.

The primary quantitative method is a trend analysis that compares the independent and dependent variables over the sampled time period. Specific relationships between the independent and dependent variables were analyzed utilizing a bivariate linear regression analysis characterized by the function Y= β0+ β1X + ε. The analysis was conducted utilizing the Excel 2010 Analytic Tool Package.

With the variables defined and the method of analysis selected, the following hypotheses can be stated:

Hypothesis 1. As annual gross domestic product per capita in each municipality decreases, the number of drug arrests and homicides will increase in both US and Mexican cities.

Hypothesis 2. As the unemployment rate increases in each municipality, the number of drug arrests and homicides will increase as well.

Hypothesis 3. The GPD per capita will maintain an inverse relationship with the dependent variables, such that an increased GPD per capita will predict lower drug arrests and homicides in US and Mexican cities over the sampled time period.

Hypothesis 4. The unemployment rate will maintain a strong positive relationship with the dependent variables, such that decreased unemployment will predict lower drug arrests and homicides in US and Mexican cities over the sampled time period.


During the project conceptualization and data collection phase, a number of limitations and deficiencies were identified. Limitations were divided into data collection and operationalization concerns and uncontrolled third variable phenomena. It was understood early on that these issues could potentially affect the strength and significance of correlation coefficients in each of the regression models.

Data Collection & Operationalization: As referenced previously, there are fundamental problems when comparing and contrasting cross-cultural data. Each independent and dependent variable has its own operational definition that can hinder the comparison of US and Mexican statistics. Early on, it was understood that official unemployment statistics in the United States differed from unemployment statistics gathered in Mexico. In the United States, someone is characterized as unemployed if “they do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work.” The unemployment rate is the “number of unemployed as a percent of the labor force.” Participation in part-time employment or temporary jobs is not counted in the official statistics (Bureau of Labor Statistics, 2013). This is in stark contrast to the unemployment definition utilized by the INEGI, which includes employment in marginal jobs, part-time positions, and even unpaid jobs. According to Widner et al., the INEGI even counts individuals who engage in one hour per week of work as “gainfully employed.” This helps to explain the artificially low Mexican municipal unemployment rates during the darkest days of the global economic recession.

There were numerous issues identified with the dependent variables as well. Though the author was eventually able to locate official municipal homicide statistics for both the United States and Mexico, these homicides represent aggregate totals in each city and not “drug-related” murders directly related to counterdrug efforts or organized crime-related turf battles. The FBI’s Uniform Crime Report and the Mexican INEGI do not differentiate between homicides stemming from interpersonal violence and those related to third-party adversarial actions. Given the quality of the information provided and the fact that individual municipalities voluntarily provide this information to federal authorities, it is difficult to rule out the influence of a third variable or directly infer that an increase in homicides is related to drug activity. Though alternate unofficial sources exist, to include the University of San Diego’s Trans-Border Institute (TBI), the statistics are often limited in scope and data collection methods are questionable; statistics are gathered from third party sources like newspapers and media outlets that are prone to sensationalizing drug murders. Even if unofficial statistics are available, the data may only encompass a two or three year period, which is not sufficient for the current study. Drug arrest statistics were no better. Though the UCR clearly compartmented drug violations into two categories, simple possession or possession with intent to distribute, the INEGI provided aggregate statistics generically referenced as “en materia de narcoticos” or “narcotics matters.” A closer look at the operational definition shows that that the term applies to a relatively large range of different drug offenses, from simple possession, to production and refinement activities, to drug and precursor smuggling.

In addition to the quality of the data collected, there was a general lack of data available on the Mexican side of the border that necessitated the occasional calculation of predictive statistics. For example, municipal homicide totals were missing for Nuevo Laredo in 2002, 2004 and 2005. Utilizing available state and municipal statistics, an average percent representation was derived and used to calculate homicide totals for the years where data was unavailable (see Table 6 in Appendix for calculation). While statistical modeling was utilized when possible, some data was unavailable during critical years and could not be inferred using calculations. Drug arrest statistics were unavailable for each of the sampled Mexican cities in 2009 and 2010. Given increased tempo of counterdrug operations by the Calderon Administration in concert with the relaxation of simple possession violations referenced by Lacey (2009), it was not feasible to produce predictive statistics that could potentially affect the results of the statistical analysis. These years were ultimately omitted from the regression analysis.

Third Variable Phenomena: When initially glancing at the data sets, it is clear that other third variables exist that could potentially affect the outcome of an empirical analysis. Many of the Mexican cities considered to be the “frontline” of the drug war experienced an enormous influx of reported murders from the mid to late 2000’s. While some of the increases are due to improved reporting and record keeping on the behalf of local governments and security services, many of the homicides are attributed to the influx of soldiers into areas traditionally controlled by DTO’s and organized crime. Consistent with Manwaring’s analysis, recent violence can be characterized as a struggle between the legitimate government and the self-serving institutions created by the cartels that have been allowed to operate with relative impunity over the last few decades. In cities like Ciudad Juarez, homicides skyrocketed from 401 in 2007 to 1,372 in 2008. Though less dramatic, Nogales experienced a similar increase in homicides, with 78 recorded in 2007 to 138 recorded in 2008 (see Table 6 in Appendix). With homicides doubling and tripling during the same year that President Calderon ordered 20,000 troops into many of these border cities, it is difficult to attribute an increase in homicides solely to disparate economic conditions (Seper, 2011).

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