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

The relationship between GDP per capita and drug arrests in Ciudad Juarez and Nogales is more complex and not as easily understood. As opposed to GDP per capita and homicides, there does not appear to be an obvious third variable influence that could affect the strength of the relationship between the input and outcome variables. A cursory glance at the data sets reveal a relatively steady increase in GDP per capita in the sampled cities, while drug arrests seemed to vary on an annual basis. There are two issues that are readily apparent; first and foremost, the regression analysis was limited to drug arrest data available from 2000 to 2008. Given the lack of available data, it is unknown how President Calderon’s policies may have affected drug arrest statistics in the years after he surged Mexican security forces into areas controlled by the cartels. In addition, 2008 drug arrest data for Ciudad Juarez represents somewhat of an outlier; as cited by the INEGI, only partial data was available for that year. An updated regression analysis excluding 2008 data reveals an Adjusted R Square of -.057with a P-Value well outside of the confidence interval. By eliminating the outlier, the relationship has become statistically insignificant.

While the act of eliminating outliers may explain the unusual relationship between GDP per capita and drug arrests in Ciudad Juarez, Nogales still possesses a moderate correlation even after excluding 2008 data. Despite the moderate statistical relationship, it is possible that the correlation has been established purely by chance. As the GDP of Nogales has increased in lockstep with the Mexican economy, so have the number of annual drug arrests within the city. While both variables seem to increase incrementally over the course of the observation period, correlation does not necessarily translate to causality. The increase in drug arrests is consistent with President Calderon’s pre-2010 “Law and Order” policy that advocated increased drug enforcement activities in Northern Mexico. In a controversial reversal of existing Mexican national security policy, Calderon embraced aspects of the multilateral Merida Initiative that were outright rejected by his predecessors. In addition to increased training and logistical assistance from the United States, the Administration placed an increased emphasis on extraditing cartel “kingpins” and disrupting the existing supply chain. As a result, both the United States and Mexico heralded a large number of arrests for illicit transportation, production and distribution activities beginning in 2006. Scherlen (2012) noted an increase in annual drug arrests in Northern Mexican cities until 2010, when internal and external pressure forced Calderon to soften his position in favor of a demand-side solution to the drug problem (Scherlen, 2012). While it is entirely possible that some sort of correlation exists between GDP per capita and drug arrests, available empirical evidence is lacking and it is likely that domestic enforcement policies directly contributed to the increased arrests.

The same can be said about the moderate to strong correlation between unemployment rates, homicides and drug arrests in Ciudad Juarez and Nuevo Laredo. On the surface, some of the data seems to affirm H2, which predicted a positive relationship between unemployment rates, homicides and drug arrests. Consistent with the unemployment rates of other industrialized countries that were affected by the global economic recession, Ciudad Juarez and Nuevo Laredo saw a gradual increase in their respective jobless rates beginning in 2008. As most of Mexico experienced a period of economic decline, President Calderon implemented his aggressive anti-drug policy, which resulted in increased arrests and homicides in select areas controlled by the cartels. While any relationship between unemployment, drug arrests and homicides appears coincidental, further research will need to be conducted to study the effects of Mexican drug policy on arrest rates and homicides in Mexican border cities that form the drug corridor. Due to the political sensitivities surrounding the drug war, the Mexican government has released very little information about security operations in Northern Mexico. Groups like the Transborder Institute based out of the University of San Diego have begun to track drug arrests and drug-related violence through media outlets and open source materials. However, most of these nongovernmental organizations have only recently begun to standardize their data collection methods and the statistics required to initiate a time series study is not currently available.

On the US side of the border, the only statistically significant relationship identified was a moderate positive correlation between GDP per capita and drug arrests in Phoenix. Again, a review of scholarly literature does not point to a strong positive association between GDP per capita and drug arrests (or many other criminal offenses for that matter). Though this correlation does seem to counter H2, it is likely that the relationship was affected by a third variable akin to correlation identified in Nogales. After almost fifteen years of steady growth, a quarterly economic review issued by the University of Arizona reported a massive contraction of the state’s economy beginning in the summer of 2008. Vest (2008) stated that as a percent representation of the aggregate national GDP per capita, Arizona’s major municipal centers slipped from 85% to 78% of the national average as the recession began to affect commerce, home sales, and employment. By 2010, that percentage decreased again, but stabilized at 77% (Vest, 2010). As the recession began affecting the GDP per capita of major cities in Arizona, Phoenix reported the lowest crime rates since 2004. From homicides and violent crime to drug arrests, the Phoenix Police Department credited “hot spotting” and other intelligence-oriented policing strategies for disrupting criminal networks and focusing resources on crime prone areas (Collom & Newton, 2008). Again, while it is possible that there is a connection between GDP per capita and drug arrests, the likelihood is that the association identified in this study is spurious.

Summary, Conclusions, and Recommendations

While a relationship between municipal economic indicators, homicides and drug arrests could not be determined from this study, Sutherland’s Rational Choice Theory was not disproved either. The Mexican drug war is a complex multi-dimensional issue influenced by a number of different sociocultural factors. While economic disparity is an important part of the conflict, the confluence of many internal and external political, social, and historical issues make this matter a particularly difficult one to solve.

Though a relationship between the two phenomena could not be established, what this study did do well is highlight some of the limitations regarding the use of official sources of data. Due to the size and scope of Mexico’s informal economy, official unemployment statistics and GDP per capita did not accurately represent the urban economy of the sampled cities in Northern Mexico. As an alternative to using unemployment and GDP data, Burdett et al. (2004) suggested comparing the percent of the population eligible for government benefits, like social security. This method would be especially effective in Mexico, where only those who participate in the formal economy and are eligible for government benefits are counted in the official unemployment rate. Even more important is the number of individuals not eligible for government benefits, which could provide a clearer picture of the exact size of the informal economy in each city.

Municipal homicide totals and drug arrests were greatly affected by the policies and actions of the Calderon Administration as he pursued the Big Four cartels in Northern Mexico. Given the degree to which government policy impacted both homicides and drug arrests, an alternative source of information is needed in order to determine if the Rational Choice Model had any impact on the surge of drug activity. While Acharya alluded to this method in her case study of Nuevo Leon, a better approach to gathering information about the participatory behaviors of city dwellers in drug-related activities could be a qualitative survey. The survey could address recent and past involvement in all aspects of drug activity, ranging from production to sales and distribution. In addition, a survey could give the researcher an opportunity to specifically address whether adverse economic conditions ever prompted the participant to consider or actually engage in activities that support drug trafficking organizations. While there are obvious technical issues regarding the cost of producing the survey, identifying a sample population, and actually administering the survey, this method has the potential of yielding valuable data in support of the Rational Choice Theory. Further, it could provide social scientists and policymakers insight into why otherwise productive members of society would actively support organized crime groups.

As a policy matter, the issue of drug violence along the US-Mexico Border is very important. Aside from the obvious human toll exacted by the drug war, the United States and various nongovernmental organizations have contributed billions of dollars with the hope of revitalizing different sectors of the Mexican economy. The US Agency for International Development alone contributed over $400 million for development projects in different parts of Mexico in 2011. That does not include the $20 billion of private remittances granted by US-based companies and NGO’s annually (US Overseas Loans and Grants, 2013). If the Merida Initiative is the proverbially “stick” being utilized to train Mexico’s military to fight the drug cartels on their own turf, aid packages and remittances represent the expensive “carrot” that will hopefully employ those who would turn to the DTO’s for work. While this sounds like an effective strategy, what if the findings produced by this study are accurate and there is absolutely no correlation between economic conditions and drug activity? The billions of dollars invested in the Mexican economy with the hope of putting people to work and depriving traffickers of a willing workforce will have been squandered. While time and resources have been invested in controlling the production and distribution of narcotics, additional funds could have been set aside for potential solutions that address the demand for illicit substances. More importantly, during a time of domestic economic hardship, would not that money have been better spent on economic initiatives that put Americans to work? While this study does not provide these answers, it does reaffirm the need to study a problem prior to contributing precious resources.

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Appendix

Table 5: Arrests for drug-related offenses by municipality in Mexico from 2000 – 2010.

			2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Ciudad Juarez			3029	2727	2717	2886	2587	2851	2824	2595	936	*	*
Monterrey			2226	2525	2713	2642	2109	1709	1448	1529	1438	*	*
Nogales			610	625	447	460	608	592	615	743	862	*	*
Nuevo Laredo			491	437	314	310	1190	1458	1305	1398	1381	*	*

Statistics gathered from the Instituto Nacional de Estadística y Geografía (INEGI) at www.inegi.org.mx. Per the INEGI, drug arrests are listed under the blanket arrest “en material de narcoticos,” which includes a large number of different drugs offenses, including but not limited to illicit drug consumption, possession with intent to distribute narcotics, and drug smuggling activities. * = Statistics unavailable for the listed year.

Table 6: Municipal homicides of sampled Mexican cities from 2000 – 2010.

			2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Ciudad Juarez			385	371	399	318	338	320	315	401	1372	2000	2545
Monterrey			497	568	533	524	510	619	658	784	732	704	1287
Nogales			31	52	32	42	43	60	58	78	138	174	226
Nuevo Laredo			115	71	79*	106	97*	154*	199	116	119	119	155

Municipal statistics gathered from the Instituto Nacional de Estadística y Geografía (INEGI) at www.inegi.org.mx. State-level statistics were gathered from the Transborder Institute at http://justiceinmexico.org/data-portal/2480-2/. For the missing years, a percent representation was derived by dividing the municipal homicides by state level homicides. The percentages were averaged together to calculate an average percent representation. The average percent was multiplied by state level statistics for the years where data is unavailable to generate a predictive statistic.  * = Homicide statistics were unavailable for the listed year.

Formula
City 2000 / State 2000 + City 2001 / State 2001 + City 2003 / State 2003 + City 2006 / State 2006 + City 2007 / State 2007 + City 2008 / State 2008 + City 2009 / State 2009 + City 2010 / State 2010 = Average Percent Representation (APR)

APR x State Homicides = Number of Homicides for No Year Data

Table 7: Gross Domestic Product (GDP) per capita of sampled Mexican cities from 2000 – 2010.

			2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Ciudad Juarez			1330	1294	1434	1560	1656	1796	2250	2174	2507	2782	715*
Monterrey			1541	1704	2118	2207	3356	2955	3476	3078	3516	3253	3272
Nogales			1464	1455	1562	1706	1864	1712	1963	2820	3376	2916	3077
Nuevo Laredo			***	4524	3656	4460	5349	3514	4177	5096	6248	8832	6581

Statistics gathered from the Instituto Nacional de Estadística y Geografía (INEGI) at www.inegi.org.mx. GDP is measured in Mexican pesos. * = Based on partial-year data; *** = Data unavailable for that year.

Table 8: Unemployment rates of sampled Mexican cities from 2000 – 2010.

			2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Ciudad Juarez			1.7	2.6	4.2	3.8	1.8	2.1	2.7	2.6	4.2	7.2	5.4
Monterrey			2.9	2.9	4.4	4.3	5.7	4.7	4.7	4.5	4.3	7.2	4.3
Nogales			2.7	3.9	6.7	5.8	5.4	3.4	2.9	2.9	4	5.6	5.6
Nuevo Laredo			2.7	3.1	3.6	3.2	4.7	4.1	4	4.1	4.5	6.4	4.8

Statistics gathered from the Instituto Nacional de Estadística y Geografía (INEGI) at www.inegi.org.mx. This data represents the statistical average over a twelve month reporting period.

Table 9: Annual drug arrest totals of sampled US cities from 2000 – 2010.

			2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Phoenix	Sale & Distribution		2,100	2,053	2,421	2,623	1,929	1,218	1,678	1,399	1,258	1,281	1,242
	Possession		3,736	3,453	3,268	3,615	5,147	5,763	5,053	5,546	5,113	5,266	4,467
	Total		5,836	5,506	5,689	6,238	7,076	6,981	6,731	6,945	6,371	6,547	5,709
Tucson	Sale & Distribution		576	648	678	674	803	826	708	853	869	973	753
	Possession		5,597	5,599	5,730	6,132	6,555	7,018	6,365	5,970	5,681	6,474	5,774
	Total		6,173	6,247	6,408	6,806	7,358	7,844	7,073	6,823	6,550	7,447	6,527
El Paso	Sale & Distribution		149	74	17	94	28	109	183	109	180	195	263
	Possession		3,355	3,862	3,398	3,516	3,364	2,930	2,685	3,902	4,561	4,473	4,028
	Total		3,504	3,936	3,415	3,610	3,392	3,039	2,868	4,011	4,741	4,668	4,291
Laredo	Sale & Distribution		*	4	2	*	*	*	*	*	3	12	13
	Possession		797	769	742	752	871	833	1,002	1,031	864	1,029	928
	Total		797	773	744	752	871	833	1,002	1,031	867	1,041	941

Data obtained from the Bureau of Justice Statistics. Statistics gathered through the Arrest Analysis Tool found at http://bjs.ojp.usdoj.gov/index.cfm?ty=datool&surl=/arrests/index.cfm#. This data represents total arrest tallies recorded by major municipal police departments in each city. Statistics are voluntarily provided by each department annually. The statistics have been subdivided into two categories: arrests for the sale or manufacturing of illicit substances and arrests for the possession substances for personal use. Drug violation totals in each of the four cities have been captured. * = Denotes a lack of available statistics in that given year.

Table 10: Municipal homicides of sampled US cities from 2000 – 2010.

		2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Phoenix		152	209	177	241	202	220	235	212	167	122	116
Tucson		60	42	47	47	55	55	51	49	65	35	51
El Paso (SD)		5	4	3	0	6	4	8	3	2	4	2
El Paso (PD)		20	20	14	21	11	14	13	17	17	12	5
El Paso (Total)		25	24	17	21	17	18	21	20	19	16	7
Laredo		10	8	7	29	15	18	22	10	10	17	9

Data obtained from the Federal Bureau of Investigation, Uniform Crime Report. Statistics gathered through the UCR Table Building tool found at http://www.ucrdatatool.gov/. This data represents homicide totals recorded by major municipal police departments in each city. Statistics are voluntarily provided by each department annually. * = Individual homicide statistics are collected and distributed to the FBI by the El Paso County Sheriff's Department and the El Paso City Police Department. Statistics are listed separately (SD for Sheriff's Department and PD for the City Police Department) and together (SD and PD added to represent the total number of homicides).

Table 11: Gross Domestic Product (GDP) per capita of sampled US cities from 2000 – 2010.

		2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Phoenix		41050	41107	41264	42494	43006	44658	46290	46112	44499	40182	40375
Tucson		29240	29222	28326	29508	29589	30605	31365	31975	31442	28870	28387
El Paso		29500	29535	30272	30062	29846	30029	30854	30505	30323	28984	29759
Laredo		22050	22119	22559	22558	22266	22521	22295	22188	22920	20991	21440

Data obtained from the US Department of Commerce, Bureau of Economic Analysis. Statistics gathered through the BEA's Interactive Data Service tool at http://www.bea.gov/itable/. This data represents the statistical average over a twelve month reporting period. GDP is measured in US dollars.

Table 12: Unemployment rates of sampled US cities from 2000 – 2010.

		2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Phoenix		3.3	4.2	5.6	5.2	4.5	4.1	3.6	3.2	5.3	9.3	9.8
Tucson		3.7	4.3	5.7	5.3	4.6	4.5	3.9	3.6	5.6	9	9.4
El Paso		6.8	7.3	8.2	8.8	7.6	7	6.7	5.9	6.3	8.8	9.8
Laredo		6.1	6.6	7.3	7.4	6.7	6	5.4	4.7	5.4	8.4	8.9

Data obtained from the US Department of Labor, Bureau of Labor Statistics. Statistics gathered through the BLS Tools application at http://data.bls.gov/cgi-bin/surveymost?la. This data represents the statistical average over a twelve month reporting period.