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

By David J. Masucci
2013, Vol. 5 No. 09 | pg. 3/4 |

Results & Discussion

Tables 1 through 4 detail the statistical output of the bivariate regression model for Mexican and US cities. Tables 1 and 3 capture descriptive statistics describing the relationship between GDP per capita, homicides and drug arrests from 2000 to 2010. Tables 2 and 4 provide descriptive statistics detailing the relationship between unemployment rates, homicides and drug arrests during the same period. The author specifically chose to capture and record the Adjusted R Square correlation coefficient, which is a more conservative estimate of the relationship between the dependent and independent variables; the T-Statistic to compare the ratio of the Adjusted R Square and the Standard Error; the P-Value at a 95% confidence interval to determine if the output was produced by chance; and the Standard Error to capture the degree of statistical variance.

Mexican Descriptive Statistics – GDP Per Capita: The results of the regression analysis varied. When analyzing the relationship between municipal GDP per capita and homicides, the correlation coefficient ranged from -.13 to .68. In Ciudad Juarez and Nogales, there was a moderate to strong positive relationship between the independent and dependent variable comfortably within the 95% confidence interval. Monterrey represented a weak statistical relationship with a higher P-Value outside of the confidence interval. Albeit weak and not statistically significant, Nuevo Laredo was the only city to have a negative correlation between GDP per capita and homicides with a P-Value outside the confidence interval.

Table 1: Effects across sampled Mexican cities of GDP per capita and municipal homicides & drug arrests from 2000 - 2010.

     

Adjusted R Square

T-Stat

P-Value (< .05)

Standard Error

Ciudad Juarez

       
 

Homicides

0.544476694

3.428920563

0.008969112

0.201404296

 

Drug Arrests

0.35986242

-2.344635039

0.051492456

0.196298428

Monterrey

       
 

Homicides

0.141138748

1.62583031

0.138430435

0.956333002

 

Drug Arrests

0.579699783

-3.469007567

0.010419856

0.340978899

Nogales

         
 

Homicides

0.684643319

4.765514209

0.001021756

1.965061441

 

Drug Arrests

0.661769322

4.080747074

0.00468527

1.070454034

Nuevo Laredo

       
 

Homicides

-0.13123078

-0.433532507

0.67978108

8.767413742

 

Drug Arrests

-0.030386475

0.890824017

0.407323506

0.669702762

Comparisons between GDP per capita and drug arrests was a little more consistent across the sampled cities. Monterrey and Nogales possessed a moderate to strong positive relationship between the independent and dependent variables with an acceptable level of statistical probability. Ciudad Juarez represented a weak to moderate positive statistical relationship with a P-Value just inside of the confidence interval. Again, Nuevo Laredo was the only city to have a negative correlation between GDP per capita and homicides with a P-Value well outside the confidence interval (See Table 1 for statistics).

Mexican Descriptive Statistics – Unemployment Rates: Like the relationship between GDP and drug indicators, the results of this regression analysis varied as well. When analyzing the relationship between municipal unemployment rates and homicides, the only city to have a moderate to strong positive relationship between the two variables was Ciudad Juarez. Monterrey, Nogales, and Nuevo Laredo all possessed relatively weak negative correlations outside of the confidence interval.

Table 2: Effects across sampled Mexican cities of unemployment rates and municipal homicides & drug arrests from 2000 - 2010.

     

Adjusted R Square

T-Stat

P-Value (< .05)

Standard Error

Ciudad Juarez

       
 

Homicides

0.629461281

4.241194756

0.002170463

0.000407756

 

Drug Arrests

0.161580691

-1.594291371

0.154900041

0.000503877

Monterrey

       
 

Homicides

-0.102671624

0.262455809

0.798878843

0.001747341

 

Drug Arrests

-0.011692969

-0.952647581

0.372497005

0.00060256

Nogales

         
 

Homicides

-0.050294589

0.721899068

0.488686839

0.007018901

 

Drug Arrests

0.255086742

-1.933780095

0.094395555

0.003449755

Nuevo Laredo

       
 

Homicides

-0.047665658

0.738261384

0.479163163

0.008965468

 

Drug Arrests

0.612666802

3.695138966

0.007704203

0.000288154

When analyzing the relationship between unemployment rates and drug arrests in the sampled cities, the correlation coefficient ranged from -.01 to .61. In general, the relationship between the independent and dependent variables was weak and not statistically significant. However, Nuevo Laredo did possess a moderate to strong positive correlation well within the confidence interval (See Table 2 for statistics).

US Descriptive Statistics – GDP Per Capita: The correlation between GDP per capita and homicides within the sampled US cities was not statistically significant. Tucson had a weak positive relationship outside of the 95% confidence interval. El Paso and Laredo both had very weak negative relationships outside of the confidence interval. Phoenix was the only city to have a weak to moderate positive correlation with an acceptable P-Value. The relationship between GDP per capita and drug arrests was a little more promising. While Tucson, El Paso and Laredo possessed statistically insignificant correlations with high P-Values, Phoenix did have a moderate positive correlation within the confidence interval (See Table 3 for statistics).

Table 3: Effects across sampled US cities of GDP per capita and municipal homicides & drug arrests from 2000 - 2010.

             
     

Adjusted R Square

T-Stat

P-Value (< .05)

Standard Error

Phoenix

         
 

Homicides

0.380319137

2.671579822

0.025556215

12.97783895

 

Drug Arrests

0.437617519

2.963358355

0.015872699

0.929542853

Tucson

         
 

Homicides

0.07757121

1.35681437

0.207891911

46.9854012

 

Drug Arrests

-0.034829979

0.766905574

0.814507936

0.436375048

El Paso

         
 

Homicides

-0.099513979

0.308103175

0.765015353

36.13363105

 

Drug Arrests

0.093131692

-1.423713237

0.188260502

0.254950273

Laredo

         
 

Homicides

-0.101800105

0.275783417

0.788942071

26.22775372

 

Drug Arrests

0.22336426

-1.968768449

0.080501433

1.365757051

US Descriptive Statistics – Unemployment Rates: The results of this regression analysis varied for US cities as well. In regards to unemployment and aggregate homicide statistics, Phoenix and El Paso both possessed a weak to moderate positive correlation with a low probability that the results were influenced by a third variable. On the other hand, Tucson and Laredo had very weak correlation coefficients with a P-Value outside of the confidence interval. With correlation coefficients ranging from -.02 to -.10 with P-Values as high as -.85, the relationship between unemployment and drug arrests was not found to be statistically significant across the four sampled cities (See Table 4 for statistics).

Table 4: Effects across sampled US cities of unemployment rates and municipal homicides & drug arrests from 2000 - 2010.

     

Adjusted R Square

T-Stat

P-Value (< .05)

Standard Error

Phoenix

         
 

Homicides

0.50914101

-3.372306262

0.008226809

1.581220162

 

Drug Arrests

-0.026934339

2.287100805

-0.858906818

0.412682922

Tucson

         
 

Homicides

0.056170372

1.946327032

-1.262985565

0.238332688

 

Drug Arrests

-0.103320966

2.104359558

0.252082732

0.806639447

El Paso

         
 

Homicides

0.371013407

0.956498609

0.02751535

2.626516717

 

Drug Arrests

-0.062416638

1.243114352

0.642264118

0.536716579

Laredo

         
 

Homicides

-0.10942067

1.363668125

0.117104385

0.909348985

 

Drug Arrests

-0.103900739

1.360271424

-0.242457204

0.813860846

Analysis: Though the data points to a weak or nonexistent relationship between selected economic indicators, homicides and drug arrests in both Mexico and the United States, a few moderate to strong correlations were identified that warrant further analysis. In Mexico, the regression model found a positive relationship between GDP per capita, municipal homicides and drug arrests in both Ciudad Juarez and Nogales. The data seemingly disproves H1, which predicted a decrease in homicides and drug arrests as GDP per capita increased in the sampled cities. However, it is worth noting that both Ciudad Juarez and Nogales recorded a precipitous increase in homicides starting in 2008. Despite steady year-to-year increases in GDP per capita in Ciudad Juarez and Nogales from 2000 to 2010, murders held steady or decreased from 2000 to 2007 (see Tables 6 & 7 for data). This dramatic increase in homicides closely coincides with Mexican President Felipe Calderon’s deployment of troops to Northern Mexican states as part of “Operation Michoacana,” coined after both the Mexican state and the organized family. Both residents and groups have directly attributed the increase in violence to confrontations between government troops and cartel enforcers, who routinely engage in running gun battles throughout the urban centers of cities like Juarez and Nogales (Wilkinson, 2009). When the data sets are analyzed excluding the 2008, 2009 and 2010 statistics, the results were drastically different for Ciudad Juarez. The Adjusted R Square was -.073 with a P-Value slightly outside of the 95% confidence interval as opposed to .544 cited in Table 1. Unfortunately, excluding the outlier statistics still does not explain the relationship between GDP per capita and homicides in Nogales; the Adjusted R Square is still .585 and slightly outside of the confidence interval.

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