Metal Mining Exploitation and Violence as a Cause of Forced Displacement

In Mexico, the Mining Law, linked to the 1992 constitutional reform of article 27, promoted an “(…) economic orientation in favor of large mining companies, both national and transnational, at the expense of the fundamental rights of Mexicans — mainly indigenous communities and peasant populations — as well as ecological rights” (^{Cardenas, 2013, p 64}). Thus, the promulgation of this Law facilitated mining exploitation and exploration.

The Anuario Estadístico de la Minería Mexicana (Statistics Yearbook of Mexican Mining; SGM, 2021, p. 6) issued by the Board of Economy states that Mexico is among the top producers of fifteen minerals worldwide, being the world’s top producer of silver. This sector (including the steel industry) accounts for 8.3% of the gross domestic industrial product and 2.3% of the national GDP. Mexico is also considered one of the top destinations in Latin America for mining exploration, and some 179 Mexican companies have foreign investments from Canada, the United States, China, Japan, the United Kingdom, Australia, South Korea, India, Spain, and France. According to the Yearbook, “(…) there are 1190 mining projects spread across 26 states of the country” (p. 29). In this context, 77% of the concessions in the Mexican territory have been granted by the federal government to Canadian companies, even in biosphere reserve areas (^{Bastidas et al., 2018}; López, 2018; ^{Cruz, 2017}; ^{Muñoz et al., 2020}; Secretaría de Economía, 2021).

This exploitation, which is associated with the alienation of these resources by entrepreneurs, either by themselves or in association with federal and State governments, has occurred coupled with characteristic forms of violence. In this respect, ^{Saade (2013, p. 36)} points out that the conflicts associated with mining operations are initially related to their high environmental impact, given their intensive use of water and their direct impact on the livelihoods of the local communities, especially those living in poverty.

Affectations to the local populations in these regions include the rights to prior informed consultation, rights to self-determination, or a-posteriori affectations to the living conditions. These include, for example, poor relocation, damage to heritage, disputes over land prices, establishment of mining companies in areas larger than those granted, or episodes of irreversible environmental affectation, among others (^{Saade, 2013, p. 38}; ^{OCMAL, 2022 [cartography]}).

These affections give rise to internal displacement because communities are no longer able to cope with everyday life. For ^{Oslender (2004a, p. 68)}, this is part of a “geography of terror,” which facilitates the establishment of extractive projects through land clearing and the co-optation of communities, not only by corporations but with the assistance of state government officials. The appropriation of these resources for mineral production processes uses high-impact strategies to disrupt local land-tenure conditions, everyday life forms, and social networks. In areas where mining takes place, the local communities have frequently suffered various acts of violence due to territorial disputes, public complaints, and resistance to accepting the imposed mining projects.

Given the complex contexts just described, it is worth noting that this article does not address all the potential causes of displacement, nor its particular characteristics in each area of Mexico. The objective of the present work is to observe at a national scale, assisted by regionalization, the potential correlations between zones showing different metal mining extractive phases and some social and demographic processes associated with FID. The following section outlines the methodological process carried out to establish the existence of these relationships and their scope. In this way, possible interpretations can be defined on the intensity of FID and its relationship to the processes associated with metal mining outlined above.

Identification of Metal Mining Activities

This analysis first regionalized the characteristics of metal mining activities in each municipality of the country (see Map 1), according to the following categories:

Source: Own elaboration based on data from the Board of Economy for 2015-2019, Mexican Geological Survey (^{Servicio Geológico Mexicano, 2021a}, ^2021b), and ^{Zaremberg et al. (2019)}.

Figure 1 Regionalization of mining activities by municipality. 

The mining potential was determined based on information from the Mineralized Regions of the Mexican Geological Survey (^{Servicio Geológico Mexicano, 2021b}). The information on mining concessions was obtained from the review of the Mining Cadastre of the Board of Economy, selecting exclusively those mining concessions in force in the study period that reported the target metals of this study as relevant elements. Mining projects were obtained from the Mexican Geological Survey database corresponding to the major mining projects in Mexico (^{Servicio Geológico Mexicano, 2021a}), again selecting only those projects reporting exploration or extraction of the target metals. Information on the incidence of mining conflicts was obtained from the database published by the Conversando con Goliath project: Participation, mobilization, and repression around neo-extractive versus environmental conflicts (^{Zaremberg et al., 2019}), reviewing exclusively those that occurred during the study period and related to the exploration and extraction of the target metals.

Displaced Population due to Criminal Insecurity and Violence

In Mexico, internal migration has been documented by the Instituto Nacional de Geografía y Estadística (National Institute of Statistics and Geography; INEGI) through population and housing censuses since 1960, although at that time the Census only recorded whether the person interviewed had previously resided in another State of the country. It was until 2000 that the Census started recording the State and municipality where the interviewee lived five years earlier. For the first time, the 2020 Census recorded in the basic questionnaire not only the State and municipality of the previous residence but also the cause of migration. Data on migration due to criminal insecurity or violence were obtained from this database. Census microdata for migration and its causes are broken down by municipality, so this was also the spatial unit used for the analysis in the present study. The total population of each municipality in 2015 was determined based on data from INEGI’s 2015 Inter-Census Survey.

Incidence of Common Criminal Offenses

The incidence of common offenses was characterized by the available open data corresponding to common criminal offenses reported by the Executive Secretariat of the National Public Security System. Data on the incidence of common criminal offenses do not consider the so-called “black figures”, that is, unreported crimes, since they relate to the alleged occurrence of offenses as recorded in preliminary (pretrial) inquiries either ongoing or in investigation files, reported by State Attorney General's Offices. The following high-impact criminal offenses associated with violent contexts were selected from the database for the present study:

The total number of criminal offenses of each type was added up for each municipality.

Spearman’s Correlation Matrix

The incidence of crime was analyzed to identify specifically which types of crimes are mostly related to migration for criminal insecurity and violence in each mining region. The correlation matrix was constructed with the municipal database on migration for criminal insecurity (standardized for the total population of each municipality), as well as the sum of the incidence of the crimes selected for each municipality in the study period (standardized for the total population of each municipality). Municipalities were grouped according to the regionalization of mining activities (creating up to five groups of municipalities: no mining, mining potential, mining concessions, mining projects, mining conflicts).

To construct the correlation matrix for each mining region, it was necessary to have a significant sample of municipalities in each region. Since, in some cases, a given State had only one or two municipalities within a mining region, it was determined that the State level could not be the unit of study. Therefore, States were grouped into geographic regions (North, Center, and South), according to the criteria by ^{Diaz and Romo (2019, p. 75)}.

Thus, for each mining region (no mining, mining potential, mining concessions, mining projects, and mining conflicts) in each geographic region of the country (North, Center, and South), we calculated the correlations between the incidence of common criminal offenses and migration for criminal insecurity and violence. As the data were non-parametric, we calculated Spearman’s correlation coefficient.

Linking Migration and Mining

This study sought to determine the correlation between forced internal displacement and mining activities, considering the phases of this extractive process at the national level. Table 3 shows the emigration rate according to the proposed mining regionalization. In each region (column), low and high emigration rates due to violence are marked in green and red, respectively.

At the national level, we observed a difference between regions with no mining activities and regions with mining activities and conflicts. In the sample of 2418 municipalities considered, 40.3% were found to include mining activities (974). Of these, 64.1% (624) have mining concessions; 24.2% (236), mining projects; and 11.7% (114), mining conflicts.

A significant incidence of migration due to violence is observed in areas with mining conflicts (130.9 migrants per 100 thousand inhabitants) and in zones with mining projects (122 migrants per 100 thousand inhabitants) (Table 3 and Figure 2). By State, the cases of Guerrero and Michoacan stand out for displaying migration dynamics associated with violence in the mining project and mining conflict phases, respectively. The cases of Mexico City and Quintana Roo are worth noting since they show high migration rates due to violence in areas with no mining activities. In both cases, migration is likely correlated with variables not considered in this study.

The map in Figure 2 uses the following criteria for symbology. Mining activity axis: 1) no mining and mining potential, 2) mining concessions, 3) mining projects, 4) mining conflicts. The migration for insecurity and violence axis uses country averages of migration rates for insecurity and violence in Table 3, corresponding to 1) no mining, 2) mining concessions, 3) mining projects, and 4) mining conflicts.

Source: Own elaboration based on estimates of migration rates for violence and insecurity, 2015-2019.

Figure 2 Migration rate by mining region. 

The statistical significance of the difference in rates between mining regions was explored using an ANOVA. This analysis determined that, given the p-value (<0.0001) associated with the F-statistic (7.502) with a 95% confidence interval, the information provided by the explanatory variables is significantly better than the one provided by the mean alone. The post-hoc REGWQ test grouped together the mining concession, mining project, and mining conflict regions and determined a statistically significant difference between this group and the one formed by the no mining and mining potential regions, with a 95% confidence interval.

In the Central Region, two States with important mining activities stand out (Estado de Mexico and Morelos). We observed high emigration rates in the mining conflict phase in Estado de México (342.13 migrants) and medium-high rates in Morelos, in territories with both mining potential and ongoing mining projects and conflicts.

In the Nort Region of Mexico, the cases of Chihuahua and Sinaloa are worth highlighting, which have a high emigration rate in territories with mining conflicts. Tamaulipas shows medium-high and high migration rates in territories, irrespective of the mining phase; therefore, it is reasonable to assume that these rates are not necessarily related to mining.

The scenario in the South Region is markedly different from the situation in the North and Center Regions. Very high emigration rates due to violence are observed in territories with mining activities, especially where there are mining conflicts and mining projects, in Michoacán (this State is located in the north-central part of the country but, according to the proposed regionalization methodology, it was included in the South Region) and Guerrero. However, it should be noted that these States have medium-high and high migration rates in municipalities with other categories of mining activity.

Relationship between the Occurrence of Criminal Offenses and Mining Activities

For Spearman’s correlations (r) and their coefficient of determination by category, the following criteria were applied (p ≤ 0.05): r ≥ 0.5, high correlation; 0.2 ≤ r < 0.5, intermediate correlation; r < 0.2, low correlation. Significant differences were observed between the three regions of Mexico analyzed, as described below.

From Tables 4, 5, and 6, in the North Region, the highest statistically significant correlation is observed between kidnapping and regions with mining potential. In the Center Region, the same finding is observed between murder with a firearm and zones with mining projects. The South Region shows a different pattern, as zones with mining projects and mining conflicts show very high statistically significant correlations with much broader types of crimes. Thus, the South Region shows a total of 57 moderate correlations and 16 high correlations with high-impact crimes, mainly in regions with mining conflicts.

In general, the mining conflict region is highly correlated with several crimes, particularly theft, intentional injuries, kidnapping, and homicide of all sorts. On the other hand, robbery of four-wheel motor vehicles and extortion stand out with a moderate correlation.

It was important to identify municipalities with mining potential that has not yet been exploited, to contrast the data versus those of municipalities with mining activities. Municipalities with no mining or with mining potential are usually territories with valleys and plains, more suitable for agriculture and urban development, which generally concentrate the largest populations, unlike areas with a more rugged landscape, less populated, where mining potential and mining activities are often found. Therefore, the best control group to contrast the data are municipalities that have mining potential but do not yet have mining concessions, projects, or conflicts.

As part of the main results, the statistical model shows that the regions with cases in the mining project phase are related to violent environments, since these show high correlations with crimes such as homicide, kidnapping, robbery, and intentional injuries. Regions with mining conflicts are highly correlated with homicide, robbery, intentional injuries, drug dealing, and kidnapping.

This evidence indicates that, as mining activities progress in a given region, there is an increasing trend in migration due to violence and insecurity. At the national level, the lowest migration rate for insecurity and violence is observed in regions with mining potential but with no mining concessions or projects, with 61 migrants per 100 thousand inhabitants; this figure is lower than the country average of 91 migrants per 100 thousand inhabitants. The rate increases as mining activities progress: 103 migrants per 100 thousand inhabitants in regions with mining concessions, 122 in regions with mining projects, and 131 in regions with mining conflicts. On average, forced displacement more than doubles from mining-free regions to regions with intense mining activity.

The most extreme cases of migration due to insecurity and violence in regions with intense mining activity are Guerrero, Michoacán, and Sinaloa. Guerrero is the most complicated case: while municipalities with no mining (mining potential only) have a rate of 174 migrants per 100 thousand inhabitants due to insecurity (the lowest in the State), municipalities with mining projects show the highest migration rate associated with insecurity in the country: 728 migrants per 100 thousand inhabitants. Municipalities with mining conflicts in Michoacán, with 613 migrants per 100 thousand inhabitants, have the second-highest country average of insecurity migration presumably linked to mining. Sinaloa has an average rate of 429 migrants per 100 thousand inhabitants for insecurity in municipalities with mining conflicts, a figure ten-fold higher than the average in municipalities with no mining in the State (42 migrants per 100 thousand inhabitants). The cases of Chihuahua and Estado de Mexico are practically identical, with 340 and 342 migrants per 100 thousand inhabitants, respectively, for insecurity in municipalities with mining conflicts, compared to only 37 in municipalities with no mining.