Violence and migration: evidence from Mexico’s drug war

3.1 Homicide data

To measure violence from the drug war, we use data on intentional homicides from municipal death records, compiled and made publicly available by the National Statistical and Geographical Institute (Instituto Nacional de Estadística y Geografía, or INEGI).^{8 9} From this we construct an annual homicide rate per 100,000 inhabitants for origin region j using annual population data from the National Council on Population (CONAPO).¹⁰ For comparison we also construct an 195 aggregated measure of violence over the drug war period under study. We calculate 196 total homicides from January 2007 to December 2010 and convert this to a homicide 197 per 100,000 inhabitants as of 2005.

The table also shows the growing disparity across areas in levels of violence following the start of the drug war. From 2005 to 2010 the standard deviation in homicides across municipalities increases more than 100%, while at the state level it increases more than 600%. The concentration of violence geographically also is evident from municipal and state maps, shown in Fig. 1. To capture violence over the drug war period under study, these maps present homicides over the 2007–2010 period per 100,000 inhabitants. As shown in panel (a) of Fig. 1, the majority of municipalities in Mexico have not exhibited high levels of homicides. Instead, a small number of municipalities exhibit high levels of both. The lower panel (b) shows a similar phenomenon at the state level.¹¹ Figure 1 also shows an absence of regional concentration in terms of where the most violent municipalities or states are located. The most violent areas are not exclusively along the US border, and several are along the Pacific and Gulf Coasts. This highlights that the intensity of the drug war is not wholly determined by proximity to the USA.¹²

3.2 Migration data

Our theoretical framework considers individual migration decisions, but in the analysis that follows we create gross migration rates at the area level. We do this since we do not know individual’s perceptions of security, and use actual violence at an area level as a proxy. We therefore examine the impact of violence in an origin area on the migration decisions of people already in those areas.

The first source of data is the 2010 Mexican Census, as accessed through IPUMS International, maintained by Population Center at the University of Minnesota. The Census is the only data set that is representative at the municipal level and provides information on domestic and international migration. For domestic migration the survey asks the current municipality of residence and the municipality of residence 5 years ago (in the year 2005). We record someone as a national migrant if they are older than five, lived in Mexico in 2005, and live in a different municipality in 2010 than in 2005.¹³ Movements across or within states are counted equally, as the goal is to capture the sheer number of people relocating during the drug war. The Census measure creates a 5-year domestic migration rate from 2005 to 2010.

For international migration the Census includes a module that asks households if a member has moved abroad in the past 5 years and, if so, the exact year of their departure. We use this information to count all individuals over the age of five, who lived in Mexico in 2005, and are reported to have moved between 2005 and 2010. It is important to note that this measure likely is a lower bound on the true incidence of international migration, as individuals who moved abroad with their entire household are not included.

The benefit of the Census is that it is representative at the municipal level, giving us a finer degree of geographic variation. The downside, however, is that individuals are not asked about the timing of their domestic relocation; hence, annual measures of municipal-level domestic migration cannot be constructed. As we detail in the next section, time variation is important for the identification assumptions of our empirical model. We therefore turn to a second data source, the National Survey of Occupation and Employment (referred to by its Spanish acronym ENOE).¹⁴ The ENOE is a rotating panel that surveys households for five consecutive quarters, is representative at the state level (not the municipal level), and keeps track of all members listed in the initial survey.¹⁵ To create annual flows, we restrict attention to households who enter the sample in the first quarter of a given year, and record an individual as a national migrant if they are reported as (a) moving to another state or (b) moving within or to another state (anywhere else in Mexico) in any of the subsequent four quarters. The former is more likely to capture costlier internal migration, while the latter also includes less costly migration in the form of moving, for example, to another neighborhood in the same city. We count individuals as international migrants if they are reported as moving abroad in any of the subsequent four quarters. The ENOE therefore captures short-term migration, as it measures the number of individuals in a given state who move between the first quarter of a given year and the first quarter of the next year. Like the Mexican Census, the ENOE also may undercount the number of domestic and international migrants, since entire households that move cannot be identified.

We calculate gross migration rates by taking the total number of individuals who moved either domestically or abroad and dividing by the population in a given area at the beginning of the period. All of the migration totals are calculated using population weights. We calculate a gross rather than a net migration rate as we do know where people move to domestically in the ENOE—our main data source for migration—making it impossible to calculate inflows.¹⁶

Second, the table shows the decline in international migration rates from 2005 to 2010. This is strongly seen in the state-level rates, which fall from 0.22% in 2005 to 0.09% in 2010. This decline in international migration, specifically to the USA, is thought to be the result of reduced job opportunities in the USA, improved job opportunities in Mexico and increased border enforcement (Passel et al. 2012). This suggests there were multiple forces acting to reduce international migration during the drug war period.

4.1 Instrumental variable: rationale and relevance

The challenge to identifying β ₂ stems from the existence of unobserved characteristics that may jointly determine migration decisions and homicides. Ex ante, it is unclear what bias these characteristics may exert. On one side, factors such as institutions may put upward bias on the coefficient, if areas with weak institutions experience a larger increase in violence due to less effective police and judicial services and greater out-migration if employers and job opportunities locate elsewhere. On the other side, factors such as the effectiveness of drug trafficking organizations may put downward bias on the coefficient if these organizations create more job opportunities—reducing the incentives to migrate—but also increase levels of violence.

To control for unobserved heterogeneity, we instrument for area homicides in period t using kilometers of federal highways interacted with quantity of cocaine seized by Colombian authorities in the same period. In this section, we outline the rationale behind this interacted variable and separately discuss each part of the instrument.

We begin with a discussion regarding the use of highways. First, the beginning of the drug war coincides with the federal government crackdown on drug trafficking organizations, which began in December of 2006. This is apparent by looking at the summary statistics in Table 1, but also has been documented by Dell (2015), who examined the impact of government crackdowns on drug trafficking. She finds that violence increased most sharply in areas where the government directly confronted drug trafficking organizations. Other potential explanations for the violence, including increased political competition that changed implicit agreements between the government and cartels, Mexico taking Colombia’s place as the major distributor of drugs to the USA, and changes in relative prices which increased the production of marijuana and opium within Mexico (Dube et al. 2014), either pre-date the conflict by many years or cannot be timed exactly to late 2006.¹⁸ These factors may work in conjunction with the government crackdown to explain the perpetuation of violence after 2007; however, in isolation they cannot explain the timing of the increase.

The government crackdown on the cartels entailed the capture and killing of members of drug trafficking organizations and the seizure of drugs and weapons (Guerrero-Gutiérrez, 2011). In doing so the government weakened previously oligopolistic organizations, leading to turf wars as organizations fought for control of the drug production and distribution networks of their weakened rivals. One argument is that increased competition was most severe over access to distribution networks—and specifically land transport routes—to the USA, the largest drug consumer market in the world and Mexico’s largest trading partner for legal goods (Rios 2012, Dell 2015). Arguably, areas with more access to distribution routes should experience the largest increases in violence.

This leads to the second part of the logic that includes federal highways in the instrument—namely, highways capture distribution networks to the USA. First, the majority of transport of goods and people from Mexico to the USA occurs via highway. The North American Transportation Statistics Database indicates that in 2011, approximately 65% of Mexican exports to the USA were transported via highway, while 82% of Mexican travel to the USA occurred via highways and rail.¹⁹ Second, federal highways are the highest quality road routes, with more stretches of paved roads and roads with four, as opposed to two, lanes. Federal highways, particularly the toll ones, frequently are the fastest and easiest way to travel between areas in Mexico. Third, the federal highway system includes the most transited and valuable routes, many of which run to and cross the US border.²⁰ For example, the federal highway system has seven crossing points into the USA, as compared to only one crossing point into Mexico’s southern neighbor, Guatemala. Finally, the U.S. Department of Justice (2010) estimates that most drugs are smuggled into the USA via land routes using commercial or private vehicles, and these are then transported across the USA using highways.²¹ It therefore is very likely that many drug shipments are transported through Mexico using the same routes as legal goods and the routes used within the USA.

To ensure that more recent factors linked to homicide rates and migration do not determine highway placement, we use federal highway values from 2005—which pre-dates the drug war. We then test the hypothesis that highways are a relevant predictor of homicides during the drug war period, by regressing homicides per 100,000 inhabitants on federal highway kilometers in 2005 for each year in the 2000 to 2010 period. Results for municipalities are shown in panel A of Table 9 in Appendix, while results for states are shown in panel B. The results show a clear relationship between highways and homicides, but only after the drug war begins. At the municipal level, the coefficients on federal highways in years prior to 2008 are insignificant, while at the state level they are insignificant prior to 2006. After the drug war begins, federal highways became a positive and significant predictor of homicides. Furthermore, the strength of this relationship increases over time, with the largest coefficients found in 2010. Hence, areas with more federal highways indeed became more violent over time.

The problem with using highways alone is that the exclusion restriction, which assumes that federal highways do not directly affect migration rates, may not hold. First, highways influence the transportation costs associated with migration to or from certain areas, which will directly affect migration rates. Second, though we use highway values from 2005 that pre-date the Great Recession and the drug war, highways might capture changes in economic activity due to linkages to the USA which could affect both homicides and migration across Mexican locations. For instance, areas that suffered more during the recession may exhibit higher migration rates, but also greater increases in violence, if drug trafficking organizations are better able to recruit members, expand their operations, and challenge rivals in these same areas. In this case federal highways may be directly correlated with our outcome variable, violating the exclusion restriction.

We therefore employ an instrumental variable that exploits time variation to capture the portion of transportation networks not directly related to migration. Specifically, following Castillo et al. (2016) we use changes to the quantity of cocaine being shipped from Colombia to Mexico to capture variation in the value of drug distribution networks to the USA over time. Unlike other drugs that reach the USA from Mexico, such as marijuana or heroin, cocaine is not produced in Mexico. All of the cultivation of coca leaves, the main input into cocaine, and the refinement of these leaves into cocaine occurs in three countries: Peru, Bolivia, and Colombia (UNODC World Drug Report 2010), with Colombia being the dominant producer. According to the 2013 United Nations World Drug Report, Colombia was responsible for 54% of all coca cultivation and 61% of all cocaine production in 2007. These numbers remain high despite a large-scale anti-drug policy enacted by Colombian authorities in the late 1990s.²² Furthermore, cocaine distribution is estimated to make up the majority of revenues generated by Mexican drug trafficking organizations. Specifically, cocaine distribution is estimated to account for 45–68% of all revenues of Mexico drug trafficking organizations (Kilmer et al. 2010). This is more than twice the estimated revenues from the distribution of marijuana, more than eight times the estimated revenues from heroin produced in Mexico, and more than five times the estimated revenues from methamphetamines produced in Mexico (Kilmer et al. 2010).²³

The amount of cocaine reaching Mexican borders partially depends on the efforts of Colombian authorities to combat drug trafficking, and specifically, their efforts to seize cocaine supplies. In recent years Colombia has increased its interdiction efforts, leading to greater external shocks to the supply of cocaine reaching Mexico (see Castillo et al. 2016 for details). These shocks likely alter the use of highways to transport drugs to the USA, and have been documented to increase violence in areas contested by Mexican drug trafficking organizations (Castillo et al. 2016). To measure these external shocks, we use data from the Colombian Defense Ministry on tons of cocaine seized by Colombian authorities in each year.²⁴ These totals are presented in panel A of Figure 3 in Appendix, and show no clear upward or downward trajectory over the drug war period. Thus the cocaine seizures do not appear to be capturing a parallel time trend to that of homicides over the time period considered.

We provide several tests of the assumption that seizures capture changes in the supply of drugs being transported through Mexico but are uncorrelated with events in the USA or Mexico that may impact migration. First, we find no positive correlation between Colombian cocaine seizures and migration to the USA, as measured by the number of new Mexican immigrants captured in the American Community Survey (ACS). As seen in panel B of Figure 3 in Appendix, the relationship between seizures and immigration is negative, even after the drug war begins. This suggests cocaine seizures are not directly associated with a rise in Mexican migration to the USA. We also estimate the relationship between monthly cocaine seizures in Colombia and monthly trade flows of “legal goods” using measures of exports from the IMF Direction of Trade Statistics over the period of January 2004 (first available in DOTS) to April 2012. As shown in panel A of Table 10 in Appendix, there is no significant relationship between cocaine seizures in Colombia and (1) bilateral trade flows between Colombia and Mexico, (2) bilateral trade flows between the USA and Colombia, (3) trade flows between Colombia and the rest of the world, (4) bilateral trade flows between the USA and Mexico, and (5) trade flows between Mexico and the world. This provides evidence that cocaine seizures are not correlated with other trade activity.

We also examine the relationship between cocaine seizures and employment rates, unemployment rates, weekly hours worked for those who are employed, and real GDP.²⁵ We use quarterly variables to increase the time variation used to estimate the correlations and show the results in panel B in Table 10 in Appendix. We find no significant correlation between any of the variables, which suggests that cocaine seizures from Colombia do not affect migration through impacts on labor market activity or legal economic activity, more generally.

Finally, we examine the relationship between Colombian cocaine seizures and seizures of opium and marijuana by Mexican authorities, using data obtained from the Mexican Ministry of Defense (SEDENA). The results of this analysis are shown in panel C in Table 10 in Appendix. We find no significant correlation between Colombian cocaine seizures and seizures of either opium or marijuana. This suggests a reduction in cocaine arriving in Mexico did not coincide with increased seizures of other drug cartel transport.

4.2 The model

Our instrument is the interaction of kilometers of federal highways in the year 2005 with thousands of tons of cocaine seized by Colombian authorities each year. The identification assumption is that shocks to cocaine supplies impact the value of highways for drug transport and violence related to control of these routes, but have no direct effect on migration.

To estimate the model we use panel data on international migration at the municipal level from the Census and panel data on domestic and international migration at the state level from labor force surveys. For comparison to previous studies, we also include results from cross-sectional data on domestic and international migration from the Census. For the Census we have annual data on the 2005 to 2010 period, while for the ENOE we have data from the 2005 to 2011 period. The outcome variable is homicides per 100,000 inhabitants in area j in time period t. This is a function of the instrument, time-invariant area characteristics (M _j ), time period fixed effects (δ _t ), and unobservable area-year factors (ε _j,t ).

Given the small number of time periods in our sample, our first stage relies heavily on cross-sectional variation to identify a relationship between the instrument and homicides. As a result, we do not include state or municipal fixed effects in our regressions. In their place we include observable state and municipal characteristics, detailed shortly. We recognize concerns about the absence of geographic fixed effects, and address these in Section 6.2. At both the state and municipal levels, we include controls for economic activity and wealth. At the state level we use annual real GDP and unemployment rates. At the municipal level, since we do not have annual data or GDP values, we use unemployment rates in 2010 and 2000, average years of education for adults, the percentage of households with running water, and household income per capita in the year 2000. We also account for pre-existing levels of violence by including average homicides per 100,000 inhabitants for the years 2003 and 2004. To account for time-invariant migration costs, we include all non-federal (state) highways as of 2005. A larger highway network should reduce the cost of moving elsewhere in Mexico and abroad, but these costs likely are general to the entire highway network, rather than specific to federal highways.²⁶ Thus our instrument captures changes in the value of the drug distribution routes conditional on the pre-existing local transportation network. Finally, we include population density to account for the possibility that both homicide and migration rates are higher in urban areas.²⁷

6.1 Local average treatment effect

The IV estimates reflect the average impact of homicides for municipalities that become more violent in 2007–2010 as a result of highways within their boundaries. One concern with these local average treatment estimates surrounds the heterogeneity with which highways predict homicides. Some areas may become more violent without having federal highways, or some areas may experience a fall in conflict despite a substantial endowment of highways. If the number of “defier” municipalities is sufficiently large they may cancel the impact of the “complier” municipalities (where presence of highways weakly increased violence or where the lack of highways weakly reduced violence). In order for our results to be representative of all locations that become more violent due to disputes over the distribution networks, the assumptions of monotonicity and independence of the instrumental variable must hold (Imbens and Angrist, 1994). Section 4.1 presents arguments for independence and shows that it was less of a concern when the instrument included time variation. In this section we focus on the requirement of monotonicity for our smallest geographic area-- municipalities.

Monotonicity holds even if the impact of highways varies across areas (the α _2,j coefficients) as long as they have the same sign. Of course, α _2,j cannot be calculated for each geographic unit j, and thus some level of aggregation is needed. To do this we place municipalities into quartiles based on the level of predicted homicides.

We predict annual homicide rates during the 2007–2010 drug war period using all observable controls except federal highway kilometers separately for the subsample of municipalities with highways and no highways. Coefficients for predicted values are calculated from the group of municipalities without highways. It is important to note that 42% of municipalities have no federal highways.³³ Predicted homicides are divided into quartiles, and municipalities are assigned accordingly. We then calculate the mean levels of actual homicides by quartile for both categories of municipalities and compare the true and predicted values. The results of this exercise are presented in Fig. 2. Average homicide rates are higher for the municipalities with highways than municipalities with no highways in all cases, except quartile 1. The difference between the two groups rises with the quartile of violence, and the largest difference is seen in quartile 4, providing evidence that the strongest effect is for municipalities with the most highways.

6.2 Falsification tests

As mentioned previously, we do not include state or municipal fixed effects in our regressions as they would absorb a large portion of the variation used to estimate the relationship between our instrument and homicides. We recognize, however, that despite controls for a number of salient characteristics across states and municipalities, the exclusion restriction will not hold if there are other time-invariant or time-varying factors that are linked to both homicides and migration. We therefore perform several falsification tests to address concerns that our instrument simply captures unobserved, confounding geographic factors rather than differences in the size of drug distribution networks.

Second, we argue that if federal highway values capture some underlying confounding factor, like spending on transportation, other measures of transportation networks should also be relevant predictors of homicides. In other words, if federal highways predict highways for a reason other than providing a drug distribution network, other measures of transport, particularly those that are less likely to be used to transport drugs to the USA, should also predict homicides.³⁴ We consider two such measures: kilometers of state highways, and airports, as measured by square kilometers of runways and square kilometers of airport platforms in national and international airports as of 2005. Note that we measure airports at the state level only, given that there are only 61 as of 2005, which means the majority of municipalities have zero values. We re-estimate the first-stage equations using these alternative transportation network measures interacted with Colombian cocaine supply shocks as our instrumental variables. The results are shown in columns 2 through 4 of panel A of Table 7. We find that airports and state highways are poor predictors of homicides, as the coefficients either have the opposite sign, in the case of airports, or have the same size but are a tenth of the size of the original, in the case of state highways. The coefficients are statistically insignificant and F and χ ² values are well below the threshold for reject the null of an under and weakly identified first stage. These results support our argument that our original instrument captures changes to the value of drug distribution networks over time, rather than other unobservable time-varying factors that predict both migration and violence.

6.3 Migration dynamics

In this section we explore the dynamics of migration in more detail. In particular, we explore the possibility that people’s perceptions about violence and the benefits of moving may be driven by previous rather than contemporaneous homicides. This is likely if people are more likely to respond to increases in violence they view as permanent rather than temporary, and lagged values can better capture the former rather than the latter. We therefore consider a one- and two-period lag in the relocation response, by estimating the interacted IV model, instrumenting for lagged homicides using kilometers of federal highways in 2005 multiplied by cocaine seizures in the previous year or two periods. All other controls remain the same.