Violence and Migration:

Evidence from Mexico’s Drug War∗

Sukanya Basu†and Sarah Pearlman‡

December 14, 2013

AbstractThe effect of violence on people’s residential location choice remains a debated topic in

the literature on crime and conflict. We examine the case of the drug war in Mexico, which

resulted from the federal government’s crackdown on drug cartels, and has dramatically in-

creased the number of homicides since late 2006. Using data from the 2010 Mexican Census,

this paper examines the impact of increased violence during 2005-2010 on the migration rates

across municipalities. We use kilometers of federal highways as an instrument for the change

in the homicide rate, since the former is a good measure of pre-existing drug distribution

networks. After controlling for observed and unobserved municipal level heterogeneity, we

find no evidence that increases in homicides have led to out migration, either domestic or

international. These conclusions are robust to tests of the exclusion restriction, the inclusion

of additional municipal controls and alternative measures of drug war violence, and investi-

gations of heterogeneity by different education, age and gender groups.

JEL classification: O12, K42, O54, J11

Keywords: Homicides, migration, drug-distribution networks.

∗We are grateful to the Ford Foundation for funding for this project. We would like to thank Olga Shemyakinafor her valuable comments. We are also grateful to Claire Oxford for excellent research assistance, Juan Trejofor assistance with the Mexican Census data, Juan Camilo Castillo and Daniel Mejia for providing the Colombiancocaine seizure data, and seminar participants at Vassar College, Bates College, the University of Rochester andSouthern Economic Association Annual Meeting for feedback. All remaining errors are our own.†Assistant Professor of Economics, Vassar College. Email: subasu@vassar.edu. Address: 124 Raymond Ave.

Box 22, Vassar College, Poughkeepsie, NY 12604.‡Corresponding Author: Assistant Professor of Economics, Vassar College. Email: sapearlman@vassar.edu.

Address: 124 Raymond Ave. Box 497, Poughkeepsie, NY 12604.

1 Introduction

Violence, and the fear of violence, induces changes in behavior. Despite this recognition, the im-

pact of violence or conflict on the affected communities is not well understood and has recently

become a topic of significant research in development and labor economics. In this paper, we

study the impact of the drug war and the related steep rise in homicides since 2006 on the resi-

dential choices of Mexicans. The drug war began after newly elected president Felipe Calderón

launched a federal assault on the drug cartels. The federal crackdown weakened pre-existing

oligopolistic cartel structures, which in turn led to turf wars and a dramatic escalation of drug-

related violence. Annual drug related homicides increased from approximately 2,120 in 2006 to

12,366 in 2011, and, in total, more than 50,000 deaths are attributed to the conflict (Trans-border

Institute, 2012). In addition, the casualties have spread beyond the combatants (which include

members of the cartels, police and army) to the civilian population. Politicians, journalists and

innocent bystanders caught in the crossfire increasingly are included among the victims.1

Hirschman (1970) states that there are two effective ways for citizens to express their discon-

tent if the advantages of being in an organization (here their residential location), and hence their

loyalties decrease: voice or political participation as a way of exerting pressure on their public of-

ficials; and exit, or migrating to a more preferred location. On the issue of voice, Dell (2012) finds

a link between election outcomes and violence in Mexican municipalities, showing that violence

escalates in areas where the party most closely aligned with the crackdown on cartels closely wins

an election. It is less clear, however, how these election results reflect people’s attitudes towards

violence and their preferences for public officials to combat it. On the other hand, researchers are

in agreement that, at least on an aggregate level, people will migrate away from their homes when

they think there is a threat of violent behavior, from governments or dissidents, to their "personal

1While comprehensive data on the victims of drug-related violence are not available, analysis based on a sub-sample of drug-related crimes reported in newspapers between 2006 and 2012 provides some idea of the civilian cost(the Violence and Victims Monitor dataset, as compiled by the Trans-border Institute). Of the 3,052 cases analyzed,the authors estimate that 1.5% of the victims are current or former mayors, while 2.4% are journalists or mediasupport workers (Molzahn et. al 2013). Applied to total drug related deaths, these percentages suggest the civiliancost has not been small.

2

integrity" (Moore and Shellman, 2008; Davenport, Moore and Poe, 2003). The drug war in Mex-

ico has not only taken a direct toll in terms of actual deaths but has also increased the overall

level of fear among the general population. Nationally representative victimization surveys show

that the percentage of adults who feel the state in which they live is unsafe rose from rose from

49% in 2004 to 61% in 2009.2 One consequence of the actual and perceived threat of violence

could be higher migration rates, as individuals flee areas most affected by the conflict. Contrasted

with traditional migration, risk aversion and lack of information may enhance violence-induced

migration (Engel and Ibáñez, 2007).

Re-location arguably is a very costly way to avoid crime victimization, but it also may be

the most effective. The expectation that rising violence may increase migration is supported by

several papers which have looked at the U.S. and found that crime does lead individuals to move.

For example, Cullen and Levitt (1999) analyze the phenomenon of population flight from city

centers to surrounding suburbs, and find that an increase in index crimes (homicide, rape, robbery,

burglary, aggravated assault, larceny, motor vehicle theft and arson) leads to a significant decline

in cities’ population. Using individual level data from the National Crime Survey, Dugan (1999)

finds that individuals who are victims of property crime are significantly more likely to move over

a three year period. Using the same data, Xie and McDowall (2008) find that victims of violent

crime are also likely to move, and do so more than victims of property crime. They also find that,

in addition to their own victimization, people move in response to the victimization of immediate

neighbors. This shows that heightened fear of crime, and not just actual victimization, can drive

re-location decisions. In general, an impending sense of danger can impact emotions and distort

beliefs and choices. As highlighted by Becker and Rubinstein (2011), while people can manage

these emotions, there is heterogeneity in their ability to do so since costs from exposure to danger

differ.3

2Author’s calculations from the National Survey of Insecurity (ENSI). This survey is conducted by Mexico’sstatistical agency, INEGI, and the data and documentation are available on INEGI’s website. For the seven mostviolent states during the drug war, this percentage rose from 53% in 2004 to close to 77% in 2009.

3The authors examine the responses of Israelis to terror attacks during the "Al-Aqsa" Intifada. Occasional users oftransportation services and coffee shops decrease their usage, but not frequent users. Media exposure and education

3

The case of Mexico can be compared with the state of Colombia, which has also experienced

a protracted conflict between the government, drug trafficking organizations and rebel groups.

Research has shown that households with greater exposure to violence in their own or surround-

ing areas were more likely to move to safer metropolitan areas (Engel and Ibáñez, 2007), while

households in major cities with higher kidnapping risks from rebel groups were likely to send

members abroad, though not to other locations within Colombia (Rodriguez and Villa, 2012).4 In

contrast to Colombia, however, the increase in drug violence in Mexico was sharp and sudden.

Over the three year period of 2006 to 2009, total homicides rose by 90%. Furthermore, the in-

crease in homicides was geographically concentrated, with only 3% of municipalities accounting

for seventy percent of the violence (Rios and Shirk, 2011). There are several anecdotal and media

reports of individuals leaving areas that have been severely affected by the violence. Most of these

accounts state that migrants have moved across the border to the U.S., and that the migrants may

be primarily concentrated among college-educated Mexicans (Rice, 2011; Arceo-Gómez, 2013).

The Internal Displacement Monitoring Center (2011) compiles these and other stories of people

within certain communities in Mexico who have moved due to drug violence. They conclude,

however, that existing estimates - which range from 220,000 to 1.6 million - are incomplete and

more reliable figures are needed.

Indeed, on a broad scale almost no study has examined if the violence in Mexico has led to

wide-spread migration and subsequent population changes. The one exception is Rios (forthcom-

ing 2014), who finds that drug related homicides are highly correlated with unpredicted population

declines at the municipal level. While Rios’s strategy is creative, the identification strategy is lim-

ited because it does not use actual migration data or control for unobserved municipal level factors

that may jointly determine drug related violence and migration decisions. This omission is im-

portant as conflict can be linked with economic prosperity (Abadíe and Gardeazábal, 2003), and

socioeconomic factors at the level have been shown to significant determinants of displacement,

affect the dissemination of information. In this paper, we study the impact of violence by level of education and age.4The increase in crime in the 1980s that plagued most of Latin America has increased the probability that people

in the region intended to move their entire household to USA (Wood, et. al., 2010).

4

even after conflict variables are controlled for (Czaika and Kis-Katos, 2008).5 In this paper we

overcome both obstacles to provide a more comprehensive picture of the impact of the drug war

on re-location. First, we use migration indexes constructed from the 2010 Mexican Census, which

contains individual level data on a person’s current location and their location within Mexico five

years ago. Second, we control for unobserved municipal level heterogeneity by instrumenting for

the increase in violence using kilometers of federal highways. We argue this variable captures

pre-existing drug distribution networks, and that the majority of violence has been about dispute

among cartels to gain control of these networks.

Overall we find no evidence that increasing homicides during the drug war period led to in-

creased migration, either domestic or international. While the OLS estimate of the relationship

between homicides and migration is positive and significant, the IV estimates are either weakly

positive or negative and insignificant. Any link between the homicides and migration can be ex-

plained by observed and unobserved municipal level variation. The confidence intervals on our

IV coefficients suggest that even dramatic increases in violence did not lead to large changes in

migration patterns. These conclusions are robust to tests of the exclusion restriction, the inclusion

of additional municipal controls and alternative measures of drug war violence, and investiga-

tions of heterogeneity by different education, age and gender groups. We propose that the results

may be explained by a low level of mobility among the Mexican population, which dampens the

incentives to take a costly re-location decision.

2 Theoretical Framework

In this paper we focus on the growth of regional violence and the effects on residential choice,

by studying the impact of the drug war on the migration decisions of the Mexican people. Drug-

violence can impact the costs of leaving versus staying in an area through multiple channels. First,

5Institutional factors, like police presence in villages of Aceh, Indonesia were important determinants of conflictand migration.

5

and most obviously, there are the direct costs of violence to life and property. If those outweigh

the moving costs, people will choose to relocate. Secondly and less obviously, drug gangs may

reduce incentives to move. Cartels aiming to dominate an area can try to prevent residents from

moving, thereby increasing migration costs. For example, the Congressional Research Service

Report for Congress (2013) points out many instances where cartels either massacred migrants

who were crossing the border, or tried to force migrants to move drugs across the border on their

behalf. Third, drug gangs may provide employment opportunities to local residents. These jobs

may be more appealing if a gang controls an area, effectively becoming a local monopoly, or if

jobs in the legitimate sector are scarce or of lower pay. The case of Bolivia is important to note -

simulated models show that a 10% increase in coca and cocaine production in Bolivia increases

GNP by 2% and lowers unemployment by about 6% (De Franco and Godoy, 1992). Given the

multiple channels through which the violence impacts the incentives to migrate, it is unclear which

effect, if any, dominates.

In general, out paper fits within the large body of literature that discusses interstate migration

in developing countries and the movement of people across national boundaries (Lucas, 1997).

The accepted notion is that individuals weigh the costs and benefits of their current place of

residence and potential places to live in when they take their moving decision (Sjaastad, 1962;

Borjas, 1987). For example, interstate migration in Mexico depends on the infrastructure and trade

ties of their state of origin with the different regions in the country and on the migratory behavior

of other individuals before them (Curran and Rivero-Fuentes, 2003). The literature is less clear,

however, about the timing of the migration decision. Do individuals first decide to leave the place

of origin, and then choose a new place to live, or are these decisions taken simultaneously after

conditions of all possible new locations are carefully considered? Finally, there is the question

whether the place of origin is valued more or less than the new destination. While we cannot view

individual’s decision making process with respect to re-location in our data, these are important

concepts to keep in mind when considering the incentives people have to move.

6

3 Data

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 Geographi-

cal Institute (Instituto Nacional de Estadística y Geografía, or INEGI).6 We use data from death

certificates rather than police records as the publicly available data cover more years and all mu-

nicipalities.78 We then calculate total homicides from January 2007 to December 2010, and con-

vert this to homicides per 100,000 inhabitants as of 2005 using population data from the National

Council on Population (CONAPO).9 The homicide variable for metropolitan j is:

HomicideV ariablej =

2010∑2007

Homicidesj

Population2005j

(3.1)

To control for pre-existing levels of violence we calculate average annual homicides per

100,000 for the years 2005 and 2006 - prior to the commencement of the drug war. Control-

ling for previous violence is important as our interest is in examining if migration changed in

areas that became more violent, not if it changed in areas that are always more violent. Some

areas that experience high levels of violence from 2007 onward also experienced high levels of

violence prior to the drug war (Cuidad Juárez, for example), and absent a change in the type or

6In the municipal death records missing data and no homicides are coded in the same way, since municipalitieswithout homicides in a given year simply do not appear. To distinguish between non-response and zero values weuse information on all deaths, either from homicides or other causes. We assume if a municipality reports values forall deaths but not homicides, there were no homicides and the value is zero. If a municipality does not report valuesfor all deaths or homicides, we assume the information is missing and code the value as a non-response.

7If death data is missing for any one year, but not for the preceding and succeeding years, we use the average ofthose two years to fill in the missing value.

8One difference between the two data sources is that the death certificate records only include intentional homi-cides, while the police record data also include manslaughter. This is helpful, as the drug related violence we areinterested in does not include unintentional homicides.

9We use the 2005 CONAPO projections because the 2010 projections, which were adjusted after the 2010 Censuswas complete, do not include revisions of earlier year estimates. Using the 2010 population projections for the mostrecent year but older projections for earlier years may lead to changes in homicides per 100,000 inhabitants that stempurely from estimation revisions. To avoid this we use the older projections which are consistent across all years.

7

levels of violence, it is unclear why the incentives for residents to move should change. As a sen-

sitivity analysis, we later also consider homicides directly attributed to drug violence. The data on

drug related homicides is from Mexico’s National Security Council (SESNSP) and are available

from December 2006 to September 2011. Given the lack of data on drug-related homicides prior

to the drug war, however, our principal measure is all homicides.

Summary statistics for municipal level totals of all homicides and drug related homicides are

shown in Table 1. The sharp rise in homicide following the beginning of the federal crackdown

on drug cartels in late 2006 is obvious. Total homicides rise from an average of 3.96 in 2005 to

10.34 in 2010 - an increase of 160% - while the maximum number of homicides registered in any

municipality jumps from 273 in 2005 to 3589 in 2010 - an increase of over one thousand percent.

The table also shows the growing disparity across municipalities in levels of violence following

the drug war. The standard deviation in homicide levels across municipalities increases almost

six-fold, while the gap between the 25th percentile and the 90th percentile doubles.

Maps of total homicides and drug related homicides provide further evidence of the concen-

tration of violence. As shown in figure 2, the vast majority of municipalities in Mexico have not

exhibited high levels of homicides or drug related homicides. Instead, a small number of munic-

ipalities exhibit high levels of both. Figure 2 also shows an absence of regional concentration in

terms of where these municipalities are located. The most violent municipalities are not exclu-

sively along the U.S. 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 U.S.

3.2 Migration Data

The data on migration is from the 2010 Mexican Census, as accessed through IPUMS Interna-

tional, maintained by Population Center at the University of Minnesota. The Census is the only

data set that provides information on migration movements and is representative of all municipal-

ities within Mexico. We use the data to examine two types of migration: national, in which an

8

individual moves to a different part of Mexico, and international, in which an individual moves

abroad. Of the two, the national migration rate is more straightforward, as all migrants are in

Mexico to answer the Census questionnaire and appear directly in the data. We label individuals

as national migrants if they report living in a different municipality within Mexico five years ago

(year 2005) than today (year 2010). Different municipalities include those within the same state

or in a different state. This is the finest level of geographic detail we can obtain, as the Census

does not provide information on re-location within a municipality.

We calculate a gross national out migration rates for a municipality j, defined as the weighted

percentage of individuals over age 5 that lived in the municipality 5 years ago but live elsewhere

in Mexico today.10

GrossMigrationRatej =IndividualsWhoMoveToAnotherMunicipality2010−2005

IndividualsWhoLivedInMunicipality2005j

(3.2)

We also calculate net national migration, defined as the gross national migration rate minus

the in-migration rate. The latter is percentage of a municipality’s current population over the

age of 5 that lived elsewhere in Mexico 5 years ago (2005). The net migration rate controls

for the possibility that some municipalities have a more transitory population.11 For example,

municipalities that offer more temporary work may experience large outflows and inflows, in

which case high gross outflows may not indicate a reduction in population.

While the Census allows us to see all municipalities, one drawback is that it does not ask

detailed information about an individual’s or household’s location history. Thus we do not know

when or exactly how many times an individual moved over the 2005 to 2010 period. It is possible

that some migrants moved prior to the commencement of the drug war. It also is possible that we

may exclude individuals who are temporarily displaced; something that might occur if violence

10The totals were calculated using population weights.11Following the literature on interstate migration in Mexico, we consider the decision to leave a location and the

choice of a new location to be separate. Our measure of out-migration aims to capture the effects of violence onindividuals who already live in an area and feel the effects of violence. We are currently not modeling the effects ofviolence on choice of destination. Violence may affect appropriate dissemination of information about the costs andbenefits of a new destination. This is less likely to be true for people who already live in the municipality.

9

subsides after a drug trafficking organization leaves an area or gains control of it from rivals. For

example, individuals who move after 2005 to avoid violence, but return to the same location before

the 2010 Census will not be marked as migrants. Despite these shortcomings, if a significant

portion of the population was displaced within Mexico between 2007 and 2010, this should be

captured by the Census’ migration variable.12

International migrants are more difficult to capture from the Census as information is available

only for individuals who have household members remaining in Mexico. Individuals who moved

abroad with their entire household do not appear in our data, making our international migration

rate a lower bound on the true value. To calculate the international out migration rate for mu-

nicipality j, we take the ratio of individuals over age 5, with household members remaining in

Mexico, who moved abroad between 2005 and 2010 to the number of individuals who lived in the

municipality in 2005.

InternationalMigrationRatej =IndividualsWhoMovedAbroad2010−2005

IndividualsWhoLivedInMunicipality2005j

(3.3)

While we do not know the extent to which the Mexican Census measure undercounts inter-

national migrants, we can gauge possible ranges using information from the U.S. Census. The

Mexico-U.S. migrant flow is the largest bilateral flow of people in the world (Ambrosini and Peri,

2012). The proportion of new adult Mexican immigrants as a proportion of all adult Mexican

migrants in the U.S. has fallen from 6.5% to 2% for the time period 2000-2010.13 On average,

two-thirds of migrants claim to move with at least one other family member, and half of them have

three or more members. Presumably these families are the missing ones in the Mexican Census.14

12Since our focus is migration decisions of individuals exposed to violence in Mexico, we remove individuals wholived outside the country five years ago. This means that return migrants are not considered in the analysis.

13Author’s calculations: A new Mexican immigrant is an individual who has arrived in the United States in the lastyear, and have resided in the U.S. for 0-1 years.

14There has also been a change in the educational distribution of new Mexican immigrants. There are now a largernumber of college graduate Mexican immigrants. This is both a result of a fall of undocumented workers, whoare mostly concentrated in the lower skill distribution, and of U.S. immigration that favors skilled migration. Anargument has been made by Arceo-Gómez (2013) that college-educated Mexicans are fleeing the drug-violence andcoming to U.S.A.

10

Summary statistics on migration rates are provided in Table 2. To compare differences across

years we calculate these rates for 2010 and 2000 - the only Censuses that include data on individ-

uals’ previous municipality of residence (earlier ones include previous state of residence). Two

main conclusions emerge. First, migration rates in Mexico are low. In 2010 the average national

migration rate across municipalities was 4.13%, while in the year 2000 it was 5.02%. This means,

on average, less than five percent of the population of municipalities moved to another location in

Mexico over a five year period. To put these numbers into perspective, according the U.S. Census,

over the 2005 to 2010 period, 12.3% of individuals in the U.S. moved to a different municipality.

Over the 1995 to 2000 period this number was 18.1% (Ihrke and Haber, 2012). Thus domestic

moving rates in Mexico are two to three times smaller than those in the U.S. Comparing Mexico’s

internal migration rates to that of another Latin American country which has been affected by

drug violence - Colombia - shows that Mexicans are indeed less mobile. Though the period of

comparison is not the same (2000-2005), Bell and Muhidin (2009) find a 6.42% to 7.39% "inter-

nal migration intensity" in Colombia using IPUMS data. This highlights that the population we

are examining is significantly less mobile than populations that have been used in other studies of

re-location responses to crime.

Second, both national and international migration rates declined in 2010 from the previous

decade. The decline in international migration, specifically to the U.S., has been well documented.

In 2012 the Pew Hispanic Center published a report entitled “Net Migration from Mexico Falls

to Zero - and Perhaps Less.", in which the authors, using both Mexican and U.S. data, show that

net migration rates from Mexico to the U.S. are no longer positive. Our own analysis of data

from the American Community Survey finds that the portion of all Mexican immigrants to the

U.S. that arrived in the past 4 years declined from 24.4% in the year 2000 to only 8.2% in 2011.

This decline is thought to be the result of reduced job opportunities in the U.S., improved job

opportunities in Mexico and increased border enforcement (Passel et. al., 2012).

The decline in national migration, on the other hand, has not been as well-documented, and

the potential causes are less clear. In Figure 1 we examine if the changes in national migration

11

differ by border and by violence status, with the latter defined as municipalities above the 90th

percentile of homicides from 2007 to 2010. For border status we see that out migration rates

increased for border municipalities between 2010 and 2000, but fell for all others. These differ-

ences are significant at the 5 percent level and may lead some to conclude that the drug war led to

increased out-migration. When we look at municipalities as grouped by violence, however, this

trend disappears. Out migration rates fell equally for municipalities that became very violent and

those that did not, suggesting increases in violence may not have caused migration to increase.

4 Estimating Migration Trends

4.1 Empirical Strategy

The migration rate of municipality j can be outlined as a linear function of homicides per 100,000

inhabitants during the drug war period (2007-2010), observable municipal level characteristics

(Zj), and unobservable municipal level characteristics (εj).

MigrationRatej = β1 + β2Homicidesj + γZj + εj (4.1)

A key part of identifying the relationship between homicides and migration is the inclusion of

municipal level characteristics that may explain both migration rates and the changes in homicide

over the drug war period. This is of particular importance since the commencement and escalation

of the drug war coincides with the Great Recession in the U.S., which had a large impact on

Mexico. As a result, areas that suffered more during the recession may exhibit higher migration

rates, as people leave to find jobs elsewhere, but these areas may also experience greater increases

in violence, if drug trafficking organizations are better able to recruit members and expand their

operations or challenge rivals.

To control for economic changes we include municipal level unemployment rates in 2010 and

2000, calculated from the Census in both years. We use this in lieu of GDP changes as these

12

are not available at the municipal level. Other observable municipal controls include average

homicides per 100,000 inhabitants for the years 2005 and 2006, the log of population, a binary

indicator variable for a municipality that shares a border with the U.S., migration rates from

the year 2000, average years of education for adults, the percentage of households with running

water, and average household income per capita in the year 2000. As mentioned previously,

pre-drug war homicides are included to control for pre-existing levels of violence. The log of

population is included to account for the fact that less populated areas may have lower migration

and violence rates than more populated ones. Border municipalities are a convenient route to the

U.S. Border status can indicate lower migration costs and these municipalities may house a larger

transitory population and hence have higher migration rates.15 Migration rates from the year 2000

are included to account for time-fixed migration costs, as previous migration rates should partially

capture the ease with which individuals can move. Finally, the last three variables aim to capture

economic activity and wealth. These are the best available measures, as, in addition to the absence

of GDP statistics, total individual or household income is not included in the 2010 Census (only

work income), and public sector measures, such as local government income and expenditure per

capita, are not available for all municipalities.16

Despite the inclusion of the municipal level controls, identification remains a challenge as

unobserved factors may jointly determine migration decisions and changes in homicides. Ex-

ante, it is unclear what direction this bias may take. On one side, factors such as institutions may

put upward bias on the coefficient, if municipalities 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 choose to locate elsewhere. On the other side, factors such as the

effectiveness of drug trafficking organizations may exert a downward bias on the coefficient. Drug

trafficking organizations might create more job opportunities, reducing the incentives to migrate,

but also increasing violence. The potential direction of the bias thus remains an open question.

15Only 31 municipalities are identified as border municipalities.16All of the variables, with the exception of population, are constructed by the authors from the 2000 and 2010

Census. Furthermore, we remove 2 municipalities with national out migration rates in excess of 50% in 2010.

13

To control for unobserved heterogeneity we instrument for municipal level homicides using

kilometers of federal highways that pass through a municipality. The rationale behind the in-

strument is two-fold. First, the increase in homicides clearly stems from the federal government

crackdown on drug trafficking organizations, which began in December of 2006. Other poten-

tial explanations for the violence, including increased political competition that changed implicit

agreements between the government and cartels, Mexico taking Colombia’s position as the ma-

jor distributor of drugs to the United States, and changes in relative prices which increased the

production of marijuana and opium within Mexico, either pre-date the conflict by many years or

cannot be timed exactly to late 2006. The dramatic increases in violence clearly occur right after

the federal crackdown begins. This is apparent by looking at the summary statistics in Table 1,

but also has been documented directly by Dell (2012), who examines the impact of government

crackdowns on drug trafficking activity. She finds that municipalities most likely to enact crack-

downs experience increases in drug related deaths and drug seizures, while municipalities less

likely to implement crackdowns do not. Her results show that violence increased most sharply in

areas where the government directly confronted drug trafficking organizations.

Why did the federal crackdown increase violence? The 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 this the government weakened previously

oligopolistic organizations, leading to turf wars as organizations fought for the drug production

and distribution networks of their weakened rivals. The increased competition has been most

severe over access to distribution networks - and specifically land transport routes - to the U.S.,

the largest drug consumer market in the world and Mexico’s largest trading partner for legal goods

(Rios, 2011; Dell, 2012). Thus areas with more access to distribution routes have experienced the

largest increases in violence.

The other rationale behind our choice of instrument is that federal highways capture the dis-

tribution networks to the U.S. The majority of transport of goods and people from Mexico to the

U.S. occurs via highway. The North American Transportation Statistics Database indicates that

14

in 2011, approximately 65% of Mexican exports to the U.S. were transported via highway, while

82% of Mexican travel to the U.S. occurred via highways and rail (no breakdown is available, but

person transport via rail in Mexico is very low). The reliance on highways for transport is higher

in Mexico than in the U.S. and Canada and largely is due to the poor state of Mexico’s railroads,

which only recently have improved under private concessions. Also, federal highways are the

highest quality road routes, with more stretches of roads that are paved and have four, as opposed

to two, lanes. Federal highways, particularly the toll ones, frequently are the fastest way to travel

between areas in Mexico. The federal highway system includes the most transited and valuable

routes, many of which run to and cross the U.S. border.1718 For these reasons, federal highways

are more likely to capture the transport of valuable goods to the U.S. - including drug shipments

- than other transport networks. We also argue that kilometers of highways themselves, rather

than a more complex measure such as the distance from the municipal center to particular highly

transited routes, do the best job of capturing the extent of drug distribution networks within our

sample. Given that, the Census does not provide disaggregated geographic data that would allow

us to see how close people live to certain trafficking routes, the length of the highway system is the

most basic way to capture the size of potential distribution networks for an entire municipality.19

Finally, by using highway values from 2005 - which pre-date the drug war - we ensure that recent

factors linked to homicides and migration do not determine their placement.

17Federal highways consist of free highways, for which no toll is charged, and toll highways. Toll highwaysare preferable (BenYishay and Pearlman, 2013), but disaggregated information on each type by municipality is notavailable for all municipalities. The data come from the 2005 Annual Reports for each state. For two states (Pueblaand Oaxaca) it was necessary to impute values, as breakdowns were not given for each municipality. The imputationwas done using data on registered passenger trucks from the Annual Reports for each state. We check the robustnessof our results to the exclusion of imputed highway values later.

18For example, the federal highway system has seven crossing points into the U.S, as compared to only one crossingpoint into Mexico’s southern neighbor, Guatemala.

19It also is difficult to weight the highways based on how much traffic they receive. This is because any dataon usage, when available, covers the entire highway line. It therefore is difficult to know to parse the traffic alongdifferent segments of a highway route.

15

4.2 Results

Our instrumental variables model is as follows. The first stage, which estimates homicides at the

municipal level is:

Homicidesj = α1 + α2HighwayKilometersj + θZj + vj (4.2)

The second stage is:

MigrationRatej = β1 + β2 ̂Homicidesj + γZj + εj (4.3)

where ̂Homicidesj are the fitted values from the first stage regression. We consider three mea-

sures of migration: (1) the gross national out migration rate; (2) the net national out migration

rate; and (3) the international out migration rate. To show the extent to which observed and un-

observed municipal level heterogeneity may bias the results we also estimate the model via OLS

with and without municipal controls.

The results of the first stage IV regressions are presented in Table 3. The results show a strong

relationship between federal highways and homicides, as the coefficients on the instrument are

large and significant. For example, the coefficient in column one implies that a one standard

deviation increase in federal highways (38 kilometers) is associated with a rise of around 8.2

homicides per 100,000 inhabitants. The model also passes under-identification and weak instru-

ment tests. The Anderson-Rubin χ2 value and Angrist-Pischke F values are sufficiently high to

reject the respective nulls of an under-identified model and an identified model that suffers from

a weak correlation between the instrument and the endogenous variable. Hence federal highways

do a good job of predicting which areas were more or less violent over the 2007-2010 period.

The second stage IV and OLS results are shown in Table 4. They show that unobserved mu-

nicipal level heterogeneity exerts an upward bias in the OLS estimates, as in all cases the IV

coefficient is smaller than the OLS coefficient. In the case of gross and net national migration,

these differences are large. As shown in columns two and five, the OLS coefficient on homi-

16

cides is positive and significant, suggesting that higher homicides led to greater gross and net

out-migration. However, once unobserved regional heterogeneity is controlled for, the IV coeffi-

cients are insignificant, and in the case of gross migration, the value is negative. Hence, regional

factors that promoted cartel presence and violence also compelled people to move. For example,

poor policing and legal authorities could allow cartels to thrive as well as drive out businesses

and employers. In which case, people would have to move to seek employment elsewhere. While

neither IV coefficient for national migration is significant, the upper bound of the 95% confi-

dence interval is quite small. For example, the upper bound coefficient for the IV estimate in

column three is 0.00005. This suggests that a two standard deviation increase in homicides per

100,000 inhabitants (188- equivalent from moving from the 25th percentile above the 95th one)

is associated with an increase in outmigration of 1% - less than one third of a standard deviation.

After controlling for unobserved municipal characteristics, there is little evidence that increasing

homicides led to higher domestic migration.

When we look at international migration the conclusions are slightly different. In this case

both the OLS and IV coefficients are negative - showing that an increase in homicides leads to a

decrease in the percentage of individuals who move abroad. Again, the IV coefficient is insignifi-

cant, but the upper bound of the 95% confidence intervals suggests muted migration response. For

example, the upper bound coefficient for column nine is 0.00002. This suggests a two standard

deviation increase in homicides is associated with an increase in international migration of 0.4%

– one quarter of one standard deviation.

The results in table 4 also highlight the importance of pre-existing levels of violence and mi-

gration in explaining current migration. Average homicides in 2005-2006 have a positive and

significant effect on gross domestic migration. This suggests that while places that always were

violent have higher migration rates, places that have become more violent have not become sig-

nificantly less attractive places of residence. We also see that previous levels of migration are

powerful explanatory variables for current levels, showing there is persistence in migration trends.

17

4.3 Are highways a valid instrument?

The exclusion restriction that underlies our IV model assumes that federal highways do not di-

rectly affect migration rates; they only indirectly affect them through changes in drug war vi-

olence. There may be reasons, however, why this assumption does not hold. First, highways

likely influence the transportation costs associated with migration to or from certain areas. We

argue above that previous migration rates capture time invariant costs linked to highways, and our

identification assumption is that these costs do not change between 2000 and 2010. If, however,

transportation costs associated with highways do change, highways may be directly correlated

with 2010 migration rates. Second, highways might capture changes in economic activity due to

linkages to the U.S. and impacts from the 2008 recession. We argue above that unemployment

rates from 2000 and 2010 control for this, but if this is not the case, and if these economic changes

predict re-location, highways may be directly correlated with our outcome variable.

To test the exclusion restriction we return to the data on international migration, as the Mex-

ican Census asks households the year in which individual members moved abroad. These re-

sponses allow us to construct annual international migration flows, and we can consider the time

variation in the value of drug distribution networks to the U.S. This variation should stem from the

quantity of drugs being transported to the U.S., and, in particular, the quantity of cocaine being

shipped from Colombia to Mexico for distribution. Unlike other drugs that reach the U.S. 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 (UNDOC, 2010). Of the three, Colombia has

long been the dominant producer. According to the 2010 United Nations World Drug Report, as

of 2008 Colombia was responsible for 43% of all coca cultivation and 52% of all cocaine pro-

duction. Thus Mexican drug trafficking organizations are reliant upon cocaine shipments from

Colombia, the world’s largest producer of this drug.

The amount of cocaine reaching Mexican borders, in turn, depends on the efforts of Colombian

authorities to combat drug trafficking, and specifically their efforts to interdict, or seize, cocaine

18

supplies. In recent years Colombia has increased its interdiction efforts, leading to greater external

shocks to the supply of cocaine reaching Mexico (Economist, 2013; Castillo et. al., 2013). These

shocks likely alter the use of highways to transport drugs to the U.S. and change the level of

violence in these areas (Castillo et. al., 2013). To measure these external shocks we use data

from the Colombian Defense Ministry on tons of cocaine seized by Colombian authorities in each

year. The benefit of using seizure data is that they capture changes to the supply of drugs being

transported through Mexico that are not correlated with events in either the U.S. or Mexico. As

a result, they should capture changes in drug trafficking violence that are de-linked from other

factors that may impact Mexican migration decisions.

Our instrument is now 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 should impact the value of highways for drug trans-

port and violence related to control of these routes, but should have no direct effect on migration.

In particular, supply shocks should not affect the value of highways for the transport of legal

goods or people, and thus should not capture impacts from the recession or changing migration

costs. All impacts should be indirect, working through the homicide variable.

The first stage of the instrumental variables model is the following:

Homicidesjt = α1 + α2(HighwayKilometersj ∗ CocaineSeizurest) + θZj + δt + ejt (4.4)

The main difference from the previous model in (4.2) is the introduction of municipal-year vari-

ation. Given that the Census covers 2005 to 2010, we have six years of observations for each

municipality. The outcome variable is homicides per 100,000 inhabitants in municipality j in

year t. This is a function of the instrument, time-invariant municipal characteristics, year fixed

effects, and unobserved municipal-year factors. The set of municipal controls remains the same as

before, with the exception of previous homicides, which we now measure with average homicides

per 100,000 inhabitants in the years 2003 and 2004.

19

The second stage is:

InternationalMigrationRatejt = β1 + β2 ̂Homicidesjt + γZj + ηt + εjt (4.5)

Results from the estimation of the two stage model are shown in table 5. To assure that any

changes in interpretation are not being driven by the introduction of time variation, we also es-

timate the model via OLS both with and without controls. These results are shown in columns

one and two, while the IV results are shown in column three. The first stage results, presented in

panel B, show that the interaction of federal highways with thousands of tons of Colombian co-

caine seizures is a significant predictor of annual homicides between the years 2005 and 2010. As

shown by the high F and χ2 statistics, the model passes the tests for weak and under-identification,

and overall the first stage remains strong.

The second stage results, shown in panel A, are surprisingly similar to those from the baseline

model. The OLS yields coefficients on homicides that are zero, while the IV model yields a

coefficient of -0.00005. This compares to a coefficient of -0.00003 from the baseline model, and

means that a one standard deviation increase in homicides per 100,000 inhabitants over a one year

period (32 homicides) leads to a decline in international migration of 0.1%. Thus, we continue

to find little evidence that increasing homicides led to greater out migration. To further test this

conclusion we estimate the model by including highways as a control variable in the estimation

of migration. We estimate both an IV model, shown in column 4, and an OLS model that includes

highways but excludes homicides, shown in column 5. In both cases the coefficient on highways

is negative and essentially zero. For the model without homicides the coefficient on highways is

significant, but the value is so small that it implies highways have a minimal direct correlation with

international migration. For the IV model, although the coefficient on homicides in the second

stage becomes positive, the value remains very small. The upper bound of the 95% confidence

interval suggests that a one standard deviation increase in annual homicides leads to an increase

in international migration of 0.3%. These results therefore provide further confirmation that even

dramatic increases in violence have not led to significant out migration.

20

4.4 Local Average Treatment Effect

As mentioned in section 4.1, the IV estimates reflect the impact of homicides for municipali-

ties that become important for cartel activity because highway kilometers in these municipalities

are an effective measure of the drug distribution network from Mexico to the United States. A

concern with these IV estimates is that there is heterogeneity in the degree to which highways

predict homicides, and principally the concern that highways might predict decreases in violence

for some areas. If the number of "defier" municipalities (those in which more highways lower vio-

lence) is sufficiently large, they may cancel the impact among "complier" municipalities (those in

which highways increased violence). In order for our results to be representative of municipalities

that became more violent due to disputes over the distribution networks within their boundaries,

the assumptions of monotonicity and independence for instrumental variables must hold (Imbens

and Angrist, 1994). The assumption of monotonicity implies that there should be no municipali-

ties that had high homicides if they had a small network of federal highways, but would see a fall

in homicides if they instead received a larger network of highways. In other words, after control-

ling for other observable characteristics, highways should not make municipalities less likely to

experience increases in violence.

To test if monotonicity holds, we predict homicide rates using all observable controls ex-

cept highway kilometers separately for the subsample of municipalities with highways and no

highways.20 Based on their predicted values, the municipalities were placed into one of the four

quartiles. We then calculated the mean levels of actual homicides by quartile for both categories of

municipalities and compare the values for both groups. The results of this exercise are presented

in Figure 3, and we wee see monotonicity holds. Average homicide rates for the municipalities

with highways are not significantly different from average homicide rates of municipalities with

no highways from quartiles one, two and three.21 The difference is statistically significant in

20There are 1050 municipalities with no federal highways; they comprise 42% of the sample.21Even though municipalities with highways in quartile 2 appear to have lower average homicide rates compared

to municipalities with no highways - 36.5 homicides as opposed to 42 homicides - this difference is not significant.Monotonicity requires that the "treated" municipalities should be at least as violent as the untreated municipalities.

21

quartile four. This quartile is characterized by the lowest average education among adults, high

unemployment as well as the highest pre-existing levels of homicide in 2005-06. This indicates

that these areas may have had cartel presence previously. Since the drug violence escalated in

2007 as a result of in-fighting between cartels, it is not surprising that the largest difference is

seen in quartile 4.

Additionally, we present the IV estimates of homicides on gross out-migration and net-out

migration, by quartile, in panels A and B of table 6, respectively. The sample size is small in each

quartile. Highways are very good predictors of homicides for municipalities in the second, third

and fourth quartiles of violence, but not in the first quartile. The IV estimate of the response of

migration (net or gross) to homicides is insignificant across all quartiles, however the difference

in signs on the coefficients is interesting. The effect of homicides on net migration in the most

violent quartiles (3 and 4) has been positive, but the effect is negative in the less violent quartiles.

This is to be expected. As has been discussed earlier, the municipalities in quartiles 3 and 4

have characteristics that suggest they may have housed cartels before the federal crackdown, and

this has had a larger impact on people’s relocation choices. Though the impact of homicides on

people’s migration decision varies by the degree of violence, we find the overall impact remains

minimal.

5 Robustness Checks

We first test if our results are robust to additional controls for municipal level economic activ-

ity and public expenditure. To measure economic activity we use information from INEGI on

megawatts used in 2010 per capita. To measure public expenditure we use information from IN-

EGI on municipal public expenditure per capita in 2010. We do not include these variables in our

main set of controls as they are not available for all municipalities. The estimates from the first

and second stage IV regressions are shown in Panel A of Table 7. Overall our results are robust

22

to the inclusion of the additional controls. The IV coefficients on homicides remain negative and

insignificant for gross national and international migration and positive and insignificant for net

national migration.

We next check the robustness of our results to using drug related homicides as opposed to all

homicides as the measure of violence. One concern could be that we fail to find a positive effect

of drug war violence on migration because we use a measure of violent death that is too broad.

We therefore re-estimate the model via OLS and IV using only drug related homicides. Since we

do not have data for drug related deaths prior to December 2006, we include the average of all

homicides per 100,000 inhabitants in 2005 and 2006 to control for pre-existing levels of violence.

The OLS and IV results of this model are presented in Panel B of Table 7. For all three outcome

variables the results are similar to the main ones. The first stage results are strong, and the second

stage results continue to yield small and insignificant coefficients on drug related homicides. This

shows that there is no strong relationship between drug-related violence and re-location, and that

the main conclusions are robust to the use of drug-related homicides as the measure of violence.

Finally, we test if our results are robust to excluding Tijuana and Cuidad Juarez, two large

border cities that have high levels of migration, both national and international, and violence.

The results, available upon request, show no differences in the estimated coefficients or their

significance. These tests suggest our results are not driven by potential outlier municipalities.22

22We perform two additional checks. First, we see if our results are robust to removing municipalities with apopulation below 1000. Areas with small populations are a concern since the homicide and migration variableson a per 100,000 inhabitant basis. The 1000 cutoff is approximately the lowest 7% of the sample, and leads toremoving 260 municipalities. The results, available upon request, are similar to the main ones and show that smallmunicipalities do not drive the conclusions. Second, we see if our results are robust to removing two states (Pueblaand Oaxaca) with imputed highway values. This removes 719 municipalities. The results, available upon request,also are similar to the main ones and show that the conclusions are not being driven by imputed highway values.

23

6 Heterogeneity in Migration Trends

We begin by investigating heterogeneity in migration rates by educational status, as the ability

to find employment in a new municipality, employment opportunities with drug trafficking orga-

nizations, and the ability to avoid victimization likely varies by years of schooling. Access of

information about violence and victimization also differs by education (Becker and Rubinstein,

2011). As shown in Panel B of Table 2, the migration rates of individuals with tertiary education

are significantly higher than those with either a primary or secondary education. Highly-educated

individuals could be more mobile because opportunities for them are more dispersed and they

have lower costs of moving or they may be more responsive to the violence.23

We examine the migration rates for three education levels; primary or less, secondary (high

school) and tertiary (college or above) education. In calculating the migrations rates we limit the

sample to adults age 25 and older, as they are more likely to have completed their education.24 We

focus on gross and net national migration, as we do not know the educational status of individuals

who moved abroad. The results from the two-stage, instrumental variables estimation of the model

are presented in Panel A Table 8. The first stage results are similar to those from the full sample.

The coefficients on the instruments are significant at the 1% level and the model passes the tests

for under and weak identification. The second stage results provide some evidence that homicides

differentially impact migration rates by level of education. Although none of the coefficients is

significant, the coefficient on gross out migration is negative for those with primary and secondary

education, but positive for those with a tertiary education. Similarly, the coefficient on net national

migration is larger for those with tertiary education. This suggests individuals with a tertiary

education have the largest migration response to the violence. This may be because migration

costs are lowest for this group, making them most able to respond with their feet.

We also look at migration rates by age and gender groups. The INEGI data shows that homi-

23We do not examine different migration rates by income or wealth, as comprehensive measures of both are notavailable in the 2010 Mexican Census. For example, the 2010 Census only includes information on work income. Inaddition to this, the rates of non-response are high, even for those who report having paid employment.

24There is no information on educational status five years ago.

24

cide rates are highest for men and people in their 30s, and we should expect heterogeneous re-

sponses by age-group and gender.25 There is some evidence, as shown in Panel B of Table 2, that

migration rates were higher among women overall. The impact of homicides on net national mi-

gration is positive for women age 16 to 24 and age 25 to 40. This compares to negative coefficients

for men in these age groups. The largest coefficient, however is for women age 25 to 40 rather

than those age 16 to 24, suggesting the latter did not respond more to the violence despite being

more mobile. Overall, the general conclusions remain. We find little evidence of strong migration

responses to increasing homicides even after stratifying the sample by education, gender and age.

7 Conclusion

In this paper we investigate if the large increase in homicides in Mexico after the beginning of the

drug war led to increased migration, both to other parts of Mexico and abroad. To identify the

relationship between violent death during the drug war period and migration rates at the municipal

level we instrument for the violence using kilometers of federal highways. We argue this variable

captures pre-existing drug-distribution networks, a key asset driving the fighting among cartels,

and between cartels and the federal government. After controlling for observable and unobserv-

able municipal level characteristics we find no evidence that homicides related to the drug war

have led to increased migration, either domestically or abroad. These conclusions are robust to

the inclusion of additional municipal controls and alternative measures of violence.

The finding that the spectacular rise in violence has not led to significant changes in indi-

viduals’ migration decisions is surprising. Several factors may explain this. First, the Mexican

population is not particularly mobile. Domestic migration rates were low prior to the commence-

ment of the drug war and have fallen further since. The low level of migration likely increases

the costs to potential migrants, as they are less likely upon re-location to find networks of people

25Author’s calculations. Breakdown by both age and sex is not possible. Graphs available upon request.

25

from the same place of origin. Thus even though life has clearly become difficult in some areas

as a result of the drug war, the average individual may find moving to be too costly.

Second, we cannot account for individuals that move abroad with their entire household. If

moving is costly, individuals with means, due to wealth, income or education, may be most likely

to move. Many of these people, in turn, also may be better able to immigrate to another country.

Indeed, in the past several years high skill immigration from Mexico has increased, while low

skill immigration has declined (Passel et. al., 2012). There also is anecdotal evidence of wealthy

Mexican families from border cities relocating in the U.S. sister city just miles away (Rice, 2011;

Rios, 2014). Thus, it is possible that the majority of the response to the conflict is comprised of

households who move to another country, rather than to other parts of Mexico. This suggests a

large heterogeneity in the response to the conflict - as individuals who face the lowest monetary

costs (moving to the next municipality) do not migrate, while individuals who face the highest

cost (moving abroad) do. We are unable to explore this explanation in this paper, given the lack of

information in the Mexican Census on entire households that migrate abroad and on household’s

assets. Further exploration of heterogeneous responses, however, would be a fruitful extension if

the data that would allow for this analysis become available.

Finally, individuals may be expressing their discontent with the rising violence in other ways.

As Hirschman (1970) suggests, citizens may put political pressure on elected representatives and

law-and-order officials to control the situation. This is less costly, monetarily, than relocation.

A comprehensive study of the effects of violence should ideally include a theoretical framework

regarding the impact of violence on people’s political preferences, and an empirical study of how

these preferences are translated into voting results.

26

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30

Table 1: Summary Statistics on Homicides across Municipalities.

Data are total homicides, not homicides per 100,000 inhabitants. Data on total homicides come from INEGI. Drug relatedhomicide data come from SESNSP. Totals are not available for the years 2005 and 2006.

31

Table 2: Summary Statistics on Migration Rates across Municipalities.

Source: Author’s calculations from the 2000 and 2010 Mexican Census, as provided by IPUMS. The migration rates foreducation are only for adults age 25 or older.

32

Table 3: First Stage Instrumental Variables Results.

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.01.

Please refer to equation 4.2 in the text for the first stage regression equation.

33

Tab

le4:

OL

San

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econ

dS

tage

IVR

esu

lts.

Rob

ust

stan

dard

erro

rsin

pare

nth

eses

***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.0

1.

34

Table 5: Exclusion Restriction Test.

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.01.

Other controls include of municipal population in 2010, percentage of HHs with running water in 2010, a binary indicatorfor sharing a border with the U.S., average years schooling adults in 2010, unemployment in 2010 and 2000, the internationalmigration rate for 2000, income per capita in 2000, and year fixed effects.

35

Tab

le6:

Loca

lA

vera

geT

reat

men

tE

ffec

ts.

Rob

ust

stan

dard

erro

rsin

bra

cket

s.***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.0

1.

Oth

erm

un

icip

al

contr

ols

incl

ud

e:aver

age

hom

icid

esp

er100,0

00

inh

ab

itants

for

2005

an

d2006,

log

of

pop

ula

tion

,u

nem

plo

ym

ent

in2010

&2000,

aver

age

yea

rs

sch

oolin

gad

ult

s,p

erce

nta

ge

of

house

hold

sw

ith

run

nin

gw

ate

r,a

bin

ary

ind

icato

rfo

rsh

ari

ng

ab

ord

erw

ith

the

U.S

.,in

com

ep

erca

pit

ain

2000

an

dth

eap

pro

pri

ate

mig

rati

on

ind

ices

for

2000.

36

Table 7: Robustness Checks.

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.01.

Other municipal controls include: average homicides per 100,000 inhabitants for 2005 and 2006, log of population,unemployment in 2010 & 2000, average years schooling adults, percentage of households with running water, a binaryindicator for sharing a border with the U.S., income per capita in 2000 and the appropriate migration indices for 2000.

37

Tab

le8:

Het

erog

enei

tyin

Mig

rati

onR

ates

.

Rob

ust

stan

dard

erro

rsin

bra

cket

s.***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.0

1.

Oth

erm

un

icip

al

contr

ols

incl

ud

e:aver

age

hom

icid

esp

er100,0

00

for

2005

an

d2006,

log

of

pop

ula

tion

,u

nem

plo

ym

ent

in2010

an

d2000,

per

centa

ge

of

hou

seh

old

sw

ith

run

nin

gw

ate

r,a

bin

ary

ind

icato

rfo

rsh

ari

ng

ab

ord

erw

ith

the

U.S

.,in

com

ep

erca

pit

ain

2000,

an

dth

eap

pro

pri

ate

mig

rati

on

ind

ices

for

2000.

Pop

ula

tion

for

edu

cati

on

lim

ited

toad

ult

sold

erth

an

25.

38

Figure 1: Average Out-Migration by Types of Municipalities

(a) Average Internal Migration

Data source : IPUMS and INEGI. Differences across years are statistically significant for border municipalities but notthe 90th percentile.

(b) Average International Migration

Data source : IPUMS and INEGI. Differences across years are statistically significant for the 90th percentile but notthe border municipalities.

39

Figure 2: Map of Homicides across Mexico

(a) Total Homicides for 2007-2010 per 100,000 Inhabitants

Source:INEGI

(b) Drug-Related Homicides for 2007-2010 per 100,000 Inhabitants

Source:SSNEP

40

Figure 3: Test of Monotonicity for the Instrumental Variable

41