Illegal drug markets and violence in Mexico: The

causes beyond Calderon

Juan Camilo Castillo∗ Daniel Mejıa† Pascual Restrepo‡

This version: February 2013

Abstract

This paper estimates the effect that drug trafficking has had on violence in

Mexico in recent years. We use two different proxies for drug trafficking ac-

tivities at the municipal level: a measure of cartels’ presence and the value of

cocaine seizures, and instrument them using simple geographic features of each

municipality interacted with cocaine seizures in Colombia. In order to motivate

our empirical exercise, we propose a simple model of the war on drugs that cap-

tures the essence of our identification strategy (e.g., the interrelationship between

aggregate supply shocks, the size of illegal drug markets and violence). Our es-

timations indicate that the rise in drug trafficking in recent years in Mexico has

generated a significant and sizable increase in the levels of violence. The effects

are especially large for violence generated by clashes between drug cartels. We

also find that this effect is mainly driven by municipalities with presence of two

or more cartels. Although we find that aggregate supply shocks originated in

drug seizures in Colombia have always had an impact on drug trafficking in Mex-

ico, violence generated by drug trafficking has been much greater since president

Calderon took office in December 2006. Our results show that government crack-

downs on drug cartels might not be the only explanation behind the rise of illegal

drug trafficking and violence observed in the last six years in Mexico. Successful

interdiction policies implemented in Colombia since 2006 have also played a ma-

jor role in explaining the worsening of the situation observed in Mexico during

Calderon’s sexenium.

Keywords: War on Drugs, Violence, Illegal Markets, Mexico.

JEL Classification Numbers: D74, K42.

∗Economics Department, Universidad de los Andes, e-mail: jc.castillo126@uniandes.edu.co†Corresponding author. Economics Department, Universidad de los Andes, e-mail: dmejia@uniandes.edu.co‡Economics Department, MIT, e-mail: pascual@mit.edu

1

1 Introduction

During the last few years, Mexico has witnessed a dramatic increase in violence: the

homicide rate in 2010 was more than twice that of 2005 (see figure 1). Most of the surge

in violence in Mexico can be attributed to illegal drug trafficking. The data shows that

drug-related homicides have had a drastic increase during the last few years, whereas

other types of homicides have increased much less. In 2007, there were 8,686 homicides,

out of which 2,760 were estimated to be drug-related. On the other hand, in 2010 there

were 25,329 homicides; according to official figures, 15,258 were drug-related. This

means that drug-related homicides increased by 453% between 2007 and 2010, whereas

non-drug-related homicides increased by 70% during the same period1.

3040

5060

70M

urde

rs /1

00 0

00 in

habi

tant

s (C

olom

bia)

510

1520

25M

urde

rs /1

00 0

00 in

habi

tant

s (M

exico

)

2000 2002 2004 2006 2008 2010Year

Mexico Colombia

(a) Homicide rates in Colombia and Mexico per

year per 100,000 inhabitants

5010

015

020

025

0Co

cain

e se

izure

s (m

etric

tons

)

510

1520

25M

urde

rs p

er 1

00 0

00 in

habi

tant

s

2000 2002 2004 2006 2008 2010Year

Homicide rate in Mexico Cocaine seizures in Colombia

(b) Homicide rate in Mexico against cocaine

seizures in Colombia

Figure 1: Comparison of violence indicators in Mexico with measures of violence and

effectiveness of interdiction in Colombia.

Mexico has been the main point of entry of drugs into the U.S. since, at least, the

turn of the century. Illicit drugs produced in the Andean countries, most importantly

cocaine, used to be shipped through the Caribbean, but with the installation of sev-

eral radars that blocked this route during the second half of the 1990s, Colombian and

Mexican drug traffickers started to use the Mexican route more intensively to smuggle

drugs into the U.S. The fights between Drug Trafficking Organizations (DTOs) over the

control of drug trafficking routes has always been a major source of violence, especially

in key locations that have a geographic comparative advantage for drug trafficking.

1An important part of the 70% increase in non-drug-related crimes may be due to spillover effects,

but this is not the focus of our analysis in this paper.

2

However, with the rise of crackdowns in Colombia since 2006, Mexican DTOs gained

even more importance. This last point is at the heart of our empirical strategy to iden-

tify and quantify the effect of the size of illegal drug markets on violence. If it is indeed

true that illegal (wholesale) drug markets cause violence, then supply shocks originated

in market crackdowns in Colombia should be felt disproportionately in those places in

Mexico where, due to their geographic location, have a comparative advantage for drug

smuggling. The disputes over the control of drug routes for the wholesale distribution

process have been referred to as the systemic violence channel by Goldstein (1985).

Although Mexico also produces drugs such as cannabis and ATS (Amphetamine-type

stimulants), most profits obtained by Mexican DTOs are generated by drug-trafficking

activities, and not by drug production.

What happened in 2006 that caused such a drastic increase in drug-related homicides

in Mexico? Many observers in Mexico have blamed the government of President Felipe

Calderon for the sudden change in violence trends. On December 11, 2006, just ten

days after taking office, president Calderon sent thousands of federal troops to the state

of Michoacan in order to try to stop drug-related violence. This was only the first act of

a new strategy against drug trafficking which involved the massive use of armed forces

to repress violence and drug-trafficking activities. Calderon’s critics do not only point

out the obvious fact that his strategy has been unsuccessful; they argue that his actions

have only worsened the situation (see, for instance, Guerrero (2011)). His actions have

beheaded many drug cartels, either by killing or capturing their leaders, which has led

to internal disputes over the control of this illegal business by competing cartels or by

lower ranked members of the same DTOs. Also, critics argue, the strategy led to the

splitting of major cartels into smaller ones 2. Therefore, the situation around 2005,

when a few DTOs with strong leaders controlled drug trafficking routes in a state of

relative calm, led to a new stage of ever-present disputes between small groups for the

control of routes, as well as common executions within cartels by low and medium-

ranked members seeking to obtain more power inside their own organization.

As compelling as the previous argument may be, Calderon’s critics seem to be

largely unaware of another major change that took place roughly at the same time that

Calderon took office: a large increase in cocaine seizures in Colombia as a result of a

change in the anti-drug strategy in this country. When Juan Manuel Santos, today

President of Colombia, was named Minister of Defense in July of 2006, he redefined the

anti-drug strategy, making less emphasis on attacking those parts of the drug production

2Grillo (2011), in his book El Narco, provides a thorough review of the history of drug trafficking

in Mexico and its nexus with crime and violence

3

chain that produce lower value added (coca crops) and more on the interdiction of drug

shipments and the detection and destruction of cocaine processing labs. This change

in the anti-drug strategy can be confirmed in the data. While the numbers of hectares

of coca crops sprayed went down from 172,000 per year in 2006 to about 104,000 in

2009 (a 40% decrease), cocaine seizures went up from 127 metric tons (MT) in 2006 to

203 MT in 2009 (a 60% increase), and the number of cocaine-processing labs detected

and destroyed increased from about 2,300 to about 2,900 during the same period (a

26% increase). This change in the Colombian strategy in the war on drugs induced a

negative supply shock in cocaine markets that was noticeable throughout the region

and even in cocaine street prices in the U.S. The price per pure gram went up from

about $135 in 2006 to about $185 in 2009 for purchases of two grams or less, and from

$40 to $68 for purchases between 10 and 50 grams during the same period3. At the

regional level, this aggregate negative supply shock in cocaine markets meant a re-

accommodation of the cocaine-trafficking business across different countries. Between

2006 and 2007, coca-plant cultivation started to increase again in Bolivia and Peru, the

processing labs moved from Colombia to Venezuela, Ecuador and Peru, and the bases of

operation of drug cartels moved from Colombia to Mexico and Central America. This

is the so-called ballooning effect at work, in which the success of authorities in some

producer nation makes drug trafficking less profitable in that nation, leading in turn

to the rise of strong drug cartels in other countries that can take the profits from the

trade.

At this point, we would like to emphasize that both causes for the rising violence

in Mexico are not mutually exclusive: the efforts by the Mexican government and

the Colombian successes may well have both pushed the homicide levels in Mexico

upwards. We simply believe that the second reason has been mostly ignored in the

Mexican debate. Thus, it is of great interest to be able to test empirically whether

Colombian drug interdiction has had an effect on Mexican drug-trafficking activities

and violence, and to measure how important this effect has been.

The purpose of this paper is twofold. First, we measure the effect that drug traffick-

ing has had on violence in the Mexican context. This is, however, not an easy task: a

simple correlation is not conclusive, since many theories point out that illegal activities

are more likely to occur in violent environments, so reverse causality would be an im-

portant issue. We therefore propose two exogenous sources of variation as instruments

in order to estimate the causal relation between illegal drug trafficking and violence:

3Source: http://www.whitehouse.gov/sites/default/files/ondcp/policy-and-

research/2011 data supplement.pdf

4

simple geographic characteristics, such as distance to the sea or to the U.S. border,

which capture the municipalities’ comparative advantage in illicit drug trade, and sup-

ply shocks in cocaine wholesale markets, measured by the amount of cocaine interdicted

by Colombian authorities. These two instruments and their interaction provide us with

enough temporal and spatial variation to be able to estimate the magnitude of the effect

that drug trafficking has had on violence in Mexico. Our second objective is to show

that the recent Colombian success in the struggle against illicit drug trafficking has

played an important role in the increase in drug trafficking activities and drug-related

violence in Mexico in recent years. This is not an easy relationship to measure, since

the fact that both are increasing is not compelling evidence for a causal link. However,

we will show that the high-frequency time series related to Colombian cocaine seizures

has a very strong correlation with homicide rates in Mexico. It would be hard to argue

that there is an underlying factor that causes both high-frequency trends.

The paper is organized as follows: Section 2 presents the relevant literature on the

relationship between illicit drug markets and violence and on the recent situation in

Mexico. We then present a simple model of drug trafficking and cartels’ competition

in section 3 in order to motivate our empirical strategy. In section 4 we describe the

data that we use in our empirical analysis and our identification strategy. The main

results are presented in section 5. In section 6 we perform various robustness checks

and falsification tests to further check the validity of our results. Finally, we present

some concluding remarks in section 7.

2 Literature review

A widespread notion is that illegal-drug markets, especially wholesale markets, are

violent. However, the empirical literature does not show the type of consensus that one

would expect from a notion that, at least intuitively, almost everybody agrees on. One

of the first attempts to address this issue is Miron (2001). He analyses a large cross

section of countries, showing that there is a strong correlation between the levels of

violence and various indicators of the so-called “war on drugs.” Of course, this kind of

correlation does not imply any type of causality, but it is some preliminary evidence

that there may be a causal link between the two.

The results of empirical papers focusing on specific countries are somewhat contra-

dictory. One the one hand, for the case of Afghanistan, Lind et al. (2009) shows that

violence increases opium cultivation, although the effect is weaker in regions with better

law enforcement. Lind’s argument is that violence affects opium cultivation through

5

the channel of lower institutional quality. On the other hand, Bove (2011) contradicts

Lind’s findings and shows that there is no clear relationship between opium cultivation

and violence in the time-series, and if that relationship exists, its magnitude is not what

most studies would expect.

For the case of Colombia, Dıaz and Sanchez (2004) use a panel of the Colombian

departments (states) and a matching methodology in order to deal with classic endo-

geneity issues. They show that the presence of armed groups leads to increased levels

of coca cultivation. Medina and Martınez (2003) use a panel of Colombian municipal-

ities and show that the rate of drug-related arrests has no correlation with violence

indicators.

Other papers have made a more convincing case when trying to resolve the endo-

geneity problem associated with disentangling the causal relationship between illegal

drug markets and violence. One such work is that of Angrist and Kugler (2008). Before

1994, the majority of cocaine in the world was produced from coca cultivated in Peru,

which was subsequently taken by plane to Colombia and used as raw material for the

production of cocaine. In 1994, however, the Peruvian government started shooting

down planes taking coca to Colombia. Coca crops then moved from Peru to Colombia

to continue the production of cocaine. The authors use this fact in a difference in dif-

ferences approach in order to compare the change in homicide rates in those regions in

Colombia where coca was cultivated before and after the Peruvian government started

shooting down planes. Their main result is that the presence of coca crops indeed

causes higher levels of violence.

Chimeli and Soares (2010) explore how illegality itself generates violence, but in

a different market: mahogany exploitation in the Brazilian amazon. Mahogany trade

in Brazil was initially legal, but became prohibited within a short period of time,

between March 1999 and October 2001. The authors also use a difference in differences

approach to compare homicide rates before and after prohibition in regions with and

without mahogany extraction. They show robust evidence of an increase in violence

(homicide rates) in mahogany areas after prohibition. Under Goldstein’s framework4, it

would be hard to argue that mahogany use or consumption cause violence through the

pharmacological channel, which allows the authors to interpret their results as further

4As explained by Goldstein (1985), drugs can cause violence through three channels. Pharmaco-

logical violence is due to the consumption of drugs, which leads people to a mental state in which they

are more prone to violence. The economic compulsive model is due to drug addicts needing resources

in order to buy more drugs, which may lead them to attack people in order to steal money. These two

channels are unrelated to drug markets. The third channel is systemic violence, and is caused by the

presence of drug markets, e.g. by disputes over territory or attacks within dealing hierarchies.

6

evidence of the existence of the systemic channel (or market-based violence).

Another work that directly addresses the issue of endogeneity is Mejıa and Re-

strepo (2013). They measure the effect of cocaine production activities on violence in

Colombia using an instrumental-variable approach. Using a panel of Colombian mu-

nicipalities, they instrument the presence of coca crops with the interaction of external

demand shocks for Colombian cocaine and an index that determines the fitness of each

municipality for the cultivation of coca. Their results indicate that cocaine production

activities explain a non-negligible fraction of homicide rates (36%), forced displacement

rates (66%) and attacks by illegal armed groups (43%). The interpretation of their

results relies on the fact that illegal armed groups compete against each other (and

against the government) over the control of territories suitable for coca cultivation and

cocaine production, and this competition is, almost always, violent5.

There is a heated debate in Mexico about the main causes behind the increase in vi-

olence in recent years, especially by pundits that point fingers at Calderon’s strategy as

the main reason for the increase in homicides observed in the last years. More precisely,

many observers and security analysts have argued that Calderon’s strategy of frontally

attacking drug cartels, especially their leaders, has been the main cause of the surge

in homicides since 2007. Some studies supporting this position are Guerrero (2010),

Merino (2011), and Guerrero (2011). Some other studies defend the government’s ac-

tions, such as Poire and Martınez (2011), who argue that the strategy of capturing or

killing DTO leaders does not increase violence, and Villalobos (2012), who supports

Calderon’s strategy by saying that in order to eliminate drug-related violence, a period

of higher levels of violence is necessary before homicide rates start going down.

Two recent papers have taken the policy endogeneity problem seriously, which arises

when trying to estimate the causal effect of Calderon’s military strategy against DTOs

on the levels of violence. Dell (2011) uses a regression discontinuity design, comparing

those municipalities where the PAN, Calderon’s party, won the local elections by a small

margin vis-a-vis those municipalities in which the PAN lost by a small margin, expecting

more violence where the PAN lost. The intuition behind her identification strategy is

that it was easier for the Federal Government to intervene in municipalities with a PAN

mayor, thus making Calderon’s war on drugs more intense on such places. The study

concludes that frontal actions against DTOs have caused an important increase in the

levels of violence. On the other hand, another study by Calderon et al. (2012) combines

5To further test this channel, Mejıa and Restrepo (2013) show that violence outcomes not related

to the disputes over the control of arable land (such as kidnappings and extortion) are not affected by

coca cultivation and cocaine production activities.

7

a difference in differences methodology with synthetic control groups and shows that

Calderon’s intervention did have an effect on the levels of violence, but it was only a

temporal effect (contrary to what Guerrero (2011) argues).

Overall, the debate on whether drug markets, anti-drug efforts and violence are

(causally) related is quite heated in the region. This paper attempts to show additional

evidence of the causal link between drug trafficking activities and violence for the case

of Mexico. Furthermore, we provide a new, perhaps complementary, explanation for

the causes behind the surge in violence observed in Mexico over the last few years.

3 A simple model of drug markets, supply shocks

and violence

Consider a municipality through which drug traffickers can potentially transport cocaine

produced in another country on its way to final consumer markets. We will denote this

municipality by i. The total gain from cocaine trafficking in municipality i is given by

pxsi, where p is the price of cocaine in wholesale markets in the final consumer country,

x is the total amount of cocaine transacted in wholesale markets in consumer countries,

and si is the share of the total cocaine trade that passes through municipality i. This

share depends on many factors, but probably the most important one is the location

of the municipality. For instance, being close to the the sea and/or to the consumer

country’s border means that the transportation of cocaine through municipality i is

much easier, meaning that a greater fraction of the drugs taken to the final consumer

country will pass through this specific municipality.

We assume that there is a pool of cartels that can participate in drug trafficking in

municipality i, but they have to pay a fixed cost, c, for participating in the trade. This

cost includes the resources needed to establish a network to run the trade (contacting

drug producers in the producer country and drug dealers in consumer countries). In a

first stage, each cartel decides whether they want to participate in the trade based on

the potential gains and the costs of doing so. In a second stage, the participating cartels

engage in a conflict over the control of the drug trade in this municipality. Cartel j

invests gi,j in the conflict over the control of municipality i, which includes the costs

of buying weapons, recruiting and training armed personnel, etc. We will assume that

cartel j obtains a fraction qi.j of the total benefit in municipality i, which is determined

by the following contest-success function:

8

qi,j =gi,j

gi,j +∑

k 6=j gi,k,

where the sum in the denominator is made over all cartels disputing municipality i’s

control. Note that if cartel j decides not to participate in trafficking in municipality i,

gi,j cannot be greater than zero.

Let us solve this problem by backward induction. In the final stage, n cartels are

participating, and each one decides the amount of resources to invest in the dispute

over the control of the trade in municipality j. The profit maximization problem cartel

j solves is therefore:

Πi,j = maxgi,j

[pxsiqi,j − gi,j] . (1)

The fixed cost does not appear at this stage since it is already a sunk cost. The first

order condition is thus pxsi∂qi,j∂gi,j

= 1, which we can solve for gi,j and obtain the following

best-response function for cartel j:

gi,j =

√pxsi

∑k 6=j

gi,k −∑k 6=j

gi,k. (2)

By assuming that all cartels are identical in all relevant dimensions, there is a unique

solution due to the concavity of qi,j(gi,j). Thus, we can find the Nash equilibrium level

of expenses in the dispute over the control of drug trafficking in municipality i by

setting gi,j = g∗i for all cartels. The equilibrium level of expenditures in conflict and

the resulting cartel’s profits are6:

g∗i = pxsini − 1

n2i

, and Π∗j =pxsini− g∗i =

pxsin2i

, (3)

where ni is the number of cartels that decided to participate in the first stage.

In the first stage, new cartels are willing to enter as long as the benefits of participat-

ing are greater than the fixed cost, e.g., ifpxsi

(ni + 1)2≥ c. Thus, the equilibrium number

of cartels is the greatest integer ni such thatpxsin2i

≥ c ⇐⇒ pxsic≥ n2

i . We define

the total level of violence in municipality i as the amount of resources invested in the

6There is, of course, a second solution, where g∗i = 0. However, this is an unstable solution since

any cartel can appropriate the full benefit in municipality i by investing an infinitesimal amount of

resources in the conflict. This seems to be a contradiction to the fact that qi,j(gi,j) is concave. To be

more precise, qi,j(gi,j) is concave as long as gi,k 6= 0 for some k 6= j

9

conflict by all cartels that decide to participate in drug trafficking in this municipality:

nig∗i = pxsi

ni − 1

ni(4)

3.1 Comparative statics

We now turn to analyze how the number of cartels, ni, and the level of violence, nig∗i ,

depend on the exogenous variables of the model. We first show how they depend on

the total value of the drug trade, Vi = pQsi.

Proposition 1. Both the number of cartels ni and the total amount of investment in

the conflict nig∗i are increasing in the total value of the drug trade in municipality i,

Vi = pQsi.

Proof. Since the number of cartels is the greatest integer ni such that Vic≥ n2

i , it is

clear that it is increasing in Vi. The amount of violence is more complicated, since it

depends on Vi both through ni and g∗i . Furthermore, ni increases discretely, so we have

to analyze two cases. When Vi increases without an increase in ni, g∗i increases, since

the partial derivative of g∗i with respect to Vi is positive. On the other hand, when ni

jumps discretely, there is an increase both in pxsi and in ni−1ni

, so from equation (4) it

is clear that the total investment in the conflict increases.

Now that although we know how our model behaves when there is a change in the

total value of drug trafficking in municipality i, we would like to know how this value

depends on the exogenous variables. The price p and the amount of cocaine consumed x

are related through the elasticity of demand, so in practice there are only two exogenous

variables, x and si. Our results, and their implications related to proposition 1, are

summarized in the following proposition:

Proposition 2. The total value of the trade in municipality i is increasing in its share

of the cocaine trade si. Therefore, the number of cartels and the total investment in the

conflict are also increasing in the share of the cocaine trade.

If the demand for drugs in consumer markets is inelastic, the total value of trade in

municipality i increases when there is a tightening in the supply of cocaine in producing

countries. In that case, both the number of cartels and the total investment in the

conflict also increase in response to supply tightenings in upstream markets. These two

results are reversed if the demand for drugs is price elastic.

Proof. It is clear from its definition that the total value of the trade increases if the share

increases. On the other hand, the change due to a supply tightening can be measured

10

by its elasticity, d ln pxsid lnx

= d ln pxsid lnx

= d ln pd lnx

+ d lnxd lnx

+ d ln sid lnx

. The third term vanishes, since

we assume the share of each municipality to be independent of the supply of drugs, and

the second term is the inverse elasticity, so that d lnVid lnx

= 1 + 1εd

. This term is negative if

εd > −1, i.e., if the demand is inelastic, and is positive if εd < −1, i.e., if the demand is

elastic. This proves the results about the response of the total value to tightenings of

the supply in upstream markets.

The results about the number of cartels and the total investment in the conflict are

a simple extension from the last paragraph, taking into account proposition 1.

3.2 Empirical application to the case of Mexico

Our model shows (equation (4)) that the amount of violence, measured by the total

investments in conflict by drug cartels, is positively correlated with two measures of drug

trafficking: the total value of the drug trade through municipality i, and the number

of cartels present in that municipality. A naive empirical strategy would be to run a

regression of violence as a function of any of these two measures of drug trafficking.

However, this kind of estimation would face the problem of reverse causality. Our model

only explains how drug trafficking causes violence, but it is silent about the ways in

which violence can increase drug trafficking. For instance, violence could diminish the

presence of the State or weaken State institutions, which would in turn make it easier

for cartels to smuggle drugs.

Any shock to the size of drug trafficking activities, Vi, that is not caused by violence

is a good candidate for an instrument and would provide us with an exogenous source

of variation to estimate the causal effect of drug trafficking on violence. Proposition 2

provides two good candidates. The first candidate is supply tightenings in upstream

markets (e.g., cocaine crackdowns in Colombia), which can be interpreted as a decrease

in x. There is plenty of evidence that the demand for drugs is inelastic (see, for instance,

Becker et al. (2006) and Mejıa and Restrepo (2008)), which, according to our model,

means that such tightenings would lead to an increase in drug-trafficking activity, and

ultimately, in the level of violence. The second candidate is any variation in the share of

the total trade that each municipality holds. Such variations can be measured in many

ways, but our proposal is to use the geographic location of the municipality and, more

specifically, its distance to the U.S. border or to the sea, both of which are strategic

locations for trafficking cocaine. These can then be used as instruments for measures of

the intensity of illegal drug markets, such as the value of the drug trade or the number

of cartels operating in each municipality. We now turn to the description of the data

11

and our empirical strategy.

4 Empirical strategy

4.1 Data

Our dataset is a monthly panel of Mexican municipalities from December 2006 until

December 2010. Our data comes from different sources. The basic geographic data,

mainly the location of municipalities, was gathered from the INEGI7. We use the de-

mographic data both from the 2010 census and from the 2005 population count, which

is also published by the INEGI. This includes population figures as well as various mea-

sures of development, such as literacy rate, GDP per capita, infant mortality, school

attendance, and a measure of the human development index. We also use the data

provided by the UNDP8 on their calculation of the human development index (HDI)

for each Mexican municipality in 2005.

The data on drug-related homicides was published by the Mexican Presidency9 in

2011, and includes the number of monthly casualties from December 2006 until De-

cember 2010. This dataset only includes homicides that, according to local authorities,

had a relation with illegal drug trade. Drug-related homicides are divided into three

broad categories: (1) executions, which involve targeted assassinations by DTOs; (2)

confrontations, which are the result of battles either between competing DTOs or be-

tween DTOs and government authorities; and (3) aggressions, which are the result of

DTOs attacking government forces. We are aware that determining whether a homicide

is drug-related and classifying it in one of the three categories we just described cannot

be done in a clearly defined and exact way, and that this might be an inexact count of

drug-related murders. However, in order to check the robustness of our results, we also

use the homicide rates published by the INEGI, which are available for the period from

January 1990 until December 2010. In contrast to those published by the Presidency,

the INEGI homicide rate is not only for drug-related incidents; instead, they include

all types of homicides.

We use two proxies for drug-trafficking activities at the municipal level. First, we

use information about the presence of the main drug cartels in each municipality. We

use the measure of presence of drug cartels proposed by Coscia and Rıos, 2012, who,

7Instituto Nacional de Estadıstica y Geografıa - www.inegi.org.mx8United Nations Development Programme - http://www.undp.org9http://www.presidencia.gob.mx/base-de-datos-de-fallecimientos/

12

based on web content such as blogs and news, build a yearly panel that provides a

measure of whether a cartel was present in each municipality during each year. The

dataset covers the period from 1990 until 2010 and contains information about the seven

most important Mexican drug cartels (Cartel de los Hermanos BeltrA¡n Leyva, Familia

Michoacana, Cartel del Golfo, Cartel de JuA¡rez, Cartel de Sinaloa, Cartel de Tijuana

and Los Zetas), as well as about the presence of other smaller DTOs. Second, we use

data on cocaine seizures in Mexico as a proxy for drug trafficking activities. This data

was taken from the Federal Government of Mexico, which lists every drug seizure from

December 2006 until April 2011, along with its date and place of occurrence. This data

is classified in four categories according to the type of drug: cocaine, heroin, cannabis,

and ATS.

We also use information from Colombian interdiction activities in order to measure

supply shocks to the Mexican drug trade in upstream markets. This database, provided

by the Colombian Ministry of Defense, includes coca-crop eradication efforts, seizures

of different types of drugs, intermediate goods, planes, ships and vehicles, and the

number of drug-processing laboratories destroyed. These variables are monthly totals

for Colombia from January 2000 until April 2012. We also have an estimate of the

total yearly production of cocaine for the years 2000-2010, which was obtained from

the same source. Additionally, the Colombian National Police publishes the yearly

seizures by each of its local divisions within the Colombian territory. We use the data

on the interdiction of cocaine at the regional level in Colombia in some of our robustness

checks.

Finally, we use the price of cocaine in the U.S. from the STRIDE (System to Re-

trieve Information from Drug Evidence) database of the DEA (U.S. Drug Enforcement

Administration).

4.2 Identification strategy

There is wide agreement among observers of the recent Mexican situation that the

illegal drug trade has intensified violence in the country. Drug cartels are in constant

turfs over the control of territories that they can use to transport narcotics from the

main producer nations into the U.S. As was explained before, many analysts also argue

that government-led crackdowns on DTOs have also increased the levels of violence.

Figure 2 presents maps of Mexico showing the most violent regions measured by their

homicide rates as well as the regions where most drug trafficking has occurred. Drug

trafficking is measured in two ways: the value of cocaine seized and the number of

13

cartels present. There is a clear relationship between the three maps, as the regions

where violence and the drug trade are most intense are mostly the same: The northeast

region, close to the Gulf of Mexico and next to the U.S. border, the northwest of the

country, and the Pacific region southwest of Mexico City, especially in the state of

MichoacA¡n. This relationship is especially strong between homicide rates and the

number of cartels present in a municipality.

(a) Homicide rates (b) Cocaine seizures

(c) Number of cartels

Figure 2: Maps of Mexico by municipality. Figure 2(a) shows the average homicide rate,

figure 2(b) shows the average cocaine seizure rate, and figure 2(c) shows the average

number of cartels. All maps show totals for the 2007-2010 period.

Measuring this relationship empirically, however, involves some problems, and sim-

ply finding a correlation between the two variables is not enough to conclude the exis-

tence of causality. The naive way of estimating the effect of drug trafficking activities

on the levels of violence would be to estimate the following equation by OLS:

14

log hi,t = µi + γt + βdi,t + δXi,t + ui,t (5)

where hi,t is the homicide rate, di,t is a measure of drug-trafficking activities, µi are

municipality fixed effects, γt are time fixed effects, Xi,t is a vector of controls, and ui,t

is a random error term. However, the estimation of equation 5 by OLS would suffer

from endogeneity issues that would make it impossible to interpret the coefficient β as

the causal effect of drug trafficking activities on violence. On the one hand, there is the

problem of reverse causality: more violence in a given region reduces opportunities for

legal activities, deeming them less profitable due to losses both of human and physical

capital. On the other hand, it may also be the case that both violence and illegal

drug trafficking share the same underlying cause, such as weak state presence or bad

institutions that enable the existence of illegal activities.

In order to solve the issue of endogeneity in the estimation of equation 5 we need to

find exogenous sources of variation that we can use as instruments for the size of illegal

drug markets in Mexico. Ideally we should use variables that vary across space and time

in order to fit the structure of our data on drug trafficking and homicides. However,

finding such a variable proves to be a very difficult task. Therefore, we follow a strategy

that uses the interaction of exogenous variables that vary in time and variables that

vary across municipalities. The choice of our instrument is motivated by the model

presented in section 3. More precisely, the spatial source of variation that we use is a

simple geographic variable that captures each municipality’s comparative advantage for

the drug trade: distance to final consumer markets (e.g., distance to the U.S. border) or

distance to the main points of entry of cocaine into Mexico (the Pacific and Caribbean

coast). Based on the model we proposed in section 3, a municipality that is strategically

located for the drug trade should have both more drug trafficking and higher levels of

violence. The main variable that we use is distance to the U.S. border: the value of

a municipality as a route for drug trafficking is presumably higher in places that are

close to the U.S., since the main objective of drug traffickers in Mexico is to smuggle

drugs across the border with this country. We thus expect drug trafficking to be more

intense in municipalities located close to the U.S. border. We also use distance to the

Atlantic and Pacific Oceans as alternative measures of how strategically well located a

municipality is for the drug trade. The maps in figure 2 are a motivation for this part

of the instrument, since violence and drug trafficking activities are concentrated in the

northern areas of Mexico, close to the U.S. border, and in some areas near the Pacific

Ocean (especially in the state of Michoacan).

We use information on interdiction rates in Colombia as a temporal source of ex-

15

ogenous variation. Our model from section 3 also provides a good motivation for the

use of supply shocks in upstream markets as an instrument. The main variable that we

use is the total amount of cocaine seized each month in Colombia, which is the largest

cocaine-producing country in the world. Large seizures by Colombian authorities in-

duce a negative (and exogenous) supply shock in wholesale drug markets in Mexico.

The higher prices caused by this kind of shock imply that the value of the cocaine mar-

kets in Mexico increases, so drug traffickers in Mexico would want to transport a larger

amount of cocaine into the U.S., thus intensifying the activities of drug cartels. As was

explained in section 3, this relies on the assumption that the demand for cocaine at

the Mexican-U.S. border is inelastic, which is justified by various studies showing that

the demand for addictive drugs is price inelastic. Figure 3 shows scatter plots of the

seizure rate in Colombia against the homicide rate in Mexico, as well as the same plot

with a one-period lag. Both plots show a positive correlation that motivates the use of

interdiction rates in Colombia as an instrument, as it suggests that cocaine seizures in

Colombia have an effect in illegal drug markets in Mexico and in the violence caused

by drug trafficking.

The intuition behind using the interaction of a municipality’s distance to the U.S.

and cocaine seizures in Colombia is the following: If it is indeed true that illegal drug

markets (drug trafficking) cause violence, then a negative supply shock induced by the

interdiction of a large cocaine shipment in Colombia should affect the size of illegal drug

markets in Mexico, but the effect should be different (e.g., heterogeneous) depending

on the location of each Mexican municipality. More precisely, the effect of the negative

supply shock on the intensity of drug trafficking activities should be larger in those

municipalities that, due to their strategic location, have a comparative advantage for

the drug trade. In other words, if our theory is correct, a negative supply shock should

be felt more strongly in the level of drug trafficking activities of those municipalities

located close to the U.S. border. The validity of using this interaction as an instrument

depends on violence not being caused directly by it, but rather, only through its effect

on the intensity of drug trafficking activities.

The first stage regression of our 2SLS framework is:

di,t = νi + ηt + αst × gi + λXi,t + ei,t (6)

where νi and ηt are municipality and time fixed effects, st is the cocaine seizure rate in

Colombia, gi is the distance of municipality i to the U.S. border, and ei,t is the error

term.

The second stage regression uses the fitted values, di,t, derived from the estimation

16

05

1015

Hom

icide

rate

in M

exico

per

100

000

inha

bita

nts

0 200 400 600 800 1000Cocaine seizure rate in Colombia

(a) Contemporary variables

05

1015

Hom

icide

rate

in M

exico

per

100

000

inha

bita

nts

0 200 400 600 800 1000Cocaine seizure rate in Colombia at t-1

(b) One-month lag of cocaine seizures

Figure 3: Scatter plots of the homicide rate in Mexico against the rate of cocaine

seizures in Colombia

of equation 6. More precisely, the second stage regression is:

log hi,t = µi + γt + ϑdi,t + ηXi,t + εi,t, (7)

where, once again, µi is a municipality fixed effect, γt is a time fixed effect, Xi,t is a

vector of controls, and εi,t is a random error term. Our coefficient of interest is ϑ, and

captures the causal effect of drug trafficking activities on the levels of violence.

5 Results

5.1 Presence of cartels as a proxy for drug trafficking

The first proxy that we use for drug trafficking activities is a measure of the presence

of cartels. This data is available yearly and at the municipal level. Since this data

includes the presence of six different cartels, we use the information in different ways in

order to build an appropriate proxy variable related to drug trafficking activities. We

also believe that this dataset is very reliable, which implies low measurement error. The

fact that this is yearly data implies that we could have two undesirable consequences.

First, the variance of our estimates could be relatively high due to the smaller number

of observations available, much less than what would be possible if our proxy were

available monthly. We will see with our results, however, that this is not the case.

Second, the yearly data will not allow us to estimate the short-term characteristics

17

of the relationship between interdiction rates in Colombia and drug trafficking and

violence in Mexico, so we will only be able to measure long-term effects. Additionally,

we will limit ourselves to the period starting in 2007 and ending in 2010, since the data

on drug-related homicide rates is only available for that period.

5.1.1 Baseline regressions: Number of cartels present

Initially, we estimate the model using a naive OLS methodology, including municipality

fixed effects. The proxy for drug trafficking activity that we use is the total number

of cartels that are present in each municipality in each year, from 2007 until 2010. A

possible issue that may arise is that the methodology used by Coscia and Rıos (2012)

based on news and blogs (whose results we use to determine the number of cartels)

may not measure the mere presence of drug cartels. Instead, it could be reporting only

those municipalities in which cartels commit acts of violence, leading to media exposure.

However, our results in section 5.1.3 provide evidence that this is not the case. The

results using this proxy are shown in table 7. The first five columns show the results

using the total drug-related homicide rate, and in each column we add different control

variables progressively. In the first column we only use two nationwide time series as

controls: GDP per capita and national military expenditures. In the first column, we

add time fixed effects, which results in both controls used in the first regression being

dropped due to their collinearity. Then, in the third column, we add the log of per

capita municipal income, which we use as a proxy for income per capita. We also

use two proxies for state presence in each municipality: social security personnel10 per

capita and the number of middle and high-school teachers per capita.

The controls that we have described so far address the first type of endogeneity that

one would think could arise in our model due to differences in income and institutional

strength, or due to time or municipality fixed effects. However, there is an additional

form of endogeneity that could arise in our model: the time series for cocaine seizures in

Colombia could be correlated with some other time series, either because of sheer luck or

because of reasons outside our model. If we run our regressions and find the results we

expect, this new time series could be driving our results. In order to control for this, we

need cross-sectional data that reflects the main differences between municipalities, such

as the main components of the HDI (education, health, and income11) in 2005. In order

10Total personnel of the IMSS (Instituto Mexicano del Seguro Social) and the ISSSTE (Instituto de

Seguridad y Servicios Sociales de los Trabajadores del Estado).11However, we are already using the municipal income, which also varies in time, as a control.

Therefore, in this new specification we only add the other two components of the HDI: education and

18

to solve this possible problem we interact the different components of the HDI with a

full set of time dummies, and use the interacted values as controls. The idea behind

including these additional controls is as follows: Suppose that there exists some time

series that is biasing our coefficients. This time series would have a differential effect

on municipalities that depend on that particular characteristic of the municipality. By

using time dummies we are controlling for any possible time series that could be causing

this type of bias by having differential effects on municipalities with better education

or health. The fifth column in table 7 presents the results of our estimation including

these controls.

We can see that the addition of controls does not alter the results in any significant

way; all regressions show a positive and significant coefficient. From now on, unless

otherwise specified, all regressions are estimated using all these variables as controls. We

also estimate the regression for each of the three homicide rates reported by the Mexican

Presidency (columns (6)-(8)). As a robustness check, we also show the results of running

the same regression on the total homicide rate reported by the INEGI (column (9)).

We can see that all regressions show a positive correlation between homicide rates and

drug-trafficking activities (as measured by the presence of drug cartels). However, these

results are not to be taken too seriously due to potential problems of endogeneity that

may be caused by reverse causality, omitted variables, or measurement errors. We

therefore proceed to use an IV methodology to correct for such problems.

As explained before, the variable that we use as an instrument for the intensity

of drug-trafficking activities is the interaction of the cocaine seizure rate in Colombia

(as a percentage of total production12) with the distance to the nearest crossing point

into U.S. territory. When we only have municipality fixed effects, we also include the

percentage of cocaine seizures in Colombia as an instrument, but not the distance to

the U.S. since this variable is collinear with the fixed effects. On the other hand, the

collinearity with the time fixed effects does not allow us to include cocaine seizures in

Colombia when we use both time and municipality fixed effects.

Table 4 shows the first-stage results. We can see that our instrument, the inter-

action of distance to the nearest point of entry into the U.S. and cocaine seizures in

Colombia, has a negative effect on drug trade in Mexico for every regression in which

time fixed effects are used. This confirms our hypothesis that supply shocks originated

in a higher rate of seizures in Colombia have a much stronger effect on drug trafficking

health. We measure education by school enrollment, and health by the infant mortality rate.12Our results are robust to using total seizures of cocaine in Colombia instead of cocaine seizures as

a percentage of potential cocaine production in Colombia.

19

in municipalities that are located close to the U.S. border. The first stage F-statistic

is in all cases above the golden-rule value of ten, thus confirming the validity of our

instrument. It is also important to note that in the regression without fixed effects, on

the first column, the coefficient for the percentage of cocaine seizures in Colombia can

be estimated. Although this regression has some degree of endogeneity because of the

fixed effects being correlated with the error term, this coefficient is positive, confirming

our hypothesis that an increase in cocaine seizures in Colombia leads to an increase in

drug trafficking activities in Mexico.

The second-stage estimates are shown in table 3. Just as with the OLS estimates,

the first five columns report the results with the total drug-related homicide rate as the

dependent variable by adding the controls one by one. In all cases, we see that there is

a strong, positive effect in the presence of drug cartels on homicides (number of drug

cartels present in a municipality each year). The only controls that change the results

in any noticeable way are the time fixed effects and the differential effects of the HDI.

First of all, this means that when we do not use time fixed effects the national trend is

biasing our results in a significant manner. Additionally, the increase in the coefficient

when the differential effects are added means that there could have been some bias due

to ignoring the possibility of a correlation between cocaine seizures in Colombia with

other time series that have important consequences in Mexico. Both biases change our

coefficients, but in no case is the change enough to modify the basic interpretation of

our results.

Columns (6)-(8) show the results of the regression with the full set of controls for

each of the three homicide rates reported by the Mexican Presidency; the final column

shows the same regression with the INEGI-reported total homicide rate. We can see

that the number of cartels present in a municipality has an important effect on each

of these homicide rates. When looking at each one separately, we see that the effect

is strongest on executions. It is also strong on confrontations, but significantly weaker

than it is on executions. Finally, there is still a significant effect on aggressions, but

it is much weaker than the estimated ones on the other two homicide rates. These re-

sults indicate that negative supply shocks due to increased cocaine seizures in Colombia

have the strongest effect on homicides caused by internal fights between drug traffickers,

which are mostly captured in the executions rate. This could be due to the rising prof-

itability of the trade when supply is tightened, which would lead greedy drug traffickers

to target their competitors in order to obtain a larger share of the benefits. Fighting

between the authorities and drug traffickers (which is related to aggressions and con-

frontations) seems to be affected only mildly by supply shocks. This makes sense for

20

two reasons. First, enforcement activities by the government are independent of the

short-term variations in the size of the market, since the incentives that authorities

face for confronting drug cartels do not depend (at least directly) on the price of drugs.

Second, even if drug traffickers would prefer to hold temporarily more routes when

supply tightens, they also know that attacking the authorities may result in immediate

retaliations that could lead to more government crackdowns precisely at a time when

such seizures are most costly to them. Thus, the only type of fighting they would be

willing to have against authorities would be related to the immediate control of routes,

such as engaging in some confrontations over the control of specific territories. On the

other hand, aggressions would be unwise in a moment of supply tightening, since they

are related to their long-term strategy against authorities and would not bring any of

the immediate control of routes that cartels would want in order to be able to transport

drugs at a high price while the effect of the negative supply shock lasts. Finally, the

result for the INEGI-reported homicide rate is presented as a robustness check, and

confirms the result obtained when we use the total drug-related homicide rate as the

dependent variable.

5.1.2 Implications

The results shown in section 5.1.1 have various implications on the current situation in

Mexico. First of all, the second-stage results mean that the violence brought by each

additional cartel in a municipality increases the number of drug-related homicides by

about 121%, if we use the coefficient from the regression using the complete set of con-

trols. The increase in other homicide rates is 109% for executions, 6% for aggressions,

and 29% for confrontations. Taking into account that the average number of cartels

present in each municipality rose from 0.194 in 2006 to 0.605 in 2010, the increase in

the number of cartels accounted for an increase of 32% in the overall level of violence

in Mexico during the same period.

We can also estimate the magnitude of the effect that the changes in Colombia

have had in Mexico. The amount of cocaine seized in Colombia has risen from 19.6%

to 41.5% of the total quantity produced. The first-stage coefficients mean that the

success of interdiction activities in Colombia has increased the number of cartels in

Mexican municipalities close to the U.S. border by about 0.46, and by about 0.23 in

municipalities 1,000 km away from the U.S. border. The last two numbers, combined

with the second-stage results, mean that, due to increased cocaine seizures in Colombia,

the homicide rate has increased 37% in municipalities close to the U.S. border, and 17%

in municipalities 1,000 km away from the border as a result of higher interdiction rates

21

in Colombia. The last results, however, should be taken with some caution. They

depend on two coefficients estimated in the first-stage regression: the coefficient for the

interaction between cocaine seizures in Colombia and the distance to the U.S., and the

coefficient for cocaine seizures alone. The latter coefficient cannot be calculated in the

regressions that use time fixed effects because it is collinear with the time dummies.

Therefore, we can only use the estimate from the regression without fixed effects, which

could be biased if the error term is somehow correlated with those time fixed effects. If

this correlation is caused by some time-dependent variable, we can use the time series

as a control to eliminate the bias. Thus, we are using two time series as controls which

we believe could cause this type of bias: the Mexican GDP per capita, and the national

military expenditure. Nevertheless, we are aware that there might be additional time

series causing some bias that we have not controlled for.

5.1.3 Results with other measures of cartels’ presence

Using the number of cartels present in a municipality is a first approach to estimating

the effects of DTOs’ activities on violence. However, this variable can be used in other

ways in order to test the type of cartel presence that matters. More precisely, we would

like to know whether the effect is due to cartels being present or not, regardless of the

amount of cartels. In order to test this, we use a dummy variable whose value is one if

there is at least one cartel in the municipality during a given year and zero otherwise.

A second alternative arises from the hypothesis that violence is not caused by cartels

being present, but by the presence of more than one cartel in the same municipality.

This makes sense since it is possible that municipalities in which a single DTO is

present have low levels of violence due to the lack of competition for routes, or, if the

monopolistic organization has enough power that it could even become a repressive

authority that discourages any type of crime. If that is the case, the higher levels

of violence would be concentrated in municipalities with the presence of two or more

DTOs. The model presented in section 3 justifies this second alternative theoretically.

To test it we construct another dummy variable that takes the value of one if there are

two or more cartels present in a municipality, and zero otherwise.

Besides calculating the coefficient related to each of these dummy variables for the

presence of cartels separately, we would like to estimate both coefficients simultaneously.

By doing so, we can distinguish whether the increase in violence caused by the larger

number of cartels is mainly due to the simple presence of cartels, or due to the presence

of more than one cartel in the same municipality. In order to do so, we need at least

one additional variable that we can use as an instrument. In one specification, we use

22

the interaction of cocaine seizures in Colombia with the distance and the square of the

distance to the Pacific Ocean. The reason why we do not use only the distance to the

Pacific is that this coefficient turns out not to be significant when it is not used together

with its square, which could be the case if the relationship is highly nonlinear. In that

case, it makes no sense to estimate two endogenous variables with a single significant

instrument, since both estimated variables would have a very high collinearity. We

also use the distance to the closest ocean (Pacific or Atlantic) as an alternative second

instrument. The motivation for these instruments is that most of the cocaine taken

to the U.S. through Mexico enters the country through either of these two coasts, but

especially through the Pacific coast, so municipalities close to the ocean would have a

comparative advantage over other municipalities as a route for taking cocaine to into

the U.S.

The IV coefficients of these specifications with the total drug-related homicide rate

as the dependent variable are shown in table 5. All regressions are estimated with both

time and municipality fixed effects, as well as with the full set of available controls. The

first column shows the results of the regression using the number of cartels present as a

benchmark. The second and third columns show that both dummies used have a large

coefficient. The coefficient for the dummy for two or more cartels is highly significant,

but the coefficient for the dummy of presence of cartels has a a very large variance

which does not allow us to determine if the coefficient is indeed different from zero (0).

When we look at the last two columns, we see that only the coefficient for the dummy

of more than one cartel is significant. The variance of the coefficient for the dummy

capturing the presence of cartels is not high in this regression, which suggests that its

coefficient is indeed zero. There is a clear implication arising from this: drug-trafficking

activities only increase homicide rates in places where more than one cartel is present,

which confirms our hypothesis that the main channel relating drug trade and violence

is the competition between DTOs over the control of territories for the drug trade.

Additionally, these results address the possible issue that we mentioned in section

5.1.1. If it were true that in our database the presence of a cartel does not simply

mean that that cartel intervenes in drug trafficking but, instead, that the cartel com-

mits violent acts, the coefficient related to the presence of cartels would be positive.

Therefore, the fact that our regression shows a non-significant sign validates that our

database indeed reports which cartels are present in a municipality, regardless of their

committing violent acts or not.

23

5.1.4 Before and after Calderon

Can the results obtained so far be generalized to the period before Calderon became

president of Mexico? This is an interesting question, as it could tell us whether the

causal relationship between drug trafficking and violence changed under Calderon’s ad-

ministration. We would thus like to estimate the same regressions as before, but only

for the time period before Calderon’s term. Unfortunately, the well-documented homi-

cide rates published by the Mexican Presidency started to be collected when Calderon

took office and started his head-on war against drugs. We are therefore limited to using

the total homicide rates published by the INEGI. However, the fact that in the previous

regressions we obtained very similar results with the INEGI total homicide rate to those

obtained with the total drug-related homicide rate assures us that these homicide rates

are nevertheless reliable and that their results can be trusted. Thus, we use the INEGI

homicide rate to compare the situation during the 2003-2006 period (before Calderon)

with the results for the 2007-2010 period (after Calderon took office).

Table 6 makes such a comparison. Panel A shows the first-stage estimates of the

regressions before and after Calderon. In both periods our instrument had an effect on

the number of cartels. If anything, that effect is larger before Calderon’s term, and in

all cases the first-stage F-test is larger than ten, supporting the validity of our second

stage. The second-stage estimates are shown in Panel B. In all regressions using time

effects the conclusion is the same: before Calderon took office the number of cartels

had no significant effect on the homicide rate. However, this situation changed after

Calderon took office, since the number of cartels started having a significant effect on

violence.

The implications of these results are striking. They mean that supply shocks from

Colombia had an important effect on drug trafficking (e.g., the number of cartels)

in regions close to the U.S. border during the whole period of 2003 to 2010. This

is what we expect from these regions being more fit for transporting drugs to North

American markets. Drug trafficking, however, only had an effect on violence after

Calderon took office. This finding may be taken as additional evidence for what most

critics of Calderon’s administration have argued. Namely, that the open war on drug

cartels has made drug trafficking much more violent. However, there is another side to

this discussion. The levels of violence observed in Mexico increase whenever Colombian

authorities seize more cocaine. What this means is that Calderon is not to be fully

blamed for the recent increase in levels of violence, since larger negative supply shocks

in wholesale cocaine markets originating in higher number of seizures in Colombia

24

temporally coincide with the start of the Calderon administration. Recall from the

introduction that it was precisely in 2007, the same year of Calderon’s first year in

office, that Colombian authorities changed the emphasis of the war on drugs, putting

less emphasis on eradicating illegal crops and more on the interdiction of drug shipments

and on attacking drug-trafficking activities in general. In short, our results indicate that

the increase in violence in Mexico since 2006 is, to a non-negligible extent, explained by

supply tightenings in wholesale cocaine markets originated in increased cocaine seizures

in Colombia.

5.2 Results with the value of cocaine seizures in Mexico as a

proxy for drug trafficking

The second variable that we use as a proxy for drug-trafficking activities is the value

of cocaine seized by Mexican authorities. We are well aware that this proxy is far

from being a perfect measure of drug-trafficking activities. First of all, cocaine seizures

also depend on the presence of government forces in each municipality, since regions

with low presence of government authorities will tend to have lower levels of cocaine

seizures. The best that we can do to partially overcome this concern is to use proxies

for state presence to isolate this effect, with the problem that these variables are only

reported yearly. We also believe that our information on cocaine seizures is subject to

significant measurement error, which further justifies the use of a 2SLS methodology.

Additionally, the price of cocaine that we use has the problem that it is only reported

for each quarter, and it is a general measure for the wholesale market in the U.S., which

could differ significantly from the price of cocaine at the Mexican points of entry into

the U.S. On the other hand, when compared to the variable used in section 5.1, our

new proxy has the advantage that we have monthly data, which greatly increases the

number of observations. This allows us to capture the effects of short-term negative

supply shocks, which was not possible with the variable measuring the presence of

cartels as a proxy for drug trafficking. When we estimate our model using the value of

cocaine seizures as a proxy for cocaine markets we are limited to the period starting in

December 2006 and ending in December 2010, since the data on both homicide rates

and cocaine seizures in Mexico is only available for that period.

5.2.1 Baseline regressions

Initially, we estimate regressions that can be directly compared to those using the

presence of drug cartels as a measure of drug-trafficking activities. That is, we first

25

use the total drug-related homicide rate as the dependent variable with controls being

added progressively13. Then we report the results for the three disaggregated measures

of drug-related homicide rates reported by the Mexican Presidency, as well as for the

INEGI homicide rate. In these regressions we include the full set of available controls.

In all cases we use both time and municipality fixed effects. Most of our results (shown

in tables 7-9) coincide with those obtained by using the presence of drug cartels as a

proxy for drug-trafficking activities. The main difference is that these new coefficients

have lower significance levels. However, we can clearly see that the main results remain:

the effect of drug trafficking and of the exogenous supply shocks is strongest for the total

drug-related homicide rate and for the executions rate. The effect on confrontations

is weaker and the effect on aggressions is still negative but it is not significant at any

level. The coefficient for the INEGI homicide rate is also slightly less than the coefficient

for total drug-related homicide rates. All these new results validate the analysis from

section 5.1.

A possible problem that these results might have is the low value of the F-test of

excluded instruments. Its value is below the golden-rule value of ten (although it is close

to it), but it is still significant at the 99% significance level. We are thus aware that

these regressions might have the problem of weak instruments. Nevertheless, the fact

that the results match those using the number of cartels as a proxy for drug-trafficking

activities validate them, and we believe them to be at least important as a robustness

check for our previous results.

5.2.2 Timing of the effects of Colombian interdiction activities on drug

trafficking and violence

Another natural question to ask is for how long do negative supply shocks originated

in increased cocaine seizures in Colombia affect drug trafficking activities and violence

in Mexico. It would be reasonable to think that this effect has a certain lag, due to the

time it takes for drugs produced in Colombia to reach Mexican territory and then to

be sent into the U.S. According to estimates by Mejıa and Rico (2010), it takes around

six weeks for the whole process that starts from drugs being shipped out of Colombia

until the money from sales comes back to drug producers. This suggests that cocaine

seizures in Colombia should affect the situation in Mexico for at least a few months

13Note, however, that our controls are only reported yearly, so they are much less meaningful than

when using the presence of drug cartels as a proxy, which was also reported for each year. We believe,

however, that using these controls is still better than using no controls, so we keep them in the

regressions.

26

after the crackdown takes place.

We will now exploit the fact that our data is reported monthly. In order to test

whether seizures in Colombia have a persistent effect in Mexico, we run the same model

as before but using different lags of the time dependent instruments. For simplicity,

we only run the regressions with the total drug-related homicide rate as the dependent

variable. We use both time and municipality fixed-effects in all cases as well as the

full set of controls (as in column (5) on table 8). We first look at the reduced-form

estimates (table 12), which show an OLS estimate of how cocaine seizures in Colombia

affect homicide rates in Mexico. We can see that cocaine interdiction rates in Colombia

have an effect on homicide rates in Mexico that lasts beyond the same period: the

coefficients decrease as the lag increases, and they are significant at the 99% level up to

a lag of two months. This is evidence that the effect on violence goes beyond the same

period, and the strength of this effect decreases over time (as expected).

The question that now remains to be answered is whether these lags are due to the

effect of activities in Colombia on drug trafficking, or due to the effect of drug trafficking

on violence. The first and second stages of our empirical strategy help us answer this

question. By looking at the first stage results, shown in table 11, we see that cocaine

seizures in Colombia only influence drug-trafficking activities during one period, since

only the contemporary coefficients are significant (with the expected sign). The second-

stage coefficients, table 12, are only significant for the first lag on the first regression,

but we can see that the coefficients for the first and second lag follow a pattern similar

to that seen in the reduced form: they are largest for the contemporary variable, and

then decrease as the time lag increases. The reason why they are not significant is due

to an increasing variance as the number of lags included in the model increases, since

we can see that the coefficients are always similar, regardless of which lags are being

included. Unfortunately, the limited period for which our data is available does not

allow us to increase the number of observations in order to decrease the variance up to

the point where we could be certain about this.

Despite the low significance of some of these results, they suggest that interdiction

in Colombia has an immediate effect on drug-trafficking activities in Mexico. Drug

trafficking in Mexico, however, has an effect on violence that goes beyond the first

period. Our estimates show that this effect lasts for at least two months, and that its

intensity decreases as time goes by. One compelling explanation for this extended effect

is the fact that any surge in violence is followed by retaliations soon afterwards. This

would explain the persistence of violence, as opposed to drug-trafficking activities, which

exhibit no persistence because increases in the flow of drugs to the U.S. due to supply

27

tightenings should take place immediately in order to benefit from the temporarily

higher prices induced by the negative supply shocks.

6 Robustness

We now turn to making various robustness checks on our estimations. The first test,

which we did consistently in the previous regressions, was to estimate the model using

the INEGI-reported total homicide rate, obtaining very similar results. We now show

some alternative tests.

6.1 Falsifying the second-stage regression

We would first like to check that we are not obtaining a spurious correlation between

drug-trafficking activities and crime in general, which would be the case if, for instance,

the presence of police forces is very weak in some regions. Our IV approach should be

enough to dismiss this type of endogeneity as long as our instrument fulfills the exclusion

restriction. However, performing additional checks further justifies the results that we

have obtained so far. The procedure we follow is based on estimating our model using

as the dependent variable some crime rate that we do not expect to be related to

drug-trafficking. Two such crime rates come to mind: thefts and the non-drug-related

homicide rate (which we construct as the difference between the INEGI-reported total

homicide rate and the drug-related homicide rate reported by the Mexican Presidency).

We certainly believe that thefts are a better candidate for this test, since homicides

unrelated to drug trafficking can have two potential problems. First, the Mexican

Government may have failed to recognize many homicides as drug-related, even though

they may have been committed for some reason connected to the activities of DTOs.

On the other hand, there may be important spillover effects. A simple example would

be a man who was murdered due to the usual fights for turf between DTOs that shortly

afterwards brings about a retaliation from his relatives that is not seen by the authorities

as a drug-related homicide.

The results of this exercise are shown in table 13. We show the IV estimates for

the effect of the number of cartels present on each one of these alternative crime rates.

The first four columns show the results of estimating the model with the difference of

the total homicide rate and the drug-related homicide rate. Even though we obtain a

positive and significant coefficient, we can see that it is much smaller than the coefficient

estimated in our original regressions. Columns (5)-(8) show the results of the regressions

28

on the total theft rate, which yields a significant coefficient only for the model without

any controls, and in that case the coefficient has the opposite sign. Finally, columns

(9)-(12) show the results of estimating our regressions on the car-theft rate, with similar

results. It should be noted that regressions where the car-theft rate is the dependent

variable can only be estimated for the years 2009 and 2010, since the INEGI only started

reporting car thefts as a separate category in 2009, meaning that the variance of this

estimate is relatively high in comparison with all other regressions we have estimated.

These results show that our model has the expected results for thefts, a type of

crime for which we find no intuitive reason that could explain a relationship with drug-

trafficking activities. The relevance of this result is that it rules out a spurious outcome

for our main models that could happen if all types of crime were correlated. We obtain

a significant result for homicides unrelated to the drug trade, but the fact that the

coefficient is much smaller suggests that the estimated effect can be fully explained by

spillover effects.

6.2 Falsifying the first-stage regression

In order to rule out a spurious correlation between cocaine seizures in Colombia and

drug trafficking in Mexico, we separate drugs seized in Colombia depending on their

presumed destination. The other major destination of cocaine produced in Colombia,

other than the U.S., is Europe. Most of this cocaine is transported through the land

border to Venezuela, and then taken across the Atlantic, either through the Caribbean

Islands or through the west coast of Africa before reaching Europe. We therefore

classify seizures according to the region in which they are seized, and group the regions

according to three groups depending on which routes pass through each region. The

first group contains all drug seizures in regions of Colombia in which most of the cocaine

interdicted is not bound for the U.S. (seizures in the border with Venezuela and the

eastern part of Colombia14). The second group contains drug seizures in regions where

most of the drug shipments are bound for the U.S. (seizures in the Pacific coast and

southwest regions in the country15). Finally, there is a third group that contains cocaine

seizures from regions where part of the drug trafficking is bound to the U.S., but an

important part is bound to other regions.

An important limitation of this test is that we only know the region where co-

14This first group includes the following departments: Cesar, Norte de Santander, Arauca, Vichada,

Guainıa, Casanare, Guaviare, Meta, and Amazonas.15This second group includes the following departments: Choco, Cauca, Valle del Cauca, Narino,

and Putumayo.

29

caine was seized when the National Police was responsible for the crackdown. This

means that our database is incomplete, since although the National Police is responsi-

ble for the majority of total seizures in Colombia, other armed forces contribute with

a non-negligible percentage. Additionally, there is another limitation due to the way

in which this database reports which division of the National Police did the seizure.

Some divisions are regionally defined, such as the local police of each city or of each

department, so it is easy to tell where the seizure took place. However, there are some

divisions within the Colombian National Police that work nationwide, such as the DI-

RAN (Anti-narcotics unit) or the DIJIN (Division of Criminal Investigations), and for

which there is no way to tell in which region they seized the drugs reported in the

database.

The results of this test are shown in table 14. All specifications contain time fixed

effects. The results show second-stage estimates that are very close to the ones esti-

mated with our baseline regression. When we look at the first-stage estimates, we can

see that cocaine seizures in regions in Colombia where cocaine is presumably bound to

the U.S. through Mexico has the expected effect, i.e., they have a negative sign. The co-

efficient related to the seizures in regions where cocaine is bound for Europe is positive,

which is the opposite of what we would expect if drug seizures in these regions indeed

had an effect on Mexican drug-trafficking activities. Finally, cocaine seizures in other

regions have a coefficient that is not significant at any level (except for the regression

without time fixed effects). We can thus conclude that our original instrument does

have an influence on violence in Mexico through the channel that we propose, and not

through an alternative channel that would have been captured in these regressions with

positive coefficients for the cocaine seized near the Caribbean or Venezuela. The fact

that cocaine seizures close to Venezuela have a positive sign is intriguing, but we can

see that seizures in different regions have different effects on Mexico just as should be

expected from our hypothesis.

Another way to falsify the first-stage regression is by looking at the effect on violence

in Mexico of drugs that are not massively produced in Colombia. A very small amount

of heroin is produced in Colombia, and most of it is not bound to the U.S. Therefore,

heroin seizures should not have an effect on drug trafficking in Mexico. In order to check

for this, we run our baseline regressions with cocaine seizures, as well as with heroin

seizures. These results are shown in table 15. The second-stage coefficients resemble

those of the original regressions. When we look at the first-stage coefficients, we see

that cocaine seizures have the expected negative sign. Heroin seizures, however, have

a positive sign. This is, again, an intriguing result, but it shows that heroin seizures at

30

least do not have the effect that is implied by our mechanism.

A final test of the first stage is to use our instrument to explain seizures of other

types of drugs in Mexico. Since cocaine seizures in Colombia should not in principle

affect the markets for other drugs (at least not directly through higher market prices),

these new first-stage regressions should not have a significant coefficient. The results

are shown in table 16. As we can see, none of these regressions show a significant

effect of cocaine seizures in Colombia either on heroin or cannabis seizures in Mexico,

as opposed to the effect that we found before on cocaine seizures (see table 4), which

is negative and significant.

7 Conclusions

In this paper we tackle two main issues. First, the effect that illegal drug trafficking (and

more specifically, cocaine trafficking) has had on violence in Mexico. And second, the

extent to which anti-drug policies implemented in Colombia have affected the intensity

of drug trafficking activities and, through this specific channel, the levels of violence in

Mexico. The main problem we face when trying to disentangle the causal effect of drug

trafficking on violence is that of the the endogeneity of any variable measuring drug-

trafficking activities, which means that we need to find an exogenous source of variation

in the intensity of drug trafficking if we want to estimate its causal effect on violence. In

order to do this, we first propose a simple model of competition among drug-trafficking

cartels that suggests two types of exogenous sources of variation. Furthermore, the

proposed model motivates (and formalizes) our empirical strategy. We derive from the

model a series of relationships between the size of illegal drug markets, the number

of cartels present in a given territory and the levels of violence that are observed in

equilibrium.

In the empirical section, we construct an instrument that has two parts. The first

one (distance to the U.S. border and/or to the Pacific or Atlantic) only varies in the cross

section (e.g., across municipalities), and the second part (cocaine seizures in Colombia)

only varies over time. The interaction between distance to the U.S. and cocaine seizures

in Colombia is our instrument for drug trafficking activities in Mexico. The intuition

behind the use of this instrument is the following: First, if the demand for cocaine

is inelastic to price changes, a negative supply shock in wholesale cocaine markets

originated in increased seizures in Colombia should increase the size of the the cocaine

market at the wholesale in Mexico (e.g., P ×Q). Second, if it is true that illegal drug

markets breed violence, the effect of the larger market size should be larger in drug

31

trafficking activities in those municipalities that have a comparative advantage, due to

their geographic location, for illegal drug trafficking activities.

Using the presence of drug cartels as a proxy for illegal drug trafficking, and instru-

menting it, we find robust empirical evidence that shows the positive effect that illegal

drug trafficking has on violence. When we explore the structure of the presence of drug

cartels more deeply we find that it is the presence of two or more cartels that mat-

ters when explaining violence. We take this result as additional evidence of our simple

model, in which drug cartels compete for the control of territories to traffic illicit drugs,

generating violence. When we use the value of cocaine seizures as our proxy for the size

of illegal drug markets at the municipal level, we confirm the previous results. Finally,

we estimate a series of robustness checks and falsification tests that further prove the

validity of our baseline results.

Three messages come out of our paper. First, although it may be true that not all

illegal markets are violent, we provide robust evidence that indicates that wholesale

drug markets are: the presence of each additional cartel in a particular location brings

an increase in the homicide rate of about 100% (that is, the homicide rate is doubled).

Second, our results show that successes in the fight against drugs in Colombia have

increased drug trafficking activities and violence in Mexico. This result provides direct

and measurable evidence of the so-called ballooning or displacement effect at work.

By suggesting that this result is one of the important messages of our paper we are

acknowledging that the first-stage results in our empirical strategy are, by themselves,

informative of the fact that successes attained in the “war on drugs” in one country don’t

necessarily solve the problem; they might just displace it to other regions or countries.

The third important message that comes out of our paper talks to the policy discussions

in Mexico about the underlying cause of the huge increase in violence observed in this

country starting in 2007. While most security analysts have pointed out that the policies

of direct confrontation of DTOs implemented by the Calderon administration are the

sole culprit of the increase in violence, our results indicate that there might be another

important reason as well: successes attained in the Colombian war against drugs. This

effect has been by no means negligible: our estimations indicate that the increase in

the seizure rates in Colombia accounts for a 37% increase in homicide rates close to the

U.S. border, and for a 17% increase in homicides in municipalities that are 1,000 km

away from the U.S. border. This is not to say that policy analysts in Mexico are wrong

on their assessment about the reasons behind the increase in violence in Mexico, but to

point out that there is a complementary explanation, based on market forces and the

transnational characteristic of illicit drug markets, that explains a non-negligible part

32

of the story.

References

Angrist, Joshua D. and Adriana D. Kugler, “Rural windfall or a new resourse

course? Coca, income and civil conflict in Colombia,” The Review of Economics and

Statistics, May 2008, 90 (2), 191–215.

Becker, Gary S., Kevin M. Murphy, and Michael Grossman, “The Market for

Illegal Goods: The Case of Drugs,” Journal of Political Economy, 2006, 114 (1).

Bove, Vincenzo, “Opium Market, Revenue Opportunities and Insurgency in

Afghanistan’s Provinces,” NEPS Working Paper, February 2011.

Calderon, Gabriela, Alberto Diaz-Ceyros, and Beatriz Magaloni, “The Tem-

poral and Spatial Dynamics of Violence in Mexico,” MIMEO, Stanford University,

2012.

Chimeli, Ariaster B. and Rodrigo R. Soares, “The Use of Violence in Illegal

Markets: Evidence from Mahogany Trade in the Brazilian Amazon,” MIMEO, Uni-

versidade Catolica de Rio, 2010.

Coscia, Michele and Viridiana Rıos, “Knowing Where and How Criminal Orga-

nizations Operate Using Web Content,” in “CIKM ’12” Maui, HI, USA October

29-November 2 2012.

Dell, Melissa, “Trafficking Networks and the Mexican Drug War,” Job Market Paper,

November 2011.

Dıaz, Ana Marıa and Fabio Sanchez, “A geography of illicit crops (coca leaf) and

armed conflict in Colombia,” Documento CEDE 001918 - Universidad de los Andes,

March 2004.

Goldstein, Paul J., “The Drugs/Violence Nexus: A Tripartite Conceptual Frame-

work,” Journal of Drug Issues, 1985, 39, 143–174.

Grillo, Ioan, El Narco, Bloomsbury Press, 2011.

Guerrero, Eduardo, “Como reducir la violencia en Mexico,” Nexos, 2010.

, “La raız de la violencia,” Nexos, 2011.

33

Lind, Jo Thori, Karl Ove Moene, and Fredrik Willumsen, “Opium for the

Masses? Conflict-Induced Narcotics Production in Afghanistan,” CESifo Working

Paper No. 2573, 2009.

Medina, Carlos and Hermes Martınez, “Violence and Drug Prohibition in Colom-

bia,” Crisis States Programme - Working papers series no. 1, August 2003.

Mejıa, Daniel and Daniel Rico, “La microeconomıa de la produccion y trafico de

cocaına en Colombia,” Documento CEDE 2010-19 - Universidad de los Andes, 2010.

and Pascual Restrepo, “The War on Illegal Drug Production and Trafficking: An

Economic Evaluation of Plan Colombia,” Documento CEDE 2008-19, 2008.

and , “Do Illegal Drug Markets Breed Violence? Evidence for Colombia,”

MIMEO, Universidad de los Andes, May 2013.

Merino, Jose, “Los operativos conjuntos y la tasa de homicidios: Una medicion,”

Nexos, June 2011.

Miron, Jeffrey A., “Violence, Guns, and Drugs: A Cross-Country Analysis,” The

Journal of Law and Economics, October 2001, 44 (S2), 615–633.

Poire, Alejandro and Marıa Teresa Martınez, “La caıda de los capos no multiplica

la violencia: el caso de Nacho Coronel,” Nexos, 2011.

Villalobos, Joaquın, “Nuevos mitos de la guerra contra el narco,” Nexos, 2012.

A Tables

34

Table 1: Descriptive statistics of the data

Variable Mean St. Dev. Observations Time period

Variables for each Mexican municipality with monthly periodicity

Homicide rates (per 100 000 inhabitants)

Total drug-related homicides 6.995 94.719 120344 Dec 2006-Dec 2010

Homicides from executions 5.519 60.6 120344 Dec 2006-Dec 2010

Homicides from aggressions 0.162 9.326 105608 Dec 2006-Dec 2010

Homicides from confrontations 1.362 67.703 117888 Dec 2006-Dec 2010

Total homicides (SIMBAD) 12.351 75.906 324192 Jan 2000-Dec 2010

Drug seizures (mt)

Cocaine 0.232 35.034 130221 Dec 2006-Dec 2010

Heroine 0.005 0.41 130223 Dec 2006-Dec 2010

Cannabis herb 42.721 1095.264 130248 Dec 2006-Dec 2010

Packed cannabis 19.829 517.853 130221 Dec 2006-Dec 2010

Variables for each Mexican municipality with yearly periodicity

Population 42804.113 127283.103 29340 2000,2005 and 2010

Presence of cartelsa

Number of cartels 0.221 0.705 27026 2000-2010

Presence of one or more cartel 0.119 0.324 27026 2000-2010

Presence of two or more cartels 0.064 0.246 27026 2000-2010

Controls

Government income 2143.766 1923.163 29340 2000-2011

School personnel 14.499 3.771 24921 2000-2011

Social security personnel 0.176 0.408 25947 2000-2011

Other crime rates

Theft 18.848 36.937 29340 2000-2011

Vehicle theft 0.994 6.22 7342 2009-2011

Monthly time series

Cocaine seizures in Colombia (mt) 11.54 72.23 144 Jan 2000-Dec 2011

Yearly time series

Cocaine production in Colombia (mt) 576.917 94.753 2000-2010

Cocaine seizures in the US (mt) 129.967 29.911 11 2000-2010

Cocaine seizures in Peru (mt) 17.577 7.494 11 2000-2010

Cocaine seizures in Bolivia (mt) 15.015 9.449 11 2000-2010

Cross-sectional data

GDP per capita (USD) 4492.045 2731.575 2418 —

Infant mortality rate (per 1000 births) 30.428 7.109 2442 —

School attendance (%) 59.958 6.288 2442 —

Distance to the US (km) 760.274 268.099 2450 —

Distance to the Pacific (km) 278.457 189.758 2450 —

Distance to the Atlantic (km) 329.237 297.939 2450 —

aThis data is available starting in 1990, but we do not use the first ten years since all our other

variables are not available for the whole period.

35

Table 2: OLS estimates of the effect of the number of cartels on various homicide rates

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Homicide rate Total Total Total Total Total Executions Aggressions Confrontations INEGI

Number of cartels 0.0518*** 0.0516*** 0.0517*** 0.0531*** 0.0490*** 0.0400*** 0.0080*** 0.0195*** 0.0332***

(0.0062) (0.0063) (0.0063) (0.0064) (0.0064) (0.0057) (0.0027) (0.0049) (0.0052)

Controls

GDP per capita (Mexico) Yes

National military expenditure Yes

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes Yes Yes

Differential effects by HDI Yes Yes Yes Yes Yes

Observations 9,780 9,780 9,780 9,624 9,608 9,608 9,608 9,608 9,608

R-squared 0.0672 0.0672 0.0674 0.0693 0.0784 0.0684 0.0115 0.0306 0.0434

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

36

Table 3: IV estimates of the effect of the number of cartels on various homicide rates

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Homicide rate Total Total Total Total Total Executions Aggressions Confrontations INEGI

Number of cartels 0.2883*** 0.5056*** 0.5082*** 0.5100*** 0.7947*** 0.7354*** 0.0573** 0.2539*** 0.6828***

(0.0410) (0.0827) (0.0833) (0.0839) (0.2005) (0.1896) (0.0289) (0.0892) (0.1801)

Controls

GDP per capita (Mexico) Yes

National military expenditure Yes

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes Yes Yes

Differential effects by HDI Yes Yes Yes Yes Yes

Observations 9,768 9,768 9,768 9,612 9,608 9,608 9,608 9,608 9,608

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

37

Table 4: First-stage estimates for the effect of the number of cartels on homicide rates

(1) (2) (3) (4) (5)

Dependent variable: Number of cartels

Cocaine seizures in Colombia -1.806*** -1.806*** -1.801*** -1.790*** -1.085***

× distance to the US (0.264) (0.264) (0.264) (0.265) (0.272)

Cocaine seizures in Colombia 2.123***

(0.235)

Controls

GDP per capita (Mexico) Yes

National military expenditure Yes

Time fixed effects Yes Yes Yes Yes

Income Yes Yes Yes

State presence Yes Yes

Differential effects by HDI Yes

Observations 9,768 9,768 9,768 9,612 9,608

R-squared 0.091 0.091 0.092 0.092 0.114

F-test 58.12 46.67 46.39 45.73 15.94

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

38

Table 5: IV estimates using different proxies for drug-trafficking related to the presence

of cartels

(1) (2) (3) (4) (5)

Dependent variable: Total drug-related homicide rate

Number of cartels 0.7723***

(0.1923)

Presence of cartels 6.7115 -0.2168 -0.6449

(5.8051) (0.5608) (0.5395)

Two or more cartels 2.1424*** 2.3191*** 2.3578***

(0.6465) (0.7113) (0.7450)

Spatial instruments used

Distance to US border Yes Yes Yes Yes Yes

Distance to Pacific Ocean Yes

Distance to Pacific Ocean squared Yes

Distance to the sea Yes

Observations 9,608 9,608 9,608 9,588 9,588

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

39

Table 6: Estimates for the INEGI homicide rates before and after Calderon took office

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Before/after Before Before Before Before Before After After After After After

Calderon took office

Panel A: First-stage estimates for number of cartels

Cocaine seizures in Colombia -0.138 -3.584*** -3.479*** -3.558*** -3.104*** 0.329*** -1.806*** -1.801*** -1.790*** -1.085***

× distance to the US (0.229) (0.737) (0.741) (0.763) (0.763) (0.083) (0.264) (0.264) (0.265) (0.272)

Panel B: IV estimates for total drug-related homicides

Number of cartels -0.6551 0.0853 0.0814 0.0590 0.0795 -0.4079** 0.4577*** 0.4585*** 0.4566*** 0.7424***

(1.7318) (0.1096) (0.1129) (0.1151) (0.1330) (0.1763) (0.0817) (0.0821) (0.0823) (0.1960)

Controls

GDP per capita (Mexico) Yes Yes

National military expenditure Yes Yes

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes

Differential effects by HDI Yes Yes

Observations 9,772 9,772 9,772 9,056 9,052 9,768 9,768 9,768 9,612 9,608

First-stage F-test 0.365 23.68 22.05 21.75 16.55 15.91 46.67 46.39 45.73 15.94

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

40

Table 7: OLS estimates of the effect of cocaine seizures on various homicide rates

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Homicide rate Total Total Total Total Total Executions Aggressions Confrontations INEGI

Value of cocaine 0.001 0.001 0.001 0.001 0.001 0.001 -0.000 -0.001 0.008***

seizures (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.000) (0.001) (0.002)

Controls

GDP per capita (Mexico) Yes

National military expenditure Yes

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes Yes Yes

Differential effects by HDI Yes Yes Yes Yes Yes

Observations 110,475 110,475 110,475 108,627 108,021 108,021 108,021 108,021 108,021

R-squared 0.008 0.009 0.009 0.009 0.013 0.011 0.002 0.004 0.008

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

41

Table 8: IV estimates of the effect of the value of cocaine seizures on various homicide rates

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Homicide rate Total Total Total Total Total Executions Aggressions Confrontations INEGI

Value of cocaine 1.553 1.039** 1.043** 1.042** 1.037** 0.846** 0.011 0.262** 1.104**

seizures (1.353) (0.427) (0.429) (0.429) (0.436) (0.357) (0.017) (0.132) (0.450)

Controls

GDP per capita (Mexico) Yes

National military expenditure Yes

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes Yes Yes

Differential effects by HDI Yes Yes Yes Yes Yes

Observations 110,475 110,475 110,475 108,627 108,021 108,021 108,021 108,021 108,021

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

42

Table 9: First stage estimates of the effect of the value of cocaine seizures on various

homicide rates

(1) (2) (3) (4) (5)

Dependent variable: Number of cartels

Cocaine seizures in Colombia -5.461 -93.958** -93.682** -93.526** -87.996**

× distance to the US (4.589) (36.468) (36.480) (36.409) (34.884)

Controls

GDP per capita (Mexico) Yes

National military expenditure Yes

Time fixed effects Yes Yes Yes Yes

Income Yes Yes Yes

State presence Yes Yes

Differential effects by HDI Yes

Observations 110,475 110,475 110,475 108,627 108,021

F-test 1.416 6.638 6.595 6.598 6.363

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

Only two first-stage estimates are reported, since the first-stage regression is

the same regardless of the homicide rate calculated.

43

Table 10: Reduced-form estimates of the effect of the value of cocaine seizures on total

drug-related homicide rates, using different lags of the instruments

(1) (2) (3) (4)

Model Contemporary Lags 0-1 Lags 0-3 Lags 0-4

Dependent variable: Total drug-related homicide rate

Cocaine seizures in Colombia -91.545*** -82.248*** -73.482*** -67.328***

at t × distance to the US (14.270) (13.776) (13.353) (13.602)

Cocaine seizures in Colombia -62.764*** -54.727*** -51.490***

at t− 1 × distance to the US (11.381) (10.880) (11.012)

Cocaine seizures in Colombia -34.680*** -32.561***

at t− 2 × distance to the US (12.167) (12.270)

Cocaine seizures in Colombia -17.019 -18.449

at t− 3 × distance to the US (11.276) (11.827)

Cocaine seizures in Colombia -15.802

at t− 4 × distance to the US (10.973)

Observations 117,721 117,721 115,296 112,871

R-squared 0.015 0.016 0.016 0.015

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

44

Table 11: First-stage estimates of the effect of the value of cocaine seizures on total drug-related homicide rates, using different

lags of the dependent variable

(1) (2) (3) (4) (5) (6) (7)

Model (lags of theNo lags Lags 0-1 Lags 0-3

dependent variable used)

First-stage independent variableContemporary Contemporary First lag Contemporary First lag Second lag Third lag

(Value of cocaine seizures)

Cocaine seizures in Colombia -87.996** -84.358*** 35.047 -81.583*** 43.483 12.046 26.113

at t × distance to the US (34.884) (31.899) (25.065) (30.008) (26.659) (26.317) (36.052)

Cocaine seizures in Colombia -18.486 -87.901*** -11.636 -80.533*** 35.627 -2.508

at t− 1 × distance to the US (34.772) (33.507) (37.577) (30.321) (24.021) (27.168)

Cocaine seizures in Colombia -4.268 -18.348 -84.141*** 29.799

at t− 2 × distance to the US (25.723) (35.038) (31.212) (23.817)

Cocaine seizures in Colombia 9.475 -10.595 -25.741 -100.302***

at t− 3 × distance to the US (35.769) (25.292) (36.056) (35.192)

Observations 108,021 105,596 105,596 100,746 100,746 100,746 100,746

R-squared 0.002 0.002 0.002 0.002 0.002 0.003 0.003

F-test 6.363 3.635 4.535 2.210 2.388 2.599 3.148

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

45

Table 12: Second-stage estimates of the effect of the value of cocaine seizures on total

drug-related homicide rates, using different lags of the instruments

(1) (2) (3)

Contemporary Lags 0-1 Lags 0-3

Dependent variable: Total drug-related homicide rate

Cocaine seizures 1.219** 1.430 1.844

in Mexico at t (0.583) (0.912) (1.647)

Cocaine seizures 0.458 0.826

in Mexico at t− 1 (0.596) (1.118)

Cocaine seizures 0.506

in Mexico at t− 2 (0.803)

Cocaine seizures 0.230

in Mexico at t− 3 (0.736)

Observations 106,941 104,540 99,738

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

46

Table 13: IV estimates for the effect of the number of cartels on alternative crime rates

Crime rate INEGI minus drug-related homicides Total thefts Car thefts

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Number of 0.2671*** 0.2673*** 0.2658*** 0.4643*** -0.0201 -0.0207 -0.0195 -0.0099 -0.0368 -0.0315 -0.0335 -0.0862

cartels (0.0687) (0.0689) (0.0692) (0.1502) (0.0418) (0.0419) (0.0421) (0.0711) (0.1308) (0.1312) (0.1375) (0.1641)

Controls

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes Yes Yes

Differential effects by HDI Yes Yes Yes

Observations 9,147 9,147 8,993 8,989 9,768 9,768 9,612 9,608 4,884 4,884 4,826 4,824

NOTE: Robust standard errors with clustering by municipality are shown in parentheses. Coefficients with *** are

significant at the 1% level. Those with ** are significant at the 5% level. Those with * are significant at the 10%

level.

47

Table 14: IV estimates, using cocaine seizures in Colombia close to the Atlantic and

close to the border with Venezuela

(1) (2) (3) (4) (5)

Panel A: First-stage estimates for number of cartels

Cocaine seizures near the -52.146*** -37.987*** -38.198*** -37.987*** -29.402***

Pacific × distance to the US (5.198) (5.697) (5.698) (5.690) (5.837)

Cocaine seizures near Venezuela and 87.749*** 66.065*** 66.144*** 69.082*** 51.630***

the Atlantic × distance to the US (18.587) (18.449) (18.453) (18.515) (19.147)

Cocaine seizures in other regions 2.900*** 0.550 0.581 0.594 0.996

of Colombia × distance to the US (0.244) (0.641) (0.641) (0.642) (0.646)

Panel B: IV estimates for total drug-related homicides

Number of cartels 0.412*** 0.592*** 0.593*** 0.593*** 0.850***

(0.054) (0.087) (0.087) (0.088) (0.168)

Controls

GDP per capita (Mexico) Yes

National expenditure in security Yes

Time fixed effects Yes Yes Yes Yes

Income Yes Yes Yes

State presence Yes Yes

Differential effects by HDI Yes

Observations 9,768 9,768 9,768 9,612 9,608

First-stage F-test 60.29 25.37 25.42 25.27 11.40

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

48

Table 15: IV estimates, using cocaine and heroin seizures in Colombia as time-varying

instruments

(1) (2) (3) (4) (5)

Panel A: First-stage estimates for number of cartels

Cocaine seizures in Colombia -1.320*** -2.020*** -2.016*** -2.001*** -1.251***

× distance to the US (0.200) (0.270) (0.270) (0.270) (0.279)

Heroin seizures in Colombia 1.294*** 0.771*** 0.776*** 0.765*** 0.602***

× distance to the US (0.125) (0.140) (0.140) (0.140) (0.142)

Panel B: IV estimates for total drug-related homicides

Number of cartels 0.4850*** 0.6245*** 0.6258*** 0.6291*** 0.9371***

(0.0651) (0.0924) (0.0926) (0.0937) (0.1908)

Controls

GDP per capita (Mexico) Yes

National expenditure in security Yes

Time fixed effects Yes Yes Yes Yes

Income Yes Yes Yes

State presence Yes Yes

Differential effects by HDI Yes

Observations 9,768 9,768 9,768 9,612 9,608

First-stage F-test 81.43 35.88 35.91 35.27 15.26

NOTE: Robust standard errors with clustering by municipality are shown in

parentheses. Coefficients with *** are significant at the 1% level. Those with

** are significant at the 5% level. Those with * are significant at the 10% level.

49

Table 16: First-stage estimates using cocaine seizures in Colombia as an instrument for the seizures of other types of drugs

Drug used as instrument Cannabis Cannabis Cannabis Cannabis Heroin Heroin Heroin Heroin

(1) (2) (3) (4) (5) (6) (7) (8)

Cocaine seizures in Colombia -7.866 -8.501 -8.128 26.754 -9.969 -9.992 -9.965 -9.017

× distance to the US (21.632) (21.621) (21.650) (25.255) (6.841) (6.844) (6.840) (6.584)

Controls

Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Income Yes Yes Yes Yes Yes Yes

State presence Yes Yes Yes Yes

Differential effects by HDI Yes Yes

Observations 120,295 120,295 118,379 117,720 120,295 120,295 118,379 117,720

R-squared 0.006 0.006 0.006 0.009 0.001 0.001 0.001 0.001

F-test 0.132 0.155 0.141 1.122 2.124 2.131 2.122 1.876

NOTE: Robust standard errors with clustering by municipality are shown in paren-

theses. Coefficients with *** are significant at the 1% level. Those with ** are

significant at the 5% level. Those with * are significant at the 10% level.

50