Crime and violence effects on economic diversity:The case of Mexico’s drug war

Viridiana Ŕıos∗

December 21, 2017

Abstract

This paper exploits a substantial increase in homicides created by a war betweendrug trafficking organizations in Mexico to estimate the causal effects of crime andviolence in the diversity of local economies. Relying on a dataset of homicidescaused by organized criminal rivalry, a text-analysis algorithm that allowed us totrack activities of Mexico’s drug trafficking cartels, and an exceptionally detailedeconomic census, we developed a panel fixed-effect model, and a model that controlsfor the possible endogeneity using instrumental variables. Our identification showsthat subnational economies afflicted with large increases in crime and violence ex-perience reductions in the diversity of its production structure and increases theirdegree of production concentration. Specifically, an increase of 10% in homicidesrates reduces the number of sectors that exists in an economy by 0.006 units. Asimilar increase in the number of criminal organizations has a reduction effect of0.032 units. Our results are robust by several specifications and various controls toaccount for the potential endogeneity of violence.

∗Author thanks Mylene Cano and Mariana Galindo for research assistance. Special thanks to all theanonymous reviewers that made this paper better, and to Mexicos Central Bank (Banxico) for theirsharp observations and feedback. Personally, this paper was made possible by the encouragement andthe friendship of Aleister Monfort, Sofia Viguri and Cesar Martinelli.

1

It has been proven that crime and violence negatively affect the economy (Blattman and

Miguel, 2010; Detotto and Otranto, 2010). However, do crime and violence also affect the

types of goods produced in an economy? In other words, are the diversity of production

structure or the degree of production concentration affected by crime and violence? This

paper develops a methodology to answering this question and by doing so, contributes to

literature on economies affected by crime and violence by focusing on the causal impacts

to economic sector diversity.

The topic of this paper is fundamental to understanding the long-term consequences on

economic development. Since the 1950s, development has been defined as a process of cre-

ating a competitive and profitable productive structure, such as diversifying from agricul-

ture and extractive industries to more sophisticated forms of services and manufacturing

(Hirschman, 1958; Rosenstein-Rodan, 1943; Singer, 1950). Most recently, in a “revived

interest in the macroeconomic role of structural transformations” (Hartmann et al., 2017),

scholars have shown how a country’s productive structure is strongly predictive of future

economic growth and development (Hidalgo and Hausmann, 2009). Finding a relation-

ship between levels of crime and violence and the diversity of local economies opens an

interesting debate as to how the lack of rule of law can affect economic development.

Mexico is an ideal case study to evaluate how the crime and violence impact production

diversity due to the large increases in homicides attributed to DTOs (Guerrero-Gutiérrez,

Guerrero-Gutiérrez; Ŕıos, 2013). Mexico is home to seven DTOs that supply cocaine and

other drugs to the United States. According to the DEA (2015), Mexican DTOs have the

largest and most powerful cocaine supply infrastructure, selling drugs in almost every U.S.

city, which provides them with yearly profits over three hundred billion dollars (Grillo,

2016). In 2007, the capture of several important drug lords unleashed an unexpected

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wave of violence between potential drug lord successors battling for turf (Dell, 2015; Rı́os,

2015; Robles et al., 2013). As a result, homicides from 2007 to 2011 doubled, causing at

least 60 thousand casualties during this period (SNSP, 2011). Levels of violence varied

in intensity and geographical location, providing an ideal range to capture the effect of

violence on subnational economic activity (Molzahn et al., 2012; Osorio, 2015).

The existence of several exceptional data sets from Mexico contributed to this research

study. First, we obtained official registries of homicides caused by organized criminal

rivalry (SNSP, 2011). Mexican authorities keep record of whether a homicide was caused

by organized criminal rivalry or not, giving us the unique ability to identify violence caused

by the war between DTOs. Second, we used the results of a text-analysis algorithm that

tracks recorded activities of Mexico’s drug trafficking cartels over at two-decade period

(Coscia and Ŕıos, 2012). The use of this data technique has been successfully applied

in many specialized academic papers on Mexico’s organized crime (Osorio, 2015; Castillo

et al., 2014; Dube et al., 2016; Holland and Ŕıos, 2017). Finally, we linked the performance

of subnational economies with the aforementioned datasets by merging them with Mexicos

national economic census (INEGI, 2014). This economic census is one of the most detailed

censuses available in the developing world with firm-level panel measures of investments,

added value and remunerations by economic sector. We were granted preferential access

to the census at the firm-level due to a specific legal agreement signed by our universities

and the Mexican Census Office.

To identify a possible causal relationship between crime, violence, and economic diversity,

we used two different identifications. We developed a panel-fixed effect model that relies

on the panel structure of our data by conditioning on municipality, period fixed effects

and standard errors clustered at the level of the municipality. Crime and violence are

3

our main independent variables of interest. Alternatively, to further control for possible

endogeneity, we developed a second specification that uses a lagged violence measure and

lagged government deterrence measure as instrumental variables. This method has been

used in several previously published academic papers (Angrist and Kugler, 2008; Camacho

and Rodriguez, 2013). With this last methodology, we find that violence and crime create

a significant reduction in the number of sectors present in a local economy. Specifically,

an increase of 10% in homicides rates reduces the number of sectors in an economy by

.006 units and a similar increase in the number of criminal organizations reduce has a

reduction effect of 0.032 units.

This paper is divided in four sections. First, we discuss the known impact of violence in

economic performance and how this paper contributes to such literature. Then, we explain

the main argument behind the paper and the unique data that we used to reinforce the

argument, including the unusual challenges involved in gathering said data. The third

section presents the strategy of analysis, reports the analysis results and conducts several

robustness tests to debunk alternative hypotheses. Finally, we conclude by discussing the

relevance of the results, and exploring further avenues of research.

The impact of crime and violence in the economy

Crime and violence significantly impacts economic performance. It has been proven that

violence has the potential to reduce GDP per capita (Alesina and Perotti, 1996; Abadie

and Gardeazabal, 2008) and to negatively affect economic growth (Blattman and Miguel,

2010; Detotto and Otranto, 2010; Bozzoli et al., 2012). It is also well known that the

effects of crime and violence are significant stronger in environments with already elevated

4

levels of macroeconomic uncertainty (Goulas and Zervoyianni, 2013) or those with highly

corrupt economic and government elites (Blackburn et al., 2017).

Several studies around the world have systematically linked crime and violence to signif-

icant economic consequences. Violence has reduced Italy’s gross domestic product and

GDP per capita (Pinotti, 2015; Carboni and Detotto, 2016), lowered the long-term poten-

tial growth of Pakistan (Ahmad et al., 2014), diminished income and employment levels

of Sierra Leone (Collier and Duponchel, 2013), inhibited economic activity in Mexico (En-

amorado et al., 2014; Robles et al., 2013), and promoted a high firm exit rate in Colombia

(Camacho and Rodriguez, 2013). Indeed, the impacts of violence and crime on trade,

national income, and economic welfare are large, global and persistent (Glick and Taylor,

2010).

The mechanisms through which crime and violence affect economic performance are

mostly linked to changes in factor accumulation. Organized crime reduces incentives

to invest in human capital (Shemyakina, 2011; Leon, 2012; Rodriguez and Sanchez, 2012;

Brown et al., 2017), discourages domestic and foreign investments (Daniele and Marani,

2011; Pshisva and Suarez, 2010; Singh, 2013), and inhibits innovation and employment

generation (Bozzoli et al., 2013; Cook et al., 2014). 1. Additionally, there is evidence

that crime and violence increase capital risks (Guidolin and La Ferrara, 2010), promote

inefficient human capital flows (Ŕıos, 2014), and reduce the value of capital assets. Waves

of violence, for example, have limited the value of tangibles (such as housing) in Ireland

(Besley and Mueller, 2012) and real estate markets in Mexico (Ajzenman et al., 2015).

Finally, crime has been associated with a significantly lower probability of enterprise

expansion and income growth (BenYishay and Pearlman, 2014), with diminished labor

productivity (Cabral et al., 2016) and increases in income inequality (Enamorado et al.,

5

2016). Spillover effects have also been tested (Murdoch and Sandler, 2004; Pan et al.,

2012).

More recently, academic studies have investigated how crime and violence may create het-

erogeneous effects. By diverting resources that could have been used in more productive

ways, crime and violence cultivates a market failure that affects investments allocated in

the informal sector (Bozzoli et al., 2012), women employees (BenYishay and Pearlman,

2013; Dell, 2015), younger and smaller firms (Camacho and Rodriguez, 2013), smaller ur-

ban areas (Enamorado et al., 2014), and firms whose inputs are predominantly imported

(Amodio and Di Maio, 2017).

This paper enriches our understanding of the economic impacts of crime and violence by

analyzing a relationship that has been mostly neglected: how crime affects the diversity

of local economies. Specifically, we study how crime and violence impact the number of

economic sectors/industries in which labor, capital and technology are allocated.

Our paper builds upon previous studies that suggest some economic sectors tend to be

more resilient to crime and violence than others. For example, investments in the oil

industry tend to be less affected by organized crime in Latin America (Maher, 2015;

Ashby and Ramos, 2013), and sectors, such as mining, manufacture & agriculture, are

more likely to remain profitable regardless of violent circumstances (Driffield et al., 2013).

Thus, when facing extended periods of violence and crime, some economic sectors may

have a higher potential to disappear than others, leading to economies with less diverse

production.

However, unlike previous literature that focuses on identifying how violence changes firm

or sector-level investment strategies (Camacho and Rodriguez, 2013; Ashby and Ramos,

6

2013; Maher, 2015), our paper is the first to explicitly provide evidence of how crime

and violence induce long-lasting changes in the production composition of subnational

economies, leading to less diversified and more concentrated economic environments.

Studying the impacts of crime and violence in the diversity of subnational economies is

critical. From pioneer studies (Hirschman, 1958; Rosenstein-Rodan, 1943; Singer, 1950)

to more recent empirical studies of economy composition (Felipe et al., 2012), there is

plenty of evidence measuring economic diversity in correlation with economic performance

(Hausmann et al., 2007; Boschma and Iammarino, 2009; Lee, 2011), productivity (Feenstra

and Kee, 2008), future growth (Cristelli et al., 2015), and income inequality (Hartmann

et al., 2017). It has been strongly established that countries tend to converge to a level of

income dictated by the number and type of specialized sectors (Hidalgo and Hausmann,

2009). Furthermore, non-diverse economies are more susceptible to suffer economic and

political failures (Engerman and Sokoloff, 1997), and tend to have limited knowledge and

capabilities in the face of external shocks (Frenken et al., 2007).

Indeed, if crime and violence affect the productive structure of an economy, establishing

rule of law is critical to creating an environment where sustained growth and democratic

prosperity can thrive.

Research question and data collection

Our testable hypothesis is that violent crime and criminal presence reduces the diversity

of local economies, limiting the types sectors that operate in an economy and favoring a

concentrated economic environment.

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We expect this result due to the distinct levels of sensitivity that different sectors demon-

strate in relation to levels of violence. Previous literatures have identified that some

economic sectors are more affected by violence than others (Ashby and Ramos, 2013;

Driffield et al., 2013). Typically, sectors that are resource-bound tend to be particularly

resilient to crime and violence. The high profits of resource-bound sectors allow these

sectors to internalize violence as a cost to their production function (Oetzel et al., 2007).

Multinational companies in old globalized and extractive industries have considerable

experience operating in violent areas with criminal presence (IPA, 2001; Bennett, 2002;

Ashby and Ramos, 2013). On the other hand, sectors lacking geographic-specific inputs

are less resilient to violence, due to a higher probability of leaving a conflict area. The

retail industry and tourism, for example, are more likely to be targets of criminal organi-

zations, and are highly movable, which makes them less resilient to violence (Daniele and

Marani, 2011). Criminal activity significantly impacts these industries because it can pro-

mote immigration (Ŕıos, 2013), reduce consumption (Castillo et al., 2014), and promoted

relocations and closures (Greenbaum et al., 2007). Size can also matter. Multinational

companies are more resilient to violence due to large high sunk costs, long investment hori-

zons, and often a developed expertise in coping with difficult regions. However, industries

with smaller firms tend to be less resilient to violence (Bennett, 2002; Oetzel et al., 2007;

Ashby and Ramos, 2013). Consequently, if crime and violence do indeed affect certain

sectors more significantly than others and ceteris paribus, we should expect to see more

diverse economies in environments that have been peaceful for extended periods of time.

To test our hypothesis, we selected Mexico as our case study. Mexico is an ideal case to

conduct our subnational analysis due to the significant increase in homicides. In recent

years, Mexico faced a large, exogenous increase in homicides created by a competitive

8

war between DTOs (Guerrero-Gutiérrez, Guerrero-Gutiérrez; Rı́os, 2013). The arrival

of a new federal administration brought an increase of enforcement operations against

drug-lords (Dell, 2015; Ŕıos, 2015; Robles et al., 2013). Through these operations, many

traffickers were killed or captured, which caused the internal structure of DTOs to desta-

bilize. The inherently complex process of replacing leadership within a trafficking orga-

nization prompts internal competition and violence within DTOs. From 2007 to 2012,

DTOs caused an estimated 60,000 casualties, more than tripling Mexico’s homicide rate

(Molzahn et al., 2012). During the peak of instability, bodies were found tortured and

decapitated with direct messages to “all those in charge of applying the law” saying, “Do

not take part in our affairs or you’ll die”, and warning society that “this war is between

us, the traffickers” (Holland and Ŕıos, 2017). This case study was ideal to estimate the

causal effects of crime and violence in the diversity of local economies due to the exogenous

variance.

Given how suitable Mexico is for this type of study, it is not surprising that academia has

already investigated the impacts of crime and violence in Mexico’s economy. However, all

studies have concentrated on illustrating the cost of violence on economic factors, such as

employment and capital investments. For example, (Dell, 2015) found that female labor

participation decreases in municipalities where drug violence erupts after law enforcement

crackdowns. It has also been shown that for every 10 drug-related homicides per a 100

thousand populace, unemployment increases by 0.5% (Arias and Esquivel, 2012). Ashby

and Ramos (2013) identified that violence deters FDI in financial services, commerce and

agriculture, and BenYishay and Pearlman (2013) illustrated that an increase in homicide

rates (per 100 thousand) reduces the weekly hours worked by 0.3. Enamorado et al. (2014)

also contributed by finding that a one standard deviation increase in the number of drug-

9

related homicides decreases income growth by 0.2 percentage points. Robles et al. (2013)

found a negative effect of violence on labor participation. Most recently, Rozo (2014)

estimated that consumption among white and blue-collard workers is reduced by 2.8% and

6.3% when homicide rates increase by 10%. Velásquez (2014) found that elevated violence

leads self-employed women to leave the labor market, (Ajzenman et al., 2015) discovered

that increases in homicides led to a 3% decrease in the price of low-income housing, and

Yepes et al. (2015) found that crime shocks are responsible for 0.25% reductions in GDP

per capita. This research study is unique as none of the aforementioned studies focused

on the impacts of violence on factor allocation and economic diversity.

Shedding light on how local economic diversity is potentially affected by crime and violence

is possible due to several, exceptional subnational panel data sets.

First, to measure violence at the local level, we accessed official registries of homicides

caused by organized criminal rivalry, also known as “crime-rivalry homicide rate” (SNSP,

2011). Mexican authorities keep record of whether a homicide was caused by organized

criminal rivalry or not, giving us the unique opportunity to identify violence caused by the

war between DTOs in a panel dataset. Additionally, these records can be geographically

identified. A homicide needs to meet six criteria to be categorized as a crime-rivalry homi-

cide. These are (i) the use of high-caliber firearms,(ii) signs of torture or severe lesions in

victims, (iii) bodies found at the crime scene or in a vehicle, (iv) victims taped, wrapped or

gagged, (v) murders committed in a prison and involving criminal organizations, and (vi)

if one of several “special circumstances” occurred. These “special circumstances” include:

if the victim was ambushed, chased or abducted prior to assassination (levantón), if the

victim was an alleged member of a criminal organization, and if a criminal organization

publicly claimed responsibility for the murder (Molzahn et al., 2012). We were also able

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to assess a sub-classification of crime-rivalry homicide rates, called “targeted execution

rates”, meaning homicides perpetrated by organized criminal rivalry where the victim was

visibly targeted (rather than assassinated as part of a shooting). This sub-classification

encompasses about 80% of all crime-rivalry homicide and is considered the most accurate

identification of the war between DTOs. Finally, we complemented these models with

general homicides rates (INEGI, 2016).

Second, to measure criminal presence, we relied on a published big-data framework that

uses a text-analysis algorithm to extract web content about recorded criminal activities by

subnational economy (Coscia and Ŕıos, 2012). The algorithm “reads digitalized records”,

news content, blogs and Google-News indexed content, searching for instances in which

DTOs operations are mentioned. The Python crawler was created to extract JSON ob-

jects using unambiguous query terms to perform text analysis. The final data, cleaned by

using an hyper-geometric cumulative distribution function, includes 2,449 sub-national

economies, and 178 “actor terms” associated with traffickers and drug trafficking organi-

zations. Each actor was classified according to 13 criminal organizations and a residual

category. A more detailed description of the methodology followed can be found within

the subsequently cited publication (Coscia and Rı́os, 2012).

This framework enabled us to obtain information that would otherwise require large scale,

expensive intelligence exercises. Most importantly, this procedure helped us to disentangle

violence from crime. Many of the recorded DTO operations are non-violent, consistent

with peacefully trading, transporting, producing or cultivating illegal drugs. The data set

has been a source to study criminal activity in several published papers (Osorio, 2015;

Castillo et al., 2014; Dube et al., 2016; Holland and Ŕıos, 2017).

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Overall, by merging our dataset of criminal rivalry homicides and criminal operations,

we obtained information of 13 DTOs operating in Mexico for 19 years (1991 - 2010). As

figure 1 shows, the disaggregation by municipal level allows us to challenge the widespread

assumption that drug traffickers control vast regions of Mexicos territory and that criminal

organizations operate in oligopolistic markets. In actuality, drug trafficking organizations

only operate in 713 of 2,441 municipalities in Mexico. Large areas of Mexico completely

lack the presence of a drug trafficking organizations. Furthermore, as of 2010, 444 (62 %)

of all municipalities with trafficking operations had more than one criminal organization

operating simultaneously.

Finally, in order to obtain information about the diversity of subnational economies, we

used Mexicos economic census. The census is a firm-level, multi-year panel classified as

one of the most detailed economic censuses of the developing world. The census contains

information about investments, added value, remunerations and yearly production, classi-

fied by economic sector according to the North American Industry Classification System

(NAICS). The census has more than 15 years of panel data for 4,231 thousand firms

among its territory (INEGI, 2014). The last iteration of the census was released during

the fall of 2015 making it highly up to date and mostly unexplored. It is important to

mention that due to confidentiality issues, accessing this data was quite difficult. We were

granted preferential access only after signing specific legal agreements by our universities

and the Mexican Census Office. The process itself took many months to be fully vetted.

Table 1 shows descriptive statistics for all the most relevant variables of our panel datasets.

The natural log of homicide rates mean is 1.92, while the value for municipality with

the highest homicide rate is 7.21. In addition, we can see that the average number of

criminal organizations present in each municipality is 0.4. Moreover, the average crime-

12

rivalry homicide rate is higher than the average targeted execution rate. Considering our

economic variables, we can see that each municipality has an average of 17.75 industries.

(Table 1 about here)

Empirical specification, results and robustness checks

Using the data described above, we now proceed to empirically estimate the effect of

crime and violence on economic diversity. We rely on two different estimation techniques:

a panel fixed effects model, and instrumental variable (IV) model. Both estimations are

better than using a simple OLS because violence and crime are likely to be endogenous.

Both estimation models disaggregate the data by year and municipality. Municipalities

are the unit of analysis, with a total of i=2,456 municipalities and data for t=[2003, 2008,

2013] years. It makes sense to use municipalities as the measure of analysis because ac-

cording to Mexican legislation, municipalities determine many important legal frameworks

and incentives used to attract domestic and foreign investment. Important development

policies, such as the recently launched “Mexican Economic Zones” have been designed

according to municipalities as the unit of analysis.

First, we present the results of a panel fixed effects model that is useful to control for

constant unobservable factors potentially affecting the outcome of interest.

We took advantage of the panel structure of our data by conditioning on municipality and

period fixed effects, and using standard errors clustered at the level of the municipality. In

other words, our first specification controls for geographic characteristics, secular trends

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in economic performance over time, and geographic linear trends to capture unobserved

local time-varying effects.

Specifically, the panel fixed effects model is given by 1:

Dit = β0 + β1 ln(νit) +l∑

k=1

βkCit + · · ·+ eit (1)

where Dit measures the diversity of the local economy. Diversity represents the number

of economic sectors that exists in the municipality i in time t. Following literature of

economic diversity (Hartmann et al., 2017; Hidalgo and Hausmann, 2009), an economic

sector is one in which added value is above zero, and added value is defined as the

difference between revenue and intermediate consumption. The economic census contains

information of more than 5 million firms.

Our causal variable ν measures crime or violence with different specifications. When ν

measures crime, it functions as the number of criminal organizations that operate in i

during t (Coscia and Ŕıos, 2012). When ν tries to measure violence, it functions as either

homicide rates (INEGI, 2016), crime-rivalry homicide rates (SNSP, 2011), or targeted

execution rates (SNSP, 2011). All rates are calculated per 100,000 inhabitants and for

the cumulative two years preceding t. Figures accumulating 24 months provide a more

representative analysis of dangerous places and takes into consideration the potentially

delayed effect of crime and violence on local economic diversity. Note that, because

zeros are considered valuable information about criminal violence, homicide rates were

logarithmically transformed according to 2:

14

ln(νit) =

ln(νit + 1) if νit ≥ 0

− ln | νit | if νit < 0(2)

To obtain the causal effect of crime and violence on the diversity of the local economy, we

need to capture within-municipality variations- after controlling for other determinants

of economic diversity. Given that the diversity of the local economy will be determined

by whether firms within a specific sector do or do not continue producing, we can assume

that an economic sector will produce if at least one firm within the specified sector of

local economy i finds that (Camacho and Rodriguez, 2013).

V At − wLt − rIt > 0 (3)

where V A is added value, wLt is the cost of payroll, and rIt is return of investments in

period t. We know that V A is given by:

V At = Yt − Ct (4)

where Yt is total revenue, and Ct is intermediate consumption.

Thus, following this equation and to capture within-municipality variations, we controlled

all the models for (a) added value, (b) remunerations (INEGI, 2014), and (c) investments

(INEGI, 2014). All values have been logged and refer to period t.

The results of the panel fixed-effects model, conditioning on municipality and period fixed

effects and with standard errors clustered at the level of the municipality, are presented

15

in Table 2.

(Table 2 about here)

In Table 2, columns (1) and (2) use homicide rates as the explanatory variable (INEGI,

2016), whereas columns (3) to (4) use the number of DTOs operating in the municipality.

We used two different dependent variables to measure economic diversity. In columns

(1) and (3), we use the number of economic sectors that have positive added value in

a municipality (see eqsector). We call these DV “sectors”. In column (2) and (4), we

use “diversity”, meaning the number of economic sectors that have positive added value

in a municipality and in which the municipality has relative comparative advantage. To

calculate Relative Comparative Advantage (RCA), we followed the definition of Hausmann

et al. (2007). The value of both dependent variables is larger when the municipality is

more diverse, i.e. has more sectors or more sectors with RCA.

The results are consistent with our expectations. We can see a strong and negative

relationship between homicide rates and the number of sectors that exist in a municipality.

As column (1) shows, a 10% increase in homicide rates is correlated with a 0.006 reduction

in the number of industries that operate in a municipality. The result is also significant

and negative for diversity measures. In column (2), a 10% increase in homicide rates is

correlated with a 0.003 reduction in diversity. As we expected, eliminating a sector with

RCA is harder than just eliminating any economic sector.

When we evaluate the impact of crime, the effects are larger. As column (3) shows, a

10% increase in the number of operational criminal organizations in a municipality is

correlated with reductions of 0.031 in the number of operational industries within the

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respective municipality. Finally, column (4) shows that an increase of 10% in the number

of operational criminal organizations in a municipality is correlated with reductions of

0.009 in the number of economic sectors with relative comparative advantage within the

respective municipality.

It is important to note that our controls are significant for added value and remunera-

tions, but not for investment. In all models, value added is positive and significant and

remunerations are negative and significant. This means that lower remunerations are cor-

related with less diversified economies, and high value-added economies tend to be more

diverse.

In Table 3, we present some robustness checks. We decided to use these subsamples to

discard the alternative hypothesis that (a) our hypothesis only applies to non-violent mu-

nicipalities that suddenly turn violent, and (b) our hypothesis only apply to municipalities

where drug traffickers DTOs operate (column 2 and 4). Specifically, we show the same

models from the previous table but with two subsamples: (a) municipalities that have

been consistently violent since the nineties (at least one homicide), and (b) municipalities

with no existing evidence of drug markets (see Dube et al. (2016) ). All models use the

number of sectors as dependent variable. Yet, when the models were also run using diver-

sity as main independent variable, the same results were attained. Columns (1) to (2) use

homicide rates as the main explanatory variable, and columns (3) to (4) use the number

of DTOs operating in the municipality. Columns (1) to (3) only present municipalities

that have been consistently violent since the nineties, and columns (2) and (4) only show

municipalities where there is no existing evidence of DTOs.

(Table 3 about here)

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Again, the results are consistent with our expectation. For municipalities that have

been consistently violent since the nineties (column 1 and 3), an increase of 10% in

homicide rates and in the number of criminal organizations are respectively correlated

with reductions of 0.007 and 0.035 in the number of industries in a municipality. For

municipalities where DTOs do not operate (columns 2 and 4), an increase of 10% in

homicide rates and in the number of criminal organizations are respectively correlated with

reductions of 0.006 and 0.032 in the number of industries in a municipality. As in previous

models, DTOs have larger effects than homicides. Moreover, as in the previous models,

the impact of crime is larger than the impacts of violence. The controls are consistent

with previously presented results. Added value and remunerations are significant, but not

for investment. In all models, value added is positive and significant and remunerations

are negative and significant. This means that lower remunerations are correlated with

less diversified economies, and high value-added economies tend to be more diverse.

Overall, all the models with municipality and period fixed effects and standard errors

clustered at the level of the municipality assume that changes in our causal variable are

strictly exogenous. Thus, our identification strategy requires that homicide rates be not

driven by economic factors that could also determine local economic diversity.

Focusing Mexico, there is convincing evidence that illustrates this to be the case. Dell

(2015) proved that the behavior of DTOs is not driven by the economic situation of

the municipality. Ajzenman et al. (2015) demonstrated that the timing and intensity

of violence are not driven by the economic situation in a municipality. In particular, he

developed an econometric specification that shows that, in the case of Mexico, employment

levels are not statistically related to homicide rates. Ajzenman et al. (2015) publication

has proven to be quite relevant given and empirically challenged the common fear that

18

economic activity could, for example, be correlated with fewer labor opportunities and

consequently, more DTO activities.

Yet, to further prove that our results are solid, we decided to add a second specification

to avoid reverse causality: an IV model. IV models address and reduce biases caused by

either omitted variables or measurement error.

The IV model is specified in equations (5) and (6). Equation (6) is the first stage of a

2SLS model containing an instrument I.

Dij = β0 + β1l̂n(νij) +l∑

k=1

βkCij + · · ·+ eij (5)

l̂n(νij) = α0 + α1Iij +l∑

k=1

αlKijl + · · ·+ uij (6)

We used two possible IV specifications, depending on the instrument we selected. Fol-

lowing published papers in the topic, we used two instruments: homicide rates during the

early nineties (INEGI, 2016), and a measure of lagged government deterrence.

There is plenty of evidence to justify the lagged values of independent variables as instru-

ments. Using lagged values of an endogenous explanatory variable as an instrument leads

to consistent estimates when (i) the lagged values are not part of the estimating equation,

and (ii) when the lagged values are sufficiently correlated with the main independent vari-

able Reed (2015). Both conditions apply to our data. Lags are less likely to be influenced

by current shocks, and have been used many published research papers, such as Buch

et al. (2014); Clemens et al. (2012); Stiebale (2011); Vergara (2010); Hayo et al. (2010).

19

There are at least 15 recently published papers in the International Studies Quarterly,

14 in the Comparative Political Studies, 10 in the American Journal of Political Science,

and 10 more in the Journal of Politics that use lagged values as instruments. In our case,

while it is plausible to assume that firms may take into account the last period’s violence

when making production decisions or even entering into business, it is less likely to believe

that production decisions of today are directly influenced by violence that happened more

than two decades ago. It is particularly hard to believe if, in between, Mexico made a

meaningful change in productive structure by signing NAFTA (Esquivel and Rodŕıguez-

López, 2003; Moreno-Brid et al., 2005; Chiquiar, 2008). We believe it is safe to assume

that any potential inertia in the violence measure on economic activity was eliminated by

Mexico’s open market shock of 1994.

Evidence has also showed that lagged government deterrence is correlated with current

crime and violence, but not with the number of current economic industries. Plenty of

published research shows that government deterrence triggers violence waves in Mexico

(Dell, 2015; Ŕıos, 2015; Robles et al., 2013; Castillo et al., 2014), and that this instru-

ment is valid (Angrist and Kugler, 2008; Camacho and Rodriguez, 2013). It is also safe

to assume that the commercial decisions of firms are not affected by central government

deterrence decisions, which are normally conducted in secret or in environments not easily

detected by citizens. Empirically, Angrist and Kugler (2008) found that when coca pro-

duction expands, it tends to increase violence, but not change the economic activity of the

producing regions. This finding is attributed to the fact that regional economies are not

necessarily tied to the drug business, instead resources of illegal activity go directly into

the hands of criminals to conduct other criminal activities (Angrist and Kugler, 2008).

Our IV specifications are presented in Table 4 and 5. Diagnostics for IV specifications

20

are shown in both tables.

Estimations with the instrument of lagged government deterrence are presented in Table

4. Columns (1) to (4) use various measures of violence and crime as the main explanatory

variables. In column (1) we use homicide rates (INEGI, 2016), in (2) the number of

operating DTOs (Coscia and Rı́os, 2012), in (3) crime-rivalry homicide rates (SNSP,

2011), and in (4) targeted execution rates (SNSP, 2011). All models from (1) to (4) use

the number of sectors in a municipality as the dependent variable.

(Table 4 about here)

The results are consistent with our expectations. They show a strong and negative re-

lationship between all different homicide rates and the number fluctuation of industries

that exist in a municipality. As column (1) shows, a 10% increase in homicide rates is

correlated with a 0.215 reduction in the number of sectors in a municipality. For column

(2), a 10% increase in the number of operational DTOs is correlated with a 1.08 reduction

in the number of sectors in a municipality. For column (3) an increase of 10% in crime-

rivalry homicide rates is correlated with a 0.186 reduction in the number of sectors in a

municipality. For column (4) an increase of 10% in targeted execution rates is correlated

with a 0.179 reduction in the number of sectors in a municipality. These results illustrate

that an increase in the number of operating DTOs in a municipality has the largest effect

over the number of sectors. Moreover, we can see how IV models show larger effects of

violence and crime over the number of sectors in a municipality than panel fixed-effects

models.

We tested all models with alternative and complex versions of the dependent variables.

In this paper, we only present the results for diversity and economic concentration. This

21

last measure is particularly interesting because it allow us to identify whether the nega-

tive effects of crime and violence on the number of economic sectors, can also affect the

concentration of economic production structures. To measure concentration, we use the

Herfindhal-Hirschman index, a classic measure used in plenty of literature (Hirschman,

1958; Boschma and Iammarino, 2009; Frenken et al., 2007; Saviotti and Frenken, 2008;

Cristelli et al., 2015; Hidalgo and Hausmann, 2009). The Herfindhal-Hirschmann index is

the sum of squares, calculated by squaring the share of added value of each industry in a

municipality and then, summing these squares (Frenken et al., 2007). Models with diver-

sity and concentration as dependent variables were also tested for different specifications

of homicides and cartels.

The results are consistent with our expectations. There are strong relationships between

increases in homicide rates and decreases in diversity, as well as, increases in concentration.

In column (5) an increase of 10% in homicide rates is correlated with reductions of 0.046 in

diversity. Column (6) shows that an increase in homicide rates is correlated with increases

in the Herfindhal-Hirschman index. Specifically, an increase of 10% in homicide rates is

correlated with an increase of 78.55 in the H-H index. Note that, to control for unobserved

time invariant, and state effects, all models use time and state and fixed effects.

Note that, all controls are significant and positive. When the dependent variable is the

number of sectors in the municipality or diversity, high value added investment and remu-

nerations are correlated with more sectors and diversity (columns 1-5). Only investment

has a negative coefficient for diversity in column (5). For column (6), the relationship is

negative due to the expected positive relationship between violence and concentration.

Table 5 illustrates the same IV specifications, but with our second instrument. The order

22

is the same as before. Columns (1) to (4) use various measures of violence and crime as

the main explanatory variables. In column (1) we use homicide rates (INEGI, 2016), in

(2) the number of operational DTOs (Coscia and Ŕıos, 2012), (3) crime-rivalry homicide

rates (SNSP, 2011), and in (4) targeted execution rates (SNSP, 2011). All models from

(1) to (4) use the number of sectors in a municipality as dependent variable.

(Table 5 about here)

As expected, an increase in homicide rates (column 1) and in the number of criminal

organizations (column 2) is correlated with reductions in the number of sectors in an

economy. However, when we evaluate crime-rivalry homicide rates (column 3) and tar-

geted execution rates (column 4), rather than homicide rates, the effects are smaller. As

column (1) shows, a 10% increase in homicide rates is correlated with reductions of 0.24

in the number of sectors in a municipality. For column (2), a 10% increase in the number

of operational DTOs is correlated with reductions of 0.070 in the number of sectors in a

municipality. For column (3) an increase of 10% in crime-rivalry homicide rates is corre-

lated with reductions of 0.186 in the number of sectors in a municipality. For column (4)

an increase of 10% in targeted execution rates is correlated with reductions of 0.179 in

the number of sectors in a municipality.

Moreover, results are solid in showing a strong and positive correlation between increases

in homicide rates with economic concentration (column 4). Specifically, an increase of

10% in homicide rates is correlated with an increase of 52.75 in the H-H index. Finally,

as expected, there is a negative and strong correlation between homicide rates and the

number of economic sectors with relative comparative advantage. Specifically, an increase

of 10% in homicide rates is correlated with reductions of 0.062 in the number of economic

23

sectors with relative comparative advantage.

The controls are positive and significant in most models. High value added economies

tend to have more sectors, higher remunerations and investment are also correlated with

having more sectors. For models 3-4, investment is not significant; for model 5, high value

added is correlated with more concentrated economies, while high levels of investment

and remunerations can be correlated to less concentrated economies; and for model 6,

high value added economies can be related to economies with less relative comparative

advantage, while high levels of investment and remunerations can be correlated to more

diversified economies.

Conclusion

Previous literature has focused its attention on identifying how crime and violence impact

economic factor accumulation, not on how crime and violence affect factor allocation,

particularly the diversity of local economies. This paper attempts to address this gap

in the literature. Using an IV specification to address problems of reverse causality and

relying on a unique dataset created with a text-analysis algorithm of web content, this

paper illustrates that increases in criminal presence and violent crime reduce economic

diversification, increased industry concentration and diminished economic complexity.

The presented results are strong, consistent, significant, and hold over a variety of speci-

fications and robustness tests.

Using a unique dataset representing the number of homicides attributed to drug cartel

rivalry, along with a text-analysis algorithm that tracks Mexicos drug trafficking activi-

24

ties, this paper demonstrates that the areas most affected by the escalation of conflict be-

tween rival drug trafficking organizations experienced a change to its productive economic

structure. Specifically, the number of economic sectors was reduced, limiting economic

diversification and increasing market concentration.

Our more conservative specification, government deterrence, shows that increases of 10%

increase in homicide rates is correlated with a 0.215 reduction in the number of sectors in a

municipality. This is equivalent to say that a 46% increase in the rate of homicides reduces

by one the number of sectors that operate in an area. The results show that increases in

the number of operational criminal organizations in a municipality, crime-rivalry homicide

rates and targeted execution rates also reduce the number of industries.

Our more innovative specification, which relies on a big data exercise of text analysis to

identify where criminal organizations operate, shows that increases of 10% in criminal

presence correlate to reductions of 0.32 in the number of industries in a municipality.

Results show that increases in homicide rates are also correlated with reductions in the

number of industries in a municipality. Finally, we show that, increases in homicide rates

and in the number of criminal organizations are correlated with reductions in the economic

diversity in a municipality.

By investigating the impact that crime has on the diversification of production factors,

this paper takes current literature one step forward: It goes from exploring the effects of

crime on the demand/supply of production factors, to analyzing its effects on economic

composition. Furthermore, our results are valuable to scholars analyzing the effects of

criminal violence on growth, and provide a strong foundation towards better understand

why highly violent areas do not always exhibit diminished growth in the short term. If,

25

as argued, the negative effects of violence in economic growth are not only explained by

reductions in investments, outflows of human capital, or increased production costs, but

by changes in the productive composition of an area, it follows that, in the short time,

violence may not necessarily reduce economic growth, but rather change the variety of

economic sectors.

Further work is needed to address whether economic growth can continue in the short

term, even with a less diverse economy. In other words, while violence will most likely

reduce economic growth in the long term (via diversification reductions), violence may

leave growth unaffected in the short term. The comparative differences of the long-term

and short-effects of violence on economic growth is a promising agenda that begs to be

explored.

26

Tables

Table 1: Descriptive statisticsVariable Mean Std. Dev. Min MaxHomicide rates (log) 1.92 1.51 0 7.21Homicide rates nineties (log) 2.76 1.37 0 6.56Criminal organizations 0.37 0.92 0 0Criminal-rivalry homicide rates (log) 0.95 1.32 0 6.45Targeted execution (log) 0.87 1.25 0 6.44Added value (log) 7.75 2.82 -13.5 17.27Total investment (log) 4.69 3.41 -11.52 15.14Total remunerations (log) 6.14 2.92 0 15.46Industries 17.75 9.41 0 42H-H index 3737.32 1962 0 10,000Diversity 5.62 2.62 1 23

27

Table 2: Panel Fixed-Effects(1) (2) (3) (4)

Independent Variables Sectors Diversity Sectors DiversityHomicide rates -0.0631** -0.0393*

(0.0273) (0.0208)Criminal organizations -0.319*** -0.0981**

(0.0507) (0.0413)Added value 0.0821*** -0.226*** 0.0838*** -0.226***

(0.0232) (0.0379) (0.0236) (0.0382)Investment 0.00903 -0.0110 0.0109 -0.0105

(0.0136) (0.0120) (0.0136) (0.0120)Remunerations 0.219*** -0.129*** 0.208*** -0.131***

(0.0484) (0.0427) (0.0482) (0.0426)Constant 14.44*** 7.886*** 14.38*** 7.831***

(0.328) (0.303) (0.320) (0.300)Observations 7,340 7,340 7,340 7,340R-squared 0.974 0.794 0.974 0.794

Note: * p

Table 3: Panel Fixed-Effects, Robustness checks(1) (2) (3) (4)

Independent Variables Sectors Sectors Sectors SectorsSub-sample Constant violence No DTOs Constant violence No DTOsHomicide rates -0.0665** -0.0635**

(0.0306) (0.0281)Criminal organizations -0.350*** -0.315***

(0.0527) (0.0526)Added value 0.0836*** 0.0921*** 0.0865*** 0.0949***

(0.0254) (0.0235) (0.0260) (0.0234)Investment 0.00975 0.00525 0.0115 0.00735

(0.0134) (0.0141) (0.0132) (0.0141)Remunerations 0.216*** 0.213*** 0.204*** 0.203***

(0.0553) (0.0501) (0.0551) (0.0499)Constant 15.46*** 14.50*** 15.39*** 14.44***

(0.382) (0.339) (0.374) (0.329)Observations 6,322 6,901 6,322 6,901R-squared 0.974 0.974 0.974 0.974

Note: * p

Tab

le4:

Inst

rum

enta

lV

aria

ble

s,G

over

nm

ent

Det

erre

nce

(1)

(2)

(3)

(4)

(5)

(6)

Var

iable

sSec

tors

Sec

tors

Sec

tors

Sec

tors

H-H

index

Div

ersi

tyH

omic

ide

rate

s-2

.158

**-1

16.8

75**

0.13

1*(0

.544

)(5

6.91

4)(0

.072

)C

rim

inal

orga

niz

atio

ns

-10.

080*

*(4

.479

)T

arge

ted

exec

uti

onra

tes

-1.8

62**

(0.8

36)

Cri

me-

riva

lry

hom

icid

era

tes

-1.7

78**

(0.8

00)

Added

valu

e0.

680*

**0.

882*

**0.

798*

**0.

793*

**16

7.50

5***

-0.1

41**

*(0

.043

)(0

.142

)(0

.069

)(0

.069

)(1

7.91

4)(0

.020

)In

vest

men

t0.

261*

**0.

514*

**0.

197*

**0.

192*

**-2

8.72

0***

0.06

8***

(0.0

23)

(0.1

17)

(0.0

33)

(0.0

33)

(9.5

73)

(0.0

11)

Rem

uner

atio

ns

2.33

1***

2.89

7***

2.26

3***

2.26

6***

-387

.291

***

0.44

9***

(0.0

58)

(0.3

29)

(0.1

02)

(0.1

03)

(24.

100)

(0.0

27)

Con

stan

t-1

.817

*-1

1.91

0***

-1.6

23-1

.202

4,12

2.51

6***

3.37

7***

(1.0

22)

(3.9

82)

(1.3

66)

(1.3

73)

(427

.090

)(0

.478

)O

bse

rvat

ions

7,33

97,

339

2,44

92,

449

7,33

97,

339

Dia

gnos

tic

chec

k(w

eak

inst

rum

ent)

1.49

e-09

***

0.02

47*

1.92

e-05

***

1.34

e-05

***

1.49

e-09

***

1.49

e-09

***

Not

e:*p<

0.1;

**p<

0.05

;**

*p<

0.01

.

30

Tab

le5:

Inst

rum

enta

lV

aria

ble

s,H

omic

ide

rate

snin

etie

s(1

)(2

)(3

)(4

)(5

)(6

)V

aria

ble

sSec

tors

Sec

tors

Sec

tors

Sec

tors

H-H

index

Div

ersi

tyH

omic

ide

rate

s-6

.741

***

1,72

9.41

3***

-2.0

69**

*(0

.870

)(2

55.2

86)

(0.3

21)

Cri

min

alor

ganiz

atio

ns

-12.

437*

**(1

.769

)T

arge

ted

exec

uti

onra

tes

-8.9

78**

*(1

.571

)C

rim

e-ri

valr

yhom

icid

era

tes

-8.1

00**

*(1

.363

)A

dded

valu

e0.

770*

**0.

917*

**0.

820*

**0.

795*

**13

7.43

9***

-0.1

07**

*(0

.084

)(0

.102

)(0

.154

)(0

.147

)(2

4.78

5)(0

.031

)In

vest

men

t0.

200*

**0.

531*

**0.

105

0.08

4-2

0.05

70.

055*

**(0

.047

)(0

.065

)(0

.072

)(0

.070

)(1

3.85

5)(0

.017

)R

emuner

atio

ns

2.53

1***

3.23

0***

2.97

7***

2.92

7***

-390

.223

***

0.51

6***

(0.0

97)

(0.1

70)

(0.2

14)

(0.2

00)

(28.

317)

(0.0

36)

Con

stan

t5.

456*

*-1

4.88

5***

-1.0

100.

169

2,31

9.43

9***

5.97

5***

(2.2

30)

(2.7

18)

(3.1

66)

(3.0

52)

(654

.106

)(0

.823

)O

bse

rvat

ions

6,32

26,

322

2,12

42,

124

6,32

26,

322

Dia

gnos

tic

chec

k(w

eak

inst

rum

ent)

1.12

e-14

***

6.73

e-14

***

5.05

e-11

***

6.92

e-12

***

1.12

e-14

***

1.12

e-14

***

Not

e:*p<

0.1;

**p<

0.05

;**

*p<

0.01

.

31

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Notes

1See also(Organski and Kugler, 1977; Imai et al., 2000; Hoeffler and Reynal-Querol, 2003; Busse and

Hefeker, 2007)

42