Impact of Crime on Mexico's FDI

The Impact Of Crime On Foreign Direct Investment:
The Case Of The Mexican War Against Organized Crime
José Eduardo Almaraz Reséndez
The following dissertation is presented in partial fulfilment of the requirements for the
MSc Applied Economics and Data Analysis.
University of Essex
United Kingdom
September 2012

ii
Table of Contents
Acknowledgments………………………………………………………………………… iii
Abstract………………………………………………………………………………….... iv
I. Introduction……………………………………………………………………………... 1
II. Background…………………………………………………………………………….. 2
III. Literature review……………………………………………………………………… 5
i. Theoretical literature………………………………………………………. 5
ii. Empirical literature………………………………………………………... 6
IV. Data description………………………………………………………………………. 8
V. Modelling crime and FDI……………………………………………………………… 11
i. Model 1: Violent crimes……………………………………………………. 11
ii. Model 2: Criminality index………………………………………………... 14
iii. Post-estimation tests………………………………………………………. 17
iv. Further results…….………………………………………………………. 21
VI. Limitations and suggestions for further research……………………………………. 23
VII. Conclusions………………………………………………………………………….. 23
Bibliography……………………………………………………………………………… 25
Appendix………………………………………………………………………………….. 28

iii
Acknowledgments
Firstly, I would like to thank my family, especially my parents José Héctor Eduardo
Almaraz Hernández and Carmen Deyanira Reséndez de Almaraz. Without their unconditional
support, both financial and moral, this work would not have been possible. I will also like to
thank my lecturers both at the University of Essex and at ITESM (Mexico) whose support and
passion for teaching were of paramount importance for the development of my work.

iv
Abstract
Violent crime has accelerated substantially in Mexico in the past five years as criminal
organizations have attempted to gain control over certain territories. Crime, especially organized
crime (which is generally more violent), deters FDI as it represents higher costs as well as higher
risks for potential or existing investors. The aim of this work is to assess the impact of organized
crime on FDI. Results from constructing a criminality index show that an increase of 1% in
typical DTO crime will reduce FDI by about 0.44% in a given state of Mexico the following year.

1
I. Introduction
Violence derived from drug-trafficking organizations (DTOs) has escalated dramatically
in the past five years in Mexico. Shortly after President Felipe Calderón took office in December
2006, he focused on the attacks on DTOs which he made the centrepiece of his administration.
However, as drug kingpins are arrested or killed, their members have fought amongst each other
to try to gain control over the various drug trafficking organizations, substantially increasing the
number of violent crimes committed. Moreover, the deployment of the army created a triangular
conflict that even further increased the number of deaths. The conflicts are greatly concentrated
in a number of states, mainly in drug trafficking routes or in states where two or more rival
DTOs clash. Therefore, these states have suffered from a consequent reduction in the amount of
Foreign Direct Investment (FDI), as investors think twice before exposing their capital to the
increasing violence.
The purpose of this essay is to assess the magnitude on FDI of violent crimes, such as
murders1
, violent thefts, kidnaps, extortions and violent thefts on highways (where a number of
typical crimes are committed by DTOs). The structure of this work is as follows. In section II, a
background is presented with a brief description of the nature of Mexican territory and its
relevance for DTOs, together with a recapitulation of the events that marked Calderón’s
administration. Section III presents a review of theories discussed in the literature for this study,
as well as a review of some previous work in similar countries and backgrounds. In section IV,
the dataset utilized is presented, which was collected mainly from different Mexican agencies,
institutions and non-governmental organizations (NGOs). In section V, two different models are
estimated, together with several post-estimation tests and other supporting results. Section VI
1
Murders are a form of premeditate or intentional homicide, classified as homicidio doloso. Not to be confused with
manslaughter or homicidio culposo.

2
presents the limitations of this work, together with recommendations for further research. Section
VII provides a conclusion for the study.
II. Background
Drug trafficking in Mexico began in the 1980s, with drugs being transported mainly from
South America into the USA. The single-party system, which predominated in Mexico for most
of the 20th century, provided a favourable environment for drug trafficking. A system network
operated that “ensured distribution rights, market access, and even official protection to drug
trafficking organizations in exchange for very lucrative bribes dispensed at very high levels of
power” (Shirk, 2011, p. 8). The first major attack on a DTO was in 1989, partly incited by
pressure from the USA, when Miguel Ángel Félix Gallardo - the boss of the Guadalajara Cartel
and head of the cocaine trade - was arrested (Shirk, 2011; Miles, 2002). Since then, however, the
power and scope of DTOs activities have grown.
In December 2006, shortly after President Felipe Calderón took office, he launched a
frontal attack on drug cartels2
. From that month and up to September 2011 (when data became
available) more than 47,500 people have died by “presumptuous delinquency rivalry”,3
according to official statistics (SESNSP, 2012; PGR, 2012). The first bold action of Calderón’s
administration was the deployment of the army to Michoacán, his home state. Some critics argue
that this was done to legitimize his government after the close elections where the runner-up
declared fraud and swore himself in as the “legitimate president” of Mexico (Molzahn, Ríos, and
Shirk, 2012; McKinley Jr., 2007). From around 2007, and after a steady decline from the late
2
Drug-trafficking organizations (DTOs) are commonly referred to as ‘cartels’. However, these should not be
confused with the term used in economics, which describes price or output setting. In fact, drug cartels are quite
competitive.
3
This classification means that at least one of the parts involved in the crime forms part of a DTO.

3
1990s, most violent crimes have increased substantially at a national scale, as depicted in Figure
1.
Figure 1. Violent Crimes (logarithmic scale)
As the figure shows, these crimes have increased as DTOs have diversified their activities
into other areas of crime (rather than just trafficking drugs) that have a greater effect on the
general population, such as murders, extortions, kidnappings for ransom,4
violent thefts and
4
Another type of kidnap that became known is as a source of recruitment - typically of Central American
immigrants - for gunmen and other roles within the criminal organization.

4
thefts on highways.5
Furthermore, the nature of the crimes committed has become more brutal.
In 2011 the number of corpses beheaded, mutilated or with evidence of torture increased
compared to 2010 (Molzahn, Ríos, and Shirk, 2012). Therefore, even though each of these
counts as a singular homicide, the impact on security perception may differ when the crime
committed is more brutal, or when the victim is high-profile (such as politicians and public
officers).
Mexico is now at an important point in time. Felipe Calderón will be leaving office on
the 1st
of December 2012, to be replaced by Enrique Peña Nieto, the elected president from the
opposition Institutional Revolutionary Party (PRI). Mr. Peña has declared that he will continue
the attack DTOs but will adopt a change of strategy. However, the outlook might appear to be
not encouraging. By 2011, about 16 major DTOs or criminal organizations were operating in the
country; these are mainly fragments of the six DTOs that operated in 2006 (Guerrero-Gutiérrez,
2011). The degree of change Peña will be able to make remains unclear and some sectors claim
for a radical change in strategy while others would prefer him to continue dismantling the drug
cartels.
Meanwhile, investors have fled the country (or have been deterred from entering), or at
least from the most violent states, to seek safer places to invest. From an economic point of view,
the increase in crime discourages investment - and thus depresses the economy - in two different
ways: it represents higher risks (i.e. investors are less confident that the value of their physical
assets will be safeguarded after incurring a sunk cost) as well as higher costs (they now need to
spend more on security and the price of their assets insurance increases).
5
It may as well be the case that other criminals (non-DTOs) take advantage of the situation as they face a lower
probability of punishment when the enforcement system gets saturated. However, since this would be an indirect
effect of DTO-ridden crime, it will not be considered explicitly.

5
III. Literature Review
i. Theoretical literature
A convenient way to determine why a certain region or country attracts FDI is to explore
firms’ perspectives. John Dunning’s ‘eclectic’ approach, first developed in 1977, is a useful
example. Dunning’s ‘OLI framework’ illustrates that firms will seek to expand to foreign
markets by means of ownership, location and internalization. Firms will seek to transform inputs
that are specific to particular locations, including not only endowments such as natural resources,
different kinds of labour and proximity to markets, but also legal and commercial environments
such as market structures and government policies. Other types of inputs Dunning specifies are
those an enterprise may purchase or create for itself (e.g. certain types of technology and
organizational skills) but needs different types of legally protected rights (e.g. patents). The
management of these inputs creates what Dunning calls the ownership-advantage. However, the
acquisition of inputs may only be possible in certain locations. The third component of the
framework, internalizing, assumes a vertical integration in which the firm may gain advantages
by growing vertically to ensure stability of supply and to control input prices in order to reduce
uncertainty (Dunning, 1980).
In a later work, more than three decades after the OLI framework was presented,
Dunning (2008) recognised the changing environment and the emergence of new players on the
world economic stage. The author points out the increasing role of institutions and governance as
determinants of capital allocation by MNEs. Furthermore, when referring to a country’s
environment, he classifies the term ‘environment’ into three types: physical, human, and
contextual. The human environment is of particular concern for the present study since, the
author points out, this involves the role of institutions such as those related to private property

6
protection, promoting freedom of enterprise and furthering social equity among others (Dunning,
2008).
Blonigen (2005) provides a thorough review of the determinants of FDI. Interest rate,
taxes, the quality of institutions and trade protection are amongst the most commonly cited
determinants of Multinational Enterprises’ (MNEs) decisions on FDI, although most of them
with mixed evidence or different magnitude. According to Bloningen, the literature on FDI
determinants might still be in its infancy and determinants of cross-country FDI are statistically
fragile. While Bolingen’s paper is not theoretical (he merely compiles theories and does not
develop them), it is a good place to start when assessing which components should be included
as determinants when modelling FDI in a macroeconomic framework.
ii. Empirical literature
A component of crime is not always included as a determinant of FDI. However, research
that has assessed the effect of crime usually concludes that at least some types of crime affect
economic growth and/or FDI. This section includes a brief summary of various papers that were
useful to this present study for assessing the impact of organized crime on FDI for the Mexican
case, as well as papers that provided insight for other components that were later included as
explanatory variables in the model.
Garfield Oneil Blake (2010) found a significant negative effect of crime on economic
growth. He uses the increase of criminal deportees from the USA as an exogenous variation of
crime. Oneil Blake’s study is relevant because most of the observations he uses are from Latin
America and the Caribbean and, more specifically, Mexico took the largest share of US
deportees from that period (1985-1996). Therefore, the results are also valid for Mexico.

7
However, his findings do not suggest the components of GDP that are most affected by the
increase of crime or the types of crime that deter growth.
Daniele and Marani (2010) explore the impact of Mafia-style crime on FDI in Italy. They
constructed an index by documenting extortion, bomb attacks, arson and criminal association per
100,000 inhabitants. Their results show that the correlation between organized crime and FDI is
both negative and significant. Their main contribution is that the index helps mitigate the
problem of under-recording of crime because, apart from extortion, these types of crime are more
likely to be reported to the authorities. However, the landscape of the Italian Mafia is quite
different from that of the Mexican DTOs. In Italy, most of the crimes are concentrated in poorer
southern areas. On the other hand, in Mexico the poor southern states have experienced
comparatively low levels of crime in the past years and Mafia-style crimes occur generally on
drug-trafficking routes or where DTOs incur in congestion costs, i.e. on routes where opponent
DTOs clash or when the route is diverted from a municipality that has more fiercely enforced the
antidrug policy (Dell, 2011).
Krkoska and Robeck (2006) developed a theoretical model based in the interaction
between a representative firm and a representative criminal. Albeit using the same empirical
linear approximation for street and organized crime, the authors performed separate regressions
for each since they acknowledged that the impact on firms can differ. They used survey data
which documented approximately 9,500 firms in 26 transition countries, as well as data on 4,000
firms of eight non-transition countries; both types were countries in Europe and Asia. The data
not only included objective indicators but also a measure of the perception of crime, which is
clearly subjective. According to the survey, the perception of crime “has a highly detrimental
impact on the willingness of foreign investors to enter a country” (Krkoska and Robeck, 2006, p.

8
21). Therefore, there is evidence of causality between crime and FDI.
Madrazo Rojas (2009) used a fixed-effects model and a pooled OLS for the Mexican case
between 1998-2006. He included a variable for homicides and a variable for kidnappings and
found a negative and significant relationship for homicides and an insignificant coefficient for
kidnappings. However, most of his determinants were insignificant, including those for kidnaps.
Moreover, the landscape of Mexico during that time period differed somewhat to the following
period, as explained above. Even though organized crime was prevalent at the time, violence
escalated between 2008 and 2010 (see Figure 1) and for most of the time period covered by
Madrazo Rojas, there was relative peace and diminishing violence. Nevertheless, even though
his results must be considered with caution, in the literature reviewed for this study, this was the
only research in the body of literature studied that directly linked violence and FDI in Mexico.
IV. Data Description
The dataset utilized is composed of annual figures mostly from 1997 to 2011 and it is
available at the state level for the 32 Mexican states.6
Since no single source provides the dataset
as a whole, it is a compilation of data generated by different agencies, including the National
Institute of Statistics and Geography (INEGI), the Executive Secretariat of the National System
of Public Security (SESNSP, a dependency of the Secretariat of the Interior), the Secretariat of
Economy (SE), the Secretariat of Finance and Public Credit (SHCP), the Mexican Social
Security Institute (IMSS), the National Council of Population (CONAPO), Mexican
Transparency, the Bank of Mexico, Eurostat and the US Bureau of Economic Analysis. A
detailed description of the different sources and the period covered, together with the summary
6
Mexico is legally composed by 31 states and a Federal District (DF). However, for data-gathering and modelling
purposes, DF serves just as another state and therefore it will be referred to as such.

9
statistics, can be found in the Appendix. However, it should be noted that the data on some types
of crime is likely to underestimate the actual situation since many crimes are not reported in
cases where people fear denouncing certain crimes (such as extortions or kidnappings) or when
there are legal loopholes that jeopardize or complicate the ability to make changes in police
reports.
The data can be grouped into three different categories. The first category is FDI, the
dependent variable, in current terms as well as a logarithmic transformation. The second group of
data is crime-related; this includes murders, violent thefts and violent thefts on highways,
kidnappings and extortions. The third group includes control variables such as socioeconomic
and demographic measures, as well as variables that control the international environment.
An examination a priori, depicted in Figure 2, sheds some evidence on the negative
correlation between FDI and the murder rate (which is probably the most important of violent
crimes.) The negative relationship is especially notable in states of the north, west and, to a lesser
extent, in the centre. In 2005, one of the safest years in the period covered by the data, FDI was
41.5% higher in real terms than in 2011, the year with most violent crimes registered.

10
Sources: Own elaboration with data from SESNSP and the Secretariat of Economy.
1999
2005
2011
Murder Rate
Murders per 100,000 inhabitants
FDI
Millions of 2005 USD
Figure 2. Murder Rate and FDI

11
V. Modelling crime and FDI
i. Model 1: Violent crimes
The first type of model to be estimated is as follows.
𝐹𝐷𝐼!" = 𝛼 + 𝛽! 𝑀𝑈𝑅!"!! + 𝛽! 𝑉𝑇!"!! + 𝛽! 𝑉𝑇𝐻!"!! + 𝛽! 𝐾𝑁!"!! + 𝛽! 𝐸𝑋𝑇!"!!
+ 𝜆! 𝐷𝑒𝑚!"#!!
!
!!!
+ 𝜆! 𝐸𝑐𝑜𝑛!"#!!
!!
!!!
+ 𝜆! 𝑃𝑜𝑙!"#!!
!"
!!!"
+ 𝜆! 𝑅𝑒𝑔𝑖𝑜𝑛!" + 𝜆! 𝐼𝑛𝑡!"#
!"
!!!"
!"
!!!"
+𝜇! + 𝜀!"
(1)
where
𝐹𝐷𝐼: Logarithmic transformation of the real FDI (in 2005 USD).
𝑀𝑈𝑅: Change in the number of murders per 100,000 inhabitants
𝑉𝑇: Change in the number of violent thefts per 100,000 inhabitants.
𝑉𝑇𝐻: Change in the number of violent thefts on highways per 100,000 inhabitants.
𝐾𝑁: Change in the number of kidnappings per 100,000 inhabitants.
𝐸𝑋𝑇: Change in the number of extortions per 100,000 inhabitants.
The subscript 𝑖𝑡 represents the observation for state 𝑖 in time period 𝑡. Observe, however,
that the five different types of crime included are lagged by one time period. This is done since
investors are likely to make the decision whether or not to enter into the market from the level of
violence they can observe. Moreover, the remaining variables are a set of demographic,
economic, political and macroeconomic variables, as well as regional dummies that were
selected based on the work of Dell (2011) and complemented by the review by Blonigen (2005)
in order to isolate the particular context of each state (such as its endowments and location, as

12
expressed by the OLI framework). Furthermore, since historically the vast majority of FDI
comes from the US and, to a lesser extent, Europe (see Appendix), a logarithmic transformation
of both GDPs was included mainly to take into account the effect of the 2008 economic crisis.
The entire set of variables is listed in Table 1 and their individual descriptions can be found in
the Appendix. Finally, 𝛼 is a constant term, 𝜇! is the individual (state) effect and 𝜀!" is the error
term.
The data set is a slightly unbalanced panel, with an average of 7.4 observations per state,
a minimum of three observations per state, and a maximum of nine. A fixed effects and a random
effects regression were run, together with a pooled OLS for comparison purposes and the results
are shown in Table 1.
Table 1. Violent crime rates on FDI
Dependent variable: Natural logarithm of FDI (2005 USD)
Pooled OLS Fixed Effects Random
Effects (GLS)
Constant -210.9365 -249.8264 -171.6913
(134.9302) (151.5879) (112.8906)
Violent crimes+
Homicides .0017* .0015 .0014
(0.0010) (0.0014) (0.0014)
Kidnaps -.0008 -.0009 -.0007
(0.0005) (0.0020) (0.0021)
Violent thefts -.0001 -.0000 -.0001
(0.0001) (0.0001) (0.0001)
Extortions -.00128 -.0015 -.0013
(0.0033) (0.0032) (0.0032)
Violent thefts on highways .0034 .0107* .0077
(0.0094) (0.0061) (0.0062)
Demographic characteristics++
Population .0003** -.0007 .0003**
(0.0001) (0.0011) (0.0001)
Population density -.0001 .0041 -.0000
(0.0003) (0.0264) (0.0003)
Migration rate .3508 .7850 .4319
(0.2416) (1.3524) (0.3030)
Economic characteristics++
Wages .0093 .0219 .0082

13
(0.0087) (0.0135) (0.0088)
No sewage -.0045 -.4114*** -.0515
(0.0460) (0.1184) (0.0521)
No electricity .1882 .3924 .0787
(0.1577) (0.2163) (0.1362)
No water -.0052 -.0187 .0059
(0.0441) (0.1365) (0.0522)
HDI 31.0632 -8.5977 25.7293*
(21.6815) (24.6256) (14.9013)
Schooling -.1883 -3.2013** -.1480
(0.5566) (1.3984) (0.6559)
Infant mortality .2452 .2534 .2261
(0.2706) (0.3463) (0.2223)
Marginality index -.8118 omitted -.5651
(0.9083) (0.8016)
Political and macroeconomic
variables++
Corruption index .0560 .0420 .0212
(0.0487) (0.0448) (0.0427)
Debt percentage .0553 .0918 .0078
(0.1238) (0.1471) (0.1154)
Interest rate .2177* .1527 .1931*
(0.1207) (0.1160) (0.1049)
Inflation -.2904* -.1608 -.2540**
(0.1453) (0.1275) (0.1215)
Foreign exchange .2341 .3406 .1177
(0.2020) (0.2505) (0.2076)
Regional dummies++
North .5777 omitted .2139
(0.7222) (0.8835)
West .5936 omitted -.0260
(0.7141) (0.8461)
South -.4401 omitted -1.1711
(0.7098) (0.7421)
International economy
US GDP -28.1160 -10.1704 -23.7858*
(16.3946) (15.6172) (12.7492)
Europe GDP 27.7342 23.2043 23.2079*
(15.4682) (14.6057) (11.9649)
Observations 237 237 237
R2
0.6886 0.1822 0.6739
Notes: +
All the violent crimes are lagged one year and per 100,000 inhabitants. ++
Variables are lagged one
year (except for the interest rate and the foreign exchange). The variable centre is omitted to avoid
multicollinearity. *, ** and *** represent significance at the 10%, 5%, and 1% levels, respectively. Robust
standard errors are shown in parentheses.

14
As the results show, the majority of variables of interest appear insignificant and two
variables (the homicide rate and violent thefts on highways) have positive signs in both the fixed
effects and random effects setups. Therefore, the results are inconclusive. Notice as well that the
coefficient of determination of the models differs greatly between the fixed effects and random
effects. This occurs because a greater share of variation occurs between states rather than within
them. Also, a Haussmann test for specification was conducted and the null hypothesis was not
rejected with a p-value of 0.1746. Hence, there is evidence that supports that the GLS is more
efficient, although the fixed effects specification is still unbiased (Hsiao, 2005).
ii. Model 2: Criminality index
In addition, following the work of Daniele and Marani (2010) for the Italian case, a
criminality index is constructed and incorporated in the model, instead of the types of violent
crimes. The index is intended to mitigate the effects of the under-recording of crimes, as
discussed above. Therefore, model 2 is as follows.
𝐹𝐷𝐼!" = 𝑎 + 𝛾! 𝐶𝑅𝐼𝑀!"!! + 𝜆! 𝐷𝑒𝑚!"#!!
!
!!!
+ 𝜆! 𝐸𝑐𝑜𝑛!"#!!
!!
!!!
+ 𝜆! 𝑃𝑜𝑙!"#!!
!"
!!!"
+ 𝜆! 𝑅𝑒𝑔𝑖𝑜𝑛!" + 𝜆! 𝐼𝑛𝑡!"#
!"
!!!"
!"
!!!"
+ 𝜇! + 𝜀!"
(2)
where 𝐶𝑅𝐼𝑀 is the criminality index (in changes), although this differs from that constructed by
Daniele and Mariani. Their index is a sum of extortion, bomb attacks, arson and criminal
association per 10,000 inhabitants. However, to the best of the author’s knowledge, only data for
extortion and criminal association is readily available of the four aforementioned crimes. Hence,
𝐶𝑅𝐼𝑀 is constructed by the sum of extortions, criminal associations, infringements to the

15
Federal Law of Firearms and Explosives7
(LFAFE), infringements to the Federal Law Against
Organized Crime (LFCDO), and murders, per 10,000 inhabitants. It is set at 100 for the index at
the national level in 2001. The criminality index is a proxy for crimes typically committed by
DTOs. The results for the estimation of equation 2 are shown in Table 2.
Table 2. Criminality index on FDI.
Dependent variable: Natural logarithm of FDI (2005 USD)
Pooled OLS Fixed Effects Random
Effects (GLS)
Constant -229.0183* -220.7951 -169.9394
(131.8738) (147.7465) (114.2551)
Criminality index+
-.0022 -.0032** -.0025
(.00134) (.0015381) (.0016)
Demographic characteristics++
Population .0003** -.0005 .0002**
(.0001) (.0011) (.0001)
Population density -.0001 -.0032 .0000
(.0003) (.0256) (.0003)
Migration rate .3197 1.1181 .4379
(.2282) (1.3224) (.3392)
Wages .0089 .0222* .0087
(.00886) (.0133) (.0091)
No sewage -.0031 -.3832** -.0633
(.0450) (.1156) (.0565)
No electricity .2133 .3730* .0900
(.1578) (.2127) (.1418)
No water -.0042 .0333 .0118
(.0446) (.1336) (.0571)
HDI 31.7517 -9.9110 22.6028
(21.6394) (24.3489) (15.5886)
Schooling -.2475 -3.0962** -.2695
(.5403) (1.3714) (.7079)
Infant mortality .2313 .1097 .1617
(.2687) (.3381) (.2334)
Marginality index -.8954 omitted -.5817
(.8745) (.835)
variables++
Corruption index .0633 .0511 .0278
7
The LFAFE penalizes not only the possession of unregistered arms but also the possession of weaponry of which
the use is confined to the army and federal armed forces (Cámara de Diputados, 2004). The latter is a crime typically
commited by DTOs.

16
(.0502) (.044) (.042)
Debt percentage .0661 .0645 .0083
(.1243) (.1458) (.1151)
Interest rate .2173* .1588 .1915*
(.1132) (.1128) (.1028)
Inflation -.2815* -.1380 -.2299*
(.1382) (.1248) (.118)
Foreign exchange .2291 .2292 .0588
(.1942) (.2420) (.2033)
Regional dummies++
North .5656 omitted .1627
(.7056) (.9727)
West .6063 omitted -.1495
(.7097) (.9346)
South -.4308 omitted -1.2936
(.7267) (.8210)
US GDP -28.311* -11.0834 -23.9984*
(16.0764) (15.213) (12.6904)
Europe GDP 28.9760* 22.2072 23.5447**
(15.0900) (14.2388) (11.9985)
Observations 237 237 237
R2
.6875 .1816 0.6647
Notes: +
The criminality index is shown in changes and lagged one year. ++
Variables are lagged one year
(except for the interest rate and the foreign exchange); The variable centre is omitted to avoid
As the results show, the coefficient of the criminality index is now negative and
statistically significant in the fixed effects model. It is also negative in the GLS regression and,
although it is insignificant at the conventional levels, it lies just below the conventional level,
with a significance of 10.1%. A Hausman specification test was performed and the null
hypothesis (which states that the difference in coefficients is not systematic) was not rejected
with a p-value of 0.2893. Therefore, there is evidence that supports that the correct specification
is, once again, the random effects setup. From this, the coefficient suggests that an increase in the
criminality index of one will reduce FDI in the next period by about 0.2543% in a given state.
Nevertheless, the fixed effects model is still unbiased and might even be preferred since the

17
sample is not random and the individual effect might be correlated with the regressors8
, as is
discussed in the following subsection (Hsiao, 2005, p. 43; Krkoska and Robeck, 2006). From the
latter, an increase in the criminality index of one will reduce FDI in the next period by about
0.312%. The fact that both models yield similar results indicates that in fact violence has a non-
negligible negative impact on crime.
Moreover, similar regressions were run but absolute values were taken of the crime
variables for model 1 and the criminality index instead of its changes for model 2; non-
significant coefficients were then obtained. This might be thought as evidence that changes in
crime have a greater impact than crime itself. Changes in crime may signal a further deterioration
of institutions and thus a greater perception of insecurity, while investors may be able to cope
with a steady level of crime. However, as shown in the following subsection, this might only be a
result of autocorrelation and hence this inference cannot be made.
iii. Post-estimation tests
Subsequent to the Hausman specification test carried out above, several tests are
performed to identify whether or not the data exhibit multicollinearity, stationarity and
endogeneity.
A. Multicollinearity
The presence of multicollinearity is a plausible concern since the model contains an
important number of variables that may be jointly determined. In fact, if the variables are
collinear, the coefficients can exhibit signs that differ from those in reality or be statistically
8
The various features of the state, such as its location, might be correlated with crime if DTOs consider it to be a
strategic drug-trafficking route. For example, Tamaulipas has both a port that connects to South America and a
border with the US. Therefore, it is an essential route for trafficking and has seen an important increase in homicides
and arson attacks as two different DTOs (the Cartel del Golfo and its former armed unit, Los Zetas) have attempted
to gain control over the state.

18
insignificant. However, the use of panel data offers more degrees of freedom that “can reduce the
gap between the information requirements of a model and the information provided by the data.”
(Hsiao, 2005, p. 312). Therefore, it becomes less of a concern than in cross sectional or time
series analysis. Nevertheless, auxiliary regressions are performed of the variables of interest.
For equation (1), the following auxiliary regressions were performed.
𝐇𝐎𝐌 = 𝐗 𝟏 𝛅 𝟏 ∗ + 𝛜 𝟏 (4)
where HOM is a 250∗1 matrix containing the differences in homicides per capita, 𝐗 𝟏 is a
250∗26 matrix containing the four other types of crimes, also given in differences, as well as the
remaining explanatory variables in Table 1 plus a column of ones (for the constant). 𝛅 𝟏 is a 26∗1
coefficients matrix and 𝛜 𝟏 is a 250∗1 error matrix. The auxiliary regression was performed using
pooled OLS and the resulting coefficient of determination is R2
=0.2632. Equivalent regressions
where performed for kidnappings, violent thefts, extortions and violent thefts on highways, with
resulting coefficients of determination of 0.0236, 0.111, 0.1456, and 0.1229, respectively.
For equation (2) the following auxiliary regression was run.
𝐂𝐑𝐈𝐌 = 𝐗 𝟐 𝛅 𝟐 ∗ + 𝛜 𝟐 (5)
where 𝐂𝐑𝐈𝐌 is a 250∗1 matrix containing the differences of the criminality index and 𝐗 is a
237∗22 matrix containing the remaining the explanatory variables described in Table 2 as well as
a column of variables. 𝛅 𝟐 is a 22∗ 1 matrix of coefficients, and 𝛜 𝟐 is a 250∗1 error matrix. The
resulting coefficient of determination is R2
=0.1689.
Therefore, in all the coefficients of interest, the low coefficients of determination (R2
)
suggest that multicollinearity is not a concern and thus can be discarded. There might still be
concern for multicollinearity in the remaining regressors. Nevertheless, as long as the
coefficients, both in sign and statistical significance, are not of interest and they guard the same

19
relation within (new) sample observations, forecasting is still possible (Griffiths, Hill, & Judge,
1993).
B. Stationarity
Since the crime variables and the crime index are given in changes and terms per capita,
it is reasonable to assume they will exhibit stationarity (crime cannot increase forever), even
when they might exhibit some sort of non-stationary process in levels. Therefore, the variable to
be tested for an autoregressive process of the first order is the dependent variable, i.e. the
logarithm of the real FDI the states attract. The standard test for unit roots in panel data is the
Levin-Li, which can be considered as a pooled Dickey-Fuller. However, the test to be used here
is the one developed by Maddala-Wu (MW) in 1999 (see for example Hoang and MacNown,
2006 for a discussion on panel data unit root tests). The MW is feasible in unbalanced panels
such as that presented.
Table 3. Maddala-Wu Panel Unit Root Test with one lag. P-values are displayed
Variable Specification without trend Specification with trend
Real FDI (logarithm) 0.003 0
Criminality index 0.25 0.578
Homicides 0.964 0.423
Kidnaps 0.611 0.955
Violent thefts 0 0.511
Extortions 0.998 0.508
Violent thefts on highways 0.539 0.999
Observations 332
Under the null hypothesis, the variables are non-stationary. Therefore, the independent
variable, logarithm of the Real FDI, is stationary while all the other logarithms have a non-

20
stationary AR(1) process. However, first-differencing them eliminates the autocorrelation and
hence the results obtained in both tables remain valid.
C. Endogeneity
The regressors may be endogenous in two different ways. Firstly, it can be as an
unobserved common factor, such as the states’ location and endowments. Secondly, when
violence reduces FDI, the lack of FDI depresses economic growth and poor economic growth
then lead to further increases in crime (simultaneity). Figure 3 outlines these two causes.
Figure 3. Forms of endogeneity
In fact, the first type of endogeneity concurs with the OLI framework developed by
Dunning (2008) and described in Section III. Both the geographic location and endowments are
variables that affect the MNEs decisions to invest in a certain place. Crime derived from drug
trafficking in Mexico, on the other hand, is also greatly influenced by the location of each state
or municipality, as described by Dell (2011). However, endogeneity caused by an individual
factor that persists over time is overcome in the fixed-effect specification, since the individual
effects are eliminated from the model. Therefore, there is further theoretical support to the fixed-
effect specification even when the Hausman specification test suggested otherwise.
Unobserved common factor Simultaneity

21
The second type of endogeneity, simultaneity, might be the case for the Italian mafia
described Daniele & Marani (2010), since the poorer southern states are also the ones where the
most crimes are committed. However, a quick glance at Figure 2 suggests that this differs for
Mexico. In fact, the poorer southern states in Mexico, which also attract less FDI, have relatively
lower homicide rates than the wealthier northern and western states. Therefore, even though it is
possible that mild endogeneity exists, it will be discarded in the absence of availability of strong
instruments.
iv. Further results
The discussion on endogeneity sheds light on the preference for the within-group
regression. Therefore, a reduced-form model is estimated, eliminating variables that appear
statistically insignificant. Most of these, such as the economic and demographic characteristics,
do not change much in the time period covered and thus can form part of the individual fixed
effect. Moreover, another regression is estimated that includes only the criminality index and
time dummies. Both results are displayed in Table 3.
Table 3. Reduced form model and model including year dummies
Dependent variable: Natural logarithm of FDI (2005 USD). Fixed effects estimations.
Reduced Form Year Dummies
Constant -166.4598* 4.4571***
(95.5427) (0.1995)
Criminality index+
-0.0032* -0.0029*
(0.0017) (0.0017)
Wages 0.0186
(0.0123)
No sewage -0.3392***
(0.0960)
No electricity 0.3235**
(0.1614)
Schooling -2.8668**

22
(1.2572)
variables++
Inflation -0.1452
(0.1155)
Foreign exchange 0.1137
(0.2064)
US GDP -12.5054
(12.7917)
Europe GDP 19.1525*
(9.7507)
Year dummies+++
2004 0.3743
(0.2722)
2005 0.3819
(0.2804)
2006 0.5299*
(0.2842)
2007 0.7685**
(0.2927)
2008 0.9560**
(0.2867)
2009 -0.0554
(0.2822)
2010 0.0352
(0.2825)
2011 -0.0562
(0.2766)
Observations 237 240
R2
0.1659 0.1085
Note: +
The criminality index is shown in changes and lagged one year. ++
Variables are lagged one year
(except for the interest rate and the foreign exchange rate);
+++
The year 2003 is omitted to avoid
As the results show, the estimated coefficient for the criminality index is similar to those
presented in Table 2. Hence, this confirms that an increase in the criminality index, which
reflects organized-crime type delinquency, will reduce real FDI by about 0.30% the following
year (it ranges from approximately 0.29% in the year dummy model in Table 3 to 0.31% in the
full model of Table 2).

23
VI. Limitations and suggestions for further research
One of the main limitations of the model was the low reliability of crime data. There is
evidence, as explained above, of a systematic under-recording of crimes. This downward bias
may vary among states (for example, if the state’s government is of the same party or not as the
federal government). Even though the criminality index helps reduce this bias, it may still be an
under-estimation of the true parameters. Moreover, this downward bias of crimes may be greater
when more crimes are committed, since it is more difficult to keep a record of them and to
monitor the actions of the government. Therefore, future research of the issue of the impact of
crime, and especially of organized crime, has on FDI in Mexico must be assessed more
thoroughly.
There are numerous ways to accomplish this. Firstly, data that is more easily captured can
be collected on crimes such as arson attacks, attacks to journalists, high-impact figures and
public officers. Secondly, the murders can be changed and use instead the Homicides by
Presumptuous Rivalry Delinquency. The latter method was used in this study, whereby the
expected signs for the index were obtained; although highly insignificant in both, the fixed
effects and random effects specification were similar. However, this might reflect the fact that
the sample was small, with a minimum of one observation per state and a maximum of three,
since this database was gathered for the first time in December 2006, and thus only four
complete years were available (the data for 2011 is still incomplete). Thirdly, although the
interpretation of results would slightly differ, the Security Perception Index can be used that
INEGI started collecting in May 2009, and which observations were too few to consider in this
work.
Another limitation of this study is that it does not point out which industries are more
affected by crime. The manufacturing industry, for example, which is capital intensive, may be
more affected by organized crime than the services industry.
VI. Conclusions
Assessing the overall economic impact of organized crime is difficult, since it acts in
both ways. There is a positive impact as money enters the country from abroad and a negative
effect as crimes become more murderous and affect the image of the country by damaging the

24
state of institutions, and more specifically, threatening the state’s monopoly on the legitimate use
of public force. The latter undermines FDI as cost on security and concerns for safety rise.
There is growing number of researches on the determinants of FDI, although, as pointed
out in Section III, it might still be in its infancy. Nevertheless, a convenient way to regard FDI is
from the perspective of MNEs. The findings of this research concur with the framework
developed by Dunning (1980), since the ownership of assets by MNEs is threatened when there
is lack of law and order. Moreover, even though some previous works have assessed the impact
of organized crime in different contexts or via surveys of MNEs, to the best of the author’s
knowledge this is the first to do so for organized crime in Mexico. Therefore, there is evidence
that an increase in the criminality index, which is a proxy for crime typically attributed to DTOs,
will deter FDI by about 0.31%. However, since the index does not have units it is difficult to
interpret the results. The results can be interpreted by examining the mean of the index, which is
141.36. Therefore, a 1% deviation from the mean, that is 1.41 points, will deter FDI by 0.44%.
The upcoming government in Mexico will face great challenges, but a challenge of
paramount importance is definitively security. If DTOs are properly managed and murders,
extortions, arson attacks, kidnaps, and violent thefts (to name some of the most important) come
into a halt, a greater inflow of FDI would be expected.

25
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methods. Manuscript in progress.

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Hsiao, C. (2005). Analysis of panel data (2nd
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28
Appendix
1. Description of variables and sources
Variable Description Sources
FDI FDI in millions of US Dollars. Secretariat of Economy
Real FDI FDI in millions of 2005 US Dollars using
the US GDP implicit deflator.
Secretariat of Economy;
US Bureau of Economic
Analysis
Murders Number of murders (homicidios dolosos). SESNSP
Murder rate Number of murders per 100,000 inhabitants. SESNSP; CONAPO
Violent thefts SESNSP
Violent thefts on
highways
Idem
Kidnaps Idem
Extortion Presented cases of extortion. Idem
LFAFE Infringements to the Federal Law Against
Firearms and Explosives (Ley Federal
Contra Armas de Fuego y Explosivos). Data
available from 2001.
SESNSP
LFCDO Infringements to the Federal Law Against
Organized Crime (Ley Federal Contra la
Delincuencia Organizada)
Idem
Wages Daily average of wages from registered
workers of the Mexican Social Security
Institute. Data available from 2002-2011
except for DF, which is available from 2005
onwards.
IMSS
Debt State debt in millions of pesos and as a
percentage of GDP.
SHCP
Interbank interest
rate
National rate. Bank of Mexico
Inflation Idem

29
Exchange rate MXP/USD exchange rate, averaged from
monthly rates.
Idem
Population Number of inhabitants per state, divided by
1,000 for visualization purposes of the
estimate.
CONAPO
Population density Number of inhabitants per squared
kilometer.
CONAPO; INEGI
Migrants per capita Migrants include interstate and international. CONAPO
Infant mortality Infant mortality per 1,000 inhabitants. Idem
No electricity Percentage of population without electricity.
Available figures for 2000, 2005 and 2010
and linear approximation made between
these dates.
Idem
No running water Percentage of population without access to
running water. Available figures for 2000,
2005 and 2010 and linear approximation
made between.
Idem
No sewage Percentage of population without sewage.
Available figures for 2000, 2005 and 2010
and linear approximation made between.
Idem
Marginality index Among the components of this index are
literacy, schooling, and access to running
water and access to electricity. Only the
value for 2005 is taken.
Idem
Schooling Average years of school attendance. Data
available for 2000, 2005, and 2010 and
linear approximations were made for the
years in between.
INEGI
GDP per capita Proxy for level of income in each state. INEGI
Corruption Proxy for the level of corruption. This index
is elaborated via a survey, which asks
citizens of each state how many times they
had to pay a bribe, and this is divided by the
number of times they used that particular
service. It is available for 2001, 2003, 2005,
Mexican Transparency

30
2007, and 2010 and the gaps between were
constructed taking averages.
US GDP US GDP in billions of 2005 USD. US Bureau of Economic
Analysis
EU + CH GDP GDP of the EU15 countries (Austria,
Belgium, Denmark, Finland, France,
Germany, Greece, Ireland, Italy,
Luxembourg, Netherlands, Portugal, Spain,
Sweden, United Kingdom) plus Switzerland.
Millions of 2000 euros.
Eurostat
North Dummy variable for Aguascalientes,
Chihuahua, Coahuila, Durango, Nuevo
León, San Luis Potosí, Tamaulipas, and
Zacatecas.
Taken from the
classification of wages
by region made by
INEGI
West Dummy variable for Baja California, Baja
California Sur, Colima, Guanajuato, Jalisco,
Michoacán, Nayarit, Sinaloa, and Sonora.
Idem
South Dummy variable for Campeche, Chiapas,
Hidalgo, Oaxaca, Puebla, Quintana Roo,
Tabasco, Tlaxcala, Veracruz, and Yucatán.
Idem
Centre Dummy variable for DF, Guerrero, State of
Mexico, Morelos, and Querétaro.
Idem
Notes: The definition for each acronym can be found in the bibliography. Some variables lack
definition as they are self-explicative. The data covers the period 1997-2011 unless otherwise
specified.
2. Descriptive statistics
Variable Observations Overall Mean Std. Dev. Min Max
FDI 457 679.4 2239 0.100 22043
Real FDI 457 689.6 2291 0.120 24296
Murders 457 450.5 562.3 0 3903
Kidnaps 457 22.96 40.29 0 408
Violent thefts 457 6641 13814 0 88636
Highway thefts 457 32.09 79.33 0 580

31
Extortions 457 92.45 170.6 0 2035
LFAFE 457 490.0 460.4 14 2881
LFCDO 332 33.84 96.20 0 881
Criminal
association 512 3376.82 4948.175 315 36794
Criminality index 332 141.3553 109.8062 28.7691 743.0095
US GDP 457 11996 1108 9846 13315
EU15+CH GDP
(Millions) 457 9.567 .725170 8.203 1.050
Population
(Millions) 457 3.219 2.777 .3965 15.20
Debt 457 5253 9382 0 56232
Debt percentage 332 1.638 1.280 0 8.447
Interest rate 457 11.65 7.185 4.820 26.89
Wages 300 182.3 40.63 100.1 337.6
Inflation 457 7.043 4.735 3.330 18.61
Migration rate 457 -0.362 0.873 -1.925 3.023
Marginality index 768 0 .9848 -1.5048 2.4121
Foreign exchange 457 10.67 1.463 7.942 13.57
Infant mortality 457 17.39 4.227 9.697 33.15
Population density 457 285.7 1044 5.362 5964
GDP
(Millions) 241 253 276 38.30 1520.

32
HDI 330 0.813 0.0382 0.708 0.923
Schooling 330 8.065 0.949 5.400 10.50
Corruption index 300 7.961 3.288 1.800 22.60
No water 330 9.106 7.807 0.985 31.34
No electricity 330 3.061 2.350 0.0799 12.01
No sewage 330 6.418 6.007 0.0819 35.29
3. Total FDI in Mexico by country of origin.
0
5000
10000
15000
20000
25000
30000
35000
Year 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Note: FDI in millions of US dollars.
Source: General Direction of FDI, Secretariat of Economy.
Others
EU15+CH
USA

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