Defence and Peace Economics
ISSN: 1024-2694 (Print) 1476-8267 (Online) Journal homepage: http://www.tandfonline.com/loi/gdpe20
Spatial Distribution of Minority Communities
and Terrorism: Domestic Concentration versus
Transnational Dispersion
Bryan J. Arva & James A. Piazza
To cite this article: Bryan J. Arva & James A. Piazza (2016) Spatial Distribution of Minority
Communities and Terrorism: Domestic Concentration versus Transnational Dispersion,
Defence and Peace Economics, 27:1, 1-36, DOI: 10.1080/10242694.2015.1055091
To link to this article: http://dx.doi.org/10.1080/10242694.2015.1055091
Published online: 09 Jul 2015.
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Date: 30 March 2016, At: 12:16
Defence and Peace Economics, 2016
Vol. 27, No. 1, 1–36, http://dx.doi.org/10.1080/10242694.2015.1055091
SPATIAL DISTRIBUTION OF MINORITY
COMMUNITIES AND TERRORISM: DOMESTIC
CONCENTRATION VERSUS TRANSNATIONAL
DISPERSION
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BRYAN J. ARVA AND JAMES A. PIAZZA*
Department of Political Science, The Pennsylvania State University, University Park, PA, USA
(Received 21 July 2014; in final form 9 May 2015)
Qualitative studies of terrorist movements frequently highlight the importance of diaspora communities as
important factors in producing and sustaining terrorist activity in countries. The underlying theoretical argument is
that bifurcation of tight-knit minority communities between countries nurtures separatist or irredentist sentiments
among affected community members, thus prompting terrorist activity, while minority community members in
other countries might mobilize financial and political resources to support terrorist activity among their compatriots.
In this study, we empirically test whether transnational dispersion, versus domestic concentration, of minority
communities in countries produces higher incidents of terrorism. Conducting a series of negative binomial estimations on a reshaped database of around 170 countries from 1981 to 2006, derived from the Minorities at Risk
database and the Global Terrorism Database, we determine that both transnational dispersion of kin minority
communities and domestic concentration of minorities within countries increase terrorism and that transnational
dispersion is a particularly robust predictor of terrorist attacks.
Keywords: Terrorism; Minority groups; Diasporas; Geographic dispersion; Geographic concentration
This study empirically examines a thus far unexamined factor in the production of terrorism1: the geographic distribution of ethnic communities within and across countries. It
investigates several questions. Are countries containing geographically concentrated minority communities within their borders, whose members suffer from past or present political,
economic and cultural discrimination, more likely to experience terrorist attacks? What
effect does transnational dispersion of a country’s minority groups have on the likelihood
that it will experience terrorism? Are countries with transnationally dispersed minority
communities – where communities within the country have kin in other countries – more or
less likely to suffer from terrorist attacks at home? These questions motivate the following
study.
*Corresponding author: Department of Political Science, The Pennsylvania State University, University Park,
PA 16802, USA. E-mail: [email protected]
In this study, we adopt the definition of terrorism used by Enders, Sandler, and Gaibulloev (2011), ‘Terrorism is
the premeditated use or threat to use violence by individuals or subnational groups against noncombatants in order
to obtain a political or social objective through the intimidation of a large audience beyond that of the immediate
victims’ (321).
1
© 2015 Taylor & Francis
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B. J. ARVA AND J. A. PIAZZA
Casual observation seems to validate both concentration and transnational dispersion as
frequent drivers of terrorism.2 A long list of countries with geographically concentrated ethnic enclaves have seen major terrorist campaigns emerging from separatist and ethnonationalist terrorist movements since World War II. For example, the ‘liberation’ of Spain’s
Basque population, with its distinct language and history of demands for greater cultural
autonomy, concentrated in the northeast of the country has been the obsession of the ETA
(Basque Homeland and Freedom) terrorist organization. Kurdish enclaves in Turkey’s
southeast and northern Iraq have provided motivation, basing opportunities and various
levels of support for terrorist activity by the Kurdish Workers Party (PKK) and Kurdish
Democratic Party. The lion’s share of contemporary terrorism in Russia is motivated by
demands for independence for Chechnya, a Muslim-majority region in southern Russia.
Muslim ethnic enclaves in Mindanao in the Philippines and in the far-South Pattani
Province of Thailand have been the source of Islamist separatist terrorism since the 1970s.
In South Asia, the mountainous regions of northeast India – in the so-called ‘Seven Sister’
states – and the Balochistan province in western Pakistan have both been the sites of active
separatist terrorist movements. Morocco has also sustained terrorist activity from the
Polisario Front, a radical separatist movement that seeks the independence of Western
Sahara from Moroccan rule.
At the same time, countries with significant minority communities that have kin in other
countries or in regional or global diasporas have experienced notable terrorist campaigns
and transnationally dispersed ethnic minority communities often fuel terrorist activity – usually by providing political support and financial resources – to terrorist groups that are
active in their home countries. There are, likewise, many prominent examples of this scenario. The Tamil and Lebanese Shi’i diaspora communities in Europe and North and South
America have been a significant factor complicating Sri Lankan and Lebanese government
efforts to address Liberation Tigers of Tamil Eelam (LTTE) and Hezbollah terrorist activity.
Both the LTTE and Hezbollah rely on kin communities outside of their home countries to
raise funds and to agitate for political support on their behalf. The Kosovo Liberation Army
(KLA) is alleged to have relied upon ethnic Albanians living across the border in Albania
and further away in Turkey and Italy for monetary support, while the KLA’s drive for
Kosovar independence was motivated, in part, by cultural identification with neighboring
Albania. Finally, terrorism within the Kurdish minority communities in Iran, Iraq, and
Turkey is motivated both by the concentrations of these communities in the geographic
extremes – and the political and economic margins – of their respective countries as well as
by the support given to it by kin communities located across borders. The Kurdish territory
in northern Iraq, for example, has provided important basing, training, and revenue-generating space for PKK terrorists within Turkey.
In this study, we empirically evaluate the proposition that countries characterized by geographically concentrated ethnic enclaves experience more terrorist activity than countries
with more internally dispersed and geographically integrated ethnic populations. We also
examine whether or not countries with ethnic groups that have kin in other countries experience more terrorism. The study contributes to scholarly understanding about the impact of
ethnic group geographic concentration and transnational dispersion on terrorist activity by
conducting a systematic, empirical cross-country analysis on a topic that has previously
been treated using individual case studies and descriptive analysis. In the next section, we
explore the relevant literature linking geographic distribution of aggrieved minority
2
Historically, terrorist attacks by groups motivated by ethnic nationalist/separatist and leftist ideologies account for
almost 70% of terrorist incidents worldwide (see Piazza 2009, 63).
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
3
communities to political violence more generally. From the relatively scant literature on the
impact of domestic concentration and transnational dispersion on political violence, we discuss the hypotheses we test in the study. The following section explains our empirical tests
and presents the results. We conclude with the implications of the findings to scholarship
on terrorism and to security policy.
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COMMUNITY CONCENTRATION, TRANSNATIONAL DISPERSION, AND
TERRORISM
The impetus for our study rests on empirical findings in a slew of recent studies revealing
that the presence of aggrieved minority populations within countries makes them significantly more likely to experience terrorism. Theoretical work by Gurr (1993, 1996, 2000)
argues that ethnic minority group experiences of discrimination and alienation from mainstream political, economic, and cultural life fosters strong group identities and gives rise to
grievances. In instances where political elites can manipulate those grievances – and overcome collective action barriers (DeNardo 1985; Hegre et al. 2001) – political violence
within countries will occur. Crenshaw (1981) and Ross (1993) use Gurr’s essential model
to depict ethnic minority group grievance as a crucial contributor to terrorist activity within
countries.
Minority Grievance and Terrorism
Recent empirical work seems to validate this theoretical model. Piazza (2011) finds that
countries with minority communities that experience economic discrimination experience,
on average, six times the level of domestic terrorist attacks than the typical country, eight
times the level of terrorism than countries containing minority groups that experience no
present economic discrimination, and ten times the number of attacks than countries lacking
any minority communities at all (Piazza 2011, 350). Further research by Piazza (2012),
building off of a rich body of qualitative literature on ethnic-based terrorist movements,
finds generally that the presence of any aggrieved minority community within a country
makes it more likely that that country will experience both domestic and transnational terrorism and that countries featuring higher levels of economic and political discrimination,
as opposed to forms of cultural, religious, and linguistic discrimination, are particularly
likely to suffer from high terrorism rates. Related studies have produced corresponding findings. These results are consistent with control findings by Lai (2007), Wade and Reiter
(2007) and Eubank and Weinberg (1994) who also find empirical evidence that poor
treatment of minority groups within countries increases terrorism.
Minority Communities, Geography, and Terrorism
So, we begin our investigation expecting the presence of minority groups, with grievances,
to be a significant predictor of patterns of terrorist attacks in countries. But how might this
dynamic be affected by population geography? While there have been many qualitative case
studies on the topic, little has been done quantitatively to assess the importance of diaspora
and transnationally dispersed ethnic or religious minority communities as important factors
in producing and sustaining terrorist activity in countries. We find this to be a curious omission, given the prominent attention paid to ethnic group geographies in the civil war literature. Many studies of internal and transnational geographic distribution of ethnic groups
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B. J. ARVA AND J. A. PIAZZA
have found such features to be important mediators for civil war onset, duration, and
intensity. (Cederman, Buhaug, and Rod 2009; Collier and Hoeffler 2001; Cunningham and
Weidmann 2011; Davis and Moore 1997; Forsberg 2008; Saideman 2002; Slack and Doyon
2001; Toft 2003; Weidmann, Rod, and Cederman 2010).
Though terrorism differs from civil war as a phenomenon3 – the former is a tactical
behavior engaged in by weak actors operating in small groups or as individuals that is
focused on grabbing attention and communicating a political message or prompting government concessions rather than securing a more traditional military victory – it is reasonable
for scholars of terrorism to look to the civil war literature for insight into how ethnic political geography might affect terrorist activity. Findley and Young (2010) point out that from
1970 to 2004 between 44 and 63% of all terrorist attacks occurred within civil conflict
zones. Additionally, 73% of all civil wars featured at least one terrorist event: This demonstrates how closely these two phenomena are related. Thus, it is our assumption that the
relationships witnessed among diasporas and transnationally dispersed ethnic or religious
minority communities and the likelihood of conflict should be similar to the relationships
they have in regard to terrorism. In many ways, this study is breaking new ground for
terrorism research. However, by looking at scholarly work done in other fields, we can feel
confident that our theory and hypotheses have solid foundations.
There seems to be agreement that conflict is more likely when ethnic groups have a
majority of their members living in a single location within a country. This is perhaps most
dramatically illustrated in the findings of Cunningham and Weidmann (2011) who determine that violence is increased when an administrative region of a country or geographic
enclave is dominated by a particular ethnic group that is in competition with other ethnicities nationally. Conversely, the more dispersed an ethnic group is the less likely they are to
become involved in a conflict. The reasoning behind this is the more members an ethnic
group has in an area, the easier it is to recruit people to join their cause. Dispersion of an
ethnic group dilutes group identity and raises organization costs, making it more difficult
for extremist and terrorist organizations to garner support. Concentration also might raise
policing and intelligence costs for states. Often concentrated pockets of ethnic communities
are quite insular, have high levels of interpersonal trust and community self-reliance, may
contain larger numbers of residents who speak local languages and communicate only with
group members, and are resistant to outside interference.4 All of these would make counterinsurgency and counterterrorism activities more difficult and terrorist activity more likely.
In addition to facilitating social mobilization that might boost the possibility of terrorist
activity, and imposing policing and intelligence costs on states, concentration of ethnic
minority groups into a particular region within a country might also reduce its sense of vulnerability. Scholars examining related forms of political violence, such as deadly ethnic
riots, have found ethnic group geographic concentration to increase group feelings of invulnerability, which facilitates outbursts of violence against individuals from rival ethnic
groups (see Horowitz 2003). In contrast, when ethnic groups are intermixed with other
groups they are more inhibited and less likely to engage in political violence. A similar phenomenon could affect patterns of terrorism in countries. Terrorist activity may be more
likely in instances where ethnic groups are concentrated, uninhibited, and feeling invulnerable vis-à-vis rivals.
3
Note, in all model estimations, we include a control for civil war using PRIO data.
Though in principle, terrorists from an ethnic minority community concentrated into a particular region or enclave
of a country might also find it more difficult to conduct activities outside of that enclave, thereby narrowing the
geographic range within which state policing assets need to be allocated. This could lower counterterrorism policing costs overall for the country.
4
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
5
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We furthermore consider the possibility that heightened minority group identity is likely
to also produce more mainstream political vehicles, such as ethnic political parties, pressure
groups and social movements, for articulating group grievances. While some would suggest
that when this occurs within the context of opportunities for political participation, it should
have a dampening effect on terrorism and political violence – because more lucrative routes
of political influence are available – empirical research by Chenoweth (2010) demonstrates
the opposite. She finds that when political competition and interest group activity increases
in democracies, the number of emergent terrorist groups increases and terrorist attacks go
up. If geographic concentration of minority groups helps to stimulate and foster minority
group engagement in the political process, then we should observe increased terrorist
activity as well.
These arguments lead us to our first hypothesis:
H1. Countries containing geographically concentrated significant minority communities are more likely to
experience terrorist attacks.
The location and density of minority groups within countries is not the only factor that
could potentially affect the likelihood of violent conflict. We also expect that transnational
dispersion of minority communities has an important impact on patterns of political violence in countries. We are aided in this expectation by the civil war literature. Though the
effects of transnational minority community kin networks on terrorist attacks have not been
empirically evaluated, scholars have found them to be important factors in fostering civil
war onset and continuance (Moore and Davis 1998; Saideman 2001; Woodwell 2004).
Seeking to explain why civil wars tend to geographically cluster and how internal armed
conflicts in one country are apt to prompt similar conflicts in neighbors – the ‘contagion’
effect – Buhaug and Gleditsch (2008: 215) find that ‘… transnational ethnic linkages are
central mechanism of conflict contagion.’
We argue that there are several ways in which transnational minority communities might
buttress terrorist activity. First, they form crucial networks of support for terrorist movements that may help to boost terrorist activity. A wide range of scholars observe that terrorist movements assiduously rely upon monetary and non-monetary support from kindred
diaspora communities (Adamson and Demetriou 2007; Carter 2005; Clark 2007; Esman
2009; Hess 2007; Levitt 2007; Wayland 2004). Diaspora communities serve as important
sources of financial support for terrorist movements by providing donations and holding
fundraisers. Perhaps even more importantly, kin communities outside of the home country
can help terrorist movements safely or clandestinely bank their assets, launder revenues,
and can help transfer money internationally. In terms of non-monetary support, ethnic terrorist movements find kindred diaspora communities to be useful for acquiring weapons
and equipment or by providing safe houses or assistance in obtaining legal or illegal entry
into foreign countries. Moreover, kin groups can serve as lobbying or pressure-group agents
on behalf of terrorist groups, or can be useful for launching public relations campaigns to
favorably portray terrorist group interests. In some instances, terrorists may use kin groups
in other countries as sources of information and intelligence. Finally, Clark (2007) notes
that diasporas are fertile recruiting grounds for terrorists. Examples of terrorist movements
that rely heavily on these types of support from their ethnic kin in other countries abound:
the LTTE, the Irish Republican Army, Hezbollah, the Mujahideen-e Khalq, various antiCastro movements, Hamas, Fatah, and Al Qaeda. Many experts maintain that these groups
would simply not have been able to function as effectively, or carry out the same level of
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B. J. ARVA AND J. A. PIAZZA
attacks, if not for the support from their kindred diaspora communities throughout the world
(Clark 2007; Hess 2007; Levitt 2007; Wayland 2004).
Second, in addition to receiving direct support from kin in other countries, terrorist movements embedded in aggrieved minority groups in countries can rely upon kin to help secure
state assistance for their cause. Saideman (2002) convincingly explains the reasons why
minority groups in some countries may be more or less likely to receive external assistance,
which then might translate into the onset or widening of armed conflict in their home country. The motivation for states to provide support to ethnic insurgencies and terrorist movements in other countries – which can take the forms of monetary resources, arms, training,
safe haven or political and diplomatic support – is essentially modulated by domestic politics within the supporting state. The consensus in the field is that all regimes need to maintain a certain level of popular support and one of the ways that states bolster their
legitimacy is to keep key domestic (majority or minority) ethnic communities content. In
addition to traditional patronage techniques, regimes may opt to please domestic constituents by pledging to support the interests of their ethnic kin in other countries (Bueno de
Mesquita and Lalman 1992; Fearon 1994; Morgan and Bickers 1992; Saideman 2002).
Though there is always the possibility of undesirable costs with this strategy – in the form
of blowback, regional instability, or international recrimination – regimes may relatively
cheaply win the support of strategic domestic constituencies by playing upon their national
pride and by constructing narratives of outrage and sympathy for the treatment of ethnic
minorities in other countries. Also, leaders may use pledges of support for foreign kin of
domestic ethnic communities as a means of mobilizing support for foreign policy objectives
vis-à-vis other states. Davis and Moore (1997) illustrate the wide appeal of this strategy on
the part of states, explaining that it applies to both democracies and to authoritarian
regimes. Dictatorships may also try to bolster support for themselves among domestic
ethnic groups by pledging to aid kin in other countries.
We expect to uncover a similar process for state support of terrorist activity. Terrorist
movements based in aggrieved minority countries are more likely to acquire direct financial,
political or military support, or to receive what Byman (2005) terms ‘passive’ support, from
foreign states that are strongly influenced or dominated by their ethnic kin. We can point to
several examples of this. The Greek Cypriot independence movement EOKA (National
Organization of Cypriot Fighters) that engaged in terrorism against British and Turkish targets in the 1950s derived active and passive support from the Greek government, while
their counterpart – the Turkish Resistance Organization – was more overtly supported by
Ankara. Throughout the 1980s, Saddam Hussein’s Arab nationalist regime in Iraq provided
weapons to Arab extremists based in the Iranian Khuzestan province, while starting in 1979
and continuing to this day, Iran provides support to Shi’i extremist groups, such as the
Mahdi Army, in Iraq. All factions of the Palestine Liberation Organization, as well as
Hamas, receive financial support from various Arab regimes, while the Pakistani government provides aid to Muslim Kashmiri terrorist movements that operate within India.
Third, Salehyan and Gleditsch (2006) demonstrate that refugee inflows often stimulate
civil wars and internal armed conflicts by providing an opportunity for rebel groups to construct and exploit cross-border networks. Similar processes could enhance terrorist activity.
Countries that share ethnic minority groups are more likely to have higher levels of transborder migration involving members of these communities. These migrations raise security
and intelligence costs for the counterterrorism officials in countries experiencing these
flows, while terrorist movements embedded in the transnationally dispersed communities
can exploit the logistical and other advantages the flows provide.
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
7
Finally, in addition to these direct material effects – financial and political support,
recruiting opportunities, logistical benefits, etc. – transnationally dispersed minority
communities also raise the possibility that terrorist activity in one country might be stimulated by public awareness of political turmoil and violence occurring in another country via
‘demonstration’ effects. Beissinger (2002) and Kuran (1998) have determined that the
demonstration of internal armed conflict by actors in one country often prompts political
activists and dissidents in neighboring countries to engage in similar activities against their
own governments. We suggest that demonstration effects should be particularly strong when
witnessed by co-ethnics in countries and likely have the same stimulating effects for lower
intensity types of political violence like terrorism.
Given all of these theoretical pathways, we test a second hypothesis:
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H2. Countries containing significant minority communities with kin in other countries are more likely to
experience terrorist attacks.
ANALYSIS
To test the above hypotheses, the study conducts a set of negative binomial regression
analyses on a dataset, compiled and shaped by the authors, of the annual count of terrorist
incidents occurring within 165 or 170, depending on the specification, countries for the period 1981–2006. The main dependent variable for the study is an annual count of the number of all domestic terrorist attacks occurring within a country-year, though we conduct
robustness estimations on counts of total – domestic and transnational – attacks in a country
per observation.
The source for the dependent variable is the Enders, Sandler, and Gaibulloev (2011)
decomposed database derived from the Global Terrorism Database (GTD), a publicly available database housed at the University of Maryland’s Center for the Study of Terrorism and
Responses to Terrorism. We opt to use the Enders, Sandler, and Gaibulloev (2011) domestic
terrorism measure for the main dependent variable because we expect that the impact of
geographic patterns of minority community distribution within a country – for example,
concentration in one region or enclave of the country – is most likely to manifest itself in
local terrorism rather than in transnational terrorism. Moreover, the motivation or ability to
conduct domestic terrorist attacks by a minority community might be enhanced by financial
or other support by kin in the Diaspora, making local attacks more likely. However, we
acknowledge that at times, domestic terrorist movements might opt to select an international
target within their home country, for example, a foreign embassy, foreign corporation, or
group of foreign tourists, in order to garner more international media attention, or ethnic
groups with kin in other countries might attack from safe havens abroad. We therefore conduct robustness models using the total count of all terrorist attacks – domestic and transnational attacks – within a country provided by GTD. The results of these estimations
produce the same results as those in the main models, so we are confident that our findings
are not dependent on how terrorism is operationalized.5
Some, though not all, of our theoretical argumentation about how patterns of domestic
concentration and transnational dispersion of ethnic minority groups prompts terrorism
focuses on ethnic group-level processes. To test the hypotheses derived from these
5
Results in Appendix 1, Table A and Table C.
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B. J. ARVA AND J. A. PIAZZA
processes, one might argue that it would be more theoretically consistent to use an ethnicgroup-year unit of analysis for the study rather than country-years. However, we opt to use
an aggregated, country-year count of attacks for the study, and this decision is informed by
the quality of data on individual terrorist attacks available to researchers.
The source of data we use for the study is based upon the GTD, an event database that
includes information on characteristics of terrorist attacks. The GTD does include a variable
for the perpetrating group name which in theory could be used to allocate counts of attacks
to ethnic minority groups on a yearly basis. However, data on terrorist attack perpetrators
are missing for around half of all observations in the GTD data-set. Patterns of missingness
for perpetrator identification in GTD vary widely by country and world region – for some
countries in Asia, for example, information on perpetrating group is absent in 80% of the
observations – and data are not missing at random (Arva and Beieler 2014). Moreover, the
perpetrator failure to claim attacks can be a component of individual group strategy, raising
a further complication. Any attempt to construct a group-level database of terrorist events
using GTD necessarily injects significant bias into the analysis, eroding confidence in the
results presented.
In contrast to the name of the perpetrator, every incident in GTD is reliably identified
with a country location of the event, allowing researchers to construct a reliable count of
attacks that occur by country per year, eschewing selection issues. Though an aggregated
country-year analysis comes with its own limitations, a country-year framework minimizes
selection problems and enables us to identify patterns of ethnic group spatial distribution as
a structural feature that may help to explain why certain countries experience more
terrorism than others.
Estimation Technique
In terms of the modeling technique, the study opts to use a negative binomial estimation
technique due to significant overdispersion in the distribution of the dependent variable: It
is a count variable where nearly 56% of the observations are zero values; around 17% of
the observations are counts of ten terrorist attacks or more; almost 5% of the observations
are counts of 100 or more terrorist incidents; and no observation can, of course, have negative values. This makes a negative binomial estimation technique more efficient than a standard, ordinary least squares technique (see Brandt et al. 2000; Cameron and Trivedi 1998;
King 1988). To correct for spatial clustering in the dependent variable, we also calculate
robust standard errors clustered on country and set the variance dispersion at constant. As a
check, we also ran the models using several alternate techniques, including zero-inflated
negative binomial estimations, country-fixed effects negative binomial estimations and rare
events logistical regression estimations. These reproduce the same overall findings as those
in the same model, though each suffers from limitations that make the negative binomial
estimation technique the most advantageous in the study.6
6
Results in Appendix 1, Table F. Note, though Vuong tests indicate that use of a zero-inflated estimation is efficient, we crucially lack a theoretical reason for using such a technique as we do not theorize that separate processes
determine whether or not a country will experience a zero count of terrorism (captured by the inflated logit model
in the zero-inflated equation) versus the number of terrorist attacks above 1 a country will experience (captured by
the negative binomial model in the zero-inflated equation). Also, because the dependent variable does not temporally vary within some country cases – it stays at zero – this complicates use of a country-fixed effect technique
because such observations are automatically dumped. These limitations support the practice of using the zero-inflated and country-fixed effects techniques for robustness checks only.
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
9
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Independent Variables
The main independent variables used in the study are derived from the Minorities at Risk
(MAR) database, which compiles indicators on the political, economic, social, and security
statuses of 282 ‘significant’ – meaning sizeable – minority communities within countries,
globally7 (Minorities at Risk Project 2009). The project defines a minority at risk group as
communities that, ‘… collectively suffer or benefit from systematic discriminatory treatment
vis-à-vis other groups in society … (Minorities at Risk Project 2009, 1)’. Around 68% of
the countries in the sample contain at least one minority at risk group, while about 47% of
countries are home to more than one minority at risk group. This actually poses a dilemma
given that the MAR database is structured as a group-year database while all of the other
indicators in the study are country-year in format. We have, therefore, reshaped the MAR
data into a country-year format by truncating the MAR variables into singular measurements for each observation. We have several reasons for doing this. First, our dependent
variable is best measured at the national level and cannot be reliably coded for specific
MAR groups, as explained above. Second, data for our controls – which have been selected
based upon their inclusion in previous empirical studies – are aggregated to the national
level and are not available at the group level. Third, we wish to adhere to the convention
established by Piazza (2011, 2012), Lai (2007) and Caprioli and Trumbore (2003) who code
aggregate country-level measures of MAR group features for countries containing more
than one MAR group by adopting the highest value exhibited by any one MAR group.
Finally, using a country-level analysis, we can capture both ethnic minority group perpetrated terrorist events along with majority group perpetrated attacks, which may be
prompted by resentments associated with MAR group features (see Piazza 2012). This has
the added advantage of introducing a conservative bias into our analysis, making us more
confident of the results. So, for the purposes of this study, our observations for MAR variables are the highest ordinal value for MAR group concentration or transnational dispersion,
rather than the average or median.8
We operationalize ethnic group spatial distribution using two variables we develop from
MAR. The first is a recoded version of the MAR variable ‘GROUPCON Group Spatial
Distribution’, or the degree to which a minority at risk group is geographically concentrated
or dispersed in a country. In its original form, this ordinal variable takes the following values: 0, indicating that the group is widely dispersed throughout the country; 1, indicating
that the group is either concentrated in urban areas only or is concentrated as a minority
population in specific regions in the country; 2, indicating that the group is concentrated
into a specific region as a majority population in that region, but that the bulk of its community is dispersed as a minority in other regions; 3, indicating that the group is concentrated
as a majority in a region with little presence dispersed in other regions of the country
(Minorities at Risk Project 2009, 7). We recode it by adding a new ordinal level for the
absence of any MAR communities in the country, changing it from a three to a four point
ordinal scale. This transformation is depicted in Table I.
7
The MAR database is the only existing source for the independent variables we use in the study: ethnic group concentration and transnational dispersion. The Ethnic Power Relations database, another frequently used data source
for scholars conducting cross-sectional time series research on ethnic minority groups in countries, does not code
variables measuring geographic patterns of ethnic group residence.
8
An added barrier to creating and analyzing minority-group-year units – in addition to missing data and the attendant selection bias problems this presents – is that the MAR database codes data on minority groups that are of a
minimum size – at least 1% of the national population – and omits smaller groups. This produces selection bias
issues that recommend that we aggregate MAR features like spatial distribution to the national level, again following the protocol established by Piazza, Lai, and others.
10
B. J. ARVA AND J. A. PIAZZA
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TABLE I Transformation of MAR Group Concentration Indicator
Original MAR Indicator
Recoded MAR Indicator
Group Spatial Distribution (GROUPCON)
= No MAR Group Present
= 0 Group is widely dispersed
= 1 Group is primarily urban or is a minority in one region
= 2 Group is a majority in one region but dispersed in others
= 3 Group is concentrated in only one region
Transnational Dispersion of Kindred Groups
=0
=1
=2
=3
=4
We transform more significantly the other MAR indicator for our other independent
variable. The transformation process is outlined in Table II.
To measure the effects of transnational dispersion – as opposed to domestic concentration
captured in the MAR Group Concentration variable previously described – we create an
ordinal scale indicator of MAR group dispersion across national borders. This indicator,
which we name MAR Group Transnational Dispersed, is a three-point scale whereby 0 indicates that the country lacks any MAR groups, 1 indicates that the country’s minority at risk
groups do not have kin across borders – i.e. they are unique to the country – and 2 indicates
that the country’s minority at risk groups do have kin, either close by right across the border
or further away in non-adjacent countries. We derive this indicator by transforming the
original MAR indicator ‘GC10, Transnational Dispersion – Kindred Groups.’ However,
because of the objective of the study and the limited nature of our hypotheses, we do not
make use of the nuanced codes in GC10 listed in Table I and instead truncate them to ease
interpretation.
These two ordinal measures of MAR group spatial distribution are the main independent
variables we use to assess the effects that domestic concentration and transnational dispersion have on terrorism.9 However, to further validate our findings we also use dummy variables coded 1 for countries containing MAR groups that are not domestically concentrated
or that are not transnationally dispersed. We, of course, expect these to be either not significant or negative predictors of terrorism. We furthermore break down into separate dummies
the original ordinal values of the MAR concentration and dispersion indicators and regress
them against counts of terrorism. Our expectation for these is that the dummies for the
higher codes – indicating higher degrees of concentration or dispersion – will be significant
predictors of terrorism.
The study also includes in all models a set of controls that previous studies have found
to be robust predictors of terrorism (see for example Eyerman 1998; Krieger and Meierrieks
2011; Li 2005; Wade and Reiter 2007). We hold constant some basic indicators that may
affect patterns of terrorism including logged measures of gross national income per capita,
national population, and surface area. We expect larger and more populous countries to
experience more terrorism because they have higher policing costs (Eyerman 1998). We
also control for level of economic development in a country, but have mixed assumptions
reflecting the divisions in the field about poverty and terrorism: Gross national income may
be a positive predictor of terrorism (Piazza 2011); a negative predictor (Freytag et al.,
9
Note, as a robustness check, we also created dummy versions of our ordinal independent variables measuring
MAR group concentration and dispersion. These were simply coded 1 for observations in which our ordinal scale
MAR indicators are 1 and higher. These provide a check of our ordinal transformation and reproduce the same
results as our main findings. These results can be found in Appendix 1, Table B. (Note also that running the original MAR indicators produces the same core results as well.)
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
11
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TABLE II Transformation of MAR Dispersion Indicators
Original MAR Indicator
Recoded MAR Indicator
Transnational Dispersion – Kindred Groups (GC 10)
Transnational Dispersion of Kindred
Groups
=0
=1
=2
= No MAR Group Present
= 1 Group has no close kindred across an international border
= 2 Group has close kindred across a border which does not adjoin its
regional base
= 3 Group has no close kindred in countries which adjoin its regional
base
= 4 Group has close kindred in one country which adjoins its regional
base
= 5 Group has close kindred in more than one country which adjoins its
regional base
2011); or may have no effect at all (Krueger and Malečková 2003). Li (2005) found income
inequality, political participation, and degree of constraints placed upon the executive
branch of countries to be significant predictors of terrorist attacks.10 We therefore include in
all models the Gini coefficient, an index measuring income inequality, level of political participation, from the Vanhanen Index, and a measure of executive constraints from Polity.
Because Eyerman (1998) determined that older, established (democratic) regimes were less
likely to experience terrorism, we also control for the number of years the current regime
has been in place. We use the ‘durable’ indicator from Polity to control for age of current
regime. Li also controlled in his study for pre- and post-Cold War temporal effects, under
the assumption that state support of terrorism during the Cold War period boosted terrorist
activity. We do the same, including a dummy variable for all Cold War years, 1945–1991.
Our expectations are, given findings of other scholars, that Gini, executive constraints –
countries with constrained executives have a limited ability to ‘‘get tough’’ on terrorists –
and the Cold War dummy variable are positive predictors of terrorism while political
participation dampens terrorist attacks. Walsh and Piazza (2010) determined that states that
protect the physical integrity rights of their citizens – rights against torture, political
imprisonment, disappearances, and extrajudicial killings – experience significantly less terrorism. We therefore include the indicator that they used – the Physical Integrity Index from
CIRI – as a control in all of our models. Also, in line with Li (2005), we insert a one-period
lag of the dependent variable in all models to account for previous terrorist attacks.
Crucially, because there is a literature demonstrating the important contributions made by
ethnic group diasporas to the onset and duration of civil wars11 – mostly by supplying
resources and lending material support to rebel groups (see, for example, Collier 2003;
Collier and Hoeffler 2004; Collier, Hoeffler, and Söderbom 2004; Fearon and Laitin 2003)
– and because terrorist activity may rise during periods of civil wars or within civil war
zones in countries (Findley and Young 2010), we control for the presence of an active civil
10
Though, interestingly, research by Østby (2008) finds that general levels of economic inequality in countries are
not a predictor of violent conflict in countries, but ‘horizontal inequalities’ – ethnic or other social-group-based
inequalities rather than inequalities between individuals – do drive violence. This dovetails with the study’s focus
on ethnic minority communities as an important factor in episodes of violent conflict such as terrorism.
11
We actually produce a similar, though tentative, finding to this using our own data. When run separately in models, MAR group transnational dispersion predicts civil wars in our sample of countries. However, when included in
the same model with group concentration, this relationship disappears.
12
B. J. ARVA AND J. A. PIAZZA
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TABLE III Descriptive Statistics
Variable
Obs.
Mean
Std. deviation
Min
Max
Terrorist Incidents (GTD Domestic, Enders, Sandler,
and Gaibulloev 2011)
MAR Group Transnationally Dispersed
MAR Group Concentration
MAR Group No Trans. Dispersion
MAR Group No Concentration
MAR GROUPCON = 0
MAR GROUPCON = 1
MAR GROUPCON = 2
MAR GROUPCON = 3
MAR GC10 (Dispersion) = 0
MAR GC10 (Dispersion) = 1
MAR GC10 (Dispersion) = 2
MAR GC10 (Dispersion) = 3
MAR GC10 (Dispersion) = 4
MAR GC10 (Dispersion) = 5
Log Gross National Income per cap
Log Population
Log Area
GINI Coefficient
Durable
Political Participation (Vanhannen)
Executive Constraints (Polity)
Civil War (PRIO)
Physical Integrity Index (CIRI)
Cold War Era
5893
7.4
32.9
0
4107
4785
5808
5808
4269
4269
4269
4269
3785
3785
3785
3785
3785
3785
5629
5800
6364
5813
5808
5719
5813
1.1
2.3
.01
.04
.39
.07
.07
.45
.35
.08
.14
.03
.15
.23
7.1
1.8
11.8
42.8
21.9
29.8
3.4
.9
1.5
.07
.19
.48
.25
.26
.49
.47
.27
.35
.19
.35
.42
1.6
1.7
2.1
8.9
27.4
22.0
1.5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.5
−2.8
5.7
17.8
0
0
0
2
4
1
1
1
1
1
1
1
1
1
1
1
1
14.1
7.1
16.6
84.8
197
71
6
3837
6364
4.8
.5
2.3
.4
0
0
8
1
524
war in all models. To do this, we use a dummy variable coded 1 for observations in which
a civil war is occurring, using data from PRIO. To provide a further check for spuriousness,
we also ran two other analyses. We conducted robustness models that control for the incidence and magnitude of civil wars, civil violence and ethnic conflict and war within countries using data from the Major Episodes of Political Violence database and evaluate. We
also reran the main models dropping all cases experiencing a civil war. All of these models
produce the same core findings as those presented in the main results table of the study.12
Table III summarizes all of the variables in the study by reporting their descriptive
statistics.
DIRECTION OF CAUSATION
All independent variables in the models are lagged one temporal period. This helps to capture lagged effects of change in them on the dependent variable and also helps to provide
some clarity about direction of causation. We note that running the models using current,
rather than lagged, versions of the independent variables produces no change in the main
12
Results in Appendix 1, Table G.
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SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
13
results. This is an empirical validation of our theoretical story that geographic distribution
of minority communities within and among countries is in most cases a product of longterm and dramatic political, social, and economic processes, whereas terrorist activity is a
more short-term, sporadic, and low-intensity phenomenon. Some evidence from the data
helps to bear this out. In the overwhelming majority of countries in our sample, geographic
living patterns of minority communities were established prior to the time period we examine and are remarkably more static than patterns of terrorist attacks. For example, nearly
every country in the sample experiences fluctuation in terrorist attacks, some dramatically:
72% of countries saw fluctuations of more than 20 more attacks during the 37 years of
observations and the average standard deviation of attacks for countries over the time series
was 18.03. However, only 60 of 172 countries saw their level of minority group concentration change at all from 1970 to 2006. Change in transnational distribution of minority
communities is even rarer in the time series. Only 9% of countries experienced a change in
transnational dispersion of their minority groups.
We are therefore dubious at the outset about the likelihood of a reversed relationship, that
terrorist attacks in t1 might cause an ethnic group to cluster into one region of a country or
to become transnationally dispersed or to see their kin come into or leave power in other
countries in t2 during the time period examined. However, to provide a further check, we
also conducted some Granger causality tests using one, two, and three-year lags of both the
independent and dependent variables. These reveal that while temporally lagged measures
of terrorist attacks and MAR group concentration and dispersion Granger-cause terrorism,
lags of terrorism do not Granger-cause the MAR group indicators.13 We furthermore ran
sets of estimations switching the dependent and independent variables. In none of these
models are counts of terrorism predictive of MAR group concentration or dispersion;
indeed, both concentration and dispersion have different country-level significant predictors
than terrorism in these models.14 This gives us further confidence that reverse causation
does not complicate interpretations of the models.
Results
The results of the analyses are summarized in Tables IV and V and provide consistent support for the hypotheses: Countries containing ethnic minority groups that are geographically
concentrated into one part of the country and ethnic groups with kin in other countries are
more likely to experience terrorism.
Model 1 finds countries with more geographically concentrated minority at risk groups to
be more likely to experience terrorist attacks while Model 2 finds MAR group transnational
dispersion to positively predict terrorism. These findings provide preliminary support for
Hypotheses 1 and 2 and are robust to the inclusion of strong covariates. Models 3 and 4
solidify this support by including an indicator coded 1 for the presence of a minority ethnic
group that is either not geographically concentrated – model 3 – or transnationally dispersed
– model 4. In model 3, countries with geographically concentrated ethnic minority groups
experience more terrorism, while countries merely possessing an ethnic minority group that
is not concentrated, that is, internally dispersed or integrated within the majority ethnic population, do not. In model 4, countries with ethnic minority groups that have kin in other
countries experience more terrorism, but actually experience less terrorism if they possess
an ethnic minority community that does not.
13
14
Results available from authors.
Results available from authors.
14
B. J. ARVA AND J. A. PIAZZA
TABLE IV Main Results, Domestic Concentration and Transnational Dispersion of Minority Groups and Domestic Terrorist Attacks
Model 1
MAR Group Concentrationt−1
Model 2
.157***
(.040)
Model 3
.160***
(.042)
.351***
(.081)
MAR Group Transnationally Dispersedt−1
MAR Group, Not Concentratedt−1
.350***
(.081)
.101
(.212)
.142**
(.044)
.350***
(.043)
−.147***
(.041)
.011*
(.006)
−.003
(.002)
.251***
(.061)
.003
(.003)
.925***
(.152)
.113*
(.047)
.329***
(.046)
−.115**
(.043)
.012*
(.006)
−.003
(.002)
.305***
(.060)
.002
(.003)
.813***
(.160)
.141**
(.045)
.348***
(.043)
−.145**
(.042)
.011*
(.006)
−.003
(.006)
.253***
(.061)
.003
(.003)
.923***
(.152)
−11.461***
(1.019)
.112*
(.047)
.329***
(.046)
−.114**
(.043)
.012*
(.006)
−.003
(.002)
.305***
(.060)
.002
(.003)
.811***
(.160)
−.123***
(.030)
−.127***
(.031)
−.124***
(.030)
−.127***
(.031)
Cold War Erat−1
.501***
(.092)
.414***
(.092)
.503***
(.092)
.413***
(.092)
Terrorist Attackst−1
.005***
(.000)
−.083
(.602)
.005***
(.000)
−.394
(.663)
.005***
(.000)
−.119
(.611)
.005
(.000)
−.384
(.663)
1046.54***
170
3285
853.98***
165
2730
1051.51***
170
3285
1002.66***
165
2730
MAR Group, Not Dispersedt−1
(ln)Gross Nat. Incomet−1
(ln)Populationt−1
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Model 4
(ln)Geog. Areat−1
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity (CIRI)t−1
Constant
Wald Chi2
No. of Countries
Observations
Notes: All models are negative binomial estimations. Robust standard errors, clustered by country, in parentheses. Dispersion set at
constant.
*p < .05, **p < .01, ***p < .000, one-tailed tests.
Table V disaggregates the MAR concentration and dispersion variables into a set of dummies measuring the ‘level’ of concentration and dispersion of minority groups in countries
separately. In all of these models, the reference category is the zero value for concentration
or dispersion, indicating that the country contains a minority group that is either not
geographically concentrated or dispersed.
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
15
TABLE V Levels of Domestic Concentration and Transnational Dispersion of Minority Groups and Terrorist
Attacks
Model 5
MAR GROUPCON = 1
MAR GROUPCON = 2
MAR GROUPCON = 3
.498*
(.218)
.302
(.245)
.609***
(.160)
MAR GC10 (Dispersion) = 1
.139**
(.044)
.340***
(.042)
−.146**
(.042)
.010
(.006)
−.003
(.002)
.246***
(.061)
.003
(.003)
.903***
(.150)
−.131***
(.030)
.353
(.282)
.392*
(.173)
.692**
(.233)
.649***
(.173)
.675**
(.198)
.105*
(.045)
.312***
(.043)
−.120**
.045
.010
(.006)
−.003
(.002)
.316***
(.063)
.001
(.003)
.818***
(.159)
−.128***
(.030)
Cold War Erat−1
.512***
(.092)
.447***
(.094)
Terrorist Attackst−1
.005***
(.000)
.005***
(.000)
.073
(.605)
−.009
(.626)
1082.58***
950.03***
MAR GC10 (Dispersion) = 2
MAR GC10 (Dispersion) = 3
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Model 6
MAR GC10 (Dispersion) = 4
MAR GC10 (Dispersion) = 5
(ln)Gross National Incomet−1
(ln)Populationt−1
(ln)Geographic Areat−1
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity Index (CIRI)t−1
Constant
Wald Chi2
(Continued)
16
TABLE V
B. J. ARVA AND J. A. PIAZZA
(Continued )
Model 5
Model 6
No. of Countries (clusters)
170
170
Observations
3153
2694
MAR GROUPCON = 0
MAR GC10 = 0
Reference Category
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Notes: All models are negative binomial estimations. Robust standard errors, clustered by country, in parentheses. Dispersion set at
constant.
*p < .05, **p < .01, ***p < .000, one-tailed tests.
In these results, we observe that, again, the higher levels of MAR group concentration
and dispersion are significant predictors of terrorism, as portrayed in Models 5 and 6.
However, when the concentration and dispersion levels are put into the same estimation,
only the higher levels of concentration – specifically codes for levels 3, 4, and 515 – are
significant. This further underscores the findings in Table IV.
In the models in Tables IV and V, the majority of the covariates are significant, many at
the highest level of significance. This, as previously noted, suggests the core findings are
particularly robust while also providing some surety that the results are not spurious. In all
models in Tables IV and V, gross national income, national population, executive constraints, civil war, the Cold War dummy, and counts of previous terrorist attacks are all significant, positive predictors of terrorism. This is as expected, given findings by other
scholars. Additionally, the CIRI measure of physical integrity rights protection is a significant negative predictor of terrorism. Again, this is as expected. The measure of income
inequality, captured by the Gini coefficient, is significant and positive in the models in
Table IV but is not significant in any estimation in Table V. Durable, or the count of years
the current regime has ruled, was expected to be a significant negative predictor of terrorism. However, it is not significant in any of the models in Tables IV and V. The same is
true for political participation. Finally, across all models, geographic area is a significant but
negative predictor of terrorism, suggesting that larger countries – which we would expect to
have higher policing costs (Eyerman 1998) – are less likely to experience terrorism. This is
an unanticipated finding.
It may be suggested that state or regime qualities that prompt the creation of diaspora
communities, such as a repression or discrimination, may also drive terrorism. Ethnic minority groups might flee repressive or discriminatory regimes, thereby producing a transnational
dispersion of their ethnic community, while the behaviors of the regime produce grievances
that fuel radicalization and political violence. To account for such a spurious relationship,
we reran all estimations while controlling for the presence of minority group discrimination
in the country, using data from the MAR database. In all of the main models, we effectively
control for state repression via the physical integrity rights protection indicator. Inclusion of
either of these variables does not change the core results of the study.16
We examined another possible source of spuriousness: namely that the process driving
the relationship between ethnic group concentration and transnational dispersion and
15
Indicating MAR group has close kin in bordering countries that do not adjoin its domestic regional base (MAR
GC10 = 3), which do adjoin its domestic regional base in one other country (MAR GC10 = 4) or which adjoin its
domestic regional base in more than one country (MAR GC10 = 5).
16
Results in Appendix 1, Table H.
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SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
17
terrorism is separatism and separatist activities. Countries containing ethnic groups that are
clustered into one region or that have kin in other countries might experience armed separatist activity, which could involve increased terrorist attacks. (Consistent with findings by
Saideman and Ayers 2000) To test for this, we reran all estimations including the MAR
separatist activity indicator (‘SEPX’). These models produce the same core findings as in
the main models.17
As a final check of spuriousness, we investigated the predictors of ethnic group
concentration and dispersion themselves. More specifically, we wanted to see whether the
country-level attributes that we find increase terrorism also increase concentration and
dispersion. We do not find evidence of this. The predictors of concentration and dispersion
are almost completely different – in terms of significance and sign – from those that predict
terrorism. This gives us another indication that separate processes produce both of these
phenomena.18
Finally, we note that the number of observations for models regressing concentration to
terrorism outnumber those for dispersion. To assuage concerns that the results of the study
might be affected by different sample sizes, or different compositions of countries, across
the estimations, we identified every country that appears in models regressing concentration
to terrorism but not transnational dispersion. We dropped these countries from Model 1 and
reran it, and produced the same findings.19
Substantive Effects
The results presented so far have demonstrated a significant relationship between ethnic
minority group concentration and dispersion and terrorism. How substantive are these relationships? To answer this question, we calculated a series of first-difference effects simulations of changes in concentration and dispersion on counts of domestic terrorism in
countries using the Clarify software developed by Tomz, Wittenburg, and King (2003)
These simulate the amount of domestic terrorism a country will experience, using
Monte-Carlo techniques, if the ordinal measure of minority group geographic concentration
is increased from zero to 1, 1 to 2, 2 to 3 and 3 to 4; and if the dispersion measure is
increased from zero to 1, 1 to 2 and 2 to 3. The results of these simulations are graphed in
Figures 1 and 2.
Both concentration and dispersion are found to have a substantive impact on terrorism.
Increasing group concentration within a country from its minimum to maximum values
more than triples the amount of domestic terrorism it experiences, as illustrated in Figure 1,
while increasing group transnational dispersion from minimum to maximum nearly doubles
domestic terrorist attacks, as shown in Figure 2. Countries with geographically concentrated
ethnic minority groups experience .25–.55 terrorist attacks per year while countries with
transnationally dispersed minority groups experience .6–1.2 attacks per year. At first glance,
the impact of both of these variables might seem minimal. However, recall that terrorism is
a rare event and that the modal country (around 66.7% of all observations) experiences zero
attacks in a given year. The figures also graph the confidence intervals for the simulated
point estimations. The lower ends of these confidence intervals are above zero, further
underscoring the strong positive relationship concentration and dispersion have on attacks.
17
Results in Appendix 1, Table I.
Results in Appendix 1, Table J.
19
Results in Appendix 1, Table K.
18
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18
B. J. ARVA AND J. A. PIAZZA
FIGURE 1
Graph of first difference effects
FIGURE 2
Graph of first difference effects
Checks for Outlier Effects
At the outset of the manuscript, we discussed prominent cases in which geographic distribution of ethnic minority groups had implications for terrorist activity, namely by producing
grievances that fuel terrorism, enhancing separatist sentiments and by engaging diasporas in
support for terrorism. These included cases such as the Kurds in Turkey and Iraq or the
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
19
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Pashtuns in Pakistan. Are such cases, however, outliers that might drive the results of our
study? We test for this possibility using two techniques. First, we truncated the dependent
variable into a dummy coded 1 for all country observations experiencing at least one terrorist attack and reran logit estimations of all of our models. The purpose of this is to flatten
out cases, like Turkey, that might experience extremely high levels of terrorism in some
observations, thereby exerting outlier effects on the results. These models reproduce the
same core findings of the study.20 Second, we also conducted a Cook’s D test to identify
potential substantive outliers. These tests revealed the top five outliers: Colombia, Germany,
Iraq, Pakistan, and Turkey. We then removed these cases from the analysis and reran the
estimations, reproducing the same core results.21 These tests increase our confidence that
the results of the study are not driven by a small handful of outliers.
CONCLUSION
To summarize our findings: we produce some evidence that countries qualified by ethnic
minority groups that are geographically clustered into enclaves, and countries containing
ethnic groups that have kin in other countries, are highly likely to experience terrorism relative to countries with ethnic minority communities that are more geographically integrated
with their majority populations, or countries lacking ethnic minority communities at all.
This is a finding that has parallels in the civil war literature and has been discussed in the
qualitative terrorism literature.
There are important limitations to this study that require mention as well as potential policy implications and directions for future research. In terms of limitations, the structure of
the data analyzed in the study prevents us from eliminating some theoretical explanations
for the results. We speculated, in the theory section, that ethnic group concentration and
transnational dispersion yielded important strategic advantages to groups wishing to employ
terrorism; for example by aiding mobilization or by facilitating fundraising or arms transfers, via kin in other countries, for terrorist movements. However, many scholars depict terrorism as a ‘weapon of the weak’ (see Crenshaw 1981), meaning that it is a tactic most
frequently adopted by weak actors with few strategic assets facing a much stronger, assetrich adversary. It is possible, therefore, due to the aggregate nature of the research design,
that terrorist activity is being adopted by weaker groups facing more concentrated minority
groups or minority groups with kin in other countries – both of which are relatively ‘‘stronger’’ – rather than the strong groups themselves.22 In order to examine this possibility,
future researches would need to develop valid, reliable, and complete data on terrorism at
the ethnic group level.
The strong positive relationship between the presence of transnationally dispersed minority communities and incidents of terrorism should signal to policy makers that they may
have to rethink the ways in which they are battling terrorism. The first suggestions these
findings make is that counterterrorism officials should monitor activities within diaspora
communities more closely. Most of the resources allocated to fighting the war on terror
domestically go to better policing mechanisms, preventing terrorist attacks from occurring
20
Results in Appendix 1, Table D.
See Appendix 1, Figure A1. Results of empirical tests in Appendix 1, Table E.
We do not suspect this is the case as we find ethnic group concentration and dispersion to also predict civil war
occurrence, in line with the previous empirical literature within the civil war genre. Civil war violence requires
much higher capacity to project force from combatants. This suggests to us that concentration and dispersion aid
and enhance terrorist movements, enabling them to commit more attacks.
21
22
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20
B. J. ARVA AND J. A. PIAZZA
while also being prepared for them, and seeking out active terrorists that are living in the
country. However, this study demonstrates that diaspora communities play a pivotal role in
the funding and functioning of terrorist groups. It would be in the best interest of governments to monitor such things as fundraising activities that could go on within these
communities because they could be giving life to terrorists (Clark 2007; Hess 2007; Levitt
2007; Wayland 2004).
These findings also indicate that the war on terror, in order to be successful, truly needs
to be a collective effort across nations. Unlike past scholarship that tended to claim that
domestic characteristics of countries were the sole predictors of terrorism, we find that
international factors need to be taken into account as well. As can be seen in many case
studies on terrorist groups such as the IRA and LTTE, the funding from diaspora communities across the world serve as the lifeblood for some of these organizations. This being said,
what diaspora communities in France are doing could directly affect the level of terrorism
seen in Sri Lanka. While the war on terror has seen a good deal of cooperation among a
number of countries, neither the level of cooperation nor the number of participants is high
enough to produce encouraging outcomes.
While this study has produced important results, and added to the literature on terrorism,
it has left many questions unanswered. As is a trait of most good research, this could prove
to be a fertile hunting ground for future scholars. Our findings have shown that diasporas
seem to have an effect on the level of terrorism, a country will incur but we do not deal
with the question of whether certain characteristics of diasporas make them more or less
likely to affect the level of terrorism. Future scholars may want to investigate whether this
relationship changes depending on how close the diaspora community is to a specific country. This may entail taking a dyadic approach to the data, and decreasing the time period to
be studied, but it could produce very important results.
Another area that should be looked into is whether the effects of diasporas on terrorism
may vary based on the nature of the terrorist group receiving support from its kindred
community. Esman (2009) seems to indicate that Muslim terrorist groups may be more
likely to receive steady support due to the strong religious connection the minority
communities have with the terrorist groups as well as the zakat tax. Does this phenomenon
only occur with Muslim terrorist groups? What are the effects of acculturation on the level
of support these diasporas are willing to provide? Are there important second generation
effects to consider? These are a few of the possible questions this research opens the door
for future scholars to study and the answers to each of them could be very valuable to the
field.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
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15
15
APPENDIX 1. SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND
TERRORISM
Iraq
Iraq
5
TurkeyColombia
Algeria
Peru
India
Colombia
Turkey
Peru
Colombia
Algeria
Guatemal
Peru
SouthAfr
Afghanis
Israel
Israel
ColombiaColombia
Algeria
SouthAfr
Turkey
Thailand
Colombia
SouthAfr
Colombia
Peru
Afghanis
Turkey
Pakistan
Pakistan
Germany
Israel
Turkey
Nicaragu Colombia
India
Banglade
Cambodia
Pakistan
Jordan
India
Japan
UnitedKi
United
S
Israel
Chile
Cambodia
Nicaragu
CzechRep
Bhutan
Thailand
Syria
UnitedKi
Russia
Germany
India
SriLanka
India
Benin
India
Spain
SouthAfr
Chile
Spain
Mozambiq
Yemen
Mongolia
Guatemal
Iran
India
Zimbabwe
Germany
Turkey
Mauritiu
Argentin
Libya
Israel
India
Denmark
UnitedKi
Angola
SaudiAra
India
Nepal
Indonesi
Mexico
France
Gabon
Georgia
Honduras
India
Israel
Pakistan
Guatemal
Laos
UnitedAr
Tajiksta
SriLanka
Iceland
Azerbaij
CongoKin
Sudan Colombia
Nepal
China
Cyprus
Banglade
Yugoslav
Pakistan
Iraq
Kuwait
Pakistan
Gambia
Zambia
India
Turkey
Mexico
Spain
France
Peru
Pakistan
Paraguay
Chile
Spain
India
UnitedKi
Peru
France
Nigeria
Canada
Greece
Switzerl
Tunisia
Russia
Costa
Ri
Ecuador
Italy
Netherla
PapuaNew
Iraq
Greece
BurkinaF
Kenya
SriLanka
Bolivia
Albania
CentralA
Venezuel
Haiti
Banglade
Italy
Thailand
Belgium
Colombia
Yugoslav
Finland
Algeria
Jamaica
Thailand
GuineaBi
Israel
Morocco
Ethiopia
Croatia
India
Banglade
Yemen
Togo
Indonesi
Vietnam
Spain
Nigeria
Haiti
Ireland
Nicaragu
Japan
Cuba
SouthAfr
Namibia
Uganda
Honduras
PapuaNew
Banglade
Greece
Senegal
Armenia
Venezuel
Guatemal
Trinidad
Indonesi
Mozambiq
Panama
Venezuel
Bolivia
Thailand
Singapor
Mozambiq
Pakistan
Nepal
Brunei
Bahrain
United
United
S
S
France
Romania
Togo
Italy
Russia
Kyrgyz
UnitedKi
Guyana
SriLanka
United
S
Peru
Coted'Iv
Namibia
Afghanis
Mexico
Israel
Guinea
Russia
Spain
Panama
Austria
Argentin
Dominica
Kenya
Indonesi
Brazil
Bolivia
Belgium
Burundi
Poland
Haiti
Luxembou
Hungary
Ireland
Zambia
Turkey
Brazil
Bolivia
Malawi
United
S
Greece
Tajiksta
Malaysia
Panama
Chile
Oman
Yemen
Greece
Lesotho
Uruguay
Niger
Spain
GuineaBi
Honduras
Ghana
Spain
Greece
France
Burma
Niger
Rwanda
Nepal
Italy
Armenia
Guatemal
Greece
Ecuador
Ecuador
Portugal
GuineaBi
Sweden
Cyprus
Portugal
Guyana
Spain
Pakistan
Guatemal
Azerbaij
Lesotho
Honduras
Mali
Mauritan
PapuaNew
Paraguay
Guatemal
Austria
France
SriLanka
Yemen
Nepal
Israel
Kuwait
Gambia
SaoTomeP
SaoTomeP
Ghana
Lesotho
Netherla
Argentin
Tajiksta
UnitedKi
Hungary
Ecuador
Mongolia
Nicaragu
Sudan
Algeria
Dominica
Greece
Thailand
Greece
PapuaNew
Luxembou
Botswana
Jamaica
SolomonI
Gambia
Costa
Cyprus
Gambia
Rwanda
Nepal
Ri
Bhutan
France
Burma
Bhutan
Norway
Mali
Senegal
SierraLe
Georgia
Comoros
Mongolia
UnitedKi
Jamaica
Bhutan
Senegal
Turkey
Jordan
Rwanda
Austria
Thailand
Burma
Honduras
Cambodia
SierraLe
Uganda
Nepal
Netherla
GuineaBi
GuineaBi
GuineaBi
Gambia
Argentin
Malawi
M
Banglade
Mali
Iceland
Mongolia
Botswana
ali
Paraguay
Ireland
GuineaBi
Costa
Grenada
Ri
Ireland
Albania
Haiti
Haiti
Lesotho
Honduras
Dominica
SolomonI
Macedoni
Jordan
Gambia
Israel
Guyana
SriLanka
Botswana
Iceland
Mali
Liberia
PapuaNew
Grenada
Iceland
Guatemal
Switzerl
Cape
Ver
Denmark
Cape
Belgium
Lesotho
Nigeria
Chad
Zimbabwe
SaoTomeP
Iceland
Iceland
Ver
Bahrain
Mali
Dominica
Nicaragu
Belgium
Panama
Ethiopia
CentralA
Belgium
Mauritan
Benin
Comoros
Mauritan
Suriname
Banglade
Mauritiu
Spain
Lesotho
Mauritiu
GuineaBi
Mongolia
Japan
Lesotho
Iceland
Seychell
Bhutan
GuineaBi
United
S
Zambia
Hungary
Estonia
Lesotho
Lebanon
Costa
Croatia
Ri
Dominica
Denmark
Gambia
Swazilan
Mauritan
Malawi
Comoros
Japan
Nepal
Haiti
auritan
Switzerl
Bhutan
United
Portugal
Panama
Malaysia
Lesotho
Benin
Honduras
Swazilan
Austria
Niger
Trinidad
Lesotho
Ukraine
Zambia
SolomonI
Poland
Laos
Nigeria
Oman
Jamaica
Suriname
Mauritiu
Chile
Argentin
Kuwait
Oman
Benin
Niger
PapuaNew
Russia
Suriname
Grenada
Banglade
Canada
Jamaica
UnitedKi
SaudiAra
Bhutan
United
Gambia
Gambia
Eritrea
Mauritiu
Pakistan
East
GuineaBi
Jamaica
Honduras
Seychell
Jamaica
Iceland
Tim
GuineaBi
Uganda
Jamaica
SouthAfr
Trinidad
Bolivia
Italy
Mali
Swazilan
Laos
Venezuel
Sweden
Ukraine
Guyana
Malawi
Yemen
Guinea
Kyrgyz
Laos
Namibia
Luxembou
Mozambiq
Suriname
Barbados
Guyana
Poland
Cuba
Tajiksta
Djibouti
Maldives
Seychell
Belize
Angola
Paraguay
Jamaica
Laos
GuineaBi
France
Swazilan
Botswana
PapuaNew
Uzbekist
Botswana
Banglade
Bhutan
Panama
Bolivia
Estonia
Latvia
Zambia
Luxembou
Mauritiu
East
Norway
Guyana
BurkinaF
Turkmeni
Botswana
Kyrgyz
UnitedAr
Tim
Armenia
Chile
Niger
Macedoni
Panama
Norway
BurkinaF
Bulgaria
Paraguay
Mauritiu
Trinidad
Lithuani
Slovakia
Netherla
Luxembou
Mali
BurkinaF
Nepal
Cyprus
SierraLe
CentralA
Oman
Jamaica
Mauritiu
Nepal
Cyprus
Luxembou
Malta
Mali
Djibouti
Namibia
Mali
Somalia
United
SSAngola
Portugal
CentralA
Uruguay
Benin
Yugoslav
East
Slovenia
Estonia
Latvia
Gabon
Sweden
Trinidad
Benin
Swazilan
Algeria
Panama
Luxembou
Greece
Lithuani
Gabon
CentralA
Oman
Guyana
Costa
Tim
Canada
Mongolia
Ri
UnitedAr
Niger
Ghana
Turkey
Norway
Iran
Tunisia
Finland
Gabon
UnitedAr
Togo
Japan
Jamaica
Senegal
PapuaNew
Estonia
Zimbabwe
Albania
Bolivia
Cyprus
Costa
Uruguay
Kyrgyz
Canada
SouthAfr
Ri
Ecuador
Ecuador
Senegal
Guatemal
Austria
Denmark
Slovenia
Paraguay
Hungary
Coted'Iv
Gabon
Eritrea
Honduras
Zambia
Eritrea
SierraLe
Oman
PapuaNew
CzechRep
Mozambiq
Uruguay
Niger
Namibia
Oman
Laos
Uzbekist
Malawi
CongoKin
Guyana
Kazakhst
Barbados
Oman
Mali
Seychell
Senegal
Brazil
Algeria
SierraLe
Namibia
Maldives
Bhutan
Georgia
Nicaragu
Malaysia
Greece
BurkinaF
Guinea
Albania
Burma
Mauritiu
Sweden
Algeria
Haiti
Qatar
Guyana
Kenya
Benin
SierraLe
Haiti
Macedoni
Sweden
Latvia
Jordan
Togo
Ecuador
Zimbabwe
Honduras
Trinidad
Denmark
Portugal
Trinidad
Albania
Slovenia
Uruguay
Lesotho
Jamaica
Hungary
Macedoni
Yemen
Panama
Uganda
Oman
Uruguay
Malawi
Croatia
Haiti
Argentin
Sweden
Botswana
Kyrgyz
Gabon
Chile
Netherla
Italy
Nepal
Ghana
Coted'Iv
Poland
CentralA
Germany
Niger
Tajiksta
Pakistan
Gabon
UnitedAr
UnitedAr
Namibia
Brunei
Brunei
Switzerl
Switzerl
Bhutan
United
SS
CzechRep
Paraguay
Slovakia
Cyprus
Mozambiq
CentralA
UnitedAr
Zimbabwe
Armenia
Uganda
Zambia
Nicaragu
Zambia
Estonia
Togo
BurkinaF
Ireland
Ethiopia
Finland
Benin
Mexico
Guinea
GuineaBi
Ghana
BurkinaF
Thailand
Jamaica
Luxembou
Gabon
Mauritiu
Poland
Finland
Namibia
Hungary
Malawi
Niger
Poland
CentralA
Yemen
Japan
Gabon
Mozambiq
Mali
Mauritan
Oman
Eritrea
Rwanda
Uruguay
UnitedKi
Kuwait
Australi
Qatar
Canada
Brunei
Brunei
Bahrain
Afghanis
Switzerl
Belize
Sudan
Bhutan
Bhutan
Bulgaria
SierraLe
Norway
Netherla
Haiti
Turkmeni
Panama
Sweden
SriLanka
Mauritan
UnitedKi
Canada
Venezuel
Honduras
Georgia
Senegal
Ghana
Ireland
Morocco
Belgium
Lesotho
Ghana
Zambia
Turkey
Iraq
Guinea
Bolivia
Albania
Uruguay
Slovakia
Armenia
BurkinaF
Poland
Coted'Iv
Rwanda
CentralA
Mauritan
Kuwait
Nigeria
PapuaNew
Guatemal
Yemen
Togo
Burma
Equatori
Brunei
Sudan
CongoKin
United
Ukraine
Honduras
Portugal
Guinea
Hungary
Dominica
CzechRep
Kazakhst
Brunei
Malaysia
Paraguay
Tunisia
Bolivia
Coted'Iv
Kenya
Laos
CzechRep
Greece
Armenia
Thailand
Turkmeni
Romania
Denmark
Turkmeni
Ireland
Coted'Iv
Portugal
Lesotho
Belgium
Spain
Kyrgyz
Belgium
Dominica
Niger
Cuba
Haiti
Rwanda
Burundi
Banglade
Equatori
Belize
United
S Rwanda
Angola
Norway
Mozambiq
Nepal
Coted'Iv
Jamaica
Indonesi
Nigeria
Dominica
Zimbabwe
Tunisia
Cambodia
Azerbaij
Romania
Latvia
Guatemal
Cambodia
Kenya
Thailand
SouthAfr
Thailand
Dominica
Nigeria
Lesotho
Swazilan
Trinidad
CongoBra
Ukraine
Mozambiq
Burma
UnitedAr
Ethiopia
Malawi
Colombia
CentralA
Libya
Haiti
CongoKin
Gabon
Canada
Hungary
Rwanda
Brunei
Chile
Belize
Morocco
ElSalvad
Denmark
Nepal
Gambia
Burundi
Burundi
UnitedAr
Belize
Switzerl
Ecuador
Portugal
Austria
Albania
Venezuel
Sudan
Ireland
Ghana
Guatemal
Nepal
Brazil
Lebanon
Iraq
Iraq
Colombia
Vietnam
UnitedAr
Zimbabwe
CongoKin
Belize
Switzerl
Poland
BurkinaF
Cuba
Algeria
Kuwait
Niger
Hungary
Morocco
Cambodia
Morocco
Macedoni
Armenia
Georgia
Nigeria
Denmark
Tunisia
Croatia
Laos
Peru
Tunisia
Laos
Somalia
Chile
Kazakhst
SouthAfr
Vietnam
Kazakhst
Mali
Oman
SaudiAra
UnitedAr
Kuwait
Vietnam
Mozambiq
CzechRep
Gabon
CongoKin
CongoKin
NorthKor
Yemen
PapuaNew
Libya
Eritrea
Libya
Rwanda
Brazil
Afghanis
Bahrain
Bahrain
Equatori
United
Senegal
Guinea
Guatemal
Latvia
Dominica
Romania
Cyprus
Japan
Belize
Cameroon
Malaysia
Coted'Iv
Azerbaij
Jamaica
Bulgaria
Senegal
Thailand
Malaysia
Tunisia
Jordan
Paraguay
Uganda
Tanzania
Italy
Guinea
Togo
Slovakia
Indonesi
Rwanda
Lebanon
Yemen
Kuwait
Angola
Yemen
Burundi
Japan
Brazil
Qatar
Somalia
Burundi
Bahrain
Mauritan
Bahrain
Bahrain
Bahrain
Switzerl
Equatori
Coted'Iv
Tunisia
Indonesi
Paraguay
Austria
Syria
Italy
Albania
Georgia
SaudiAra
Togo
Kuwait
Singapor
GuineaBi
Angola
Kenya
Bulgaria
Romania
Tunisia
Panama
Uganda
Albania
Kazakhst
Japan
Nigeria
UnitedAr
Jordan
Ukraine
Panama
Romania
Indonesi
Iran
Ethiopia
Netherla
Senegal
Cambodia
Thailand
Namibia
Uzbekist
Niger
SouthAfr
Nepal
Rwanda
Angola
Cuba
Nepal
Kuwait
Vietnam
Mexico
Banglade
Libya
Mauritan
Singapor
Belize
United
Yemen
United
S
Syria
Haiti
Mauritan
Vietnam
Poland
Singapor
Belize
Georgia
Ecuador
Syria
Peru
Kenya
Ukraine
Egypt
Togo
Hungary
Angola
Portugal
CongoBra
Banglade
Singapor
Egypt
Morocco
Jordan
Sweden
Burma
Uzbekist
Burundi
Hungary
Brazil
SaudiAra
Albania
Cuba
Yugoslav
SaudiAra
Ethiopia
Vietnam
Togo
CongoKin
Equatori
Coted'Iv
Peru
Zambia
Mali
Cambodia
Togo
Italy
Coted'Iv
Albania
Georgia
Kuwait
Uganda
Uganda
Honduras
Syria
Yugoslav
Azerbaij
Honduras
Ecuador
Paraguay
Nicaragu
Chad
Iran
Malawi
Angola
Argentin
Yugoslav
Burma
Kuwait
UnitedKi
Togo
CongoBra
Libya
Libya
SaudiAra
CongoKin
Liberia
Argentin
Malaysia
Denmark
Algeria
Malaysia
Spain
Tunisia
Cyprus
PapuaNew
Ethiopia
Rwanda
Germany
Cuba
Libya
China
Haiti
Syria
Algeria
Eritrea
Bulgaria
Bulgaria
China
Singapor
Malaysia
Coted'Iv
Ecuador
Dominica
Zimbabwe
Yugoslav
Bolivia
Portugal
Vietnam
Kazakhst
Ethiopia
Cuba
Romania
Cuba
NorthKor
Afghanis
CongoBra
NorthKor
Panama
Libya
Brazil
Sudan
Guyana
Algeria
Zimbabwe
Iran
Ethiopia
Azerbaij
Hungary
Azerbaij
Austria
Mozambiq
Romania
Haiti
Yemen
Nigeria
Pakistan
Poland
Rwanda
Brazil
CzechRep
Poland
Mexico
Nigeria
Argentin
Yugoslav
Cuba
Vietnam
Spain
Poland
Sudan
China
NorthKor
NorthKor
Burundi
Cuba
United
United
S
S
Venezuel
Oman
Thailand
Paraguay
Portugal
Iran
Venezuel
Mexico
Syria
Mexico
Ethiopia
Algeria
Uzbekist
Kyrgyz
Brazil
NorthKor
CzechRep
Poland
Venezuel
Chile
Ukraine
Lesotho
Italy
Yugoslav
Mexico
Panama
Italy
Nigeria
Pakistan
Russia
Venezuel
Syria
Brazil
Poland
Bulgaria
Sudan
Singapor
Libya
Sudan
Libya
Senegal
Morocco
Togo
Guatemal
Georgia
Nigeria
Morocco
Thailand
Argentin
Kuwait
Japan
SriLanka
Syria
Mexico
Ethiopia
China
Guatemal
Banglade
China
Bulgaria
Russia
Bulgaria
Cuba
Libya
Morocco
SierraLe
Zimbabwe
Kenya
Belgium
Mexico
Burma
Cambodia
Cuba
Poland
Turkey
Mexico
Yugoslav
Nicaragu
Greece
Peru
SouthAfr
Romania
Iran
Iran
Indonesi
Ghana
Rwanda
SriLanka
Poland
Iraq
SriLanka
Yemen
Russia
Romania
Sudan
Israel
Bulgaria
Libya
Sudan
Bosnia
Guinea
Brazil
Zimbabwe
Sudan
Bulgaria
SierraLe
Mexico
Mozambiq
Iran
Netherla
Burma
Iran
Burma
Yemen
Russia
Yugoslav
SriLanka
Sudan
Romania
SriLanka
Yugoslav
Ethiopia
Russia
Syria
China
Iraq
United
SSS
Honduras
France
Ukraine
Sudan
Angola
Thailand
Guatemal
Israel
UnitedKi
Georgia
Russia
Honduras
Venezuel
Mali
Uganda
Croatia
Iran
Macedoni
Uzbekist
Angola
Venezuel
Japan
SriLanka
Sudan
China
China
Romania
Ethiopia
Afghanis
Somalia
CongoKin
Turkey
CongoKin
Spain
Trinidad
Greece
Burma
Indonesi
Burma
Mozambiq
Tajiksta
Mexico
Iraq
Chile
Tajiksta
SouthAfr
Russia
China
Syria
Angola
Austria
Venezuel
Burma
Sudan
Syria
SriLanka
Afghanis
Somalia
Zambia
GuineaBi
Costa
Iraq
Mexico
Bahrain
Ri
Yugoslav
United
SS
Italy
Thailand
Mexico
CentralA
Haiti
Indonesi
Iran
Banglade
Croatia
Afghanis
Uganda
Finland
BurkinaF
Belgium
France
Banglade
Russia
Haiti
Syria
Yugoslav
Brunei
Iraq
Coted'Iv
Senegal
Algeria
PapuaNew
UnitedKi
Afghanis
Thailand
Nepal
Cambodia
Uzbekist
India
Azerbaij
Uganda
Bahrain
Coted'Iv
Russia
Haiti
Afghanis
SriLanka
Kenya
Albania
Nigeria
Colombia
SaudiAra
Sudan
CongoKin
Haiti
Israel
Cuba
China
SouthAfr
Burma
Sudan
Singapor
Coted'Iv
Brazil
Yemen
UnitedKi
SouthAfr
Cambodia
Jamaica
Iraq
Kenya
Malaysia
Uganda
Mexico
Italy
Rwanda
Afghanis
Angola
Sudan
Dominica
Canada
Iceland
Russia
Argentin
Honduras
Belgium
Dominica
PapuaNew
Argentin
Sudan
Sudan
Morocco
Guatemal
France
Pakistan
Paraguay
Guatemal
Zambia
Cyprus
Iraq
Vietnam
France
Japan
Gambia
Rwanda
Turkey
Indonesi
Tunisia
Laos
Senegal
Tajiksta
United
France
Togo
Germany
Panama
Netherla
Argentin
India
Tajiksta
Algeria
Switzerl
Croatia
Nepal
Israel
Chile
Bolivia
Gabon
UnitedKi
UnitedAr
Greece
Venezuel
Guatemal
Venezuel
Spain
Nepal
Mongolia
Honduras
France
Russia
Peru
Peru
Nicaragu
Pakistan
Ecuador
Chile
Nigeria
France
Israel
Mozambiq
Italy
Brazil
Burundi
Mauritiu
Guatemal
Benin
Ethiopia
Nicaragu
Russia
Guatemal
United
SS
Denmark
Kuwait
Nicaragu
Indonesi
Zimbabwe
Spain
Yugoslav
Peru
Burundi
SaudiAra
Iraq
CongoKin
Angola
SriLanka
Chile
Georgia
Bhutan
Azerbaij
China
Colombia
Libya
Indonesi
Colombia
Sudan
Yemen
Peru
Angola
CzechRep
Jordan
Japan
Slovakia
Turkey
Iran
Turkey
Germany
Syria
India
Cambodia
UnitedKi
India
Algeria
Mexico
India
India
Pakistan
Banglade
Nicaragu
SouthAfr
Turkey
Israel
-5
0
Studentized residuals
5
0
-5
Studentized residuals
10
10
Pakistan
Germany
Peru
Israel
SouthAfr
Guatemal
Algeria
Downloaded by [Penn State University] at 12:16 30 March 2016
-10
-10
Peru
0
.02
.04
.06
Leverage
FIGURE A1
Influence plots of the Cook’s D statistics
Chile
Colombia
Turkey
0
CongoKin
CongoKin
.02
Pakistan
Germany
Colombia
.04
.06
24
B. J. ARVA AND J. A. PIAZZA
TABLE A Main Models Using GTD Instead of GTD Domestic
Model 1
MAR Group Concentrationt−1
.091**
(.035)
MAR Group Trans. Dispersedt−1
(ln)Gross National Incomet−1
(ln)Populationt−1
(ln)Geographic Areat−1
Downloaded by [Penn State University] at 12:16 30 March 2016
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity Index (CIRI)t−1
Cold War Erat−1
Terrorist Attackst−1
Constant
Wald Chi2
No. of Countries (clusters)
Observations
***p = .000;**p = .01; *p = .05.
Model 2
.139**
(.045)
.292***
(.044)
−.087*
(.036)
.012*
(.005)
−.004*
(.002)
.160
(.085)
.001
(.003)
.998**
(.137)
−.046
(.031)
.134*
(.103)
.005***
(.000)
.159
(.581)
771.43
(170)
3271
.270***
(.068)
.123**
(.046)
.278***
(.045)
−.093**
(.036)
.013*
(.005)
−.004*
(.002)
.206*
(.087)
−.001
(.003)
.884***
(.143)
−.043
(.032)
.089
(.104)
.005***
(.000)
.199
(.628)
609.29
(165)
2716
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
25
TABLE B Main Models Using Dichotomous Versions of Independent Variables
Model 1
Concentration Dummyt−1
.559***
(.147)
Dispersion Dummyt−1
(ln)Gross National Incomet−1
(ln)Populationt−1
(ln)Geographic Areat−1
Downloaded by [Penn State University] at 12:16 30 March 2016
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity Index (CIRI)t−1
Cold War Erat−1
Domestic Terrorist Attackst−1
Constant
Wald Chi2
No. of Countries (clusters)
Observations
***p = .000; **p = .01; *p = .05.
Model 2
.137**
(.045)
.347***
(.043)
−.147**
(.044)
.013*
(.006)
−.003*
(.002)
.258***
(.062)
.003
(.003)
.944***
(.149)
−.127***
(.030)
.502***
(.092)
.005***
(.001)
−.084
(.621)
1075.93
(170)
3285
.695***
(.158)
.114*
(.047)
.330***
(.046)
−.115**
(.044)
.013
(.007)
−.004
(.003)
.304***
(.060)
.003
(.003)
.811***
(.161)
−.127***
(.031)
.411***
(.092)
.005***
(.001)
−.405
(.664)
848.39
(165)
2730
26
B. J. ARVA AND J. A. PIAZZA
TABLE C Levels of Domestic Concentration and Transnational Dispersion and GTD
Model 1
MAR GROUPCON = 1
MAR GROUPCON = 2
MAR GROUPCON = 3
Model 2
Model 3
.139**
(.046)
.284***
(.044)
−.083*
(.036)
.011*
(.005)
−.004*
(.002)
.148
(.085)
.002
(.003)
.965***
(.139)
−.056
(.032)
.142
(.104)
.005***
(.000)
.304
(.579)
803.73
(170)
3139
−.056
(.228)
.244
(.149)
.553*
(.231)
.363*
(.153)
.403*
(.175)
.111*
(.046)
.264***
(.042)
−.080*
(.037)
.011*
(.005)
−.004*
(.002)
.191*
(.091)
−.000
(.003)
.911***
(.141)
−.041
(.032)
.071
(.101)
.005***
(.001)
.488
(.584)
754.26
(170)
2680
.227
(.222)
−.121
(.262)
.063
(.176)
−.126
(.251)
.182
(.180)
.518
(.267)
.308
(.213)
.347
(.222)
.113*
(.046)
.262***
(.043)
−.084*
(.038)
.011*
(.005)
−.004*
(.002)
.189*
(.091)
−.000
(.003)
.890***
(.142)
−.048
(.034)
.075
(.101)
.005***
(.001)
.553
(.591)
783.99
(170)
2680
MAR GROUPCON = 0
MAR GC10 = 0
.308
(.174)
.068
(.249)
.345*
(.136)
MAR GC10 (Dispersion) = 1
MAR GC10 (Dispersion) = 2
Downloaded by [Penn State University] at 12:16 30 March 2016
MAR GC10 (Dispersion) = 3
MAR GC10 (Dispersion) = 4
MAR GC10 (Dispersion) = 5
(ln)Gross National Incomet−1
(ln)Populationt−1
(ln)Geographic Areat−1
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity Index (CIRI)t−1
Cold War Erat−1
Transnational Terrorist Attackst−1
Constant
Wald Chi2
No. of Countries (clusters)
Observations
Reference Category(ies)
***p = .000; **p = .01; *p = .05.
MAR GROUPCON = 0
MAR GC10 = 0
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
27
TABLE D Adjusting for Outlier Effects: Main Models Using Dichotomous-Dependent Variable
Model 1
MAR Group Concentrationt−1
.140***
(.037)
.158**
(.054)
.307***
(.077)
.142*
(.060)
.402***
(.060)
−.107*
(.045)
.007
(.008)
−.005*
(.003)
.190**
(.017)
.000
(.004)
.932***
(.173)
−.106**
(.036)
.392***
(.108)
1.379***
(.103)
−2.778***
(.743)
522.95
(170)
3285
.428***
(.063)
−.106*
(.048)
.012
(.009)
−.007*
(.003)
.268***
(.065)
−.000
(.004)
.793***
(.183)
−.120**
(.039)
.300**
(.118)
1.250***
(.108)
−2.983***
(.812)
493.15
(165)
2730
MAR Group Trans. Dispersedt−1
(ln)Gross National Incomet−1
(ln)Populationt−1
Downloaded by [Penn State University] at 12:16 30 March 2016
(ln)Geographic Areat−1
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity Index (CIRI)t−1
Cold War Erat−1
Domestic Terrorist Attackst−1
Constant
Wald Chi2
No. of Countries (clusters)
Observations
***p = .000; **p = .01; *p = .05.
Model 2
28
B. J. ARVA AND J. A. PIAZZA
TABLE E Main Models Rerun Dropping Top 5 Outliers (Turkey, Pakistan, Germany, Colombia, and Iraq)
Model 1
MAR Group Concentrationt−1
Model 2
Model 3
.159***
(.586)
.363***
(.085)
MAR Group Trans. Dispersedt−1
MAR GROUPCON = 1
.544*
(.246)
.358
(.255)
.625***
(.165)
MAR GROUPCON = 2
MAR GROUPCON = 3
Downloaded by [Penn State University] at 12:16 30 March 2016
MAR GC10(Dispersion) = 1
.153**
(.047)
.359***
(.044)
−.157***
(.042)
.013*
(.006)
−.003
(.002)
.260***
(.060)
.002
(.003)
1.04***
(.156)
.127**
(.050)
.344***
(.046)
−.127**
(.046)
.014*
(.007)
−.004
(.003)
.314***
(.060)
.002
(.003)
.921***
(.170)
.151**
(.047)
.352***
(.043)
−.157***
(.043)
.012*
(.006)
−.003
(.002)
.257***
(.060)
.003
(.003)
1.020***
(.154)
.404
(.276)
.403*
(.186)
.686**
(.259)
.657***
(.180)
.699**
(.210)
.119*
(.048)
.323***
(.043)
−.134**
(.046)
.012
(.006)
−.003
(.002)
.327***
(.064)
.001
(.003)
.921***
(.166)
−.108***
(.029)
.470***
(.093)
.006***
(.001)
−.395*
(.586)
1091.33
(165)
3183
−.111***
(.030)
.376***
(.091)
.006***
(.001)
−.769
(.663)
1145.26
(160)
2644
−.114***
(.029)
.485***
(.092)
.006***
(.001)
−.264
(.592)
1106.65
(165)
3051
MAR GROUPCON = 0
−.116***
(.030)
.412***
(.096)
.006***
(.001)
−.603
(.620)
1064.74
(165)
2606
MAR GC10 = 0
MAR GC10 (Dispersion) = 2
MAR GC10 (Dispersion) = 3
MAR GC10 (Dispersion) = 4
MAR GC10 (Dispersion) = 5
(ln)Gross National Incomet−1
(ln)Populationt−1
(ln)Geographic Areat−1
Gini Coefficientt−1
Durablet−1
Executive Constraints (Polity)t−1
Political Participation (Van.)t−1
Civil War (PRIO)t−1
Physical Integrity Index (CIRI)t−1
Cold War Erat−1
Domestic Terrorist Attackst−1
Constant
Wald Chi2
No. of Countries (clusters)
Observations
Reference Category(ies)
***p = .000; **p = .01; *p = .05
Model 4
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
29
TABLE F Part 1: Footnote 6 Robustness Check Using Zero-Inflated Negative Binomial
Variables
Model 1
Model 2
logGNI
.183***
(.044)
.488***
(.049)
−.192***
(.038)
.018**
(.006)
−.004**
(.001)
.097**
(.037)
.001
(.003)
.021
(.030)
.819***
(.121)
−.085**
(.026)
.214*
(.092)
.015***
(.001)
.203***
(.029)
.177***
(.044)
.487***
(.049)
−.177***
(.038)
.018**
(.006)
−.004**
(.001)
.089*
(.036)
.003
(.003)
.021
(.031)
.796***
(.122)
−.093***
(.026)
.263**
(.092)
.014***
(.001)
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
AggSF
civwarprio
PhysInt
ColdWar
GTDDomestic
MAR Group Concentration
MAR Group Transnationally Dispersed
Constant
−.262
(.507)
.433***
(.056)
−.482
(.503)
Observations
2717
2717
Note: Standard errors in parentheses.
***p < .001, **p < .01, *p < .05.
30
B. J. ARVA AND J. A. PIAZZA
TABLE F Part 2: Footnote 6 Robustness Check Using Country-Fixed Effects
Variables
Model 1
Model 2
logGNI
.032
(.037)
.113*
(.045)
−.043
(.037)
−.011*
(.005)
−.001
(.002)
.206***
(.042)
.002
(.002)
.037
(.020)
.598***
(.090)
−.079***
(.020)
.412***
(.059)
.003***
(.000)
.074*
(.031)
.052
(.040)
.163**
(.051)
−.073
(.043)
−.008
(.005)
−.002
(.002)
.234***
(.046)
.001
(.002)
.038
(.022)
.466***
(.100)
−.075***
(.021)
.318***
(.063)
.002***
(.001)
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
AggSF
civwarprio
PhysInt
ColdWar
GTDDomestic
MAR Group Concentration
−1.655***
(.466)
.1111a
(.067)
−1.616**
(.521)
3060
148
2534
139
MAR Group Transnationally Dispersed
Constant
Observations
Number of COW
Note: Standard errors in parentheses.
a
It should be noted that in order to run a country-fixed effects analysis, we had to drop 26 countries from the sample because they
never experienced any terrorist activity. This is most likely the reason that the variable falls from significance and also why we feel
fixed effects models are not the best way to test this.
***p < .001, **p < .01, *p < .05.
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
31
TABLE F Part 3: Footnote 6 Robustness Check Using Rare Events Logit
Variables
Model 1
Model 2
logGNI
.156***
(.036)
.400***
(.044)
−.109***
(.033)
.006
(.006)
−.005***
(.002)
.183***
(.054)
.001
(.003)
.107*
(.045)
.693***
(.163)
−.099***
(.028)
.379***
(.095)
1.365***
(.090)
.136***
(.029)
.140***
(.040)
.427***
(.050)
−.109**
(.035)
.011
(.007)
−.007***
(.002)
.260***
(.060)
.000
(.003)
.106*
(.049)
.535**
(.184)
−.112***
(.031)
.291**
(.100)
1.241***
(.098)
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
AggSF
civwarprio
PhysInt
ColdWar
GTDDomesticDummy
MAR Group Concentration
MAR Group Transnationally Dispersed
Constant
Observations
Note: Robust standard errors in parentheses.
***p < .001, **p < .01, *p < .05.
−2.728***
(.503)
.301***
(.057)
−2.906***
(.538)
3285
2730
32
B. J. ARVA AND J. A. PIAZZA
TABLE G Footnote 14 Main Models Where All Cases Experiencing Civil War are Dropped
Variables
Model 1
Model 2
logGNI
.119*
(.060)
.324***
(.071)
−.071
(.054)
.002
(.009)
−.006
(.004)
.251***
(.075)
.001
(.004)
.099
(.081)
−.092*
(.037)
.420***
(.104)
.013*
(.005)
.185***
(.046)
.090
(.063)
.330***
(.072)
−.071
(.053)
.004
(.010)
−.007
(.006)
.303***
(.071)
.001
(.004)
.125
(.118)
−.102**
(.037)
.348**
(.122)
.012
(.006)
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
AggSF
PhysInt
ColdWar
GTDDomestic
MAR Group Concentration
MAR Group Transnationally Dispersed
Constant
−.944
(.899)
.380***
(.082)
−.871
(.999)
Observations
2704
2263
Note: Robust standard errors in parentheses.
***p < .001, **p < .01, *p < .05.
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
33
TABLE H Footnote 19 Main Models Including Control for Minority Group Discrimination
Variables
Model 1
Model 2
logGNI
.111*
(.052)
.385***
(.056)
−.184***
(.055)
.005
(.008)
−.000
(.002)
.108
(.092)
.005
(.004)
1.043***
(.174)
−.094*
(.041)
.893***
(.152)
.005***
(.001)
.145*
(.061)
.082
(.053)
.391***
(.061)
−.150**
(.055)
.005
(.008)
−.000
(.002)
.144
(.093)
.005
(.005)
.879***
(.191)
−.094*
(.041)
.704***
(.146)
.004**
(.001)
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
civwarprio
PhysInt
ColdWar
GTDDomestic
MAR Group Concentration
MAR Group Transnationally Dispersed
MAR Group Discrimination
Constant
Observations
Note: Robust standard errors in parentheses.
***p < .001, **p < .01, *p < .05.
.239
(.170)
.631
(.782)
.333**
(.128)
.087
(.188)
.432
(.798)
2137
1681
34
B. J. ARVA AND J. A. PIAZZA
TABLE I Footnote 20 Main Models including Control for MAR Separatist Activity
Variables
Model 1
Model 2
logGNI
.128**
(.046)
.361***
(.045)
−.146***
(.043)
.008
(.006)
−.003
(.002)
.244***
(.062)
.003
(.003)
1.013***
(.160)
−.120***
(.030)
.509***
(.094)
.005***
(.001)
.196***
(.048)
.096*
(.049)
.333***
(.045)
−.102*
(.045)
.009
(.007)
−.003
(.003)
.300***
(.060)
.003
(.003)
.896***
(.164)
−.126***
(.031)
.410***
(.093)
.005***
(.001)
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
civwarprio
PhysInt
ColdWar
GTDDomestic
MAR Group Concentration
MAR Group Transnationally Dispersed
Separatist Group
Constant
Observations
Note: Robust standard errors in parentheses.
***p < .001, **p < .01, *p < .05.
−.092
(.050)
.125
(.615)
.404***
(.085)
−.088*
(.044)
−.263
(.672)
3153
2598
SPATIAL DISTRIBUTION OF MINORITY COMMUNITIES AND TERRORISM
35
TABLE J Footnote 21 Main Models Where MAR Group Concentration and Discrimination are Dependent Variables
Variables
Model 1
MAR Group Concentration DV
Model 2
MAR Group Transnationally Dispersed DV
−.068
(.047)
.121*
(.057)
.106*
(.049)
.011
(.007)
−.000
(.002)
.031
(.039)
.006*
(.003)
.060
(.081)
−.060**
(.023)
−.121**
(.040)
.001
(.000)
−.780
(.624)
−.054
(.050)
.135*
(.060)
.062
(.057)
.011
(.007)
−.000
(.002)
.007
(.033)
.006*
(.003)
.095
(.081)
−.041
(.021)
−.057
(.034)
.000
(.000)
−.968
(.651)
3452
2871
logGNI
logpop
logarea
GINI
Downloaded by [Penn State University] at 12:16 30 March 2016
Durable
Executive
VanPart
civwarprio
PhysInt
ColdWar
GTDDomestic
Constant
Observations
Note: Robust standard errors in parentheses
***p<.001, **p<.01, *p<.05
36
B. J. ARVA AND J. A. PIAZZA
TABLE K Model 1 Rerun Removing Countries Lacking MAR Group Transnational Dispersion
Model 1
logGNI
logpop
logarea
GINI
Durable
Downloaded by [Penn State University] at 12:16 30 March 2016
Executive
VanPart
PhysInt
ColdWar
GTDDomestic
Mar Group Concentration
Constant
Observations
Note: Robust standard errors in parentheses.
***p < .001, **p < .01, *p < .05.
.084
(.049)
.344***
(.048)
−.137**
(.048)
.010
(.008)
−.004
(.003)
.324***
(.061)
.002
(.004)
−.195***
(.032)
.427***
(.090)
.006***
(.001)
.183***
(.040)
.714
(.683)
2730