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Public Choice

, Volume 178, Issue 3–4, pp 329–347 | Cite as

Terrorism and affinity of nations

  • Khusrav Gaibulloev
  • Todd SandlerEmail author
Open Access
Article

Abstract

This article investigates whether a country’s political proximity to the United States, France, Germany, and the United Kingdom exposes the country to terrorist attacks. We merge information on political proximity between countries and terrorism data to construct a panel of world countries for 1968–2014. Various measures of terrorism are included—i.e., number of transnational terrorist attacks, number of domestic terrorist attacks, and number of terrorist attacks with casualties. In addition, two measures of political proximity or affinity are employed. A host of control variables are included to account for standard determinants of terrorism. We find a clear relationship indicating that countries displaying a political affinity with key Western countries’ policy views attract more transnational and domestic terrorist attacks. The results are robust to alternative empirical specifications. Furthermore, countries’ affinity with the United States puts US citizens in greater peril from terrorist attacks in those countries.

Keywords

Affinity of nations Transnational and domestic terrorism Policy interdependence US victims Ideal points 

JEL Classification

D74 F50 H56 

1 Introduction

This article addresses an important terrorism question, first raised by Dreher and Gassebner (2008). Namely, does the adoption of foreign policy positions similar to those of the United States, which is a prime target of transnational terrorism, put a country at greater peril for terrorism? In the literature, some evidence already exist that exercising active foreign policy, being allied with the United States, or both may spawn transnational terrorist attacks (Savun and Phillips 2009).1 In the current article, we broaden that question to ask: does the adoption of foreign policy stances similar to those of the United States and its three primary Western European allies (i.e., France, Germany, and the United Kingdom) raise a country’s risk of being a target of transnational and domestic terrorism? If the answer is affirmative, then these at-risk countries must be vigilant to the enhanced terrorism risk as their voting behavior in, say, the UN General Assembly becomes more closely aligned with that of the United States and its prime allies. Thus, there can be a direct link between a country’s political affinity with some set of target countries and the country’s own need for greater counterterrorism efforts. Moreover, limiting such political ties may reduce the country’s counterterrorism needs, as was true for Spain after the election following the March 11, 2004 Madrid commuter train bombing attack. At the time, the incoming Spanish government withdrew its support for the US-led War on Terror, which had been a demand of the terrorists, to limit its terrorism risks (Gaibulloev and Sandler 2019).

Researchers relate countries’ foreign policy positions to UN General Assembly votes, because these votes are highly visible and provide a revealed preference of dyadic alignments among country pairs (Dreher and Jensen 2007; Gartzke 1998; Signorino and Ritter 1999; Voeten 2004). UN voting alignment is said to capture other countries’ agreement with Western liberal attitudes when the voting behavior of the United States and its main allies are highlighted (Bailey et al. 2017). Since the start of the modern era of transnational terrorism in the late 1960s (Hoffman 2017), affinity with the United States and its three allies can reflect shared opposition to Western-directed terrorism. For example, the United States and the United Kingdom lost the most people in the 9/11 attacks (Enders and Sandler 2012). London and Paris have sustained many terrorist attacks, while German cities likewise have suffered terrorist attacks since the start of 1968. The rise of lone wolf terrorists in recent years has put these four countries and their politically aligned allies in jeopardy (Hoffman 2017).

The notion of affinity-generated terrorism risks captures an important transnational externality in which countries attract terrorist attacks even when they are not directly responsible for the perceived grievance behind the incident. One country’s terrorism-creating grievance may induce an attack in another country that adopts similar policy positions, so that the causes of terrorist events are less direct than usually supposed. Terrorist groups may direct their attacks to convenient weaker target countries that display some of the same political philosophies of strong prime-target countries. In some instances, prime-target countries’ interests may be hit in the weaker countries (Gaibulloev and Sandler 2019). Affinity-based terrorist attacks are likely to blindside some venue countries, which may have little direct grounds for anticipating an attack.

The purpose of the current study is to expand earlier affinity and terrorism analyses in a number of important ways. First, we include an ideal-points measure along with the standard affinity measure used in the terrorism literature (Bapat 2011; Campos and Gassebner 2013; Dreher and Fischer 2010, 2011; Dreher and Gassebner 2008). Previously, the ideal-points measure has not been applied to terrorism studies. Bailey et al. (2017) indicate that the ideal-points measure can better distinguish changes in preferences from changes in policy agendas, thereby providing more signal to noise. In the current study, the ideal-points and the standard affinity measures work well, but the former does, indeed, yield more precise estimates of the consequence of political affinity on terrorism. Second, our investigation includes not only political affinity with the United States and its three major Western European allies, but also political affinity with just the United States or with just its three major Western European allies as generators of transnational terrorist events. Third, the current study contains a rich set of controls and robustness tests than do Dreher and Gassebner (2008). Fourth, a comprehensive analysis of political affinity and domestic terrorism is presented here. Previous studies ignore the relationship between political affinity and domestic terrorism, or in one instance uncover a very weak relationship.2 Fifth, we investigate potential channels through which affinity affects transnational terrorism by distinguishing attacks against US interests from those against non-US interests. If affinity with the United States is a driving force of terrorism, then more attacks against US interests abroad in affinity-aligned countries are anticipated as the United States hardens its own borders and potential domestic targets. Throughout the analysis, we estimate fixed-effects negative binomial regressions applied to 159 countries for ten five-year periods, starting in the late 1960s and ending in 2014.

The article is rich in findings. Both measures of political affinity with the United States and its three main European allies are robust determinants of transnational terrorism for two alternative datasets—namely, International Terrorism: Attributes of Terrorist Events (ITERATE), 1968–2014 (Mickolus et al. 2016) and the Global Terrorism Database (GTD), 1970–2014 (National Consortium for the Study of Terrorism and Response to Terrorism (START) 2016).3 Additionally, political affinity with the United States or with France, Germany, and the United Kingdom combined are significant determinants of aligned countries’ transnational terrorist attacks. For domestic terrorism, political affinity with the United States and/or with its three prime European allies results in countries attracting more terrorist attacks at home. Finally, political affinity with the United States is an inducer of terrorist attacks against US victims abroad.

The remainder of the article contains six sections. In Sect. 2, preliminaries include three essential concepts for our study. Section 3 briefly reviews past literature, while theoretical considerations and statements of three hypotheses follow in Sect. 4. Section 5 contains a description of the variables, the data, and empirical methodology; Sect. 6 reports results and robustness tests. Finally, concluding remarks follow in Sect. 7.

2 Preliminaries

Terrorism is the premeditated use or threat to use violence by individuals or subnational group against noncombatants to obtain a political objective through the intimidation of an audience beyond that of the immediate victims (Enders and Sandler 2012; Hoffman 2017). The essential ingredients in this definition are the political motive, the nonstate perpetrator, and the intended general audience. Assassinations, armed attacks, bombings, or hostage taking that are intended to coerce political change or concessions from a targeted country are terrorist incidents. In contrast, violence solely for extortion purposes constitutes a criminal activity. Thus, kidnappings or bombings meant to secure ransoms, but not intended to further a political aim, are not terrorist acts. State-sponsored, but not state-conducted, terrorism is permitted by this definition.4 Terrorists seek a large audience in order to create a general atmosphere of anxiety, such that the public feels sufficiently at risk that it applies pressure on officeholders or rulers to concede to the political demands of the terrorists. Our definition agrees with those that underlie ITERATE and GTD from which we draw our terrorism data for the empirical tests.

An important distinction for the current study is between transnational and domestic terrorism. Based on the nationalities of its victims, targets, supporters, or perpetrators, transnational terrorism involves two or more countries (Enders et al. 2011). If, for example, a letter bomb is mailed from Brussels to Paris to induce political change there, then the letter bombing is a transnational terrorist incident. Politically motivated skyjackings that commence in one country and terminate in another constitute transnational terrorism. That also is true of a skyjacking in which the victims and/or the perpetrators are citizens from more than a single country. The January 2002 kidnapping and subsequent beheading of Daniel Pearl, a Wall Street Journal reporter, in Karachi, Pakistan, by al-Qaida-affiliated terrorists, is classified as a transnational terrorist incident.

Domestic terrorism is homegrown: the victims and perpetrators are citizens of the venue country (the scene of the attack). If, for example, a flight solely with nationals aboard is hijacked to another domestic destination for political purposes, then the flight diversion is a domestic terrorist incident. Politically inspired bombings carried out by nationals that impact other citizens of the venue country only are domestic terrorist attacks. Most terrorist acts during a struggle for independence are domestic terrorist incidents. Domestic terrorist attacks far outnumber transnational terrorist events (Enders et al. 2011). As we argue at a later point, political affinity may inspire nationals to engage in terrorist attacks at home to protest their country’s foreign policy alignment with the United States or its key allies. Even though the inspiration has a foreign flavor, the act remains domestic when it does not involve foreign nationalities in terms of victims, perpetrators, or the venue.

Next, we turn to alternative measures of political affinity, employed in the literature to gauge a country’s foreign policy alignment with, say, the United States. The US political proximity index, used by Dreher and Gassebner (2008, p. 27) and many others, is “the share of roll call votes cast in line with the United States in the UN General Assembly.” That measure varies between 1 and 0, where 1 indicates that a country’s UN General Assembly votes precisely match those of the United States on all issues put to a vote, and 0 indicates that a country’s votes depart from those of the United States on all issues put to a vote. An alternative affinity measure is the S score, put forward by Signorino and Ritter (1999). The score represents a cardinal measure of revealed foreign policy preferences based on the votes cast by a country relative to a comparison country, such as the United States. Consider the S score between country a and the United States, which is computed as one minus the absolute difference between the United States’ and country a’s vote scores, summed over all included votes and divided by the number of votes (Bailey et al. 2017, p. 433). The value of a country’s vote is 1 for a yea, 2 for abstain, and 3 for a nay. If the United States and country a vote the same on all issues, then S equals 1; if the two countries display maximal disagreement, then S equals −1. A potential drawback of both measures of political proximity or affinity is that they implicitly assume a simple correspondence between the two countries’ foreign policy preferences and how often they vote relative to one another. For the UN voting data in this paper, the S score and the UN voting share measures are perfectly correlated (see Table A2 in the Online Appendix), so that we can use either. Given what is favored in the literature, we rely on the S score as our first affinity measure.

Our other affinity index is the ideal-points measure, which uses the absolute distance between a country’s ideal (or most favored) policy position and that of the United States, some other reference country, or the average ideal policy position for a set of countries. The ideal-points affinity measure is somewhat related to the median-voter model, wherein agents’ most preferred policy positions are identified along a single dimension. For this third affinity measure, countries, whose ideal points are closer together, are viewed as displaying greater political affinity. Bailey et al. (2017) apply a spatial model to estimate single-dimensional ideal points for a state. To ensure more valid intertemporal comparisons, they match UN resolutions that possess identical positions over time on a US-held liberal world-order scale and use that information for their estimation. Their model ensures that the ideal-points estimates—in contrast to the S score—are not sensitive to agenda shifts that may change the types of issues put to a vote. The model also mitigates the impact of votes that create division among countries for reasons unrelated to the states’ foreign policy positions in relation to the Western liberal order. As a result, the Bailey et al. measure better distinguishes agenda changes from true preference changes, thereby identifying more precisely foreign policy orientation between two countries.

3 Previous literature

Over the years, a large literature has developed for which a measure of political affinity is used as either a dependent or independent variable regarding a host of political-economy questions (Bailey et al. 2017). In terms of an independent variable, Gartzke (1998) examines whether foreign policy affinity influences the likelihood of conflict. Bapat (2011) investigates if US political affinity, US-provided military aid, and other controls affect the duration of terrorists resident in aid-recipient countries. Political affinity with the United States is shown by Bapat to increase the longevity of resident terrorist groups despite US-supported counterterrorism assistance to the host country. That result follows because the United States is more tolerant of aid-recipient countries’ poor counterterrorism performances owing to their support for US-held foreign policy positions in the United Nations. The greater longevity of the terrorist groups stems from a moral-hazard problem, whereby the aid recipient has no incentive to annihilate the resident group because doing so will end its US-provided aid. In a different exercise, Dreher and Jensen (2007) show that International Monetary Fund (IMF) conditionality is relaxed for recipient countries that display enhanced political affinity with the United States in terms of UN General Assembly votes. The underlying reason is that such political affinity provides a direct benefit to the United States that motivates it to soften loan conditionally. Using political proximity as a dependent variable, Dreher and Jensen (2013) indicate that regime change can alter foreign policy directions in terms of countries’ affinity with the United States. The above studies are merely representative of more than 70 studies using political proximity as an explanatory or response variable.

Very few articles investigate political affinity as a potential cause of terrorism. Dreher and Gassebner (2008) establish a somewhat weak relationship (at the 0.10 level) between political affinity and the number of transnational terrorist incidents for 116 countries during 1975–2001, a sample that includes hardly any post-9/11 terrorist attacks. Their measure of affinity corresponds to the share of a country’s UN General Assembly votes that agree with those of the United States. That share measure cannot gauge changes in preferences over time as is possible with the ideal-points measure employed here. For the Dreher and Gassebner (2008) study, political affinity displays a much stronger relationship in affecting the average and median number of deaths from transnational terrorist attacks, sustained by countries with political affinity with the United States. In an extreme-bounds analysis (EBA) of the determinants of terrorism, Gassebner and Luechinger (2011) indicate that US affinity is a robust determinant of transnational terrorism in venue and victim countries, but not in the perpetrators’ countries. A few terrorism articles focus on other determinants of transnational terrorism, such as political instability (Campos and Gassebner 2013), government decentralization (Dreher and Fischer 2010), and foreign aid (Bapat 2011). Those articles consider only political affinity with the United States, based on the simplest measure of political proximity—a country’s voting correspondence to the United States on UN General Assembly votes.

4 Theoretical considerations

Countries face vastly different risks of their citizens being the victims of transnational terrorist attacks. Since the start of 1968, US interests have been targeted disproportionately. Based on ITERATE data, the solid curve in Fig. 1 indicates the annual percentage of transnational terrorist attacks directed against US citizens during 1968–2014. For 1968–1990, the average percentage of US-victim attacks was 36.3%; for 1991–2014, this average percentage of US-victims attacks fell to 30.5%. Clearly, US victims attracted the lion’s share of transnational terrorist incidents, most of which occurred abroad. Next, we find and display the annual percentage of transnational terrorist attacks against citizens from the United States, France, Germany, and the United Kingdom.5 Those observations are represented by the dashed plot in Fig. 1. During 1968–2014, the annual percentage of transnational terrorist incidents directed at those four countries’ citizens averaged 49.6%. Both series’ annual percentages have declined since 2003.
Fig. 1

Percentage of transnational terrorist attacks against US interests or against US and its three European allies’ interests

Taken together, the four countries attract about half of all transnational terrorist attacks because of terrorists’ grievances and media-generated attention. Savun and Phillips (2009) hypothesize that foreign policy positions can create grievances that result in transnational terrorism. Surely, the four countries considered here are active in the foreign policy arena and, as such, can attract terrorist attacks. Three of the four countries are permanent members of the UN Security Council, which can dispatch troops to address civil and interstate conflicts. UN peacekeeping troop deployments also may create grievances that motivate terrorism.

Rich industrial countries with active foreign policy agendas often take measures to harden their borders against foreign terrorists. To the extent possible, these targeted countries also deploy defenses to protect targets at home. In response, terrorists often choose to attack these countries’ citizens abroad, where they are more vulnerable. Political affinity offers terrorist groups a means of lashing out at targeted countries’ values by hitting softer venue countries, espousing or following similar foreign policy positions. Terrorist attacks based on political proximity represent “attack transference” to more vulnerable countries. Such transference makes the most of terrorists’ limited resources and may have far-reaching political influence by affecting the foreign policy positions of many intimidated countries. As a consequence, affinity-based terrorist attacks may have larger effects than terrorist attacks directed at grievance-generating countries, thus making displaced attacks desirable and cost-effective from the viewpoint of the terrorists. Even a few terrorist attacks to protest political proximity can intimidate many countries into not supporting the policy positions of the United States and its three major allies. As an added bonus, political-affinity attacks are less apt to result in retaliation if the (weak) venue country cannot project its power abroad to the terrorist group’s home base or its supporters. Given the crucial foreign policy roles assumed by the United States and its three European partners, we state the following hypothesis:

Hypothesis 1

Political affinity to the United States, France, Germany, and the United Kingdom results in transnational terrorist attacks.

A second hypothesis follows because affinity-motivated attacks often seek out US citizens. US presence in terms of US residents and US visitors likely is more visible in countries that have a strong political affinity with the United States, which raises the number of US targets for transnational terrorist groups. This hypothesis is stated in terms of transnational terrorist attacks, because incidents involving US citizens abroad necessarily are transnational in nature. Thus, we have a second hypothesis:

Hypothesis 2

Political affinity with the United States leads to attacks against US victims abroad in policy-aligned countries.

Domestic terrorism may morph into transnational terrorism as resident terrorist groups seek wider exposure for their attacks and grievances; thus, Palestinian terrorists (e.g., the Popular Front for the Liberation of Palestine) engaged in high-profile skyjackings to gain worldwide attention starting in 1968 (Hoffman 2017). In fact, Enders et al. (2011) demonstrate that domestic terrorism Granger-causes transnational terrorism, but the latter does not Granger-cause the former. Blomberg et al. (2011) show that most terrorist groups employ both types of terrorism, with a much larger share of domestic terrorist attacks, which are simpler logistically and less risky than transnational terrorist operations. Domestic terrorism is less risky because borders do not have to be crossed and foreign powers are not motivated to retaliate since their interests are not hit. Thus, if political affinity can result in transnational terrorism, then affinity can also generate domestic terrorism as terrorist groups diversify their attack portfolios and seek less risky attack modes for gaining attention.

Political affinity may motivate a country’s terrorist groups to resort to domestic terrorism to protest a home country’s adoption of Western liberal values. That was the case for some domestic terrorist attacks in Greece, Spain, Germany, Italy, and elsewhere when leftist terrorists constituted the dominant terrorist influence during 1968–1989 (Gaibulloev and Sandler 2019). Those groups objected to Western-style capitalism, their country’s membership in NATO, and other US-associated policies. Many of the resident terrorist groups’ attacks targeted home-country institutions, power figures, and firms. Even subsequent domestic attacks by religious fundamentalist terrorist groups in the 1990s and beyond were at times the result of backlashes against the home country’s relationships with key Western powers and their political posture. In many instances, Western powers gave aid with the implicit condition that the aid-recipient countries support the donor’s votes in the UN General Assembly (see Bapat 2011; Dreher et al. 2008).6 As a consequence, resident terrorist groups lashed out at their home countries owing to their Western-based political affinity, thereby giving rise to domestic terrorist attacks.

Piazza (this issue) shows that public opinion can support domestic and transnational terrorist attacks when democratic governance is adopted by countries in the Middle East. Democracy is viewed by some as representing Western liberal values that are un-Islamic (Piazza this issue). Countries introducing democratic rule often possess a political affinity with the United States and its three major allies. Terrorist backlashes to democracy thus may foster a relationship between domestic terrorism and political affinity when attacks are staged at home against domestic targets. Thus, we have a third hypothesis:

Hypothesis 3

Political affinity to the United States, France, Germany, and the United Kingdom results in domestic terrorist attacks.

5 Data and methodology

We construct a panel dataset that includes 159 countries over the 1966–2014 period, giving us a much longer dataset than the pioneering study by Dreher and Gassebner (2008). The full sample is divided into ten periods: 1966–1969, 1970–1974, 1975–1979, 1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, and 2010–2014. Grouping years into longer periods generates more within-country variation, which allows for more accurate fixed-effects estimates of slowly changing variables, such as political affinity. The descriptive statistics are presented in Online Appendix Table A1.

Our dependent variable is the total count of terrorist attacks in a country in a given (five-year) period. A terrorist attack is ascribed to the country, where the attack takes place. We use ITERATE transnational terrorist attacks (Mickolus et al. 2016), GTD transnational terrorist events, and GTD domestic terrorist incidents (START 2016) as alternative dependent variables. The GTD data are separated into domestic and transnational terrorist attacks following the decomposition method devised by Enders et al. (2011). In robustness analyses, we also examine the 5-year counts of ITERATE transnational incidents with casualties (injuries and deaths), GTD transnational incidents with casualties, and GTD domestic incidents with casualties. For 1966–1969, the averaged transnational terrorist attack count is based on ITERATE’s reported attacks in 1968 and 1969. The averaged domestic terrorist attack count starts in the 1970–1974 period, given GTD’s coverage only after 1969.

Two measures of political proximity or affinity between states are employed in the regressions. Our first proximity measure is constructed using data on the absolute difference between estimated ideal points of paired countries (Bailey et al. 2017). We create four dissimilarity indices by separately calculating the five-year averaged values of the absolute distance in ideal points between each sample country and France, Germany, the United Kingdom, and the United States, respectively. For example, France’s ideal-points distance index represents the difference between a sample country’s ideal policy position and that of France, with larger values indicative of greater dissimilarity between the two countries’ preferences over foreign policy. The pairwise correlations between these four variables range from 0.84 to 0.995. The strong correlations are anticipated given that the sampled countries are like-minded as they adhere to the Western liberal order. Our index of political proximity, Ideal Points Distance, is the average of those four variables. In our robustness analysis, we also examine separately the distances of the United States’ ideal points (Ideal Points Distance US) and Europe’s ideal points (Ideal Points Distance EUR). The latter is constructed by calculating the average of the dissimilarity indices of France, Germany, and the United Kingdom. The correlation between those two variables is around 0.88—see Online Appendix Table A2. The second measure of political affinity between a sample country and France, Germany, the United Kingdom, and the United States, which we label Affinity, is computed using the S score data from Bailey et al. (2017) and Voeten (2013); those measures follow our steps in constructing Ideal Points Distance.

A number of control variables are added from the literature on the determinants of terrorist attacks (e.g., Gaibulloev et al. 2017; Gassebner and Luechinger 2011). We rescale annual Polity 2 scores from the Polity IV Project’s dataset (Marshall et al. 2016), which range between − 10 (complete autocracy) and + 10 (full democracy), so that the rescaled normalized index varies between 0 and 1. Our measure of regime type, Polity2, is the averaged value of the normalized index for a country in a given five-period period. Following Gaibulloev et al. (2017), who find a non-linear impact of regime type on terrorism, we use both Polity2 and its squared value. Studies suggest that long-lasting regimes endure less terrorism (e.g., Eyerman 1998). Therefore, we obtain information on regime durability in years from the Polity IV Project’s database and compute its five-year averaged value for a given country (Durable). Political instability may lead to more terrorism. Furthermore, terrorism might be applied as a strategy in civil conflicts. The five-year averaged incidence of civil conflicts for each country, Civil War, is calculated using the major episodes of political violence dataset (Marshall 2017). The Civil War variable’s magnitude ranges between 0 and 7, with larger values indicative of a more severe conflict. To control for ethnic discrimination, we compute the average share of a country’s population that faces ethnically motivated political discrimination during a given period, which we label Discriminated POP. The information required for this variable is taken from the Ethnic Power Relations dataset (Wimmer et al. 2009, updated through 2010). Ethnic discrimination is expected to induce terrorism as the marginalized population turns to political violence to express grievances.

To account for a country’s economic conditions and population size, we use the natural logarithm of the averaged value of its GDP per capita (in constant 2010 US dollars) and the natural logarithm of the averaged size of its total population during a given period, which are labeled log(GDP/POP) and log(POP), respectively. The data for those right-hand side variables are taken from the World Development Indicators database (World Bank 2018). In the terrorism literature, the findings on the impact of income per capita on terrorism are mixed (e.g., Abadie 2006; Enders et al. 2016; Li 2005). Population and terrorism are found to be positively correlated because of higher exposure to attacks, larger pool of potential terrorist recruits, and greater challenges in preventing attacks (e.g., Eyerman 1998; Gaibulloev 2015). Globalization may either reduce terrorism by addressing terrorism risks associated with a country’s underdevelopment, or facilitate terrorism by lowering the costs of terrorists’ movements and illegal activities (Li and Schaub 2004). Moreover, the number of potential international targets in a country—e.g., tourists, foreign companies, and embassies—increases with globalization. The degree of a country’s integration into the global economy (e.g., trade volume, absence of trade barriers, and FDI flows) and its integration into the international community (e.g., number of hosted embassies, memberships in international organizations, and number of signatories to treaties) are represented by the country’s five-year averaged economic globalization index (Econ. Globalization) and its five-year averaged political globalization index (Polit. Globalization), respectively. The data are taken from Dreher (2006), updated in Dreher et al. (2008). Finally, past studies have shown that foreign aid is negatively associated with transnational terrorism (e.g., Azam and Thelen 2008, 2010; Young and Findley 2011). We compute each country’s averaged foreign aid per capita during a given period using information in the World Development Indicators (World Bank 2018) on population, net official development assistance, and official aid received (in constant 2013 US dollars).

A negative binomial model is applied to estimate our panel regressions. The conditional mean of the number of terrorist events, \( T_{it} \), in country \( i \) at time period \( t \) is
$$ E\left( {\left. {T_{it} } \right|PP_{it} ,{\mathbf{X}}_{{{\mathbf{it}}}} ,\mu_{i} ,\eta_{t} } \right) = \exp \left( {\alpha PP_{it} + \beta {\mathbf{X}}_{{{\mathbf{it}}}} + \mu_{i} + \eta_{t} } \right), $$
(1)
where \( PP_{it} \) is a measure of political proximity, \( {\mathbf{X}}_{{{\mathbf{it}}}} \) is a vector of other explanatory variables, \( \mu_{i} \) denotes country fixed effects to control for country-specific unobserved heterogeneities, and \( \eta_{t} \) represents time effects to account for time-specific common shocks.

6 Results and robustness

Table 1 presents the baseline negative binomial regressions that include a measure of political proximity, country dummies, and time dummies. Two alternative measures of political proximity—Ideal Points Distance (Columns 2, 4) and Affinity (Columns 3, 5)—and two measures of transnational terrorism—ITERATE (Columns 2, 3) and GTD Transnational (Columns 4, 5)—are entered. Both measures of political proximity are statistically significant across all models. The estimates of Ideal Points Distance and Affinity are negative and positive, respectively; their opposite coefficient signs are expected because closer political proximity corresponds to lower values of Ideal Points Distance and higher values of Affinity. Based on incidence-rate ratios, a one-standard-deviation increase in Ideal Points Distance (0.63) is associated with about an 18% (a 23%) reduction in the rate of ITERATE (GTD) transnational terrorist events. Thus, a country that has foreign policy preferences similar to the four major Western powers is likely to have more transnational terrorism, which supports Hypothesis 1.
Table 1

Baseline regressions for political affinity and transnational terrorism

Variables

Iterate

Iterate

GTD trans

GTD trans

Ideal points distance

− 0.345*** (0.113)

 

− 0.446*** (0.160)

 

Affinity

 

1.493*** (0.395)

 

1.315** (0.536)

Country fixed effects

Yes

Yes

Yes

Yes

Time fixed effects

Yes

Yes

Yes

Yes

N

158

158

158

158

Observations

1413

1413

1293

1293

Political affinity measures are with the United States and its three major Western allies. Significance levels: *** is < .01, ** is < .05, and * is < .10. Cluster-robust standard errors (clustered on country) are in parentheses

We now estimate our full regression models, which expand the baseline models to include key and standard determinants of terrorism—see Table 2. Our main conclusion of positive correlation between transnational terrorism and political proximity holds. The Affinity variable is based on yea and nay votes, but our finding holds if we use S scores that incorporate abstentions (available upon request). The effects of other statistically significant variables concur with the literature. The estimates of Polity2 and its squared term are positive and negative, respectively, indicating an inverted U-shaped relationship between regime type and terrorism (Gaibulloev et al. 2017). The impact of regime durability is negative, whereas the effects of civil war and population are positive (see our earlier discussion, which anticipated such findings).
Table 2

Main regressions for political affinity and transnational terrorism

Variables

Iterate

Iterate

GTD trans

GTD trans

Ideal points distance

− 0.840*** (0.277)

 

− 0.973*** (0.300)

 

Affinity

 

2.696*** (0.995)

 

2.024* (1.183)

Polity2

3.234** (1.571)

3.351** (1.551)

4.951*** (1.859)

5.469*** (1.874)

Polity2 squared

− 3.346** (1.339)

− 3.499*** (1.325)

− 4.753*** (1.499)

− 5.230*** (1.502)

Durable

− 0.027** (0.011)

− 0.027** (0.011)

− 0.033*** (0.013)

− 0.031** (0.013)

Civil war

0.258** (0.105)

0.262*** (0.101)

0.364*** (0.107)

0.358*** (0.101)

Discriminated POP

− 0.104 (1.101)

− 0.252 (1.128)

0.297 (1.184)

0.215 (1.303)

log(GDP/POP)

0.237 (0.375)

0.243 (0.361)

0.732 (0.454)

0.700 (0.449)

log(POP)

2.815*** (1.076)

2.999*** (1.070)

2.516** (1.257)

2.770** (1.221)

Econ. globalization

0.001 (0.017)

0.004 (0.016)

− 0.023 (0.017)

− 0.017 (0.017)

Polit. globalization

0.006 (0.011)

0.006 (0.011)

0.026** (0.013)

0.026** (0.013)

Foreign aid/POP

0.001 (0.001)

0.002 (0.001)

0.002 (0.002)

0.002 (0.002)

Constant

− 42.829** (17.064)

− 48.067*** (16.990)

− 43.345** (20.744)

− 49.496** (20.017)

Country fixed effects

Yes

Yes

Yes

Yes

Time fixed effects

Yes

Yes

Yes

Yes

N

116

116

116

116

Observations

811

811

811

811

Political affinity measures are with the United States and its three major Western allies. Significance levels: *** is < .01, ** is < .05, and * is < .10. Cluster-robust standard errors (clustered on country) are in parentheses

We perform a number of robustness analyses using a political proximity measure based on the absolute distance between countries’ ideal points. The results generally are similar to those reported above if we use S scores (Affinity); the Ideal Points Distance measure is less noisy and more robust, consistent with Bailey et al. (2017). First, we replace ITERATE and GTD transnational incidents with terrorist incidents with casualties. Attacks with casualties are more likely to be reported consistently by the media across all countries. This alternative terrorism variable ameliorates undercounting problems, particularly in countries where the international media is less represented. Second, European and North American countries have strong political affinity with the major Western powers. Many European countries experienced transnational terrorist attacks. Thus, we examine whether our results hold if European and North American countries are removed from the sample.

Third, we include a number of additional control variables in our regression models. Interventionist foreign policies or being allied with the United States may make a country a target of transnational terrorist attacks (Savun and Phillips 2009), because those alignments may either challenge the interests of terrorist groups or create grievances that translate into terrorist attacks. Three international affairs variables are constructed: We compute the five-year averaged count of a country’s foreign interventions (Intervention) using the information from the International Military Interventions dataset (Pearson and Bauman 1993), which has been updated through 2005 by Kisangani and Pickering (2008). We also create a dummy variable that equals one if a country has experienced an international crisis during a given five-year time period (International Crisis), and another dummy variable that equals one if a country was allied with the United States during a given five-year time period (U.S. Alliance). The data on international crises come from the International Crisis Behavior (ICB) dataset (Wilkenfeld et al. 2010), which records countries facing one or more international crises over a three-year period. The data on alliances are taken from Gibler (2009); these data are extended through 2012. Finally, we replace our economic globalization measure with five-year averaged trade as a percentage of GDP (Trade), a common indicator of trade openness, and we enter the logarithm of the five-year averaged military expenditure per capita (constant 2010 US dollars) [log(Mil.Exp/POP)], a standard control for a country’s military capacity. Those variables are constructed using information on trade, military expenditures (as percentages of GDP), and GDP per capita (in constant 2010 US dollars) from the World Development Indicators (World Bank 2018). The 2010–2014 period is dropped from this robustness analysis owing to missing observations on international interventions and crises.

Fourth, we lag the political proximity measure by one year to ameliorate potential simultaneity concerns. Fifth, to ascertain that the correlation between political proximity and terrorism is not driven solely by the affinity with the United States, we separately enter the ideal-points difference with the United States and the ideal-points difference with Europe (France, Germany, and the United Kingdom). We cannot enter both measures in the same regression because of a very high correlation (0.88) between them.

Tables 3 and 4 report the findings for the political proximity variables; the complete results are given in the Online Appendix. Our main result—a negative correlation between dissimilarity in states’ preferences and transnational terrorism—holds across all of the robustness regressions, thus supporting Hypothesis 1. Hence, political proximity is associated with greater transnational terrorism and the relationship holds using either affinity with the United States or affinity with the three European countries. Moreover, to account for a significant shift in the counterterrorism policies of the United States and its allies following 9/11, we interact Ideal Points Distance with a dummy variable for the post-2000 period. The results suggest that the impact of political affinity on terrorism is smaller after 2000. However, the effect is not statistically significant for GTD transnational terrorist events (available upon request).
Table 3

Robustness analyses for political affinity and transnational terrorism

Variables

Attacks with casualties

Drop North America and Europe

Add more control variables

Iterate

GTD trans

Iterate

GTD trans

Iterate

GTD trans

Ideal points distance

− 0.774** (0.305)

− 1.037*** (0.287)

− 0.845*** (0.293)

− 0.926*** (0.308)

− 0.756*** (0.249)

− 0.890*** (0.296)

N

116

116

97

97

116

116

Observations

811

811

743

743

661

661

Political affinity measures are with the United States and its three major Western allies. Significance levels: *** is < .01, ** is < .05, and * is < .10. Cluster-robust standard errors (clustered on country) are in parentheses. See Table 2 for all variables included in columns 2–5. Columns 6 and 7 replace the economic globalization variable with trade and enter additional variables (foreign interventions, international crises, alliance with U.S., and military expenditure). The full results are reported in the Online Appendix

Table 4

Further robustness analyses for political affinity and transnational terrorism

Variables

Iterate

GTD trans

Iterate

GTD trans

Iterate

GTD trans

Ideal points distance

(lag)

− 0.456**

(0.191)

− 0.539***

(0.191)

    

Ideal points distance

  

− 0.852***

− 0.945***

  

EUR

  

(0.278)

(0.308)

  

Ideal points distance

    

− 0.752***

− 0.961***

US

    

(0.276)

(0.269)

N

116

116

116

116

116

116

Observations

793

793

811

811

811

811

Significance levels: *** is < .01, ** is < .05, and * is < .10. Cluster-robust standard errors (clustered on country) are in parentheses. See Table 2 for all variables included in the models. The full results are reported in the Online Appendix

Next, we examine the statistical association between domestic terrorism and political proximity. Table 5 presents the regression outputs for two measures of domestic terrorism: the number of domestic terrorist incidents (upper panel) and the number of domestic terrorist events with casualties (lower panel). The model specifications are similar to those of Tables 2, 3 and 4 with the exception of economic globalization being replaced with the trade variable in all models owing to convergence issues. In particular, for each dependent variable, Model 1 uses the main specification (see Table 2), Model 2 removes European and North American countries, Model 3 enters additional explanatory variables, which are foreign interventions, international crises, alliance with the United States, and military expenditures (see Table 4), Model 4 replaces the ideal-points measure with its one-year lagged value, and Model 5 uses the Affinity variable as a measure of political alignment. Ideal Points Distance is used in all other models. Furthermore, similar to a transnational-terrorism specification in Table 4, we enter separately the ideal-points difference with the United States and the ideal-points difference with Europe in Models 6 and 7. The results for the political affinity variables are presented in Table 5, with the full results given in the Online Appendix. Consistent with Hypothesis 3, we find a robust positive relationship between political proximity and domestic terrorism, which is represented by the negative coefficient on Ideal Points Distance and the positive coefficient on Affinity. This result is statistically significant across all specifications reported in Table 5.
Table 5

Political affinity and domestic terrorism

Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Number of domestic terrorist events

 Ideal points distance

− 1.216***

− 1.126***

− 0.926***

    
 

(0.360)

(0.366)

(0.339)

    

 Ideal points distance

   

− 0.806***

   

 (lag)

   

(0.247)

   

 Affinity

    

2.366*

  
     

(1.343)

  

 Ideal points distance

     

− 1.217***

 

 EUR

     

(0.369)

 

 Ideal points distance

      

− 1.121***

 US

      

(0.343)

Number of domestic terrorist events with casualties

 Ideal points distance

− 1.244***

− 1.132***

− 0.938***

    
 

(0.357)

(0.360)

(0.350)

    

 Ideal points distance

   

− 0.832***

   

(lag)

   

(0.249)

   

 Affinity

    

2.482*

  
     

(1.391)

  

 Ideal points distance

     

− 1.241***

 

 EUR

     

(0.366)

 

 Ideal points distance

      

− 1.153***

 US

      

(0.344)

N

118

99

116

118

118

118

118

Observations

818

750

661

798

818

818

818

Significance levels: *** is < .01, ** is < .05, and * is < .10. Cluster-robust standard errors (clustered on country) are in parentheses. See Table 2 for all variables included in the models. Economic globalization is replaced with trade due to convergence issue. Model 2 excludes North American and European countries. Model 3 enters additional variables (foreign interventions, international crises, alliance with U.S., and military expenditure). The full results are reported in the Online Appendix

Finally, we examine the effect of political proximity with the United States, measured by the ideal-points difference from the United States, on incidents involving non-US victims (Models 1, 2) and incidents involving US victims (Models 3, 4)—see Table 6. The two dependent variables are constructed by disaggregating ITERATE attacks and computing five-year counts of attacks against US victims and those against all other countries’ victims. Using ITERATE, we cannot fully identify attacks against German, French, and British victims, in contrast to attacks against US victims. The results in Table 6 show that a country with closer political proximity to the United States experiences more attacks against US and non-US targets. Those findings hold after removing North American and European countries from the sample (Models 3 and 4). The positive relationship between the political affinity with the United States and terrorist attacks against the US citizens abroad is consistent with Hypothesis 2. Political ties to the United States likely increase the number of potential US victims in a given country (e.g., businessmen, aid workers, and tourists), which, combined with a weaker security protection than in the United States, may attract international terrorists who aim to harm US citizens. Again, the findings for political proximity are shown in Table 6 and the complete results are reported in the Online Appendix.7
Table 6

Political affinity with the US and Iterate attacks against US versus non-US targets

Variables

Model 1

Model 2

Model 3

Model 4

Non-US victim

Non-US victim

US victim

US victim

Ideal points distance

− 0.584*

− 0.612*

− 0.643**

− 0.578**

US

(0.302)

(0.321)

(0.264)

(0.279)

N

116

97

116

97

Observations

811

743

811

743

Significance levels: *** is < .01, ** is < .05, and * is < .10. Cluster-robust standard errors (clustered on country) are in parentheses. See Table 2 for all variables included in the models. Models 2 and 4 exclude North American and European countries. The full results are reported in the Online Appendix

7 Concluding remarks

For two alternative measures of political proximity or affinity with the United States and its three primary Western allies (Britain, France, and Germany), countries are shown to be in greater peril from transnational and domestic terrorist attacks when their foreign policy positions revealed in UN General Assembly votes are more aligned with those four countries. The ideal-point measure of proximity outperforms the standard “S” affinity measure in the reported regressions. The inclusion of US primary allies in the political proximity variable is a novel feature of our analysis. The careful examination of political proximity and domestic terrorism also is a unique feature of this study. Our findings are robust to attacks with casualties, dropping North America and Europe, lagging the ideal-point measure, and adding a host of further controls. Important determinants of terrorism—population, regime durability, regime type, and civil wars—are robust controls with significant coefficients in the anticipated direction. When the proximity measure is disaggregated, countries with closer political proximity to the United States or to its three key allies attract more terrorism, so that our results are not just driven by political proximity to the United States.

A key takeaway is that being aligned with the policy positions of the United States, its principal allies, or both invites a potential cost in terms of transnational and domestic terrorist attacks. The domestic terrorism risk is novel to this study, but stems from citizens resorting to violence at home to demonstrate their displeasure with their country’s foreign policy alignments with key Western policy preferences. Such discontentment may be enhanced if the United States or its prime European allies provide foreign assistance, because citizens view their government’s Western-oriented policy positions as a betrayal of their own values. That perception may be exacerbated if the aid-recipient country then is more supportive of the aid-giving countries’ UN positions as a way of staying on good terms in order to keep the aid coming (see, e.g., Bapat 2011). As a consequence, countries must prepare for such attacks as their leaders’ foreign policy stances become more Western-oriented. Thus, counterterrorism efforts may need to be strengthened when voting patterns in the United Nations become more closely aligned with Western powers. The finding that political proximity to the United States also increases terrorist attacks on US victims abroad councils that the US government take steps to alert its citizens living in or visiting countries that are more closely aligned with US foreign policy positions to the greater risk. That is another important takeaway. Potential-target countries likewise must take measures to protect their US visitors and their own citizens from affinity-related terrorist attacks. Terrorism risks can work through circuitous routes!

Footnotes

  1. 1.

    Gaibulloev et al. (2017) do not sustain the alleged alliance link on the basis of updated data and alternative empirical methods.

  2. 2.

    In their analysis of the influence of government decentralization on domestic terrorism, Dreher and Fischer (2011) include voting with the United States in the UN General Assembly as a control. Political affinity is, however, a significant influence on domestic terrorism in only two of their six models. Affinity is marginally significant at the 0.10 level in one of those two models.

  3. 3.

    We use Enders et al.’s (2011) methods to separate domestic and transnational terrorist incidents in the GTD.

  4. 4.

    State-sponsored terrorism involves a government surreptitiously assisting a terrorist group’s operations (Mickolus 1989).

  5. 5.

    ITERATE possesses more detail on attacks against US victims than against French, German, and British victims, so some undercounting of attacks is likely for the latter three countries.

  6. 6.

    Recent statements by the Trump administration on this regard are not so subtle.

  7. 7.

    We also control for bilateral trade with the United States using trade data from Glick and Rose (2016). Political affinity remains statistically significant, but the trade variable is not significant (results are available upon request).

Notes

Acknowledgements

The authors have profited from comments from William F. Shughart II and an anonymous referee. Responsibility for the article’s content is solely that of the authors.

Funding

Sandler’s research was funded by the Vibhooti Shukla Endowment at the University of Texas at Dallas. No actual or perceived conflict of interest is connected with that financial support.

Supplementary material

11127_2018_611_MOESM1_ESM.docx (49 kb)
See materials associated with this article at https://www.utdallas.edu/~tms063000/website/downloads.html (DOCX 48 kb)

References

  1. Abadie, A. (2006). Poverty, political freedom, and the roots of terrorism. American Economic Review, 96(2), 50–56.CrossRefGoogle Scholar
  2. Azam, J.-P., & Thelen, V. (2008). The roles of foreign aid and education in the War on Terror. Public Choice, 135(3–4), 375–397.CrossRefGoogle Scholar
  3. Azam, J.-P., & Thelen, V. (2010). Foreign aid versus military intervention in the War on Terror. The Journal of Conflict Resolution, 54(2), 237–261.CrossRefGoogle Scholar
  4. Bailey, M. A., Strezhov, A., & Voeten, E. (2017). Estimating dynamic state preferences from United Nations voting data. The Journal of Conflict Resolution, 61(2), 430–456.CrossRefGoogle Scholar
  5. Bapat, N. A. (2011). Transnational terrorism, US military aid, and the incentive to misrepresent. Journal of Peace Research, 48(3), 303–318.CrossRefGoogle Scholar
  6. Blomberg, S. B., Gaibulloev, K., & Sandler, T. (2011). Terrorist group survival: Ideology, tactics, and base of operations. Public Choice, 149(3–4), 441–463.CrossRefGoogle Scholar
  7. Campos, N. S., & Gassebner, M. (2013). International terrorism, domestic political instability, and the escalation effect. Economics and Politics, 25(1), 27–47.CrossRefGoogle Scholar
  8. Dreher, A. (2006). Does globalization affect growth? Empirical evidence from a new index. Applied Economics, 38(10), 1091–1110.CrossRefGoogle Scholar
  9. Dreher, A., & Fischer, J. A. V. (2010). Government decentralization as a disincentive for transnational terror? An empirical analysis. International Economic Review, 51(4), 981–1002.CrossRefGoogle Scholar
  10. Dreher, A., & Fischer, J. A. V. (2011). Does government decentralization reduce domestic terror? An empirical test. Economics Letters, 111(3), 223–225.CrossRefGoogle Scholar
  11. Dreher, A., & Gassebner, M. (2008). Does political proximity to the U.S. cause terror? Economics Letters, 99(1), 27–29.CrossRefGoogle Scholar
  12. Dreher, A., Gaston, N., & Martens, P. (2008). Measuring globalisation: Gauging its consequences. New York: Springer.CrossRefGoogle Scholar
  13. Dreher, A., & Jensen, N. (2007). Independent actor or agent? An empirical analysis of the impact of U.S. interests on International Monetary Fund conditions. The Journal of Law and Economics, 50(1), 105–124.CrossRefGoogle Scholar
  14. Dreher, A., & Jensen, N. (2013). Country or Leader? Political change and UN General Assembly voting. European Journal of Political Economy, 29, 183–196.CrossRefGoogle Scholar
  15. Enders, W., Hoover, G. A., & Sandler, T. (2016). The changing nonlinear relationship between income and terrorism. The Journal of Conflict Resolution, 60(2), 195–225.CrossRefGoogle Scholar
  16. Enders, W., & Sandler, T. (2012). The political economy of terrorism (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
  17. Enders, W., Sandler, T., & Gaibulloev, K. (2011). Domestic versus transnational terrorism: Data, decomposition and dynamics. Journal of Peace Research, 48(3), 319–337.CrossRefGoogle Scholar
  18. Eyerman, J. (1998). Terrorism and democratic states: Soft targets or accessible systems. International Interactions, 24(2), 151–170.CrossRefGoogle Scholar
  19. Gaibulloev, K. (2015). Terrorist group location decision: An empirical investigation. Oxford Economic Papers, 67(1), 21–41.CrossRefGoogle Scholar
  20. Gaibulloev, K., Piazza, J. A., & Sandler, T. (2017). Regime types and terrorism. International Organization, 71(3), 491–522.CrossRefGoogle Scholar
  21. Gaibulloev, K., & Sandler, T. (2019). What we have learned about terrorism since 9/11. Journal of Economic Literature, 57(2) (forthcoming).Google Scholar
  22. Gartzke, E. (1998). Kant we all just get along? Opportunity, willingness, and the origins of the democratic peace. American Journal of Political Science, 42(1), 1–27.CrossRefGoogle Scholar
  23. Gassebner, M., & Luechinger, S. (2011). Lock, stock, and barrel: A comprehensive assessment of the determinants of terror. Public Choice, 149(3–4), 235–261.CrossRefGoogle Scholar
  24. Gibler, D. M. (2009). International military alliances, 1648–2008. Washington, DC: CQ Press.CrossRefGoogle Scholar
  25. Glick, R., & Rose, A. K. (2016). Currency unions and trade: A post-EMU reassessment. European Economic Review, 87, 78–91.CrossRefGoogle Scholar
  26. Hoffman, B. (2017). Inside terrorism (3rd ed.). New York: Columbia University Press.CrossRefGoogle Scholar
  27. Kisangani, E. N. F., & Pickering, J. (2008). International military interventions, 1989–2005. Inter-University Consortium for Political and Social Research (ICPSR). http://www.k-state.edu/polsci/intervention/. Accessed July 21, 2014.
  28. Li, Q. (2005). Does democracy produce or reduce transnational terrorist incidents? The Journal of Conflict Resolution, 49(2), 278–297.CrossRefGoogle Scholar
  29. Li, Q., & Schaub, D. (2004). Economic globalization and transnational terrorism: A pooled time-series analysis. The Journal of Conflict Resolution, 48(2), 230–258.CrossRefGoogle Scholar
  30. Marshall, M. G. (2017). Major episodes of political violence (MEPV) and conflict regions, 19462016. Center for Systemic Peace. http://www.systemicpeace.org/inscrdata.html. Accessed February 12, 2017.
  31. Marshall, M. G., Gurr, T. R., & Jaggers, K. (2016). Polity IV dataset. Center for Systemic Peace. http://www.systemicpeace.org/inscrdata.html. Accessed February 12, 2016.
  32. Mickolus, E. F. (1989). What constitutes state support to terrorism? Terrorism and Political Violence, 1(3), 287–293.CrossRefGoogle Scholar
  33. Mickolus, E. F., Sandler, T., Murdock, J. M., & Flemming, P. (2016). International terrorism: Attributes of terrorist events, 1968–2015 (ITERATE). Ponte Vedra, FL: Vinyard Software.Google Scholar
  34. National Consortium for the Study of Terrorism and Responses to Terrorism (START). (2016). Global terrorism database (GTD). www.start.umd.edu/gtd/. Accessed June 14, 2016.
  35. Pearson, F. S., & Bauman, R. A. (1993). International military interventions, 1946–2005. International Consortium for Political and Social Research. http://www.k-state.edu/polsci/intervention. Accessed March 30, 2018.
  36. Piazza, J. A. (this issue). Democratic skepticism and support for terrorism in the Palestinian Territories. Public Choice.  https://doi.org/10.1007/s11127-018-0596-3.
  37. Savun, B., & Phillips, B. J. (2009). Democracy, foreign policy, and terrorism. The Journal of Conflict Resolution, 53(6), 878–904.CrossRefGoogle Scholar
  38. Signorino, C. S., & Ritter, J. M. (1999). Tau-b or not tau-b: Measuring the similarity of foreign policy positions. International Studies Quarterly, 43(1), 115–144.CrossRefGoogle Scholar
  39. Voeten, E. (2004). Resisting the lonely superpower: Responses of states in the United Nations to U.S. dominance. The Journal of Politics, 66(3), 729–754.CrossRefGoogle Scholar
  40. Voeten, E. (2013). Data and analyses of voting in the United Nations General Assembly. In B. Reinalda (Ed.), Routledge handbook of international organization (pp. 54–66). London: Routledge.Google Scholar
  41. Wilkenfeld, J., Brecher, M., Hewitt, J., Beardsley, K., & Eralp, P. (2010). International crisis behavior datasets, version 10 (1918-2007), CIDCM, University of Maryland. http://www.cidcm.umd.edu/icb/data/. Accessed July 21, 2014.
  42. Wimmer, A., Cederman, L.-E., & Min, B. (2009). Ethnic politics and armed conflict: A configurational analysis of a new global dataset. American Sociological Review, 74(2), 316–337.CrossRefGoogle Scholar
  43. World Bank. (2018). World development indicators (WDI). http://data.worldbank.org/data-catalog/world-development-indicators. Accessed February 18, 2018.
  44. Young, J. K., & Findley, M. G. (2011). Can peace be purchased? A sectoral-level analysis of aid’s influence on transnational terrorism. Public Choice, 149(3–4), 365–381.CrossRefGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of EconomicsAmerican University of SharjahSharjahUAE
  2. 2.Department of Economics, School of Economics, Political and Policy SciencesUniversity of Texas at DallasRichardsonUSA

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