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The effects of the electoral calendar on terrorist attacks
Accepted Manuscript The Effects of the Electoral Calendar on Terrorist Attacks Valentina A. Bali, Johann Park
PII:
S0261-3794(14)00033-X
DOI:
10.1016/j.electstud.2014.03.002
Reference:
JELS 1451
To appear in:
Electoral Studies
Received Date: 28 January 2013 Revised Date:
4 March 2014
Accepted Date: 4 March 2014
Please cite this article as: Bali, V.A., Park, J., The Effects of the Electoral Calendar on Terrorist Attacks, Electoral Studies (2014), doi: 10.1016/j.electstud.2014.03.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Highlights
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We study the linkage between terrorist events in democracies and elections. We examine transnational and domestic terrorist trends over a 40 year span. Transnational terrorist events decline before elections. Domestic terrorist events increase before elections.
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The Effects of the Electoral Calendar on Terrorist Attacks
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JELS-D-13-00023: (R2): FINAL SUBMISSION
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Valentina A. Bali a*, Johann Park b
a
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(Corresponding Author) Department of Political Science, Michigan State University, 324 South Kedzie Hall, East Lansing, 48824, Michigan, Tel: 517-432-4491, email: [email protected]. b
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Political Science and Public Administration, Mississippi State University, 105 Bowen Hall, P.O. Box PC, Mississippi State, MS, 39762, Tel: 662-325-2711, email: [email protected].
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The Effects of the Electoral Calendar on Terrorist Attacks
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Abstract
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The goal of this paper is to study the linkages between the timing of terrorist events and elections. As strategic actors terrorists may respond to electoral environments by altering the frequency of their attacks around election times. Focusing on democracies, we examine variations in transnational and domestic terrorist incidents before elections over a 40 year span. We find distinct pre-electoral changes in the incidence of terrorist events. In the ITERATE dataset, where only transnational terrorist events are included, terrorist activities decline in election months, while in the partitioned GTD dataset, where only domestic terrorist events are kept, terrorist activities rise in election months. The findings suggest electoral calendars can dissuade and attract terrorist threats, depending on the origin of the threat, but these effects occur only very close to election time.
1. Introduction
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Keywords: Terrorism, Elections, Domestic Terrorism, Transnational Terrorism.
Since a seminal study by Sandler et al. (1983), scholars have long contended that many facets
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of terrorists’ actions are not capricious but in fact can respond to specific strategic goals and political circumstances (Berman and Laitin, 2008; Berrebi and Klor, 2006; Berrebi and
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Lakdwalla, 2007; Bueno de Mesquita, 2005; Kydd and Walter, 2002; Lapan and Sandler, 1988; Pape, 2003). Who is recruited, and then who is targeted, how they are targeted, and when they are targeted, among others, are dimensions that scholars of terrorism have found to be nonarbitrary in particular when examining long-standing conflicts (Barros et al., 2006; Benmelech and Berrebi, 2007; Clauset et al., 2010; Sanchez-Cuenca, 2001). From this conception of terrorists as relatively “rational” or strategic actors, it is reasonable to hypothesize that terrorists might also take into consideration the electoral calendar as a relevant dimension in their
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decisions. After all, the eventual consequences can be substantial. For example, the Madrid train bombings of 2004, three days before the Spanish general elections and with scores of casualties and injured, provide a striking example of what the timing of a terrorist event can produce. While
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pre-electoral polls suggested a winning margin for the incumbent Popular Party, the terrorist event, later established to have been conducted by Islamic militants, seemed to have helped derail in a matter of days the 4% advantage of the government party (Bali, 2007; Colomer, 2005;
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Montalvo, 2012; Torcal and Rico, 2004). While other transnational and domestic terrorist events have been clearly smaller in scale than the Madrid bombings, and in fact some may have
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responded to an opposite logic, one of de-escalation right before an election, the research puzzle still remains: in general, have terrorists exploited the electoral calendar in deciding the timing of their events?
The goal of this paper is to examine the linkages between the timing of terrorist events,
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transnational and domestic, and elections. If, as some scholars have argued, one of terrorists’ goals is to influence policymaking (Pape, 2005) then electoral times could prove to be particularly fecund times to do so. Remarkably, this line of inquiry has not been systematically
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explored in both broad cross-national settings and across extended periods of time although both the elections and terrorism literatures, as we detail in the next section, imply this line of inquiry
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merits attention. For example, pre-electoral periods may incite more terrorist activities because the electorates are more attentive to politics at those times, and interest groups are more strenuously vying for influence. On the other hand, pre-electoral times may inhibit terrorist activities because terrorist groups may have more opportunities to non-violently channel their dissent, or they may fear more aggressive retaliation from the government. In general, election times may enhance certain integral features of democracies and consequently further influence
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the prospects for terrorist events. There may be various ways in which electoral calendars influence, up or down, terrorist activity. The main goal of this study is to estimate the net impact of these electoral influences.
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This is important in order to build upon our previous knowledge on elections and terrorism, by providing a much needed empirical benchmark, but also in relation to security considerations and potential electoral consequences. In this study, we analyze country-month level data from the
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broad cross-national terrorism datasets ITERATE (1968-2008) and domestic GTD (1970-2008) to elucidate whether electoral months display differential levels of terrorist events. The analyses
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reveal that election months can both significantly deter and attract terrorist threats, depending on the origin of the threat, transnational or domestic; these effects occur close to election day.
2. Background Considerations and Expectations
The concept and definitions of terrorism are multiple and some of them are disputed
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(Gibbs, 1989; Hoffman, 2006; Norris et. al., 2003; Schmid and Jongman, 1988; Wilkinson, 2001; Young and Findley, 2011), yet, by now many definitions share several elements in common. In one such definition terrorism is understood to be “the unlawful use of violence or threat of
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violence to instill fear and coerce governments or societies” (U.S. Department of Defense, 2012). One frequent ingredient found in many of the definitions of terrorism refer to these acts of
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violence or threats as being “calculated,” “premeditated,” “systematic,” “purposive,” or “deliberate” (Hoffman, 2006; Norris et al., 2003; Wilkinson, 2001). Although many terrorist events seem after the fact indiscriminate and irrational, they often respond to a clear logic of purposeful intimidation. This intimidation may be aimed at internal or external audiences. More specifically, terrorist incidents can be classified as domestic or transnational depending on the nationality of the key actors involved. Domestic terrorist incidents occur when the main
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perpetrators, victims, and target audience are all from the same country; otherwise, if any of these three actors differ in nationality the event is deemed transnational (Mickolous et al., 2009; Li and Schaub, 2004).
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This study will explore whether the schedule of terrorist acts, domestic and transnational, is deliberately linked with the calendar of electoral politics. There are several strands of research on terrorism and elections that are relevant for developing expectations. Broadly, this research
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stems from statistical work on terrorist trends, conflict-specific work on governments and terrorist groups, institutional research on regime type and terrorist activity, research on elections
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and citizen engagement, and finally research on rally-effects and the diversionary use of force. At this point, there is little previous research that directly examines the relation between terrorist timing and elections across both a broad array of democracies and extended periods of time. There are two notable exceptions. In a preliminary study, Aksoy (2010) finds in the European
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context that proximity to elections increases domestic terrorist attacks among countries with more electoral disproportionality. In a descriptive study, Newman (2013) finds terrorist violence generally (as measured in GTD) increases closer to an election date among a sample of 117
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countries between the years 2000 and 2005. Both studies then contribute to the line of research addressed in this study, but the first study is geographically bounded while the second study
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focuses on a very short period of time, without simultaneously controlling for background country characteristics.
2.1 Trends in Terrorist Event Series To begin with, the research on trends of terrorist events has suggested the presence of cycles when examining aggregate series. More specifically, Enders and Sandler (1993, 1999, 2000, 2002, 2005) have examined in depth long term trends in transnational terrorist events using
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time series techniques with the popular ITERATE dataset. Their spectral analyses reveal that terrorist events with casualties display cycles between 4 and 5 years long (Enders and Sandler, 2000). That is, this is the period (or primary frequency) that most explains the variance of the
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series after de-trending. Secondary cycles are close to 2 years. As Enders and Sandler (2000) conjecture the cycles might be explained through various mechanisms of contagion, world political events, and swings in public opinion, including those related to electoral calendars.
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Thus, global trend analyses of transnational terrorism have identified some periodicities and terrorists’ strategic attention to election times may be contributing to them.
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2.2 Conflict-specific research
Plenty of conflict-specific research and game-theoretic work has addressed the calculated interplay between governments and terrorist groups (for early work see, Lapan and Sandler, 1988; Sandler et al., 1983), often finding that the activities of terrorist groups are highly
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responsive to the existing political context (Bueno de Mesquita, 2005, 2007; Clauset et al., 2010; Enders and Sandler, 1993, 2005; Gassebner et al., 2008; Gould and Klor, 2010; Hoffman, 2006; Kydd and Walter, 2002; Pape, 2003). Two cases of long-standing struggles with terrorism, those
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of Israel and Spain, have motivated much of this work. In the case of the Israeli-Palestinian conflict, Berrebi and Klor (2006, 2008) find that
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terrorist activity levels vary depending on the party in power (e.g., more events when the leftwing party is in power) and in turn they influence the electorate’s support for a given party. Clauset et al. (2010) find that Palestinian groups’ strategies, including violent attacks, are very sensitive to existing political circumstances (e.g., public support, inter-group competition, government countermeasures, and approaching elections) and are therefore dynamic. Finally, Bloom (2004, 2005) proposes that Palestinian suicide bombing attacks can be motivated, in part,
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by competition among factions to influence and mobilize public opinion through ramped-up violence. This “outbidding hypothesis” has been tested in the Palestinian-Israeli conflict with mixed or conditional support (Brym and Araj, 2008; Clauset et al., 2010; Jaeger et al., 2013). For
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example, Jaeger et al. (2013) find that the gains to each group from successful terrorist violence accrue within secular and Islamic factions, respectively, but not across them.
In the case of Spain’s conflict with the Basque separatist organization Euskadi Ta
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Askatasuna (ETA), this strand of research has also revealed evolving and context-dependent terrorist strategies (Barros et al., 2006; Mees, 2003; Sanchez-Cuenca, 2001). For example,
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Barros et al. (2006) find that ETA attacks increase in the summer and decrease with deterrence, repressive governments, and political accords. Motivated by both the Israeli and Spanish cases, Hodler and Rohner (2010) predict, in the sole game theoretic study on the timing of terrorist attacks and elections, that the risk of terrorist attacks can be higher at the beginning of electoral
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terms rather than later. In their analyses terrorists are more likely to strike early in an incumbent’s term in order to gain early informational advantages but also to avoid possible governmental retaliations closer to election time: “initially weak government may sometimes
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react aggressively to terror attacks later in their terms to show toughness to the voters” (p.188, Hodler and Rohner, 2010). Overall conflict-motivated studies provide rationales and evidence
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compatible with an association between the timing of terrorist attacks and elections but few general predictions, with the sole exception of Hodler and Rohner (2010). 2.3 Regime type and levels of terrorism Turning next to institutions and terrorism studies, the research on the origins of terrorism has examined at length the role of regime type. The vast majority of the evidence suggests a positive linkage between democracies and higher levels of terrorism, in particular transnational
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terrorism (Chenoweth, 2010; Eubank and Weinberg, 1994, 2001; Li, 2005; Li and Schaub, 2004; Pape, 2003; Schmid, 1992) but not all (e.g., Eyerman, 1998; Piazza, 2008). Multiple causal mechanisms have been proposed to link terrorism to regime characteristics. Earlier on, as
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Eyerman (1998) posits, the arguments could be categorized as stemming from two competing and opposing schools of thought on the links between democracy and terrorism: (1) the “political access school” which argued that democracies decrease the attraction of violent action by
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providing alternative channels of engagement, and (2) the “strategic school” which argued that democracies increase the likelihood of terrorist activity because of their constrained and open
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institutions (Brooks, 2009; Crenshaw, 1981; Schmid, 1992). Li (2005) addresses these opposing schools by including separate measures for each and finds that both are operational: higher voter turnout reduces transnational terrorist events while more governmental constraints and press freedoms increase them. More recent work has found further positive associations between
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democracies and terrorism due to democracies’ higher number of policy veto players (Young and Dugan, 2011) and competing political groups (Chenoweth, 2010) which can result in more deadlock and conflict. In addition, from a foreign policy perspective, democracies may be more
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prone to transnational terrorism since they may be in general more active in international affairs, which in turn can spur increased resentment from abroad (Savun and Phillips, 2009).1
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Given the inherent characteristics of democratic regimes which aspects get further highlighted during election times, potentially influencing terrorist attacks? One possibility is that during election times the “political access” dimension, that is the opportunity for political engagement, gets intensified since it is less likely that governmental constraints, press freedoms, or established veto players change dramatically during the last months before an election. It is 1
Further work on the determinants of terrorism has extended the analyses beyond regime type to examine the influences of regime history, economic development (Abadie, 2006; Krueger and Laitin, 2008; Sanchez-Cuenca, 2009), and operating costs (Lai, 2007), factors that are more impervious to the electoral calendar.
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also conceivable that right before an election democracies may be less likely to increase their level of engagement in international affairs (see Section 2.5). However, consistent with some of the more recent findings (i.e., Chenoweth, 2010) election times may also heighten political
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competition among societal groups. From an institutional perspective then elections may dampen terrorist attacks by providing rebels and dissidents with a time with more non-violent channels of expression and fewer causes for resentment, but they may also incite more events due to political
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competition. 2.4 Elections and citizen engagement
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Terrorists’ timing may also be influenced by when citizens are politically attentive and engaged. Elections and electorate studies provide many insights with regards to citizens’ levels of engagement. By now, there is a vast empirical literature on the degree of competence and attentiveness of electorates (Bartels, 1996, 2008; Campbell et al., 1960; Lau and Redlawsk,
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2006; Lewis-Beck et al., 2008; Popkin, 1991; Page and Shapiro, 1992; Zaller, 1992). For example, within the American context the electorate is found to display large variations in knowledge and attention to politics, with the verdict on how rational voters are when making up
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their minds at voting time still very much in the air (Bartels, 2008; Lau and Redlawsk, 2006; Lewis-Beck et al., 2008). However, when elections are near voters’ levels of engagement
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increase in particular as they become more receptive to campaign efforts (Arcenaux, 2005; Banducci and Karp, 2003). In fact, Gelman and King (1993) find that much of the engagement and learning occurs about six weeks before the election while, in a study of five western countries, Blais (2004) finds that close to 1 voter out of 6 typically changes their vote intention during the month preceding the election. Campaigns and the media can play a substantial role in mobilizing the electorate (Banducci and Karp, 2003; Hillygus and Shields, 2008), but many of
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these effects may take place close to the election and are short-lived (Gerber et al., 2011; Hill et al., 2008; Johnston et al., 2004). Since high publicity is a focal interest of terrorists (Nacos, 2002), election times seem then a unique period to influence the electorate given their level of
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interest. From this perspective, election times may especially attract domestic attacks as terrorists would take the opportunity to make a political statement upon a more receptive domestic audience.
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2.5 Rally effects and retaliations
Unexpected events can also induce changes of mind. In particular, there is ample
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evidence in relation to foreign policy that dramatic external threats, as from a surprise attack, can induce substantial changes in incumbent approval or “rally-around-the-flag” effects (Baker and Oneal, 2001; Baum, 2002; Brody and Shapiro, 1989; Colaresi, 2007; Edwards and Swenson, 1997; Hetherington and Nelson, 2003; Mueller, 1973). These effects are generally not long-lived
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and eventually peter out (Brody, 1991; Mueller, 1973). Nevertheless, transnational terrorist attacks, by quickly boosting national cohesion in the target society, may alter the political environment. A sudden rally before an election can be sufficient to improve the electoral chances
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of an incumbent. In fact, recent research suggests higher levels of terrorism in an electoral year incite a direct political response from electorates: they turn up at higher rates (Robbins et al.,
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2013). In addition, a transnational terrorist event close to an election may incite the incumbent to take harsh retaliatory actions to prove his problem-solving abilities. Indeed, scholars have found that heightened external threats by transnational terrorists can make people in democracies more supportive for hawkish foreign policies against the terrorists and their constituencies (Davis and Silver, 2004; Gadarian, 2010; Merolla and Zechmeister, 2009; Viscusi and Zeckhauser, 2003). But any action taken by an incumbent government close to an election time will also face high
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levels of scrutiny. In general then there may be considerable strategic calculations at work for both transnational terrorists and democratic leaders when they respectively consider actions, of aggression or retaliation, around election times.
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From the perspective of terrorist groups, they may dampen transnational terrorist incidents close to election time in order to avoid forceful retaliations. There is a large and evolving research agenda that examines the diversionary use of force by governments against
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external threats when incumbent leaders are domestically vulnerable (Clark, 2003; Fordham, 1998; Leeds and Davis, 1997; Meernik, 1994, 2001; Mitchell and Prins, 2004; Oneal and Tir,
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2006; Ostrom and Job, 1986; Smith, 1996, 1998). One strand in this diversionary literature has argued that potential target states strategically dodge conflict with large democracies that are facing domestic troubles (Kisangani and Pickering, 2009; Leeds and Davis, 1997; Smith, 1996). Similarly, international terrorist groups may also avoid attacks against democracies that are
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domestically vulnerable and close to election time. This might suggest that the “outbidding hypothesis” (Bloom, 2004, 2005), by which dissidents groups carry out attacks in order to “outbid” each other for public support, may not hold when the targeted nation is near an election.
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At those times, terrorist events may lead to rally-effects and forceful retaliations, which in turn may stifle support among the aggrieved population the terrorists claim to represent.
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From the perspective of democratic leaders, as elections approach they may reduce their tendencies towards higher involvement in international affairs (Savun and Phillips, 2009), in order to avoid ill-timed and potentially critical scrutiny. Again, from the diversionary literature, there is little evidence that democracies are more likely to use military force before an election. For example, Leeds and Davis (1997), Kisangani and Pickering (2009), and Oneal and Tir (2006) find no linkages between the use of force and elections. Colaresi (2007) argues that a
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dramatic foreign policy action around election time may bring about suspicions to voters about its true intentions and finds in the American context that foreign policy initiatives launched just before elections tend to generate in many cases smaller and even negative rally effects. It seems
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that democratic leaders have strategic reasons to avoid pursuing provocative and aggressive polices abroad when there is a high chance of political and electoral backlashes (Williams, 2013). In fact, as Oneal and Tir, (2006) argue, Gaubatz (1991) and Huth and Allee (2002) find more use
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of military force by democracies not before but just after an election, when their leaders are safest politically.
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2.6 Expectations
In sum, there are several possible mechanisms linking the timing of terrorist attacks to elections. Some mechanisms imply higher levels of terrorism at election times mainly due to increased attention from the public and increased political competition among societal groups.
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These arguments are more suitable when explaining domestic terrorist events. Other mechanisms imply lower levels of terrorism close to elections mainly due to increased channels of engagement for dissidents and more incentives to avoid rally-effects and armed retaliation.
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Having more political access may deter in particular domestic terrorist events while increased chances of forceful retaliation may discourage above all transnational terrorist events.
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The central purpose of the present study is not to weigh and assess each of these influences, for which specific measures may be unobtainable at this time for a broad crossnational study, but to evaluate empirically their net impact. With this in mind, we present expectations about their overall impact. First, we hypothesize the net influence will be positive for domestic events:
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Hypothesis 1 (Domestic events): the level of domestic terrorist activity will increase on election months. The rationale for this expectation is that attention effects and “outbidding” competition
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effects in general may be larger than participatory and retaliatory effects. That is, in the days around elections domestic dissident groups may be more likely to seek citizens’ attention, which is already heightened, and, as a result of this, they may also be more likely to compete for public
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support. On the other hand, around election times domestic dissident groups may not be dissuaded by, or find credible, last-minute political access or threats of retaliation.
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In the case of transnational terrorist events, our initial expectations may be somewhat less clear than those for domestic events. Nevertheless, we still think a negative relationship between elections and transnational terrorism is more plausible. The motivation for this expectation is that the potential for rally-effects and armed retaliation may swamp heightened attention and
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competition effects, and in general act as a strong deterrent for transnational actions close to an election. That is, election times may reduce the incidence of transnational terrorist acts as nondomestic groups seek to avoid unfavorable rally-effects and forceful retaliation from weak or
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threatened incumbent governments. Elections may also reduce the frequency of transnational terrorism by dampening at those times democracies’ tendencies towards high levels of
events:
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international involvement. We hypothesize then a ratcheting down (or comparable levels) of
Hypothesis 2 (Transnational events): the level of transnational terrorist activity will decrease (or remain unchanged) on election months. In addition, for domestic terrorist events in particular we expect the net electoral impact will be larger among countries experiencing little electoral competition since in those instances
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dissidents will have fewer opportunities to channel their grievances (Hypothesis 3: Electoral Competition). Finally, we conjecture any electoral effects will be larger in more recent times since technology and media advances may have increased the potential publicity and attention
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from dramatic events (Hypothesis 4: Period Effects).
3. Data and Methodology
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3.1 Dependent variable
The dependent variable is the number of terrorist events that occur in a democratic
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country in a month, and the unit of analysis is therefore a country-month. Countries are included in the analyses only for those years in which they are deemed democratic as per yearly categorizations of Golder (2004) and Cheibub et al. (2010) of regime types. Since we have different expectations for transnational and domestic terrorist events we need separate measures
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for them. For transnational terrorism we use data from ITERATE (International Terrorism Attributes of Terrorist Events), specifically collected for and most widely used in transnational event studies. For domestic terrorism, however, there is no data collection that has sufficient
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cross-sectional and time-series coverage. Another much widely used data collection is GTD (Global Terrorism Database). Although GTD includes both domestic and transnational incidents,
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it does not consistently distinguish between the two for the whole time period of study. Recently, Enders, Sandler and Gaibulloev (2011) partitioned out domestic events from the GTD dataset. We use then the GTD split data that includes only domestic events, as constructed by Enders, Sandler, and Gailbulloev (2011).2
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We are very much thankful to Prof. Sandler for making the partitioned GTD dataset available. For more details on collection criteria for ITERATE and GTD see, respectively, Flemming et al. (2008) and LaFree and Dugan (2006). ITERATE is proprietary data which can be acquired at http://www.vinyardsoftware.com while GTD can be obtained at http://www.start.umd.edu/gtd.
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The estimation sample from ITERATE includes incidents from 83 democratic countries from the years 1968 to 2008. The estimation sample from the domestic partitioned GTD data includes incidents from 95 democratic countries from the years 1970 to 2008 (except for 1993
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for which GTD does not have data). 3.2 Independent variables
The key independent variable is Election Month, a discrete binary indicator flagging
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whether an election (presidential or legislative) took place in that month. Details on the coding and sources for all independent variables can be found in Appendix A. We also include two other
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discrete binary indicators, Prior Months (1-3) and Prior Months (4-6). The former indicator flags the previous three months to an election, and the latter indicator flags three months earlier. For example, if an election took place in November then Prior Months (1-3) would flag the months of August, September, and October, and Prior Months (4-6) would flag the months of May, June,
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and July. We also examined various other ways of modeling proximity to elections, such as with a counter variable that counts down the months towards an election or monthly dummy indicators spanning up to a year before the election.
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The variable Past Events is the logged monthly running average, up to the previous month, of past terrorist attacks within each country since the start of the dataset (1968 or 1970)
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or the country becoming democratic, as in Li (2005). In general, long term levels of terrorist activity, whether high or low, may systematically persist across time due to general historical propensities of a country or the specific focus of a terrorist group (Li, 2005; Midlarsky et al., 1980). This variable also allows us to control for any persisting serial correlation. Previous work on terrorist activity, in particular transnational terrorism, has found past incidence measures to be needed (e.g., Lai, 2007; Li and Schaub, 2004; Young and Dugan, 2011). By this control, if a
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country never has a terrorist incident in the whole period of study then it is excluded from the main study. However, as an alternative specification we also consider the simple lag of the dependent variable, instead of the log of the running average, in which case these countries with
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no terrorist experiences are also included.
With regards to political variables influencing the incidence of terrorist events, there is much guidance based on previous literature. Specifically, five variables related to a country’s
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political institutions and political conditions are included: Presidential, Executive Constraint, Participation, Electoral Competition and Conflict. Starting with political institutions,
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Presidential is a discrete binary indicator that identifies those countries with a presidential system, based on Cheibub et al.’s (2010) categorization. This variable is expected to have a positive effect on terrorist incidents. Previous research has argued and found that under certain specifications majoritarian systems may have higher terrorist incidents than proportional
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systems. According to Li (2005) there is a reduced motivation to resort to terrorism in proportional systems since they may be more inclusive, have fewer group rebellions, and more policy congruence with citizens’ preferences (see also, Huber and Powell, 1994; Reynal-Querol,
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2002). Executive Constraint is based on the POLITY IV executive constraint variable, in a scale from 1-7, where 1 indicates unlimited authority by the executive and 7 indicates parity or
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subordination (Marshall et al., 2009). This variable is also expected to have a positive effect on terrorist incidents since countries with more institutional constraints may be more limited in the range of their antiterrorist strategies (Crenshaw, 1981; Li, 2005). Participation and Electoral Competition are two variables from Vanhanen’s (2011) data collection that measure, respectively, the percentage of the population that voted in the most recent general election and the percentage of votes gained in these elections by smaller parties.
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These two measures reflect existing political opportunities and conditions. Both are expected to have a negative effect on terrorism since they capture dimensions of a democracy related to political access. Li (2005) found greater voter turnout had a negative impact on transnational
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terrorist incidents while Aksoy (2010) found in the European context that countries where it is easier to gain legislative seats have fewer domestic terrorist incidents.3 Finally, Conflict is a discrete binary indicator that denotes whether a country is experiencing an interstate armed
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conflict when examining the ITERATE data, and it flags an intrastate conflict when examining the domestic GTD data. External and internal general armed conflicts may accompany and
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attract terrorist incidents (Li and Schaub, 2004), since they highlight the existence of dissidence and violence.
We also include a large array of control variables that have by now become standard in the empirical terrorism literature. These variables account for military capability, economic
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development, population size, regime durability, post-cold war status, and regional indicators. Military Capability is the logged military expenditures per capita, lagged one year. Previous work had hypothesized that a country’s higher levels of capabilities and resources, including
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military ones, would discourage terrorist attacks; however, in all instances the opposite was found (Chenoweth, 2010; Li, 2005; Li and Schaub, 2004). Having more military resources may
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make the target state more valuable to terrorists due to wider visibility (Li, 2005); but it may also induce more attacks if those military resources are linked to political conflicts. GDP per Capita is the logged Gross Domestic Product (GDP) per capita, lagged one year, and the expectation is that higher levels of development reduce the likelihood of grievances (Li and Schaub, 2004).
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Chenoweth (2010) conjectured political competition, or “the ability of citizens to influence government without restrictions” as measured in POLITY IV, has a positive impact on transnational terrorism by capturing crowding effects of one group on the other. We included as well POLITY IV’s political competition measure in all specifications but it never achieves statistical significance.
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Population Size is the log of the population and controls for an increased likelihood of terrorism in the larger states due to increased heterogeneity, difficulty to control, and larger number of potential dissidents (Lai, 2007; Li and Schaub, 2004; Eyerman, 1998). Regime Durability is the
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logged number of years since the most recent regime change, with the expectation that more unstable countries may be more prone to terrorist incidents (Eyerman, 1998; Li and Schaub, 2004; Piazza, 2008; Sanchez-Cuenca, 2009; Weinberg and Eubank, 1998). Post-Cold War is a
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discrete indicator that flags the years after 1991. This variable in conjunction with the regional variables can capture temporal and general geographic differences (Enders and Sandler, 1999; Li
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and Schaub, 2004).
Since the dependent variable is a count of the number of terrorist incidents in a month, the appropriate statistical method is an event count model, as opposed to ordinary least squares. In particular we use the negative binomial regression, a variant of count model specifications that
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has been commonly used in past studies of terrorist incidents since, among others, it addresses the features of over-dispersion present in this data (Chenoweth, 2010; Li, 2005; Li and Schaub, 2004; Young and Dugan, 2011). As detailed in the Robustness section we also examined
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alternative specifications through a zero-inflated count model that allows for two processes instead of one for generating the zeroes, and by incorporating country-level fixed effects. While
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the zero-inflated specification prevailed by some measures of fit over the basic negative binomial regression model, with or without country-level fixed effects, substantively entirely comparable results were obtained. We opted then, as in many previous studies, to focus the presentation on the negative binomial regression specification. Finally, the estimations were carried out with robust standard errors clustered at the country level to account for country-based correlations and all significance tests were two-tailed.
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3.3 Summary breakdown Table 1 highlights some of the characteristics of the dependent variable. Based on the availability of the independent variables, the estimation sample from the ITERATE database
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includes 7,336 incidents and the estimation sample from the GTD domestic database includes 28,254 incidents. The mean number of terrorist incidents per month among democratic countries for the ITERATE and GTD domestic datasets are, respectively, 0.32 and 1.2 in non-election
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months. Focusing on election months, which hover above 600 in each data set, the average number of incidents decreases to 0.27 in ITERATE and increases to 1.5 in domestic GTD. The
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distribution of terrorist incidents is highly skewed, with a preponderance of zeroes, or countrymonth observations without incidents. In the ITERATE data there are around 85 percent of zeroes, in election and non-election months, while in the GTD domestic data there are 77 percent of zeroes in non-election months and 73 percent in election months. Given the distributional
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characteristics, non-parametric tests such as Kruskall-Wallis and Mann-Whitney are better suited than normality-based tests to compare the distribution of terrorist events between election and non-election month samples. These tests reveal no statistically significant differences in the
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ITERATE data but statistically significant differences in the GTD data. These are suggestive
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aggregate comparisons though only indicative since further controls are needed. [Table 1 about here]
4. Findings
Tables 2 and 3 present the statistical results for the ITERATE and GTD domestic analyses. Each model is a negative binomial regression predicting the number of terrorist events in a country-month for a given array of controls or sub-samples. Models 1 and 2 in either table
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are the starting points of reference for this study. Model 1 is the full model while Model 2 is the full model omitting the indicators controlling for previous quarters to an election. [Tables 2 and 3 about here.]
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Starting with the key independent variable, Election Month, we find it has a negative and statistically significant effect in the ITERATE data (Table 2), where only transnational terrorist events are included. But, this variable has a positive and statistically significant effect with the
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domestic GTD data (Table 3).4 Substantively, the electoral effect translates in ITERATE into a decrease in the expected number of transnational incidents in election months of about 19
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percent, holding all other variables at their mean, and in GTD into an increase in the expected number of domestic incidents in election months of about 53 percent. For example, for a “modal” European country, before the Cold War, the expected number of transnational terrorist incidents is 0.31 in non-election months but decreases to 0.25 in election months. On the other
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hand, in such a country, the expected number of domestic GTD terrorist incidents is 0.42 in nonelection months but increases to 0.64 events in election months. Our first main hypothesis was that the net impact would be positive for domestic terrorist
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events, and we find this to be the case. This result would be in line with the logic of heightened payoffs. That is, domestic terrorist groups may engage in more activities around electoral times
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due to the heightened attention from the public and increased competition among groups. For transnational events the negative result corresponds to our second main hypothesis and in general is in line with the logic of decreased payoffs. Terrorist groups may engage in fewer activities around electoral times due to possible forceful retaliation and public rallies or, due to lack of saliency if domestic issues prevail. The argument for decreased terrorist activities due to more
4
The baseline, or omitted category, in Model 1 is all months more than 6 months away from an election and in Model 2 it is all non-election months.
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channels of engagement during election times is less convincing in the case of transnational terrorism since the dissident groups are external. The observed electoral effects are very focused in time. We explored other alternative
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specifications, such as a monthly counter decreasing in value towards an election as well as monthly dummy indicators for the span of a year before an election. The general counter was not statistically significant, in either ITERATE or domestic GTD, which a posteriori is not surprising
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since the average span between elections is 22 months and the observed effect occurs only at the very end, upon the election. In ITERATE when including monthly dummy indicators up to a
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year before an election, that is twelve indicators, only the indicators for the election month and for the month 6th before the election are statistically significant and negative (i.e., drops in events). In the domestic GTD dataset the analogous specification has only the election month and the month before it statistically significant, and positive (i.e., increases in events). We also
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examined the month and quarter following the election, with no influences found in either data set. The fact that the electoral effects are so concentrated in time may be related to the limited window of opportunity to induce or engage electorates.
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Our hypotheses also included the expectation that countries with little electoral competition would be more prone to attacks, in particular domestic, during election times and
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that election effects would be more prominent in recent times. We examined these hypotheses by generating sub-samples and then carrying out appropriate Wald tests. Models 3 and 4 present respectively the benchmark models for countries with high electoral competition (larger than 53%, the country-level mean) and low electoral competition (less than 53%). Models 5 and 6 present respectively the benchmarks models for the period before 1991 (pre-Cold War) and for the period after 1991 (post-Cold War). To assess election-related differences we then carried out
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Wald tests of equality of the election month coefficients across sub-samples. Results for these tests can be found at the bottom of the tables. In the ITERATE data (Table 2) there are no statistically distinguishable differential
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election effects between low and high electoral competition countries (chi2(1) = 1.03, p-v = 0.31). However, there are period differences. The election month decreases in ITERATE terrorist incidents mainly take place after the Cold War (chi2(1) = 3.22, p-v = 0.07). All else constant,
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after the Cold War the expected number of transnational terrorist incidents decreases by about 35 percent on election months. This implies the disincentives to carry out a transnational terrorist
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attack around election times have increased in recent decades. Recent times may have brought higher levels of exposure through around-the-clock media coverage, which could further induce public rallies, as well as higher levels of governmental preparedness through additional international cooperation on counterterrorism.
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In the domestic GTD data (Table 3) there are no distinguishable period effects, that is, the election month increases in terrorist attacks are comparable before and after 1991 (chi2(1) = 1.87, p-v = 0.17). However, as conjectured there are election competition effects. That is, countries
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with less electoral competition have more domestic GTD terrorist incidents in election months than countries with more electoral completion (chi2 (1) = 5.82, p-v = 0.02). All else constant,
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among countries with less electoral competition the expected number of domestic GTD terrorist incidents increases by about 110 percent on election months. This would suggest domestic terrorist incidents are quite susceptible to the existing political opportunities in a country such that when there are fewer political outlets the motivations to strike around election times increase.
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With regards to the control variables, and focusing back on the benchmark Models 1 and 2, most were in line with previous research. Concentrating first on past trends and the general political context, as expected, Past Incident has a large and positive significant effect on terrorist
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incidents whether in ITERATE or domestic GTD. Thus, historical trends of calm or activity tend to persist. The Presidential control does not achieve statistical significance in ITERATE and achieves statistical significance in the domestic GTD data but in the opposite direction than
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expected: presidential systems experience fewer domestic terrorist events than non-presidential systems. However, this result is not robust to key alternative specifications. For example, if past
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incidents is operationalized by the lag of the dependent variable, instead of a long-term running average of incidents, the presidential systems indicator does not achieve statistical significance, nor does it achieve significance when a zero-inflated count model is used (see Table 5, Models 12 and 13). The Executive Constraints variable does not achieve statistical significance in neither
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the ITERATE nor the GTD domestic data. Regarding Participation and Electoral Competition only the participation variable has a statistically significant effect and in both the ITERATE and domestic GTD data: higher voter turnout corresponds to a decreased number of terrorist
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incidents.5 Finally, Conflict has a positive and statistically significant effect in the domestic GTD data, with internal armed conflicts having a substantial effect on domestic terrorist incidents. In
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sum, the political environment matters in that trends in transnational or domestic terrorist activities tend to persist, countries involved in an internal armed conflict have more domestic terrorist incidents, and countries with higher turnout experience fewer terrorist incidents in general. 5
Examining non-democracies as well as democracies for the period 1975-1997 Li (2005) finds that Executive Constraints and an operationalization of participation have significant effects on transnational incidents. Focusing only on democracies we also find the significant dampening effect for higher turnout while our null finding for executive constraints is to be expected given the more limited variation in this variable when restricting the sample to democracies.
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When considering the remaining standard controls, the findings align with expectations and with results in previous works. Military Capability, GDP per Capita, and Population Size have all a statistically significant effect in both datasets and in the expected directions. Generally,
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countries with more military strength, less developed, and more populous are associated with more terrorist incidents. Regime Durability does not achieve statistical significance in ITERATE but does so in the GTD domestic data: more stable countries are less likely to experience
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domestic terrorist events. Finally, as others have noted, the number of transnational and domestic terrorist incidents have declined since the Cold War (Enders and Sandler 1999, 2005; Li, 2005;
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Young and Dugan, 2011).
5. Robustness and Alternative Specifications
We evaluated the robustness of the results to the following issues: 1) the endogeneity of elections, 2) the type of elections, 3) the influence of outlier countries, 4) the presence of
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country-level fixed effects, 5) the specification of a zero-inflated model, 6) the operationalization of past events, and 7) the severity of terrorist events. The results for these alternative examinations are presented in Tables 4 (ITERATE) and 5 (GTD). Beginning with endogeneity,
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elections may occur on some occasions before constitutionally pre-determined times, more particularly so in the case of parliamentary systems that expressly allow for early dissolutions.
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The timing of an election then can be endogenous to the current political circumstances. To address this we re-estimated the benchmark Model 1 separately for presidential countries and for non-presidential countries (parliamentarian and mixed), as seen in Models 7 and 8 in Tables 4 and 5. Based on Wald tests (see results at the bottom of the tables), there are no statistically significant differences in election month effects across presidential and non-presidential systems in neither dataset. In fact, the average span between elections for both presidential and non-
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presidential countries is exactly the same, at around 22 months. In addition, we carried out a test of endogeneity for the Election Month variable through a variant of the Hausman test for count models (Staub, 2009) and found no evidence for it. However, this test has limitations since we do
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not have good domestic predictors of electoral occurrences apart from the calendar itself. All in all, with some caution we think endogeneity is not unduly influencing the results. [Tables 4 and 5 about here.]
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Elections may carry different levels of saliency depending on the function of the election. In a presidential system, presidential elections have long-garnered more public and
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campaign attention than mid-term elections, often resulting in higher turnout rates (U.S. Census, 2012; McDonald, 2010). We re-estimated the benchmark Model 2 for the ITERATE and GTD domestic data with three indicators flagging, respectively, presidential only election months, legislative only election months, and concurrent presidential and legislative election months. The
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results are found in Models 9. There is limited evidence of saliency effects at work. In the ITERATE data all election month indicators are negative, though just the presidential election month coefficient is statistically significant. In the domestic GTD data all election month
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indicators are positive though now just the legislative election month coefficient is statistically significant. However, when further tests are carried out to distinguish the indicators’ coefficients
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amongst themselves, no statistical differences arise in both ITERATE and GTD domestic. There is substantial variability in the general trends of terrorist incidents across countries. Some of this variability may not be entirely explained by the control variables and may overly influence the results. To address this we examined for possible outlier countries. Specifically we inspected Pearson residuals averaged by country that were two standard deviations above the
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overall country average of residuals, in the ITERATE and GTD domestic data respectively.6 We then re-estimated the benchmark Model 1 removing each of the outlier countries one by one as well as all of the outlier countries simultaneously, as seen in Models 10. In each instance of
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removal the election month effects persisted in both the ITERATE and GTD domestic analyses. At the aggregate level, in online Supplements A and B (ITERATE and GTD domestic, respectively) of this study average terrorist event rates by country can be found broken down for
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non-election and election months. At a glance, these country summaries already reveal that the
driven by a few select countries.
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distinct election month effects found for transnational and domestic terrorist events are not
Methodologically we considered alternative specifications to the negative binomial that addressed country-level effects as well as excess, or differentiated, zeroes. In particular, we considered first negative binomial regressions with country-level fixed effects and country-level
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random effects. Hausman tests revealed that, both in ITERATE and domestic GTD, fixed effects are preferred to random effects (chi2(14) = 65.1, p-v < 0.01, and (chi2(14) = 340.5, p-v < 0.01, respectively). Models 11 in Tables 4 and 5 present the estimates when including country-level
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fixed effects in both data. In ITERATE the election month effect is still negative but no longer statistically significant at any conventional level, while in GTD the election month persists in
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magnitude and significance.
We then also considered a zero-inflated negative binomial model that allows zeroes to be generated by two different processes (a binary process and the count process). For the binary (inflated-zero) equation we included as independent variables all the political variables, age of
6
By this rule in the ITERATE data 6 countries were deemed outliers: Colombia, France, India, Israel, Italy, and Netherlands and in the GTD domestic data 8 countries: United Kingdom, Spain, Russia, Peru, Philippines, El Salvador, Colombia, and Indonesia. The countries derive from different regions of the world suggesting the idiosyncrasies are localized to each particular country.
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regime, and the measure of past incidents. It may be reasonable to expect that political-related factors shape whether a country persistently has no terrorism, and some research has preliminarily begun to test this (i.e., Young and Dugan, 2011). Further, as Drakos and Gofas
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(2006) suggest no events may also be a sign of under-reporting due to political constraints. Based on measures of fit, AIC and BIC, specifically, the zero-inflated negative binomial model was preferred to both the basic model and the basic model with fixed country-level effects.
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However, as can be seen in Models 12 in Table 4 and 5, substantively and in terms of statistical significance entirely comparable election month results were obtained with the zero-inflated
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count model. That is, with a zero-inflated model in ITERATE the expected number of transnational incidents in election months decreases by 19 percent, the same percentage factor as in the basic model, and in GTD the expected number of domestic incidents in election months increases by about 43 percent, comparable to the 53 percent in the basic model. Comparable
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substantive results are obtained for all other factors.
To account for past trends in terrorist incidence we included the logged monthly average of past terrorist attacks within each country since the start of the dataset (1968 or 1970) or the
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country becoming democratic, as in Li (2005). This operationalization, by taking the log, drops those countries that never have an attack throughout the whole period of analysis and includes
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those country-months thereafter once an event has occurred. As an alternative we re-estimated Model 1 with the lag of the dependent variable instead of the logged running average. One known and clear disadvantage of using the lagged dependent variable is that potentially too little is left to be explained. But, as can be seen in Models 13, fully comparable results to those in
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Model 1 were obtained. Substantively, they translate into a 17 percent reduction in transnational ITERATE terrorist incidents and a 47 percent increase in domestic GTD terrorist incidents.7 Finally, the dependent variable of our analyses is the number of events without
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accounting for their severity. We re-estimated Model 1 with the total number of fatalities from incidents in a month instead of the number of incidents. The results are found in Models 14. Interestingly, in the ITERATE data an election month has now a strong and positive significant
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effect. That is, election months are predicted to have more fatalities while in our main analyses of the ITERATE data election months were found to have fewer incidents. However, an outlier
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analysis, as was done before with counts of events, reveals that this result is entirely driven by Spain. When Spain is omitted from the analyses (Model 15 in Table 4) no statistically significant electoral month effects are found in terms of fatalities in the ITERATE data. With the GTD domestic data no election month effects are found with regards to fatalities to begin with, and
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this result holds after examining as well for potential outlier effects.
6. Discussion
This study has found that the number of terrorist events is distinct on election months and
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the variation depends on the origin of the threat. More specifically, when examining terrorist event data where only transnational events are included (e.g., ITERATE), events are less
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frequent on election months compared to non-election months. In contrast, when examining terrorist event data when only domestic events are included (e.g., GTD domestic), events are more frequent on election months compared to other months. Substantively the effects translate in the main specification into a predicted 19 percent reduction in ITERATE transnational 7
We also examined a zero-inflated negative binomial specification, as in Model 12, but using the lagged dependent variable instead of the logged monthly past average of events. In this specification, the election month effect remains statistically significant at the 0.10 level for both ITERATE and GTD domestic data, translating again into a 17 percent reduction in transnational ITERATE terrorist incidents and this time a 25 percent increase in domestic GTD terrorist incidents.
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terrorist incidents and a 53 percent increase in GTD domestic terrorist incidents. Moreover, these election month effects are mediated by time period and level of electoral competition. In particular, the decrease in transnational terrorist incidents in election months occur mainly after
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the Cold War (or 1991) while the increase in domestic terrorist events in election months occur more distinctively among countries with low levels of electoral competition.
How do we begin to interpret these results in light of the various mechanisms of
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influence suggested from previous research (e.g., attention, competition, channeling of dissent, and retaliation)? Focusing first on the ITERATE-based results, or the dampening of transnational
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terrorist events around elections, strategic considerations related to possible retaliation and illtimed attention may be playing a key role. In the complex back-and-forth between governments and terrorist actors, terrorist groups appear to de-escalate activities, in particular large-scale ones, at election times in order to avoid forceful retaliation (Hodler and Rohner, 2010). By the logic of
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diversionary foreign policy, leaders in critical domestic circumstances may especially wish for a legitimate opportunity to use force abroad (e.g., after a newly imposed external threat) to boost reelection chances. But by the counterpart logic of strategic avoidance (Leeds and Davis, 1997),
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terrorist groups may have strategic reasons to avoid targeting governments around election times to precisely scuttle any chances of hostile public rallies and armed retribution, which in turn can
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be detrimental to their public support. In addition, from the perspective of electoral attention cycles a decrease in transnational events, especially if small-scaled, during election months may also be expected if non-domestic terrorist groups find their grievances less likely to garner public attention during electoral times, which in general tend to prioritize domestic issues. If governments’ abilities to pre-empt and re-act against transnational terrorist attacks have
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increased in the post-Cold War era, we would expect electoral dampening of transnational terrorist attacks to be more of a post-Cold War phenomenon, which is indeed what we found. Focusing on the GTD results, or the increase in domestic terrorist events around
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elections, we found that attention-related incentives may be at work. Domestic terrorist events close to election times can garner the spotlight on the domestic grievance at hand and on the inadequacies of the incumbent government. The fact that the observed electoral effect is so
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concentrated in time is consistent with the electorate’s attention being a likely explanatory mechanism. That is, what critically change during the last months before an election are the
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public’s level of engagement and the level of competition among societal groups to favorably garner this attention. Long-term institutions (e.g., number of veto players, party thresholds, and institutional constraints) do not dramatically change in those last pre-election months. In addition, the finding that the election-month increase in domestic attacks is mediated by electoral
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competition is in congruence with the “access school” explanation that curtailed political access, an evolving political opportunity, increases terrorist activity (see also, Aksoy, 2010). All in all, this present study provided a much needed empirical benchmark on election
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time influences on the incidence of terrorist events across an ample cross section of countries and for an extended period. From a policy perspective, the results imply that counter-terrorism
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strategies need to attune to the electoral calendar in order to further prevent terrorist events, especially domestic ones. The fact that transnational terrorist events decrease on election months but only after the Cold War suggests that dimensions of our current times, such as continuous media coverage of on-going events, which could further encourage a public rally, as well as international treaties of cooperation on counterterrorism, may be contributing as deterrents.
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We conclude with suggestions for future research. First, more work still waits to probe the relative importance of the various possible mechanisms of influence related to the electoral calendar. Such designs will require more detailed information on both the terrorist groups
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themselves and the domestic political context. This type of collection effort can then be accompanied by a dyadic research design, prevalent in the interstate conflict literature, to fully explore the dual nature of government and terrorist actors’ interactions. For example, it would be
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useful to assess the ideological leanings of incumbent governments and those of terrorists groups. With these measures, we may then probe how event rates vary with the degree of
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ideological consonance between governments and dissident groups, including close to election times. Second, future work may also investigate the relative role of regime history, development, and power. It is very likely that considerations of short-term political circumstances, such as fluctuations in attention cycles and relative strength of government leaders, are critical for the
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revealed association between election time and terrorism trends. But, long-term structural traits
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may further influence terrorists’ strategic decisions around the electoral calendar.
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Table 1. Mean and Frequency of Terrorist Incidents in ITERATE and Domestic GTD in Non-Election and Election Months GTD Domestic (1970-2008)
Democratic Countries= 83
Democratic Countries= 95
Country-Month Observations= 23,311
Country-Month Observations= 22,588
Country-Month Observations with Elections= 644
Country-Month Observations with Elections= 629
Mean
0.32
0.27
Percent "Zeroes"
85.15%
85.56%
Kruskall-Wallis Test Mann-Whitney Test
Chi2= 0.042 Z = 0.35
p-v = 0.83 p-v = 0.73
SC
TE D
Election Months
Total Number of Incidents= 28,254
M AN U
Total Number of Incidents= 7,336
Non-Election Months
EP
Note: Percent "zeroes" is the percent of country-month observations with no incidents.
AC C
RI PT
ITERATE (1968-2008)
Non-Election Months
Election Months
1.24
1.48
77.33%
72.65%
Chi2 = 3.89 Z = -2.68
p-v = 0.05 p-v = 0.01
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Table 2. Effect of Electoral Proximity on Monthly Terrorist Incidents in ITERATE (1968-2008) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 High Comp. -0.293** (0.13) 0.196 (0.25) 0.017 (0.17) 0.748*** (0.07) 0.085 (0.16) 0.090* (0.05) -0.022*** (0.007)
-0.308 (0.19) 0.167** (0.07) -0.283** (0.14) 0.075 (0.07) 0.081 (0.11) -1.075*** (0.16) 1.621 (1.58) 9,819
0.365 (0.26) 0.328*** (0.07) -0.363*** (0.10) 0.068* (0.04) -0.098 (0.07) -1.06*** (0.16) 3.01 (0.99) 13,492
Pre-CW -0.049 (0.13) 0.253 (0.24) 0.017 (0.10) 0.671*** (0.08) 0.517** (0.25) 0.051 (0.06) -0.015** (0.006) 0.006 (0.01) -0.375** (0.15) 0.196*** (0.07) -0.235** (0.10) 0.132*** (0.05) -0.047 (0.07)
Post-CW -0.426** (0.17) -0.097 (0.18) -0.002 (0.20) 0.972*** (0.09) -0.194 (0.28) -0.052 (0.08) -0.013* (0.007) -0.002 (0.01) 0.671*** (0.26) 0.474*** (0.08) -0.580*** (0.12) 0.022 (0.06) -0.078 0.11)
-0.095 (0.79) 9,081
5.79 (1.63) 14,230
Wald χ2(Overall)
1815***
1568***
2662***
1220***
1075***
744***
Alpha
2.8 (0.4)
2.8 (0.4)
1.9 (0.4)
3.3 (0.5)
2.2 (0.4)
4.6 (0.8)
Executive Constraint Participation Competition Interstate Conflict Military Expenditures GDP per Capita Population Age Regime Post Cold War
AC C
Constant
SC
Presidential
0.786*** (0.06) 0.232 (0.14) 0.040 (0.05) -0.016*** (0.005) 0.002 (0.01) 0.178 (0.23) 0.287*** (0.05) -0.367*** (0.08) 0.071* (0.04) -0.027 (0.06) -1.028*** (0.12) 2.650 (0.91) 23,311
M AN U
Past Incident
TE D
Prior Months (4- 6)
RI PT
Observations
Prior Months (1-3)
-0.217* (0.11)
Low Comp. -0.068 (0.18) 0.101 (0.10) -0.01358 (0.14) 0.818*** (0.07) 0.557** (0.23) -0.024 (0.07) -0.012 (0.01)
-0.205* (0.11) 0.126 (0.17) 0.015 (0.13) 0.787*** (0.06) 0.230 (0.14) 0.040 (0.05) -0.016*** (0.005) 0.002 (0.01) 0.177 (0.23) 0.287*** (0.05) -0.368*** (0.08) 0.071* (0.04) -0.029 (0.06) -1.026*** (0.12) 2.659 (0.92) 23,311
EP
Election Month
χ2 (1)= 1.03 p-v=0.31 χ2 (1)= 3.22 p-v=0.07 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. * p < 0.10, ** p < 0.05, *** p <0.01.
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Table 3. Effect of Electoral Proximity on Monthly Terrorist Incidents in GTD Domestic (1970-2008) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Presidential Executive Constraint Participation Competition Intrastate Conflict Military Expenditures GDP per Capita Population Age Regime Post Cold War
AC C
Constant
0.643*** (0.04) -0.315** (0.15) -0.014 (0.05) -0.011** (0.005) 0.005 (0.01) 0.975*** (0.20) 0.189*** (0.05) -0.143** (0.07) 0.217*** (0.04) -0.169*** (0.06) -0.827*** (0.11) -0.98 (0.82) 22,588 3961*** 2.4 (0.3)
1.399*** (0.20) 0.202** (0.09) -0.243** (0.12) 0.317*** (0.06) -0.225** (0.09) -0.782*** (0.13) -2.51 (1.18) 10,006 1596***
0.542** (0.25) 0.175*** (0.05) -0.196** (0.09) 0.122** (0.05) -0.098 (0.07) -0.807*** (0.15) 1.845 (1.47) 12,582 2814***
2.2 (0.3)
2.3 (0.4)
Pre-CW 0.208 (0.18) 0.220* (0.13) 0.126 (0.11) 0.746*** (0.04) 0.084 (0.28) -0.036 (0.06) -0.011 (0.01) 0.023** (0.01) 0.436** (0.17) -0.001 (0.07) -0.171 (0.13) 0.141*** (0.05) -0.042 (0.09)
Post-CW 0.566*** (0.18) -0.138 (0.10) -0.160 (0.12) 0.586*** (0.06) -0.398** (0.17) 0.034 (0.06) -0.007 (0.006) -0.005 (0.01) 1.270*** (0.22) 0.348*** (0.09) -0.263** (0.10) 0.240*** (0.06) -0.183** (0.09)
-0.89 (1.21) 6,748 3367***
-0.85 (1.02) 15,840 1145***
1.7 (0.3)
2.7 (0.4)
RI PT
Past Incident
High Comp. 0.103 (0.15) -0.073 (0.11) -0.134 (0.10) 0.711*** (0.04) -0.562** (0.22) -0.062 (0.08) -0.012 (0.008)
SC
2.4 (0.3)
Prior Months (4- 6)
Low Comp. 0.746*** (0.21) 0.172* (0.09) 0.112 (0.11) 0.545*** (0.05) -0.277 (0.20) 0.023 (0.05) -0.002 (0.006)
M AN U
Alpha
Prior Months (1-3)
0.4228*** (0.13)
TE D
Observations LR χ2(Overall)
0.425*** (0.13) 0.042 (0.08) -0.019 (0.09) 0.643*** (0.04) -0.314** (0.15) -0.014 (0.05) -0.011** (0.005) 0.005 (0.01) 0.975*** (0.20) 0.190*** (0.05) -0.144** (0.07) 0.217*** (0.04) -0.169*** (0.07) -0.827*** (0.10) -0.98 (0.82) 22,588 4133***
EP
Election Month
χ2 (1)=5.82 p-v=0.02 χ2 (1)= 1.87 p-v=0.17 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. * p < 0.10, ** p < 0.05, *** p <0.01.
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Table 4. Alternative Specifications in ITERATE (1968-2008)
Pres. & Leg. Election Month
Prior Months (4- 6) Past Incident
0.329 (0.26) 0.026 (0.19) 0.804*** (0.07)
-0.182* (0.11) 0.010 (0.13) 0.816*** (0.11)
-0.049 (0.04) 0.003 (0.01) 0.001 (0.01) 0.174 (0.26) 0.298*** (0.07) -0.392*** (0.08) 0.087* (0.05)
0.145 (0.09) -0.026*** (0.007) -0.003 (0.01) -0.06 (0.23) 0.201** (0.09) -0.220 (0.16) 0.032 (0.07)
Participation Competition Interstate Conflict Military Expenditures GDP per Capita Population
0.786*** (0.06) 0.234 (0.14) 0.040 (0.05) -0.017*** (0.005) 0.002 (0.01) 0.177 (0.23) 0.286*** (0.05) -0.367*** (0.08) 0.070* (0.037)
AC C
Presidential Executive Constraint
Model 13 Lagged DV
Model 14 Fatalities
-0.259* (0.15)
-0.177 (0.17)
-0.208* (0.12)
-0.185* (0.11)
1.59* (0.84)
-0.054 (0.11) 0.023 (0.14) 0.831*** (0.04) 0.071 (0.10) 0.006 (0.05) -0.012*** (0.004) 0.003 (0.01) 0.391 (0.25) 0.290*** (0.04) -0.341*** (0.08) 0.033 (0.04)
0.112 (0.10) 0.048 (0.09) 0.524*** (0.07) 0.437 (0.39) -0.083 (0.05) -0.014** (0.006) 0.0003 0.004 0.134 (0.12) 0.169** (0.08) -0.471*** (0.09) -0.041 (0.28)
0.093 (0.14) 0.033 (0.12) 0.322*** (0.05) 0.777*** (0.29) 0.007 (0.07) -0.015 (0.009) 0.003 (0.01) 0.472 (0.31) 0.589*** (0.07) -0.388*** (0.10) 0.332*** (0.06)
-0.09 (0.34) 0.349 (0.43) 0.563*** (0.14) 0.300 0.41 -0.193 (0.13) -0.021* (0.01) -0.011 (0.01) -0.745 (0.53) 0.880*** (0.22) -0.748** (0.31) 0.493*** (0.13)
-0.351* (0.18) -0.137 (0.13) -0.487 (0.34)
Legislative Election Month
Prior Months (1-3)
Model 12 ZINB Count Model
RI PT
Saliency
Model 11 Country Fixed Eff.
SC
Presid. -0.175 (0.21)
Model 10 Without Outliers
M AN U
-0.242* (0.13) Presidential Election Month
Model 9
TE D
Election Month
Model 8
EP
Model 7 Non Presid.
0.096 (0.16) 0.001 (0.12) 0.790*** (0.05) 0.026 (0.16) 0.044 (0.05) -0.019*** (0.004) 0.004 (0.01) 0.225 (0.25) 0.259*** (0.05) -0.368*** (0.08) 0.078* (0.04)
Model 15 Fatalities ~ Spain -0.265 (0.23)
-0.094 (0.34) 0.355 (0.43) 0.552*** (0.14) 0.299 (0.40) -0.217 (0.14) -0.021* (0.01) -0.009 (0.01) -0.652 (0.52) 0.925*** (0.23) -0.835** (0.31) 0.466*** (0.13)
ACCEPTED MANUSCRIPT Table 4 Continued Age Regime Post Cold War
-0.042 (0.06) -0.944*** (0.15)
0.024 (0.12) -1.23*** (0.15)
-0.027 (0.06) -1.028*** (0.12)
-0.034 (0.07) -1.048*** (0.13)
-0.308*** (0.06) -0.541*** (0.09)
0.048 (0.08) -1.015*** (0.13)
-0.069 (0.08) -1.342*** (0.13)
0.216* (0.12) -1.29*** (0.39)
0.249* (0.13) -1.34*** (0.40)
RI PT
Zero Binary Model Past Incident
SC
Presidential
-0.166 (0.34) -18.96 (1.14)*** -0.315 (0.27) -0.143 (0.04)*** 0.043 (0.02)* 2.611 (0.82)*** -5.48 (1.63)
M AN U
Executive Constraint Participation Competition
Constant (Binary Model)
2.68 (1.20)
1.92 (2.13)
2.65 (0.91)
Observations LR χ2(Overall) Alpha
15,344 1724*** 3.2 (0.5)
7,967 1180*** 2.1 (0.5)
23,331 1633*** 2.8 (0.4)
3.35 (0.99)
7.65 (5.36)
2.45 (0.99)
-1.640 (1.29)
2.30 (3.5)
3.46 (3.6)
20,461 2405*** 2.9 (0.4)
23,331 8721*** 2.6 (0.8)
23,331 1167*** 2.52 (0.34)
26,872 952*** 3.4 (0.5)
23,311 207*** 46.8 (8.4)
22,950 228***
AC C
EP
Constant
TE D
Age Regime
Vuong 3.33***
χ2 (1)= 0.07 p-v=0.78 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. Controls for regions (not shown) were also included.
Model 12 is a zero-inflated negative binomial regression, with a count equation and a "zero" binary equation. * p < 0.10, ** p < 0.05, *** p <0.01.
46.6 (8.4)
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Table 5. Alternative Specifications in GTD Domestic (1970-2008)
Presid.
Saliency
0.488*** (0.18)
0.212 (0.16)
Pres. & Leg. Election Month
Prior Months (4- 6) Past Incident
0.111 (0.11) -0.041 (0.10) 0.717*** (0.03)
-0.044 (0.07) 0.030 (0.14) 0.407*** (0.10)
-0.032 (0.05) -0.009* (0.005) 0.0001 (0.01) 0.711*** (0.21) 0.173*** (0.04) -0.165* (0.09) 0.176*** (0.04)
0.118 (0.08) -0.008 (0.01) 0.012* (0.006) 1.891*** (0.28) 0.395*** (0.12) -0.365** (0.17) 0.240*** (0.07)
Competition Intrastate Conflict Military Expenditures GDP per Capita Population
AC C
Participation
0.643*** (0.04) -0.313** (0.16) -0.014 (0.05) -0.011** (0.005) 0.005 (0.005) 0.975*** (0.20) 0.189*** (0.05) -0.144** (0.07) 0.217*** (0.04)
EP
Presidential Executive Constraint
Model 12 Model 13 ZINB Lagged Count Model DV
Model 14
0.484*** (0.15)
0.342*** (0.11)
0.360*** (0.13)
0.385** (0.15)
0.256 (0.17)
0.054 (0.07) -0.029 (0.08) 0.397*** (0.05) -0.345** (0.16) 0.069 (0.05) -0.011** (0.005) -0.001 (0.005) 1.098*** (0.24) 0.161*** (0.06) -0.142* (0.07) 0.202*** (0.04)
0.035 (0.06) -0.085 (0.06) 0.152*** (0.03) 0.188 (0.27) 0.003 (0.06) -0.0008 (0.01) -0.0007 (0.01) 1.442*** (0.23) 0.454*** (0.08) -0.234* (0.13) 0.516*** (0.06)
0.242 (0.29) -0.251* (0.14) 0.569*** (0.07) 0.215 (0.38) -0.099 (0.07) -0.008 (0.01) 0.0004 (0.01) 2.099*** (0.26) 0.298*** (0.11) -0.527*** (0.15) 0.404*** (0.07)
0.086 (0.23) 0.499*** (0.16) 0.379 (0.33)
Legislative Election Month
Prior Months (1-3)
Model 11 Country Fixed Eff.
0.022 (0.09) -0.103 (0.09) 0.634*** (0.04) -0.306* (0.16) -0.052 (0.04) -0.008* (0.004) 0.005 (0.004) 0.957*** (0.16) 0.232*** (0.05) -0.172** (0.07) 0.230*** (0.04)
0.083 (0.07) 0.014 (0.07) 0.737*** (0.03) 1.271** (0.65) -0.060** (0.03) -0.011*** (0.003) 0.011*** (0.002) 0.992*** (0.07) 0.104** (0.05) -0.488*** (0.06) -1.559*** (0.24)
M AN U
Presidential Election Month
Model 10 Without Outliers
RI PT
Model 9
SC
Model 8
TE D
Election Month
Model 7 Non Presid.
Fatalities
ACCEPTED MANUSCRIPT Table 5 Continued Age Regime Post Cold War
-0.116* (0.06) -0.688*** (0.16)
-0.184* (0.09) -0.819*** (0.12)
-0.169 *** (0.06) -0.827*** (0.11)
-0.212*** (0.06) -0.799*** (0.12)
-0.321*** (0.04) -0.155** (0.07)
-0.152** (0.08) -0.825*** (0.11)
-0.380*** (0.09) -0.267** (0.11)
-0.074 (0.08) -0.273 (0.18)
-5.80 (1.71) 26,175 579*** 3.2 (0.4)
-1.71 (1.62) 22,588 939*** 6.7 (1.5)
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Zero Binary Model
Past Incident
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Presidential
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Executive Constraint Participation Competition
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Age Regime
Observations LR χ2(Overall) Alpha
0.077 (0.74) 14,913 1944*** 2.4 (0.5)
-1.33 (1.45) 7,675 1400*** 2.0 (0.4)
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Constant
-0.98 (0.82) 22,588 4043*** 2.4 (0.3)
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Constant (Binary Model)
-0.63 (0.69) 20,625 3628*** 2.6 (0.4)
35.58 (4.61) 22,588 29985*** 2.1 (0.05)
-0.804*** (0.06) -0.05 (0.37) 0.162 (0.11) 0.010 (0.010) -0.022** (0.01) 0.004 (0.14) -1.64 (0.74) -0.86 (0.89) 22,588 711*** Vuong 5.49***
χ2 (1)= 1.31 p-v=0.25 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. Controls for regions (not shown) were also included. Model 12 is a zero-inflated negative binomial regression, with a count equation and a "zero" binary equation. * p < 0.10, ** p < 0.05, *** p <0.01.
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Appendix A. List of Independent Variables Definition
Source
Election Month
1 if an election month; 0 otherwise. Only first round of an election is considered.
Prior Months (1-3)
1 for the three preceding months to an election; 0 otherwise. 1 for the preceding months 4, 5 and 6 to an election; 0 otherwise. Log of the average monthly number of terrorist incidents up to the previous month. 1 if the country has a presidential system of government; 0 otherwise. Executive constraint variable from POLITY IV: "the extent
Parline database of national parliaments http://www.ipu.org/parline-e/parlinesearch.asp Psephos : http://psephos.adam-carr.net/countries Center on Democratic Performance (ERA) http://cdp.binghamton.edu/era/index.html See above
Presidential Executive Constraint
GDP per Capita Population Age of Regime Post Cold War
Log of Gross Domestic Product per capita, lagged one year. Log of Population. Log of years of the current regime (democracy). 1 from 1991 onwards; 0 otherwise.
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Inter/Intrastate Conflict
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Competition
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Military Expenditures
of institutionalized constraints on the decision-making powers chief executives, whether individuals or collectivities." It is a 1- 7 scale. Vanhanen's (2011) measure on the percent of the total population who voted on an election, lagged one year. Vanhanen's (2011) measure on the percent achieved by small parties (or 100% minus the vote of the largest), lagged one year. 1 if a country is involved in an inter/intra state conflict; 0 otherwise. Log of military expenditures per capita, lagged one year.
Participation
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Past Incident
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Prior Months (4- 6)
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Independent Variable
See above ITERATE and GTD (see text and footnotes) Cheibub et al. (2010) http://www.systemicpeace.org/polity/polity4.htm
http://www.fsd.uta.fi/en/data/catalogue/ FSD1289/meF1289e.html See above
Gleditsch et al. (2002). http://www.prio.no/ /CSCW/Datasets/Armed-Conflict/UCDP-PRIO/ Correlates of War (2010): National Material Capabilities (http://www.correlatesofwar.org/) World Bank (http://data.worldbank.org/indicator) World Bank (http://data.worldbank.org/indicator) Cheibub et al. (2010)
ACCEPTED MANUSCRIPT Supplement A. Summary Statistics by Country for ITERATE
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4 13 7 16 18 3 12 6 7 9 12 7 19 10 8 14 6 1 15 13 9 11 5 17 1 10 3 10 9 5 6 4 10 2 18 11 11 9 13 2 4 4 6
Average # Attacks Average # Attacks Per Month Per Election Month 0.055 0 0.455 0.154 0 0 0.093 0.188 0.188 0.167 0.031 0 0.262 0 0.114 0.167 0.107 0 0.010 0 0.101 0.083 0.258 0 0.810 0.895 0.133 0.2 0.051 0.125 0.321 0.357 0.006 0 0.083 0 0.078 0 0.056 0.154 0.084 0.111 0.188 0.273 0.006 0 1.282 1.059 0 0 1.416 1 0.007 0 0.836 0.400 0.350 0.556 0.011 0 0.182 0 0.015 0 0.411 0.2 0.237 0 0.188 0.167 0.594 1 0.814 0.727 0.033 0.111 0.128 0.077 0.037 0 0.030 0 0.012 0 0 0
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# of Elections
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Years in Sample Model 1- Table 3 Albania 1994 - 2008 Argentina 1974 - 2008 Armenia 1992 - 2008 Australia 1968 - 2008 Austria 1968 - 2008 Bangladesh 1994 - 2008 Belgium 1969 - 2008 Bolivia 1982 - 2008 Brazil 1986 - 2008 Bulgaria 1991 - 2008 Canada 1968 - 2008 Chile 1968 - 2008 Colombia 1968 - 2008 Costa Rica 1969 - 2008 Croatia 1992 - 2008 Cyprus 1973 - 2008 Czech Republic 1994 - 2008 Czechoslovaquia 1991 - 1992 Denmark 1968 - 2008 Dominican Republic 1968 - 2008 Ecuador 1980 - 2008 El Salvador 1985 - 2008 Estonia 1993 - 2008 France 1968 - 2008 Georgia 2005 - 2008 Germany/West Germany 1971 - 2008 Ghana 1995 - 2008 Greece 1975 - 2008 Guatemala 1968 - 2008 Haiti 1995 - 2003 Honduras 1982 - 2008 Hungary 1991 - 2008 India 1968 - 2008 Indonesia 2000 - 2008 Ireland 1970 - 2008 Israel 1968 - 2008 Italy 1968 - 2008 Jamaica 1970 - 2008 Japan 1969 - 2008 Kenya 1999 - 2008 Kyrgyzstan 1999 - 2008 Latvia 1993 - 2008 Lithuania 1995 - 2008 Supplement A Continued
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Country Name
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0 0 0 0 0 0 0 0.308 0 0 0 0.100 0.250 0.333 0 0 2.429 0 0.118 0 0.250 0 0 0.125 1.625 0.125 na 0 0 0 0 0.20 0.40 na 0 0.778 0.550 0 0.111 0
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0.022 0 0 0.012 0 0.063 0.032 0.359 0.009 0.060 0.495 0.034 0.355 0.134 0.021 0.746 0.897 0.014 0.155 0.015 0.218 0 0.032 0.114 0.670 0.101 0.375 0.116 0.169 0.008 0.161 0.013 0.700 0.156 0.017 1.409 1.467 0.100 0.226 0.006
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6 2 6 2 4 1 3 13 12 4 2 10 4 3 4 3 7 10 17 4 8 2 2 8 8 8 0 12 10 6 10 10 10 0 8 9 20 4 9 3
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1996 - 2008 2003 - 2008 1994 - 2008 2001 - 2008 1995 - 2008 2000 - 2008 1992 - 2008 1969 - 2008 1970 - 2008 1985 - 2008 1999 - 2008 1969 - 2008 1973 - 2008 1990 - 2008 1988 - 2008 1981 - 2008 1987 - 2008 1990 - 2008 1977 - 2008 1991 - 2008 1992 - 2008 2002 - 2008 1995 - 2008 1989 - 2008 1978 - 2008 1971 - 2008 1986 - 1988 1970 - 2008 1969 - 2008 1997 - 2008 1983 - 2008 1971 - 2008 1968 - 2008 1981 - 1984 1993 - 2008 1969 - 2008 1968 - 2008 1968 - 2008 1970 - 2008 1992 - 2008
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Macedonia Madagascar Mali Mexico Moldova Namibia Nepal Netherlands New Zealand Nicaragua Nigeria Norway Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Romania Russia Sierra Leone South Africa South Korea Spain Sri Lanka Sudan Sweden Switzerland Taiwan Thailand Trinidad y Tobago Turkey Uganda Ukraine United Kingdom United States Uruguay Venezuela Zambia
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5 13 7 11 16 3 9 7 6 6 9 0 7 3 5 17 0 8 8 13 6 1 11 12 9 11 6 9 14 1 10 3 10 7 0 2 5 6 4 9 2 15 10
Average # Attacks Average # Attacks Per Month Per Election Month 0.223 2.000 1.074 1.077 0.038 0 0.086 0 0.106 0.313 1.710 15.667 0.100 0.444 0.024 0.143 0.622 0.333 0.299 0.333 0.088 0.444 0.077 na 0.054 0.143 0.006 0 1.375 0.200 10.676 12.647 0.125 na 0.048 0 0.051 0.125 0.129 0.308 0.053 0 0.042 0 0.029 0 0.158 0.5 0.302 0.556 4.968 5.727 0.027 0 0.012 0 1.671 1.071 0 0 0.796 0.3 0.012 0.333 1.115 2.300 2.246 1.571 0.033 na 0.023 0.500 0.642 1.400 0.439 0.500 0.140 0 7.766 12.111 1.557 2.000 0.102 0 2.112 0.900
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# of Elections
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Years in Sample Model 1- Table 4 Albania 1992 - 2008 Argentina 1974 - 2008 Armenia 1995 - 2008 Australia 1978 - 2008 Austria 1972 - 2008 Bangladesh 1992 - 2008 Belgium 1978 - 2008 Benin 1994 - 2008 Bolivia 1982 - 2008 Brazil 1986 - 2008 Bulgaria 1991 - 2008 Burundi 2007 - 2008 Canada 1980 - 2008 Central African Republic 1994 - 2008 Chile 1990 - 2008 Colombia 1975 - 2008 Congo 1994 - 1996 Costa Rica 1976 - 2008 Croatia 1992 - 2008 Cyprus 1975 - 2008 Czech Republic 1995 - 2008 Czechoslovaquia 1991 - 1992 Denmark 1979 - 2008 Dominican Republic 1971 - 2008 Ecuador 1980 - 2008 El Salvador 1985 - 2008 Estonia 1992 - 2008 Finland 1986 - 2008 France 1973 - 2008 Georgia 2005 - 2008 Germany/West Germany 1971 - 2008 Ghana 1994 - 2008 Greece 1976 - 2008 Guatemala 1976 - 2008 Guinea-Bissau 2005 - 2008 Guyana 1993 - 2004 Haiti 1995 - 2003 Honduras 1982 - 2008 Hungary 1992 - 2008 India 1976 - 2008 Indonesia 2000 - 2008 Ireland 1976 - 2008 Israel 1972 - 2008 Supplement B Continued
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Country Name
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1.889 0.714 1.273 0 0 0.200 0 0.333 0 0.167 0 0 0 0 1.333 0.167 0 0.250 0 2.000 0 4.000 0 0.250 4.667 5.571 0 0.294 0 2.000 0 0 0 0.167 0 0 6.250 5.500 na 0 0 0 4.556 0 5.111 na
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2.153 0.043 0.729 0.109 0.064 0.032 0.010 0.388 0.051 0.050 0 0.165 0.016 0.054 1.035 0.067 0.013 1.430 0 0.327 0.004 6.671 0.286 0.168 16.383 4.593 0.080 0.123 0 2.808 0.059 0 0.061 0.022 0.420 0.026 4.992 3.649 0.250 0.057 0.077 0 2.244 0.038 3.834 0.457
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M AN U
9 7 11 2 4 5 8 6 5 6 4 2 5 3 3 12 9 4 1 2 5 4 3 4 3 7 10 17 3 8 3 2 6 6 2 8 8 8 0 7 10 6 9 7 9 0
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1972 - 2008 1979 - 2008 1975 - 2008 2000 - 2008 1996 - 2008 1992 - 2008 1992 - 2008 1995 - 2008 1996 - 2008 1993 - 2008 1989 - 2008 2001 - 2008 1992 - 2008 1991 - 2008 1991 - 2008 1972 - 2008 1981 - 2008 1985 - 2008 1994 - 1995 1999 - 2008 1987 - 2008 1975 - 2008 1990 - 2008 1989 - 2008 1981 - 2008 1987 - 2008 1990 - 2008 1977 - 2008 1995 - 2008 1992 - 2008 2001 - 2008 2002 - 2008 1994 - 2008 1996 - 2008 1995 - 2008 1989 - 2008 1978 - 2008 1989 - 2008 1986 - 1988 1983 - 2008 1970 - 2008 1997 - 2008 1986 - 2008 1981 - 2008 1971 - 2008 1981 - 1984
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Italy Jamaica Japan Kenya Kyrgyzstan Latvia Lithuania Macedonia Madagascar Mali Mauritius Mexico Moldova Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Romania Russia Senegal Sierra Leone Slovakia Slovenia South Africa South Korea Spain Sri Lanka Sudan Sweden Switzerland Taiwan Thailand Trinidad y Tobago Turkey Uganda Supplement B Continued
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8 8 19 4 8 3
0.100 0.955 2.018 0.097 0.293 0.057
0.375 1.250 1.053 0.750 0.125 0
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EP
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M AN U
SC
RI PT
Ukraine United Kingdom United States Uruguay Venezuela Zambia
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0
.5
M AN U
0
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AveAttacksPerELECTIONMonth .5 1 1.5 2
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2.5
ITERATE
1
1.5
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AveAttacksPerMonth
0
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EP
AveAttacksPerELECTIONMonth 5 10 15
20
GTD Domestic
0
5
10 AveAttacksPerMonth
15
20
PII:
S0261-3794(14)00033-X
DOI:
10.1016/j.electstud.2014.03.002
Reference:
JELS 1451
To appear in:
Electoral Studies
Received Date: 28 January 2013 Revised Date:
4 March 2014
Accepted Date: 4 March 2014
Please cite this article as: Bali, V.A., Park, J., The Effects of the Electoral Calendar on Terrorist Attacks, Electoral Studies (2014), doi: 10.1016/j.electstud.2014.03.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Highlights
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We study the linkage between terrorist events in democracies and elections. We examine transnational and domestic terrorist trends over a 40 year span. Transnational terrorist events decline before elections. Domestic terrorist events increase before elections.
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• • • •
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The Effects of the Electoral Calendar on Terrorist Attacks
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JELS-D-13-00023: (R2): FINAL SUBMISSION
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Valentina A. Bali a*, Johann Park b
a
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(Corresponding Author) Department of Political Science, Michigan State University, 324 South Kedzie Hall, East Lansing, 48824, Michigan, Tel: 517-432-4491, email: [email protected]. b
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Political Science and Public Administration, Mississippi State University, 105 Bowen Hall, P.O. Box PC, Mississippi State, MS, 39762, Tel: 662-325-2711, email: [email protected].
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The Effects of the Electoral Calendar on Terrorist Attacks
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Abstract
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The goal of this paper is to study the linkages between the timing of terrorist events and elections. As strategic actors terrorists may respond to electoral environments by altering the frequency of their attacks around election times. Focusing on democracies, we examine variations in transnational and domestic terrorist incidents before elections over a 40 year span. We find distinct pre-electoral changes in the incidence of terrorist events. In the ITERATE dataset, where only transnational terrorist events are included, terrorist activities decline in election months, while in the partitioned GTD dataset, where only domestic terrorist events are kept, terrorist activities rise in election months. The findings suggest electoral calendars can dissuade and attract terrorist threats, depending on the origin of the threat, but these effects occur only very close to election time.
1. Introduction
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Keywords: Terrorism, Elections, Domestic Terrorism, Transnational Terrorism.
Since a seminal study by Sandler et al. (1983), scholars have long contended that many facets
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of terrorists’ actions are not capricious but in fact can respond to specific strategic goals and political circumstances (Berman and Laitin, 2008; Berrebi and Klor, 2006; Berrebi and
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Lakdwalla, 2007; Bueno de Mesquita, 2005; Kydd and Walter, 2002; Lapan and Sandler, 1988; Pape, 2003). Who is recruited, and then who is targeted, how they are targeted, and when they are targeted, among others, are dimensions that scholars of terrorism have found to be nonarbitrary in particular when examining long-standing conflicts (Barros et al., 2006; Benmelech and Berrebi, 2007; Clauset et al., 2010; Sanchez-Cuenca, 2001). From this conception of terrorists as relatively “rational” or strategic actors, it is reasonable to hypothesize that terrorists might also take into consideration the electoral calendar as a relevant dimension in their
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decisions. After all, the eventual consequences can be substantial. For example, the Madrid train bombings of 2004, three days before the Spanish general elections and with scores of casualties and injured, provide a striking example of what the timing of a terrorist event can produce. While
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pre-electoral polls suggested a winning margin for the incumbent Popular Party, the terrorist event, later established to have been conducted by Islamic militants, seemed to have helped derail in a matter of days the 4% advantage of the government party (Bali, 2007; Colomer, 2005;
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Montalvo, 2012; Torcal and Rico, 2004). While other transnational and domestic terrorist events have been clearly smaller in scale than the Madrid bombings, and in fact some may have
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responded to an opposite logic, one of de-escalation right before an election, the research puzzle still remains: in general, have terrorists exploited the electoral calendar in deciding the timing of their events?
The goal of this paper is to examine the linkages between the timing of terrorist events,
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transnational and domestic, and elections. If, as some scholars have argued, one of terrorists’ goals is to influence policymaking (Pape, 2005) then electoral times could prove to be particularly fecund times to do so. Remarkably, this line of inquiry has not been systematically
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explored in both broad cross-national settings and across extended periods of time although both the elections and terrorism literatures, as we detail in the next section, imply this line of inquiry
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merits attention. For example, pre-electoral periods may incite more terrorist activities because the electorates are more attentive to politics at those times, and interest groups are more strenuously vying for influence. On the other hand, pre-electoral times may inhibit terrorist activities because terrorist groups may have more opportunities to non-violently channel their dissent, or they may fear more aggressive retaliation from the government. In general, election times may enhance certain integral features of democracies and consequently further influence
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the prospects for terrorist events. There may be various ways in which electoral calendars influence, up or down, terrorist activity. The main goal of this study is to estimate the net impact of these electoral influences.
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This is important in order to build upon our previous knowledge on elections and terrorism, by providing a much needed empirical benchmark, but also in relation to security considerations and potential electoral consequences. In this study, we analyze country-month level data from the
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broad cross-national terrorism datasets ITERATE (1968-2008) and domestic GTD (1970-2008) to elucidate whether electoral months display differential levels of terrorist events. The analyses
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reveal that election months can both significantly deter and attract terrorist threats, depending on the origin of the threat, transnational or domestic; these effects occur close to election day.
2. Background Considerations and Expectations
The concept and definitions of terrorism are multiple and some of them are disputed
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(Gibbs, 1989; Hoffman, 2006; Norris et. al., 2003; Schmid and Jongman, 1988; Wilkinson, 2001; Young and Findley, 2011), yet, by now many definitions share several elements in common. In one such definition terrorism is understood to be “the unlawful use of violence or threat of
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violence to instill fear and coerce governments or societies” (U.S. Department of Defense, 2012). One frequent ingredient found in many of the definitions of terrorism refer to these acts of
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violence or threats as being “calculated,” “premeditated,” “systematic,” “purposive,” or “deliberate” (Hoffman, 2006; Norris et al., 2003; Wilkinson, 2001). Although many terrorist events seem after the fact indiscriminate and irrational, they often respond to a clear logic of purposeful intimidation. This intimidation may be aimed at internal or external audiences. More specifically, terrorist incidents can be classified as domestic or transnational depending on the nationality of the key actors involved. Domestic terrorist incidents occur when the main
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perpetrators, victims, and target audience are all from the same country; otherwise, if any of these three actors differ in nationality the event is deemed transnational (Mickolous et al., 2009; Li and Schaub, 2004).
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This study will explore whether the schedule of terrorist acts, domestic and transnational, is deliberately linked with the calendar of electoral politics. There are several strands of research on terrorism and elections that are relevant for developing expectations. Broadly, this research
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stems from statistical work on terrorist trends, conflict-specific work on governments and terrorist groups, institutional research on regime type and terrorist activity, research on elections
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and citizen engagement, and finally research on rally-effects and the diversionary use of force. At this point, there is little previous research that directly examines the relation between terrorist timing and elections across both a broad array of democracies and extended periods of time. There are two notable exceptions. In a preliminary study, Aksoy (2010) finds in the European
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context that proximity to elections increases domestic terrorist attacks among countries with more electoral disproportionality. In a descriptive study, Newman (2013) finds terrorist violence generally (as measured in GTD) increases closer to an election date among a sample of 117
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countries between the years 2000 and 2005. Both studies then contribute to the line of research addressed in this study, but the first study is geographically bounded while the second study
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focuses on a very short period of time, without simultaneously controlling for background country characteristics.
2.1 Trends in Terrorist Event Series To begin with, the research on trends of terrorist events has suggested the presence of cycles when examining aggregate series. More specifically, Enders and Sandler (1993, 1999, 2000, 2002, 2005) have examined in depth long term trends in transnational terrorist events using
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time series techniques with the popular ITERATE dataset. Their spectral analyses reveal that terrorist events with casualties display cycles between 4 and 5 years long (Enders and Sandler, 2000). That is, this is the period (or primary frequency) that most explains the variance of the
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series after de-trending. Secondary cycles are close to 2 years. As Enders and Sandler (2000) conjecture the cycles might be explained through various mechanisms of contagion, world political events, and swings in public opinion, including those related to electoral calendars.
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Thus, global trend analyses of transnational terrorism have identified some periodicities and terrorists’ strategic attention to election times may be contributing to them.
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2.2 Conflict-specific research
Plenty of conflict-specific research and game-theoretic work has addressed the calculated interplay between governments and terrorist groups (for early work see, Lapan and Sandler, 1988; Sandler et al., 1983), often finding that the activities of terrorist groups are highly
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responsive to the existing political context (Bueno de Mesquita, 2005, 2007; Clauset et al., 2010; Enders and Sandler, 1993, 2005; Gassebner et al., 2008; Gould and Klor, 2010; Hoffman, 2006; Kydd and Walter, 2002; Pape, 2003). Two cases of long-standing struggles with terrorism, those
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of Israel and Spain, have motivated much of this work. In the case of the Israeli-Palestinian conflict, Berrebi and Klor (2006, 2008) find that
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terrorist activity levels vary depending on the party in power (e.g., more events when the leftwing party is in power) and in turn they influence the electorate’s support for a given party. Clauset et al. (2010) find that Palestinian groups’ strategies, including violent attacks, are very sensitive to existing political circumstances (e.g., public support, inter-group competition, government countermeasures, and approaching elections) and are therefore dynamic. Finally, Bloom (2004, 2005) proposes that Palestinian suicide bombing attacks can be motivated, in part,
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by competition among factions to influence and mobilize public opinion through ramped-up violence. This “outbidding hypothesis” has been tested in the Palestinian-Israeli conflict with mixed or conditional support (Brym and Araj, 2008; Clauset et al., 2010; Jaeger et al., 2013). For
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example, Jaeger et al. (2013) find that the gains to each group from successful terrorist violence accrue within secular and Islamic factions, respectively, but not across them.
In the case of Spain’s conflict with the Basque separatist organization Euskadi Ta
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Askatasuna (ETA), this strand of research has also revealed evolving and context-dependent terrorist strategies (Barros et al., 2006; Mees, 2003; Sanchez-Cuenca, 2001). For example,
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Barros et al. (2006) find that ETA attacks increase in the summer and decrease with deterrence, repressive governments, and political accords. Motivated by both the Israeli and Spanish cases, Hodler and Rohner (2010) predict, in the sole game theoretic study on the timing of terrorist attacks and elections, that the risk of terrorist attacks can be higher at the beginning of electoral
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terms rather than later. In their analyses terrorists are more likely to strike early in an incumbent’s term in order to gain early informational advantages but also to avoid possible governmental retaliations closer to election time: “initially weak government may sometimes
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react aggressively to terror attacks later in their terms to show toughness to the voters” (p.188, Hodler and Rohner, 2010). Overall conflict-motivated studies provide rationales and evidence
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compatible with an association between the timing of terrorist attacks and elections but few general predictions, with the sole exception of Hodler and Rohner (2010). 2.3 Regime type and levels of terrorism Turning next to institutions and terrorism studies, the research on the origins of terrorism has examined at length the role of regime type. The vast majority of the evidence suggests a positive linkage between democracies and higher levels of terrorism, in particular transnational
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terrorism (Chenoweth, 2010; Eubank and Weinberg, 1994, 2001; Li, 2005; Li and Schaub, 2004; Pape, 2003; Schmid, 1992) but not all (e.g., Eyerman, 1998; Piazza, 2008). Multiple causal mechanisms have been proposed to link terrorism to regime characteristics. Earlier on, as
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Eyerman (1998) posits, the arguments could be categorized as stemming from two competing and opposing schools of thought on the links between democracy and terrorism: (1) the “political access school” which argued that democracies decrease the attraction of violent action by
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providing alternative channels of engagement, and (2) the “strategic school” which argued that democracies increase the likelihood of terrorist activity because of their constrained and open
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institutions (Brooks, 2009; Crenshaw, 1981; Schmid, 1992). Li (2005) addresses these opposing schools by including separate measures for each and finds that both are operational: higher voter turnout reduces transnational terrorist events while more governmental constraints and press freedoms increase them. More recent work has found further positive associations between
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democracies and terrorism due to democracies’ higher number of policy veto players (Young and Dugan, 2011) and competing political groups (Chenoweth, 2010) which can result in more deadlock and conflict. In addition, from a foreign policy perspective, democracies may be more
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prone to transnational terrorism since they may be in general more active in international affairs, which in turn can spur increased resentment from abroad (Savun and Phillips, 2009).1
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Given the inherent characteristics of democratic regimes which aspects get further highlighted during election times, potentially influencing terrorist attacks? One possibility is that during election times the “political access” dimension, that is the opportunity for political engagement, gets intensified since it is less likely that governmental constraints, press freedoms, or established veto players change dramatically during the last months before an election. It is 1
Further work on the determinants of terrorism has extended the analyses beyond regime type to examine the influences of regime history, economic development (Abadie, 2006; Krueger and Laitin, 2008; Sanchez-Cuenca, 2009), and operating costs (Lai, 2007), factors that are more impervious to the electoral calendar.
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also conceivable that right before an election democracies may be less likely to increase their level of engagement in international affairs (see Section 2.5). However, consistent with some of the more recent findings (i.e., Chenoweth, 2010) election times may also heighten political
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competition among societal groups. From an institutional perspective then elections may dampen terrorist attacks by providing rebels and dissidents with a time with more non-violent channels of expression and fewer causes for resentment, but they may also incite more events due to political
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competition. 2.4 Elections and citizen engagement
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Terrorists’ timing may also be influenced by when citizens are politically attentive and engaged. Elections and electorate studies provide many insights with regards to citizens’ levels of engagement. By now, there is a vast empirical literature on the degree of competence and attentiveness of electorates (Bartels, 1996, 2008; Campbell et al., 1960; Lau and Redlawsk,
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2006; Lewis-Beck et al., 2008; Popkin, 1991; Page and Shapiro, 1992; Zaller, 1992). For example, within the American context the electorate is found to display large variations in knowledge and attention to politics, with the verdict on how rational voters are when making up
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their minds at voting time still very much in the air (Bartels, 2008; Lau and Redlawsk, 2006; Lewis-Beck et al., 2008). However, when elections are near voters’ levels of engagement
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increase in particular as they become more receptive to campaign efforts (Arcenaux, 2005; Banducci and Karp, 2003). In fact, Gelman and King (1993) find that much of the engagement and learning occurs about six weeks before the election while, in a study of five western countries, Blais (2004) finds that close to 1 voter out of 6 typically changes their vote intention during the month preceding the election. Campaigns and the media can play a substantial role in mobilizing the electorate (Banducci and Karp, 2003; Hillygus and Shields, 2008), but many of
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these effects may take place close to the election and are short-lived (Gerber et al., 2011; Hill et al., 2008; Johnston et al., 2004). Since high publicity is a focal interest of terrorists (Nacos, 2002), election times seem then a unique period to influence the electorate given their level of
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interest. From this perspective, election times may especially attract domestic attacks as terrorists would take the opportunity to make a political statement upon a more receptive domestic audience.
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2.5 Rally effects and retaliations
Unexpected events can also induce changes of mind. In particular, there is ample
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evidence in relation to foreign policy that dramatic external threats, as from a surprise attack, can induce substantial changes in incumbent approval or “rally-around-the-flag” effects (Baker and Oneal, 2001; Baum, 2002; Brody and Shapiro, 1989; Colaresi, 2007; Edwards and Swenson, 1997; Hetherington and Nelson, 2003; Mueller, 1973). These effects are generally not long-lived
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and eventually peter out (Brody, 1991; Mueller, 1973). Nevertheless, transnational terrorist attacks, by quickly boosting national cohesion in the target society, may alter the political environment. A sudden rally before an election can be sufficient to improve the electoral chances
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of an incumbent. In fact, recent research suggests higher levels of terrorism in an electoral year incite a direct political response from electorates: they turn up at higher rates (Robbins et al.,
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2013). In addition, a transnational terrorist event close to an election may incite the incumbent to take harsh retaliatory actions to prove his problem-solving abilities. Indeed, scholars have found that heightened external threats by transnational terrorists can make people in democracies more supportive for hawkish foreign policies against the terrorists and their constituencies (Davis and Silver, 2004; Gadarian, 2010; Merolla and Zechmeister, 2009; Viscusi and Zeckhauser, 2003). But any action taken by an incumbent government close to an election time will also face high
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levels of scrutiny. In general then there may be considerable strategic calculations at work for both transnational terrorists and democratic leaders when they respectively consider actions, of aggression or retaliation, around election times.
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From the perspective of terrorist groups, they may dampen transnational terrorist incidents close to election time in order to avoid forceful retaliations. There is a large and evolving research agenda that examines the diversionary use of force by governments against
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external threats when incumbent leaders are domestically vulnerable (Clark, 2003; Fordham, 1998; Leeds and Davis, 1997; Meernik, 1994, 2001; Mitchell and Prins, 2004; Oneal and Tir,
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2006; Ostrom and Job, 1986; Smith, 1996, 1998). One strand in this diversionary literature has argued that potential target states strategically dodge conflict with large democracies that are facing domestic troubles (Kisangani and Pickering, 2009; Leeds and Davis, 1997; Smith, 1996). Similarly, international terrorist groups may also avoid attacks against democracies that are
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domestically vulnerable and close to election time. This might suggest that the “outbidding hypothesis” (Bloom, 2004, 2005), by which dissidents groups carry out attacks in order to “outbid” each other for public support, may not hold when the targeted nation is near an election.
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At those times, terrorist events may lead to rally-effects and forceful retaliations, which in turn may stifle support among the aggrieved population the terrorists claim to represent.
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From the perspective of democratic leaders, as elections approach they may reduce their tendencies towards higher involvement in international affairs (Savun and Phillips, 2009), in order to avoid ill-timed and potentially critical scrutiny. Again, from the diversionary literature, there is little evidence that democracies are more likely to use military force before an election. For example, Leeds and Davis (1997), Kisangani and Pickering (2009), and Oneal and Tir (2006) find no linkages between the use of force and elections. Colaresi (2007) argues that a
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dramatic foreign policy action around election time may bring about suspicions to voters about its true intentions and finds in the American context that foreign policy initiatives launched just before elections tend to generate in many cases smaller and even negative rally effects. It seems
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that democratic leaders have strategic reasons to avoid pursuing provocative and aggressive polices abroad when there is a high chance of political and electoral backlashes (Williams, 2013). In fact, as Oneal and Tir, (2006) argue, Gaubatz (1991) and Huth and Allee (2002) find more use
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of military force by democracies not before but just after an election, when their leaders are safest politically.
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2.6 Expectations
In sum, there are several possible mechanisms linking the timing of terrorist attacks to elections. Some mechanisms imply higher levels of terrorism at election times mainly due to increased attention from the public and increased political competition among societal groups.
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These arguments are more suitable when explaining domestic terrorist events. Other mechanisms imply lower levels of terrorism close to elections mainly due to increased channels of engagement for dissidents and more incentives to avoid rally-effects and armed retaliation.
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Having more political access may deter in particular domestic terrorist events while increased chances of forceful retaliation may discourage above all transnational terrorist events.
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The central purpose of the present study is not to weigh and assess each of these influences, for which specific measures may be unobtainable at this time for a broad crossnational study, but to evaluate empirically their net impact. With this in mind, we present expectations about their overall impact. First, we hypothesize the net influence will be positive for domestic events:
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Hypothesis 1 (Domestic events): the level of domestic terrorist activity will increase on election months. The rationale for this expectation is that attention effects and “outbidding” competition
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effects in general may be larger than participatory and retaliatory effects. That is, in the days around elections domestic dissident groups may be more likely to seek citizens’ attention, which is already heightened, and, as a result of this, they may also be more likely to compete for public
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support. On the other hand, around election times domestic dissident groups may not be dissuaded by, or find credible, last-minute political access or threats of retaliation.
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In the case of transnational terrorist events, our initial expectations may be somewhat less clear than those for domestic events. Nevertheless, we still think a negative relationship between elections and transnational terrorism is more plausible. The motivation for this expectation is that the potential for rally-effects and armed retaliation may swamp heightened attention and
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competition effects, and in general act as a strong deterrent for transnational actions close to an election. That is, election times may reduce the incidence of transnational terrorist acts as nondomestic groups seek to avoid unfavorable rally-effects and forceful retaliation from weak or
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threatened incumbent governments. Elections may also reduce the frequency of transnational terrorism by dampening at those times democracies’ tendencies towards high levels of
events:
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international involvement. We hypothesize then a ratcheting down (or comparable levels) of
Hypothesis 2 (Transnational events): the level of transnational terrorist activity will decrease (or remain unchanged) on election months. In addition, for domestic terrorist events in particular we expect the net electoral impact will be larger among countries experiencing little electoral competition since in those instances
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dissidents will have fewer opportunities to channel their grievances (Hypothesis 3: Electoral Competition). Finally, we conjecture any electoral effects will be larger in more recent times since technology and media advances may have increased the potential publicity and attention
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from dramatic events (Hypothesis 4: Period Effects).
3. Data and Methodology
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3.1 Dependent variable
The dependent variable is the number of terrorist events that occur in a democratic
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country in a month, and the unit of analysis is therefore a country-month. Countries are included in the analyses only for those years in which they are deemed democratic as per yearly categorizations of Golder (2004) and Cheibub et al. (2010) of regime types. Since we have different expectations for transnational and domestic terrorist events we need separate measures
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for them. For transnational terrorism we use data from ITERATE (International Terrorism Attributes of Terrorist Events), specifically collected for and most widely used in transnational event studies. For domestic terrorism, however, there is no data collection that has sufficient
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cross-sectional and time-series coverage. Another much widely used data collection is GTD (Global Terrorism Database). Although GTD includes both domestic and transnational incidents,
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it does not consistently distinguish between the two for the whole time period of study. Recently, Enders, Sandler and Gaibulloev (2011) partitioned out domestic events from the GTD dataset. We use then the GTD split data that includes only domestic events, as constructed by Enders, Sandler, and Gailbulloev (2011).2
2
We are very much thankful to Prof. Sandler for making the partitioned GTD dataset available. For more details on collection criteria for ITERATE and GTD see, respectively, Flemming et al. (2008) and LaFree and Dugan (2006). ITERATE is proprietary data which can be acquired at http://www.vinyardsoftware.com while GTD can be obtained at http://www.start.umd.edu/gtd.
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The estimation sample from ITERATE includes incidents from 83 democratic countries from the years 1968 to 2008. The estimation sample from the domestic partitioned GTD data includes incidents from 95 democratic countries from the years 1970 to 2008 (except for 1993
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for which GTD does not have data). 3.2 Independent variables
The key independent variable is Election Month, a discrete binary indicator flagging
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whether an election (presidential or legislative) took place in that month. Details on the coding and sources for all independent variables can be found in Appendix A. We also include two other
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discrete binary indicators, Prior Months (1-3) and Prior Months (4-6). The former indicator flags the previous three months to an election, and the latter indicator flags three months earlier. For example, if an election took place in November then Prior Months (1-3) would flag the months of August, September, and October, and Prior Months (4-6) would flag the months of May, June,
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and July. We also examined various other ways of modeling proximity to elections, such as with a counter variable that counts down the months towards an election or monthly dummy indicators spanning up to a year before the election.
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The variable Past Events is the logged monthly running average, up to the previous month, of past terrorist attacks within each country since the start of the dataset (1968 or 1970)
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or the country becoming democratic, as in Li (2005). In general, long term levels of terrorist activity, whether high or low, may systematically persist across time due to general historical propensities of a country or the specific focus of a terrorist group (Li, 2005; Midlarsky et al., 1980). This variable also allows us to control for any persisting serial correlation. Previous work on terrorist activity, in particular transnational terrorism, has found past incidence measures to be needed (e.g., Lai, 2007; Li and Schaub, 2004; Young and Dugan, 2011). By this control, if a
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country never has a terrorist incident in the whole period of study then it is excluded from the main study. However, as an alternative specification we also consider the simple lag of the dependent variable, instead of the log of the running average, in which case these countries with
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no terrorist experiences are also included.
With regards to political variables influencing the incidence of terrorist events, there is much guidance based on previous literature. Specifically, five variables related to a country’s
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political institutions and political conditions are included: Presidential, Executive Constraint, Participation, Electoral Competition and Conflict. Starting with political institutions,
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Presidential is a discrete binary indicator that identifies those countries with a presidential system, based on Cheibub et al.’s (2010) categorization. This variable is expected to have a positive effect on terrorist incidents. Previous research has argued and found that under certain specifications majoritarian systems may have higher terrorist incidents than proportional
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systems. According to Li (2005) there is a reduced motivation to resort to terrorism in proportional systems since they may be more inclusive, have fewer group rebellions, and more policy congruence with citizens’ preferences (see also, Huber and Powell, 1994; Reynal-Querol,
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2002). Executive Constraint is based on the POLITY IV executive constraint variable, in a scale from 1-7, where 1 indicates unlimited authority by the executive and 7 indicates parity or
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subordination (Marshall et al., 2009). This variable is also expected to have a positive effect on terrorist incidents since countries with more institutional constraints may be more limited in the range of their antiterrorist strategies (Crenshaw, 1981; Li, 2005). Participation and Electoral Competition are two variables from Vanhanen’s (2011) data collection that measure, respectively, the percentage of the population that voted in the most recent general election and the percentage of votes gained in these elections by smaller parties.
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These two measures reflect existing political opportunities and conditions. Both are expected to have a negative effect on terrorism since they capture dimensions of a democracy related to political access. Li (2005) found greater voter turnout had a negative impact on transnational
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terrorist incidents while Aksoy (2010) found in the European context that countries where it is easier to gain legislative seats have fewer domestic terrorist incidents.3 Finally, Conflict is a discrete binary indicator that denotes whether a country is experiencing an interstate armed
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conflict when examining the ITERATE data, and it flags an intrastate conflict when examining the domestic GTD data. External and internal general armed conflicts may accompany and
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attract terrorist incidents (Li and Schaub, 2004), since they highlight the existence of dissidence and violence.
We also include a large array of control variables that have by now become standard in the empirical terrorism literature. These variables account for military capability, economic
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development, population size, regime durability, post-cold war status, and regional indicators. Military Capability is the logged military expenditures per capita, lagged one year. Previous work had hypothesized that a country’s higher levels of capabilities and resources, including
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military ones, would discourage terrorist attacks; however, in all instances the opposite was found (Chenoweth, 2010; Li, 2005; Li and Schaub, 2004). Having more military resources may
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make the target state more valuable to terrorists due to wider visibility (Li, 2005); but it may also induce more attacks if those military resources are linked to political conflicts. GDP per Capita is the logged Gross Domestic Product (GDP) per capita, lagged one year, and the expectation is that higher levels of development reduce the likelihood of grievances (Li and Schaub, 2004).
3
Chenoweth (2010) conjectured political competition, or “the ability of citizens to influence government without restrictions” as measured in POLITY IV, has a positive impact on transnational terrorism by capturing crowding effects of one group on the other. We included as well POLITY IV’s political competition measure in all specifications but it never achieves statistical significance.
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Population Size is the log of the population and controls for an increased likelihood of terrorism in the larger states due to increased heterogeneity, difficulty to control, and larger number of potential dissidents (Lai, 2007; Li and Schaub, 2004; Eyerman, 1998). Regime Durability is the
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logged number of years since the most recent regime change, with the expectation that more unstable countries may be more prone to terrorist incidents (Eyerman, 1998; Li and Schaub, 2004; Piazza, 2008; Sanchez-Cuenca, 2009; Weinberg and Eubank, 1998). Post-Cold War is a
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discrete indicator that flags the years after 1991. This variable in conjunction with the regional variables can capture temporal and general geographic differences (Enders and Sandler, 1999; Li
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and Schaub, 2004).
Since the dependent variable is a count of the number of terrorist incidents in a month, the appropriate statistical method is an event count model, as opposed to ordinary least squares. In particular we use the negative binomial regression, a variant of count model specifications that
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has been commonly used in past studies of terrorist incidents since, among others, it addresses the features of over-dispersion present in this data (Chenoweth, 2010; Li, 2005; Li and Schaub, 2004; Young and Dugan, 2011). As detailed in the Robustness section we also examined
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alternative specifications through a zero-inflated count model that allows for two processes instead of one for generating the zeroes, and by incorporating country-level fixed effects. While
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the zero-inflated specification prevailed by some measures of fit over the basic negative binomial regression model, with or without country-level fixed effects, substantively entirely comparable results were obtained. We opted then, as in many previous studies, to focus the presentation on the negative binomial regression specification. Finally, the estimations were carried out with robust standard errors clustered at the country level to account for country-based correlations and all significance tests were two-tailed.
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3.3 Summary breakdown Table 1 highlights some of the characteristics of the dependent variable. Based on the availability of the independent variables, the estimation sample from the ITERATE database
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includes 7,336 incidents and the estimation sample from the GTD domestic database includes 28,254 incidents. The mean number of terrorist incidents per month among democratic countries for the ITERATE and GTD domestic datasets are, respectively, 0.32 and 1.2 in non-election
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months. Focusing on election months, which hover above 600 in each data set, the average number of incidents decreases to 0.27 in ITERATE and increases to 1.5 in domestic GTD. The
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distribution of terrorist incidents is highly skewed, with a preponderance of zeroes, or countrymonth observations without incidents. In the ITERATE data there are around 85 percent of zeroes, in election and non-election months, while in the GTD domestic data there are 77 percent of zeroes in non-election months and 73 percent in election months. Given the distributional
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characteristics, non-parametric tests such as Kruskall-Wallis and Mann-Whitney are better suited than normality-based tests to compare the distribution of terrorist events between election and non-election month samples. These tests reveal no statistically significant differences in the
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ITERATE data but statistically significant differences in the GTD data. These are suggestive
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aggregate comparisons though only indicative since further controls are needed. [Table 1 about here]
4. Findings
Tables 2 and 3 present the statistical results for the ITERATE and GTD domestic analyses. Each model is a negative binomial regression predicting the number of terrorist events in a country-month for a given array of controls or sub-samples. Models 1 and 2 in either table
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are the starting points of reference for this study. Model 1 is the full model while Model 2 is the full model omitting the indicators controlling for previous quarters to an election. [Tables 2 and 3 about here.]
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Starting with the key independent variable, Election Month, we find it has a negative and statistically significant effect in the ITERATE data (Table 2), where only transnational terrorist events are included. But, this variable has a positive and statistically significant effect with the
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domestic GTD data (Table 3).4 Substantively, the electoral effect translates in ITERATE into a decrease in the expected number of transnational incidents in election months of about 19
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percent, holding all other variables at their mean, and in GTD into an increase in the expected number of domestic incidents in election months of about 53 percent. For example, for a “modal” European country, before the Cold War, the expected number of transnational terrorist incidents is 0.31 in non-election months but decreases to 0.25 in election months. On the other
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hand, in such a country, the expected number of domestic GTD terrorist incidents is 0.42 in nonelection months but increases to 0.64 events in election months. Our first main hypothesis was that the net impact would be positive for domestic terrorist
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events, and we find this to be the case. This result would be in line with the logic of heightened payoffs. That is, domestic terrorist groups may engage in more activities around electoral times
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due to the heightened attention from the public and increased competition among groups. For transnational events the negative result corresponds to our second main hypothesis and in general is in line with the logic of decreased payoffs. Terrorist groups may engage in fewer activities around electoral times due to possible forceful retaliation and public rallies or, due to lack of saliency if domestic issues prevail. The argument for decreased terrorist activities due to more
4
The baseline, or omitted category, in Model 1 is all months more than 6 months away from an election and in Model 2 it is all non-election months.
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channels of engagement during election times is less convincing in the case of transnational terrorism since the dissident groups are external. The observed electoral effects are very focused in time. We explored other alternative
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specifications, such as a monthly counter decreasing in value towards an election as well as monthly dummy indicators for the span of a year before an election. The general counter was not statistically significant, in either ITERATE or domestic GTD, which a posteriori is not surprising
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since the average span between elections is 22 months and the observed effect occurs only at the very end, upon the election. In ITERATE when including monthly dummy indicators up to a
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year before an election, that is twelve indicators, only the indicators for the election month and for the month 6th before the election are statistically significant and negative (i.e., drops in events). In the domestic GTD dataset the analogous specification has only the election month and the month before it statistically significant, and positive (i.e., increases in events). We also
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examined the month and quarter following the election, with no influences found in either data set. The fact that the electoral effects are so concentrated in time may be related to the limited window of opportunity to induce or engage electorates.
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Our hypotheses also included the expectation that countries with little electoral competition would be more prone to attacks, in particular domestic, during election times and
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that election effects would be more prominent in recent times. We examined these hypotheses by generating sub-samples and then carrying out appropriate Wald tests. Models 3 and 4 present respectively the benchmark models for countries with high electoral competition (larger than 53%, the country-level mean) and low electoral competition (less than 53%). Models 5 and 6 present respectively the benchmarks models for the period before 1991 (pre-Cold War) and for the period after 1991 (post-Cold War). To assess election-related differences we then carried out
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Wald tests of equality of the election month coefficients across sub-samples. Results for these tests can be found at the bottom of the tables. In the ITERATE data (Table 2) there are no statistically distinguishable differential
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election effects between low and high electoral competition countries (chi2(1) = 1.03, p-v = 0.31). However, there are period differences. The election month decreases in ITERATE terrorist incidents mainly take place after the Cold War (chi2(1) = 3.22, p-v = 0.07). All else constant,
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after the Cold War the expected number of transnational terrorist incidents decreases by about 35 percent on election months. This implies the disincentives to carry out a transnational terrorist
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attack around election times have increased in recent decades. Recent times may have brought higher levels of exposure through around-the-clock media coverage, which could further induce public rallies, as well as higher levels of governmental preparedness through additional international cooperation on counterterrorism.
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In the domestic GTD data (Table 3) there are no distinguishable period effects, that is, the election month increases in terrorist attacks are comparable before and after 1991 (chi2(1) = 1.87, p-v = 0.17). However, as conjectured there are election competition effects. That is, countries
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with less electoral competition have more domestic GTD terrorist incidents in election months than countries with more electoral completion (chi2 (1) = 5.82, p-v = 0.02). All else constant,
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among countries with less electoral competition the expected number of domestic GTD terrorist incidents increases by about 110 percent on election months. This would suggest domestic terrorist incidents are quite susceptible to the existing political opportunities in a country such that when there are fewer political outlets the motivations to strike around election times increase.
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With regards to the control variables, and focusing back on the benchmark Models 1 and 2, most were in line with previous research. Concentrating first on past trends and the general political context, as expected, Past Incident has a large and positive significant effect on terrorist
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incidents whether in ITERATE or domestic GTD. Thus, historical trends of calm or activity tend to persist. The Presidential control does not achieve statistical significance in ITERATE and achieves statistical significance in the domestic GTD data but in the opposite direction than
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expected: presidential systems experience fewer domestic terrorist events than non-presidential systems. However, this result is not robust to key alternative specifications. For example, if past
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incidents is operationalized by the lag of the dependent variable, instead of a long-term running average of incidents, the presidential systems indicator does not achieve statistical significance, nor does it achieve significance when a zero-inflated count model is used (see Table 5, Models 12 and 13). The Executive Constraints variable does not achieve statistical significance in neither
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the ITERATE nor the GTD domestic data. Regarding Participation and Electoral Competition only the participation variable has a statistically significant effect and in both the ITERATE and domestic GTD data: higher voter turnout corresponds to a decreased number of terrorist
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incidents.5 Finally, Conflict has a positive and statistically significant effect in the domestic GTD data, with internal armed conflicts having a substantial effect on domestic terrorist incidents. In
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sum, the political environment matters in that trends in transnational or domestic terrorist activities tend to persist, countries involved in an internal armed conflict have more domestic terrorist incidents, and countries with higher turnout experience fewer terrorist incidents in general. 5
Examining non-democracies as well as democracies for the period 1975-1997 Li (2005) finds that Executive Constraints and an operationalization of participation have significant effects on transnational incidents. Focusing only on democracies we also find the significant dampening effect for higher turnout while our null finding for executive constraints is to be expected given the more limited variation in this variable when restricting the sample to democracies.
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When considering the remaining standard controls, the findings align with expectations and with results in previous works. Military Capability, GDP per Capita, and Population Size have all a statistically significant effect in both datasets and in the expected directions. Generally,
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countries with more military strength, less developed, and more populous are associated with more terrorist incidents. Regime Durability does not achieve statistical significance in ITERATE but does so in the GTD domestic data: more stable countries are less likely to experience
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domestic terrorist events. Finally, as others have noted, the number of transnational and domestic terrorist incidents have declined since the Cold War (Enders and Sandler 1999, 2005; Li, 2005;
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Young and Dugan, 2011).
5. Robustness and Alternative Specifications
We evaluated the robustness of the results to the following issues: 1) the endogeneity of elections, 2) the type of elections, 3) the influence of outlier countries, 4) the presence of
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country-level fixed effects, 5) the specification of a zero-inflated model, 6) the operationalization of past events, and 7) the severity of terrorist events. The results for these alternative examinations are presented in Tables 4 (ITERATE) and 5 (GTD). Beginning with endogeneity,
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elections may occur on some occasions before constitutionally pre-determined times, more particularly so in the case of parliamentary systems that expressly allow for early dissolutions.
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The timing of an election then can be endogenous to the current political circumstances. To address this we re-estimated the benchmark Model 1 separately for presidential countries and for non-presidential countries (parliamentarian and mixed), as seen in Models 7 and 8 in Tables 4 and 5. Based on Wald tests (see results at the bottom of the tables), there are no statistically significant differences in election month effects across presidential and non-presidential systems in neither dataset. In fact, the average span between elections for both presidential and non-
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presidential countries is exactly the same, at around 22 months. In addition, we carried out a test of endogeneity for the Election Month variable through a variant of the Hausman test for count models (Staub, 2009) and found no evidence for it. However, this test has limitations since we do
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not have good domestic predictors of electoral occurrences apart from the calendar itself. All in all, with some caution we think endogeneity is not unduly influencing the results. [Tables 4 and 5 about here.]
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Elections may carry different levels of saliency depending on the function of the election. In a presidential system, presidential elections have long-garnered more public and
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campaign attention than mid-term elections, often resulting in higher turnout rates (U.S. Census, 2012; McDonald, 2010). We re-estimated the benchmark Model 2 for the ITERATE and GTD domestic data with three indicators flagging, respectively, presidential only election months, legislative only election months, and concurrent presidential and legislative election months. The
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results are found in Models 9. There is limited evidence of saliency effects at work. In the ITERATE data all election month indicators are negative, though just the presidential election month coefficient is statistically significant. In the domestic GTD data all election month
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indicators are positive though now just the legislative election month coefficient is statistically significant. However, when further tests are carried out to distinguish the indicators’ coefficients
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amongst themselves, no statistical differences arise in both ITERATE and GTD domestic. There is substantial variability in the general trends of terrorist incidents across countries. Some of this variability may not be entirely explained by the control variables and may overly influence the results. To address this we examined for possible outlier countries. Specifically we inspected Pearson residuals averaged by country that were two standard deviations above the
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overall country average of residuals, in the ITERATE and GTD domestic data respectively.6 We then re-estimated the benchmark Model 1 removing each of the outlier countries one by one as well as all of the outlier countries simultaneously, as seen in Models 10. In each instance of
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removal the election month effects persisted in both the ITERATE and GTD domestic analyses. At the aggregate level, in online Supplements A and B (ITERATE and GTD domestic, respectively) of this study average terrorist event rates by country can be found broken down for
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non-election and election months. At a glance, these country summaries already reveal that the
driven by a few select countries.
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distinct election month effects found for transnational and domestic terrorist events are not
Methodologically we considered alternative specifications to the negative binomial that addressed country-level effects as well as excess, or differentiated, zeroes. In particular, we considered first negative binomial regressions with country-level fixed effects and country-level
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random effects. Hausman tests revealed that, both in ITERATE and domestic GTD, fixed effects are preferred to random effects (chi2(14) = 65.1, p-v < 0.01, and (chi2(14) = 340.5, p-v < 0.01, respectively). Models 11 in Tables 4 and 5 present the estimates when including country-level
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fixed effects in both data. In ITERATE the election month effect is still negative but no longer statistically significant at any conventional level, while in GTD the election month persists in
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magnitude and significance.
We then also considered a zero-inflated negative binomial model that allows zeroes to be generated by two different processes (a binary process and the count process). For the binary (inflated-zero) equation we included as independent variables all the political variables, age of
6
By this rule in the ITERATE data 6 countries were deemed outliers: Colombia, France, India, Israel, Italy, and Netherlands and in the GTD domestic data 8 countries: United Kingdom, Spain, Russia, Peru, Philippines, El Salvador, Colombia, and Indonesia. The countries derive from different regions of the world suggesting the idiosyncrasies are localized to each particular country.
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regime, and the measure of past incidents. It may be reasonable to expect that political-related factors shape whether a country persistently has no terrorism, and some research has preliminarily begun to test this (i.e., Young and Dugan, 2011). Further, as Drakos and Gofas
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(2006) suggest no events may also be a sign of under-reporting due to political constraints. Based on measures of fit, AIC and BIC, specifically, the zero-inflated negative binomial model was preferred to both the basic model and the basic model with fixed country-level effects.
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However, as can be seen in Models 12 in Table 4 and 5, substantively and in terms of statistical significance entirely comparable election month results were obtained with the zero-inflated
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count model. That is, with a zero-inflated model in ITERATE the expected number of transnational incidents in election months decreases by 19 percent, the same percentage factor as in the basic model, and in GTD the expected number of domestic incidents in election months increases by about 43 percent, comparable to the 53 percent in the basic model. Comparable
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substantive results are obtained for all other factors.
To account for past trends in terrorist incidence we included the logged monthly average of past terrorist attacks within each country since the start of the dataset (1968 or 1970) or the
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country becoming democratic, as in Li (2005). This operationalization, by taking the log, drops those countries that never have an attack throughout the whole period of analysis and includes
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those country-months thereafter once an event has occurred. As an alternative we re-estimated Model 1 with the lag of the dependent variable instead of the logged running average. One known and clear disadvantage of using the lagged dependent variable is that potentially too little is left to be explained. But, as can be seen in Models 13, fully comparable results to those in
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Model 1 were obtained. Substantively, they translate into a 17 percent reduction in transnational ITERATE terrorist incidents and a 47 percent increase in domestic GTD terrorist incidents.7 Finally, the dependent variable of our analyses is the number of events without
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accounting for their severity. We re-estimated Model 1 with the total number of fatalities from incidents in a month instead of the number of incidents. The results are found in Models 14. Interestingly, in the ITERATE data an election month has now a strong and positive significant
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effect. That is, election months are predicted to have more fatalities while in our main analyses of the ITERATE data election months were found to have fewer incidents. However, an outlier
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analysis, as was done before with counts of events, reveals that this result is entirely driven by Spain. When Spain is omitted from the analyses (Model 15 in Table 4) no statistically significant electoral month effects are found in terms of fatalities in the ITERATE data. With the GTD domestic data no election month effects are found with regards to fatalities to begin with, and
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this result holds after examining as well for potential outlier effects.
6. Discussion
This study has found that the number of terrorist events is distinct on election months and
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the variation depends on the origin of the threat. More specifically, when examining terrorist event data where only transnational events are included (e.g., ITERATE), events are less
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frequent on election months compared to non-election months. In contrast, when examining terrorist event data when only domestic events are included (e.g., GTD domestic), events are more frequent on election months compared to other months. Substantively the effects translate in the main specification into a predicted 19 percent reduction in ITERATE transnational 7
We also examined a zero-inflated negative binomial specification, as in Model 12, but using the lagged dependent variable instead of the logged monthly past average of events. In this specification, the election month effect remains statistically significant at the 0.10 level for both ITERATE and GTD domestic data, translating again into a 17 percent reduction in transnational ITERATE terrorist incidents and this time a 25 percent increase in domestic GTD terrorist incidents.
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terrorist incidents and a 53 percent increase in GTD domestic terrorist incidents. Moreover, these election month effects are mediated by time period and level of electoral competition. In particular, the decrease in transnational terrorist incidents in election months occur mainly after
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the Cold War (or 1991) while the increase in domestic terrorist events in election months occur more distinctively among countries with low levels of electoral competition.
How do we begin to interpret these results in light of the various mechanisms of
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influence suggested from previous research (e.g., attention, competition, channeling of dissent, and retaliation)? Focusing first on the ITERATE-based results, or the dampening of transnational
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terrorist events around elections, strategic considerations related to possible retaliation and illtimed attention may be playing a key role. In the complex back-and-forth between governments and terrorist actors, terrorist groups appear to de-escalate activities, in particular large-scale ones, at election times in order to avoid forceful retaliation (Hodler and Rohner, 2010). By the logic of
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diversionary foreign policy, leaders in critical domestic circumstances may especially wish for a legitimate opportunity to use force abroad (e.g., after a newly imposed external threat) to boost reelection chances. But by the counterpart logic of strategic avoidance (Leeds and Davis, 1997),
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terrorist groups may have strategic reasons to avoid targeting governments around election times to precisely scuttle any chances of hostile public rallies and armed retribution, which in turn can
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be detrimental to their public support. In addition, from the perspective of electoral attention cycles a decrease in transnational events, especially if small-scaled, during election months may also be expected if non-domestic terrorist groups find their grievances less likely to garner public attention during electoral times, which in general tend to prioritize domestic issues. If governments’ abilities to pre-empt and re-act against transnational terrorist attacks have
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increased in the post-Cold War era, we would expect electoral dampening of transnational terrorist attacks to be more of a post-Cold War phenomenon, which is indeed what we found. Focusing on the GTD results, or the increase in domestic terrorist events around
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elections, we found that attention-related incentives may be at work. Domestic terrorist events close to election times can garner the spotlight on the domestic grievance at hand and on the inadequacies of the incumbent government. The fact that the observed electoral effect is so
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concentrated in time is consistent with the electorate’s attention being a likely explanatory mechanism. That is, what critically change during the last months before an election are the
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public’s level of engagement and the level of competition among societal groups to favorably garner this attention. Long-term institutions (e.g., number of veto players, party thresholds, and institutional constraints) do not dramatically change in those last pre-election months. In addition, the finding that the election-month increase in domestic attacks is mediated by electoral
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competition is in congruence with the “access school” explanation that curtailed political access, an evolving political opportunity, increases terrorist activity (see also, Aksoy, 2010). All in all, this present study provided a much needed empirical benchmark on election
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time influences on the incidence of terrorist events across an ample cross section of countries and for an extended period. From a policy perspective, the results imply that counter-terrorism
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strategies need to attune to the electoral calendar in order to further prevent terrorist events, especially domestic ones. The fact that transnational terrorist events decrease on election months but only after the Cold War suggests that dimensions of our current times, such as continuous media coverage of on-going events, which could further encourage a public rally, as well as international treaties of cooperation on counterterrorism, may be contributing as deterrents.
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We conclude with suggestions for future research. First, more work still waits to probe the relative importance of the various possible mechanisms of influence related to the electoral calendar. Such designs will require more detailed information on both the terrorist groups
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themselves and the domestic political context. This type of collection effort can then be accompanied by a dyadic research design, prevalent in the interstate conflict literature, to fully explore the dual nature of government and terrorist actors’ interactions. For example, it would be
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useful to assess the ideological leanings of incumbent governments and those of terrorists groups. With these measures, we may then probe how event rates vary with the degree of
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ideological consonance between governments and dissident groups, including close to election times. Second, future work may also investigate the relative role of regime history, development, and power. It is very likely that considerations of short-term political circumstances, such as fluctuations in attention cycles and relative strength of government leaders, are critical for the
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revealed association between election time and terrorism trends. But, long-term structural traits
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may further influence terrorists’ strategic decisions around the electoral calendar.
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Table 1. Mean and Frequency of Terrorist Incidents in ITERATE and Domestic GTD in Non-Election and Election Months GTD Domestic (1970-2008)
Democratic Countries= 83
Democratic Countries= 95
Country-Month Observations= 23,311
Country-Month Observations= 22,588
Country-Month Observations with Elections= 644
Country-Month Observations with Elections= 629
Mean
0.32
0.27
Percent "Zeroes"
85.15%
85.56%
Kruskall-Wallis Test Mann-Whitney Test
Chi2= 0.042 Z = 0.35
p-v = 0.83 p-v = 0.73
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Election Months
Total Number of Incidents= 28,254
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Total Number of Incidents= 7,336
Non-Election Months
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Note: Percent "zeroes" is the percent of country-month observations with no incidents.
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ITERATE (1968-2008)
Non-Election Months
Election Months
1.24
1.48
77.33%
72.65%
Chi2 = 3.89 Z = -2.68
p-v = 0.05 p-v = 0.01
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Table 2. Effect of Electoral Proximity on Monthly Terrorist Incidents in ITERATE (1968-2008) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 High Comp. -0.293** (0.13) 0.196 (0.25) 0.017 (0.17) 0.748*** (0.07) 0.085 (0.16) 0.090* (0.05) -0.022*** (0.007)
-0.308 (0.19) 0.167** (0.07) -0.283** (0.14) 0.075 (0.07) 0.081 (0.11) -1.075*** (0.16) 1.621 (1.58) 9,819
0.365 (0.26) 0.328*** (0.07) -0.363*** (0.10) 0.068* (0.04) -0.098 (0.07) -1.06*** (0.16) 3.01 (0.99) 13,492
Pre-CW -0.049 (0.13) 0.253 (0.24) 0.017 (0.10) 0.671*** (0.08) 0.517** (0.25) 0.051 (0.06) -0.015** (0.006) 0.006 (0.01) -0.375** (0.15) 0.196*** (0.07) -0.235** (0.10) 0.132*** (0.05) -0.047 (0.07)
Post-CW -0.426** (0.17) -0.097 (0.18) -0.002 (0.20) 0.972*** (0.09) -0.194 (0.28) -0.052 (0.08) -0.013* (0.007) -0.002 (0.01) 0.671*** (0.26) 0.474*** (0.08) -0.580*** (0.12) 0.022 (0.06) -0.078 0.11)
-0.095 (0.79) 9,081
5.79 (1.63) 14,230
Wald χ2(Overall)
1815***
1568***
2662***
1220***
1075***
744***
Alpha
2.8 (0.4)
2.8 (0.4)
1.9 (0.4)
3.3 (0.5)
2.2 (0.4)
4.6 (0.8)
Executive Constraint Participation Competition Interstate Conflict Military Expenditures GDP per Capita Population Age Regime Post Cold War
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Constant
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Presidential
0.786*** (0.06) 0.232 (0.14) 0.040 (0.05) -0.016*** (0.005) 0.002 (0.01) 0.178 (0.23) 0.287*** (0.05) -0.367*** (0.08) 0.071* (0.04) -0.027 (0.06) -1.028*** (0.12) 2.650 (0.91) 23,311
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Past Incident
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Prior Months (4- 6)
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Observations
Prior Months (1-3)
-0.217* (0.11)
Low Comp. -0.068 (0.18) 0.101 (0.10) -0.01358 (0.14) 0.818*** (0.07) 0.557** (0.23) -0.024 (0.07) -0.012 (0.01)
-0.205* (0.11) 0.126 (0.17) 0.015 (0.13) 0.787*** (0.06) 0.230 (0.14) 0.040 (0.05) -0.016*** (0.005) 0.002 (0.01) 0.177 (0.23) 0.287*** (0.05) -0.368*** (0.08) 0.071* (0.04) -0.029 (0.06) -1.026*** (0.12) 2.659 (0.92) 23,311
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Election Month
χ2 (1)= 1.03 p-v=0.31 χ2 (1)= 3.22 p-v=0.07 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. * p < 0.10, ** p < 0.05, *** p <0.01.
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Table 3. Effect of Electoral Proximity on Monthly Terrorist Incidents in GTD Domestic (1970-2008) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Presidential Executive Constraint Participation Competition Intrastate Conflict Military Expenditures GDP per Capita Population Age Regime Post Cold War
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Constant
0.643*** (0.04) -0.315** (0.15) -0.014 (0.05) -0.011** (0.005) 0.005 (0.01) 0.975*** (0.20) 0.189*** (0.05) -0.143** (0.07) 0.217*** (0.04) -0.169*** (0.06) -0.827*** (0.11) -0.98 (0.82) 22,588 3961*** 2.4 (0.3)
1.399*** (0.20) 0.202** (0.09) -0.243** (0.12) 0.317*** (0.06) -0.225** (0.09) -0.782*** (0.13) -2.51 (1.18) 10,006 1596***
0.542** (0.25) 0.175*** (0.05) -0.196** (0.09) 0.122** (0.05) -0.098 (0.07) -0.807*** (0.15) 1.845 (1.47) 12,582 2814***
2.2 (0.3)
2.3 (0.4)
Pre-CW 0.208 (0.18) 0.220* (0.13) 0.126 (0.11) 0.746*** (0.04) 0.084 (0.28) -0.036 (0.06) -0.011 (0.01) 0.023** (0.01) 0.436** (0.17) -0.001 (0.07) -0.171 (0.13) 0.141*** (0.05) -0.042 (0.09)
Post-CW 0.566*** (0.18) -0.138 (0.10) -0.160 (0.12) 0.586*** (0.06) -0.398** (0.17) 0.034 (0.06) -0.007 (0.006) -0.005 (0.01) 1.270*** (0.22) 0.348*** (0.09) -0.263** (0.10) 0.240*** (0.06) -0.183** (0.09)
-0.89 (1.21) 6,748 3367***
-0.85 (1.02) 15,840 1145***
1.7 (0.3)
2.7 (0.4)
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Past Incident
High Comp. 0.103 (0.15) -0.073 (0.11) -0.134 (0.10) 0.711*** (0.04) -0.562** (0.22) -0.062 (0.08) -0.012 (0.008)
SC
2.4 (0.3)
Prior Months (4- 6)
Low Comp. 0.746*** (0.21) 0.172* (0.09) 0.112 (0.11) 0.545*** (0.05) -0.277 (0.20) 0.023 (0.05) -0.002 (0.006)
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Alpha
Prior Months (1-3)
0.4228*** (0.13)
TE D
Observations LR χ2(Overall)
0.425*** (0.13) 0.042 (0.08) -0.019 (0.09) 0.643*** (0.04) -0.314** (0.15) -0.014 (0.05) -0.011** (0.005) 0.005 (0.01) 0.975*** (0.20) 0.190*** (0.05) -0.144** (0.07) 0.217*** (0.04) -0.169*** (0.07) -0.827*** (0.10) -0.98 (0.82) 22,588 4133***
EP
Election Month
χ2 (1)=5.82 p-v=0.02 χ2 (1)= 1.87 p-v=0.17 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. * p < 0.10, ** p < 0.05, *** p <0.01.
ACCEPTED MANUSCRIPT
Table 4. Alternative Specifications in ITERATE (1968-2008)
Pres. & Leg. Election Month
Prior Months (4- 6) Past Incident
0.329 (0.26) 0.026 (0.19) 0.804*** (0.07)
-0.182* (0.11) 0.010 (0.13) 0.816*** (0.11)
-0.049 (0.04) 0.003 (0.01) 0.001 (0.01) 0.174 (0.26) 0.298*** (0.07) -0.392*** (0.08) 0.087* (0.05)
0.145 (0.09) -0.026*** (0.007) -0.003 (0.01) -0.06 (0.23) 0.201** (0.09) -0.220 (0.16) 0.032 (0.07)
Participation Competition Interstate Conflict Military Expenditures GDP per Capita Population
0.786*** (0.06) 0.234 (0.14) 0.040 (0.05) -0.017*** (0.005) 0.002 (0.01) 0.177 (0.23) 0.286*** (0.05) -0.367*** (0.08) 0.070* (0.037)
AC C
Presidential Executive Constraint
Model 13 Lagged DV
Model 14 Fatalities
-0.259* (0.15)
-0.177 (0.17)
-0.208* (0.12)
-0.185* (0.11)
1.59* (0.84)
-0.054 (0.11) 0.023 (0.14) 0.831*** (0.04) 0.071 (0.10) 0.006 (0.05) -0.012*** (0.004) 0.003 (0.01) 0.391 (0.25) 0.290*** (0.04) -0.341*** (0.08) 0.033 (0.04)
0.112 (0.10) 0.048 (0.09) 0.524*** (0.07) 0.437 (0.39) -0.083 (0.05) -0.014** (0.006) 0.0003 0.004 0.134 (0.12) 0.169** (0.08) -0.471*** (0.09) -0.041 (0.28)
0.093 (0.14) 0.033 (0.12) 0.322*** (0.05) 0.777*** (0.29) 0.007 (0.07) -0.015 (0.009) 0.003 (0.01) 0.472 (0.31) 0.589*** (0.07) -0.388*** (0.10) 0.332*** (0.06)
-0.09 (0.34) 0.349 (0.43) 0.563*** (0.14) 0.300 0.41 -0.193 (0.13) -0.021* (0.01) -0.011 (0.01) -0.745 (0.53) 0.880*** (0.22) -0.748** (0.31) 0.493*** (0.13)
-0.351* (0.18) -0.137 (0.13) -0.487 (0.34)
Legislative Election Month
Prior Months (1-3)
Model 12 ZINB Count Model
RI PT
Saliency
Model 11 Country Fixed Eff.
SC
Presid. -0.175 (0.21)
Model 10 Without Outliers
M AN U
-0.242* (0.13) Presidential Election Month
Model 9
TE D
Election Month
Model 8
EP
Model 7 Non Presid.
0.096 (0.16) 0.001 (0.12) 0.790*** (0.05) 0.026 (0.16) 0.044 (0.05) -0.019*** (0.004) 0.004 (0.01) 0.225 (0.25) 0.259*** (0.05) -0.368*** (0.08) 0.078* (0.04)
Model 15 Fatalities ~ Spain -0.265 (0.23)
-0.094 (0.34) 0.355 (0.43) 0.552*** (0.14) 0.299 (0.40) -0.217 (0.14) -0.021* (0.01) -0.009 (0.01) -0.652 (0.52) 0.925*** (0.23) -0.835** (0.31) 0.466*** (0.13)
ACCEPTED MANUSCRIPT Table 4 Continued Age Regime Post Cold War
-0.042 (0.06) -0.944*** (0.15)
0.024 (0.12) -1.23*** (0.15)
-0.027 (0.06) -1.028*** (0.12)
-0.034 (0.07) -1.048*** (0.13)
-0.308*** (0.06) -0.541*** (0.09)
0.048 (0.08) -1.015*** (0.13)
-0.069 (0.08) -1.342*** (0.13)
0.216* (0.12) -1.29*** (0.39)
0.249* (0.13) -1.34*** (0.40)
RI PT
Zero Binary Model Past Incident
SC
Presidential
-0.166 (0.34) -18.96 (1.14)*** -0.315 (0.27) -0.143 (0.04)*** 0.043 (0.02)* 2.611 (0.82)*** -5.48 (1.63)
M AN U
Executive Constraint Participation Competition
Constant (Binary Model)
2.68 (1.20)
1.92 (2.13)
2.65 (0.91)
Observations LR χ2(Overall) Alpha
15,344 1724*** 3.2 (0.5)
7,967 1180*** 2.1 (0.5)
23,331 1633*** 2.8 (0.4)
3.35 (0.99)
7.65 (5.36)
2.45 (0.99)
-1.640 (1.29)
2.30 (3.5)
3.46 (3.6)
20,461 2405*** 2.9 (0.4)
23,331 8721*** 2.6 (0.8)
23,331 1167*** 2.52 (0.34)
26,872 952*** 3.4 (0.5)
23,311 207*** 46.8 (8.4)
22,950 228***
AC C
EP
Constant
TE D
Age Regime
Vuong 3.33***
χ2 (1)= 0.07 p-v=0.78 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. Controls for regions (not shown) were also included.
Model 12 is a zero-inflated negative binomial regression, with a count equation and a "zero" binary equation. * p < 0.10, ** p < 0.05, *** p <0.01.
46.6 (8.4)
ACCEPTED MANUSCRIPT
Table 5. Alternative Specifications in GTD Domestic (1970-2008)
Presid.
Saliency
0.488*** (0.18)
0.212 (0.16)
Pres. & Leg. Election Month
Prior Months (4- 6) Past Incident
0.111 (0.11) -0.041 (0.10) 0.717*** (0.03)
-0.044 (0.07) 0.030 (0.14) 0.407*** (0.10)
-0.032 (0.05) -0.009* (0.005) 0.0001 (0.01) 0.711*** (0.21) 0.173*** (0.04) -0.165* (0.09) 0.176*** (0.04)
0.118 (0.08) -0.008 (0.01) 0.012* (0.006) 1.891*** (0.28) 0.395*** (0.12) -0.365** (0.17) 0.240*** (0.07)
Competition Intrastate Conflict Military Expenditures GDP per Capita Population
AC C
Participation
0.643*** (0.04) -0.313** (0.16) -0.014 (0.05) -0.011** (0.005) 0.005 (0.005) 0.975*** (0.20) 0.189*** (0.05) -0.144** (0.07) 0.217*** (0.04)
EP
Presidential Executive Constraint
Model 12 Model 13 ZINB Lagged Count Model DV
Model 14
0.484*** (0.15)
0.342*** (0.11)
0.360*** (0.13)
0.385** (0.15)
0.256 (0.17)
0.054 (0.07) -0.029 (0.08) 0.397*** (0.05) -0.345** (0.16) 0.069 (0.05) -0.011** (0.005) -0.001 (0.005) 1.098*** (0.24) 0.161*** (0.06) -0.142* (0.07) 0.202*** (0.04)
0.035 (0.06) -0.085 (0.06) 0.152*** (0.03) 0.188 (0.27) 0.003 (0.06) -0.0008 (0.01) -0.0007 (0.01) 1.442*** (0.23) 0.454*** (0.08) -0.234* (0.13) 0.516*** (0.06)
0.242 (0.29) -0.251* (0.14) 0.569*** (0.07) 0.215 (0.38) -0.099 (0.07) -0.008 (0.01) 0.0004 (0.01) 2.099*** (0.26) 0.298*** (0.11) -0.527*** (0.15) 0.404*** (0.07)
0.086 (0.23) 0.499*** (0.16) 0.379 (0.33)
Legislative Election Month
Prior Months (1-3)
Model 11 Country Fixed Eff.
0.022 (0.09) -0.103 (0.09) 0.634*** (0.04) -0.306* (0.16) -0.052 (0.04) -0.008* (0.004) 0.005 (0.004) 0.957*** (0.16) 0.232*** (0.05) -0.172** (0.07) 0.230*** (0.04)
0.083 (0.07) 0.014 (0.07) 0.737*** (0.03) 1.271** (0.65) -0.060** (0.03) -0.011*** (0.003) 0.011*** (0.002) 0.992*** (0.07) 0.104** (0.05) -0.488*** (0.06) -1.559*** (0.24)
M AN U
Presidential Election Month
Model 10 Without Outliers
RI PT
Model 9
SC
Model 8
TE D
Election Month
Model 7 Non Presid.
Fatalities
ACCEPTED MANUSCRIPT Table 5 Continued Age Regime Post Cold War
-0.116* (0.06) -0.688*** (0.16)
-0.184* (0.09) -0.819*** (0.12)
-0.169 *** (0.06) -0.827*** (0.11)
-0.212*** (0.06) -0.799*** (0.12)
-0.321*** (0.04) -0.155** (0.07)
-0.152** (0.08) -0.825*** (0.11)
-0.380*** (0.09) -0.267** (0.11)
-0.074 (0.08) -0.273 (0.18)
-5.80 (1.71) 26,175 579*** 3.2 (0.4)
-1.71 (1.62) 22,588 939*** 6.7 (1.5)
RI PT
Zero Binary Model
Past Incident
SC
Presidential
M AN U
Executive Constraint Participation Competition
TE D
Age Regime
Observations LR χ2(Overall) Alpha
0.077 (0.74) 14,913 1944*** 2.4 (0.5)
-1.33 (1.45) 7,675 1400*** 2.0 (0.4)
AC C
Constant
-0.98 (0.82) 22,588 4043*** 2.4 (0.3)
EP
Constant (Binary Model)
-0.63 (0.69) 20,625 3628*** 2.6 (0.4)
35.58 (4.61) 22,588 29985*** 2.1 (0.05)
-0.804*** (0.06) -0.05 (0.37) 0.162 (0.11) 0.010 (0.010) -0.022** (0.01) 0.004 (0.14) -1.64 (0.74) -0.86 (0.89) 22,588 711*** Vuong 5.49***
χ2 (1)= 1.31 p-v=0.25 Test of Equality of "Election Month" Note: Robust standard errors clustered by country are in parentheses. All tests are two-sided. Controls for regions (not shown) were also included. Model 12 is a zero-inflated negative binomial regression, with a count equation and a "zero" binary equation. * p < 0.10, ** p < 0.05, *** p <0.01.
ACCEPTED MANUSCRIPT
Appendix A. List of Independent Variables Definition
Source
Election Month
1 if an election month; 0 otherwise. Only first round of an election is considered.
Prior Months (1-3)
1 for the three preceding months to an election; 0 otherwise. 1 for the preceding months 4, 5 and 6 to an election; 0 otherwise. Log of the average monthly number of terrorist incidents up to the previous month. 1 if the country has a presidential system of government; 0 otherwise. Executive constraint variable from POLITY IV: "the extent
Parline database of national parliaments http://www.ipu.org/parline-e/parlinesearch.asp Psephos : http://psephos.adam-carr.net/countries Center on Democratic Performance (ERA) http://cdp.binghamton.edu/era/index.html See above
Presidential Executive Constraint
GDP per Capita Population Age of Regime Post Cold War
Log of Gross Domestic Product per capita, lagged one year. Log of Population. Log of years of the current regime (democracy). 1 from 1991 onwards; 0 otherwise.
EP
Inter/Intrastate Conflict
AC C
Competition
TE D
Military Expenditures
of institutionalized constraints on the decision-making powers chief executives, whether individuals or collectivities." It is a 1- 7 scale. Vanhanen's (2011) measure on the percent of the total population who voted on an election, lagged one year. Vanhanen's (2011) measure on the percent achieved by small parties (or 100% minus the vote of the largest), lagged one year. 1 if a country is involved in an inter/intra state conflict; 0 otherwise. Log of military expenditures per capita, lagged one year.
Participation
SC
Past Incident
M AN U
Prior Months (4- 6)
RI PT
Independent Variable
See above ITERATE and GTD (see text and footnotes) Cheibub et al. (2010) http://www.systemicpeace.org/polity/polity4.htm
http://www.fsd.uta.fi/en/data/catalogue/ FSD1289/meF1289e.html See above
Gleditsch et al. (2002). http://www.prio.no/ /CSCW/Datasets/Armed-Conflict/UCDP-PRIO/ Correlates of War (2010): National Material Capabilities (http://www.correlatesofwar.org/) World Bank (http://data.worldbank.org/indicator) World Bank (http://data.worldbank.org/indicator) Cheibub et al. (2010)
ACCEPTED MANUSCRIPT Supplement A. Summary Statistics by Country for ITERATE
EP
AC C
RI PT
4 13 7 16 18 3 12 6 7 9 12 7 19 10 8 14 6 1 15 13 9 11 5 17 1 10 3 10 9 5 6 4 10 2 18 11 11 9 13 2 4 4 6
Average # Attacks Average # Attacks Per Month Per Election Month 0.055 0 0.455 0.154 0 0 0.093 0.188 0.188 0.167 0.031 0 0.262 0 0.114 0.167 0.107 0 0.010 0 0.101 0.083 0.258 0 0.810 0.895 0.133 0.2 0.051 0.125 0.321 0.357 0.006 0 0.083 0 0.078 0 0.056 0.154 0.084 0.111 0.188 0.273 0.006 0 1.282 1.059 0 0 1.416 1 0.007 0 0.836 0.400 0.350 0.556 0.011 0 0.182 0 0.015 0 0.411 0.2 0.237 0 0.188 0.167 0.594 1 0.814 0.727 0.033 0.111 0.128 0.077 0.037 0 0.030 0 0.012 0 0 0
SC
# of Elections
M AN U
Years in Sample Model 1- Table 3 Albania 1994 - 2008 Argentina 1974 - 2008 Armenia 1992 - 2008 Australia 1968 - 2008 Austria 1968 - 2008 Bangladesh 1994 - 2008 Belgium 1969 - 2008 Bolivia 1982 - 2008 Brazil 1986 - 2008 Bulgaria 1991 - 2008 Canada 1968 - 2008 Chile 1968 - 2008 Colombia 1968 - 2008 Costa Rica 1969 - 2008 Croatia 1992 - 2008 Cyprus 1973 - 2008 Czech Republic 1994 - 2008 Czechoslovaquia 1991 - 1992 Denmark 1968 - 2008 Dominican Republic 1968 - 2008 Ecuador 1980 - 2008 El Salvador 1985 - 2008 Estonia 1993 - 2008 France 1968 - 2008 Georgia 2005 - 2008 Germany/West Germany 1971 - 2008 Ghana 1995 - 2008 Greece 1975 - 2008 Guatemala 1968 - 2008 Haiti 1995 - 2003 Honduras 1982 - 2008 Hungary 1991 - 2008 India 1968 - 2008 Indonesia 2000 - 2008 Ireland 1970 - 2008 Israel 1968 - 2008 Italy 1968 - 2008 Jamaica 1970 - 2008 Japan 1969 - 2008 Kenya 1999 - 2008 Kyrgyzstan 1999 - 2008 Latvia 1993 - 2008 Lithuania 1995 - 2008 Supplement A Continued
TE D
Country Name
ACCEPTED MANUSCRIPT
EP
0 0 0 0 0 0 0 0.308 0 0 0 0.100 0.250 0.333 0 0 2.429 0 0.118 0 0.250 0 0 0.125 1.625 0.125 na 0 0 0 0 0.20 0.40 na 0 0.778 0.550 0 0.111 0
RI PT
0.022 0 0 0.012 0 0.063 0.032 0.359 0.009 0.060 0.495 0.034 0.355 0.134 0.021 0.746 0.897 0.014 0.155 0.015 0.218 0 0.032 0.114 0.670 0.101 0.375 0.116 0.169 0.008 0.161 0.013 0.700 0.156 0.017 1.409 1.467 0.100 0.226 0.006
SC
6 2 6 2 4 1 3 13 12 4 2 10 4 3 4 3 7 10 17 4 8 2 2 8 8 8 0 12 10 6 10 10 10 0 8 9 20 4 9 3
M AN U
TE D
1996 - 2008 2003 - 2008 1994 - 2008 2001 - 2008 1995 - 2008 2000 - 2008 1992 - 2008 1969 - 2008 1970 - 2008 1985 - 2008 1999 - 2008 1969 - 2008 1973 - 2008 1990 - 2008 1988 - 2008 1981 - 2008 1987 - 2008 1990 - 2008 1977 - 2008 1991 - 2008 1992 - 2008 2002 - 2008 1995 - 2008 1989 - 2008 1978 - 2008 1971 - 2008 1986 - 1988 1970 - 2008 1969 - 2008 1997 - 2008 1983 - 2008 1971 - 2008 1968 - 2008 1981 - 1984 1993 - 2008 1969 - 2008 1968 - 2008 1968 - 2008 1970 - 2008 1992 - 2008
AC C
Macedonia Madagascar Mali Mexico Moldova Namibia Nepal Netherlands New Zealand Nicaragua Nigeria Norway Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Romania Russia Sierra Leone South Africa South Korea Spain Sri Lanka Sudan Sweden Switzerland Taiwan Thailand Trinidad y Tobago Turkey Uganda Ukraine United Kingdom United States Uruguay Venezuela Zambia
ACCEPTED MANUSCRIPT Supplement B. Summary Statistics by Country for GTD Domestic
EP
AC C
RI PT
5 13 7 11 16 3 9 7 6 6 9 0 7 3 5 17 0 8 8 13 6 1 11 12 9 11 6 9 14 1 10 3 10 7 0 2 5 6 4 9 2 15 10
Average # Attacks Average # Attacks Per Month Per Election Month 0.223 2.000 1.074 1.077 0.038 0 0.086 0 0.106 0.313 1.710 15.667 0.100 0.444 0.024 0.143 0.622 0.333 0.299 0.333 0.088 0.444 0.077 na 0.054 0.143 0.006 0 1.375 0.200 10.676 12.647 0.125 na 0.048 0 0.051 0.125 0.129 0.308 0.053 0 0.042 0 0.029 0 0.158 0.5 0.302 0.556 4.968 5.727 0.027 0 0.012 0 1.671 1.071 0 0 0.796 0.3 0.012 0.333 1.115 2.300 2.246 1.571 0.033 na 0.023 0.500 0.642 1.400 0.439 0.500 0.140 0 7.766 12.111 1.557 2.000 0.102 0 2.112 0.900
SC
# of Elections
M AN U
Years in Sample Model 1- Table 4 Albania 1992 - 2008 Argentina 1974 - 2008 Armenia 1995 - 2008 Australia 1978 - 2008 Austria 1972 - 2008 Bangladesh 1992 - 2008 Belgium 1978 - 2008 Benin 1994 - 2008 Bolivia 1982 - 2008 Brazil 1986 - 2008 Bulgaria 1991 - 2008 Burundi 2007 - 2008 Canada 1980 - 2008 Central African Republic 1994 - 2008 Chile 1990 - 2008 Colombia 1975 - 2008 Congo 1994 - 1996 Costa Rica 1976 - 2008 Croatia 1992 - 2008 Cyprus 1975 - 2008 Czech Republic 1995 - 2008 Czechoslovaquia 1991 - 1992 Denmark 1979 - 2008 Dominican Republic 1971 - 2008 Ecuador 1980 - 2008 El Salvador 1985 - 2008 Estonia 1992 - 2008 Finland 1986 - 2008 France 1973 - 2008 Georgia 2005 - 2008 Germany/West Germany 1971 - 2008 Ghana 1994 - 2008 Greece 1976 - 2008 Guatemala 1976 - 2008 Guinea-Bissau 2005 - 2008 Guyana 1993 - 2004 Haiti 1995 - 2003 Honduras 1982 - 2008 Hungary 1992 - 2008 India 1976 - 2008 Indonesia 2000 - 2008 Ireland 1976 - 2008 Israel 1972 - 2008 Supplement B Continued
TE D
Country Name
ACCEPTED MANUSCRIPT
EP
1.889 0.714 1.273 0 0 0.200 0 0.333 0 0.167 0 0 0 0 1.333 0.167 0 0.250 0 2.000 0 4.000 0 0.250 4.667 5.571 0 0.294 0 2.000 0 0 0 0.167 0 0 6.250 5.500 na 0 0 0 4.556 0 5.111 na
RI PT
2.153 0.043 0.729 0.109 0.064 0.032 0.010 0.388 0.051 0.050 0 0.165 0.016 0.054 1.035 0.067 0.013 1.430 0 0.327 0.004 6.671 0.286 0.168 16.383 4.593 0.080 0.123 0 2.808 0.059 0 0.061 0.022 0.420 0.026 4.992 3.649 0.250 0.057 0.077 0 2.244 0.038 3.834 0.457
SC
M AN U
9 7 11 2 4 5 8 6 5 6 4 2 5 3 3 12 9 4 1 2 5 4 3 4 3 7 10 17 3 8 3 2 6 6 2 8 8 8 0 7 10 6 9 7 9 0
TE D
1972 - 2008 1979 - 2008 1975 - 2008 2000 - 2008 1996 - 2008 1992 - 2008 1992 - 2008 1995 - 2008 1996 - 2008 1993 - 2008 1989 - 2008 2001 - 2008 1992 - 2008 1991 - 2008 1991 - 2008 1972 - 2008 1981 - 2008 1985 - 2008 1994 - 1995 1999 - 2008 1987 - 2008 1975 - 2008 1990 - 2008 1989 - 2008 1981 - 2008 1987 - 2008 1990 - 2008 1977 - 2008 1995 - 2008 1992 - 2008 2001 - 2008 2002 - 2008 1994 - 2008 1996 - 2008 1995 - 2008 1989 - 2008 1978 - 2008 1989 - 2008 1986 - 1988 1983 - 2008 1970 - 2008 1997 - 2008 1986 - 2008 1981 - 2008 1971 - 2008 1981 - 1984
AC C
Italy Jamaica Japan Kenya Kyrgyzstan Latvia Lithuania Macedonia Madagascar Mali Mauritius Mexico Moldova Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Romania Russia Senegal Sierra Leone Slovakia Slovenia South Africa South Korea Spain Sri Lanka Sudan Sweden Switzerland Taiwan Thailand Trinidad y Tobago Turkey Uganda Supplement B Continued
ACCEPTED MANUSCRIPT 1992 - 2008 1970 - 2008 1970 - 2008 1970 - 2008 1974 - 2008 1996 - 2008
8 8 19 4 8 3
0.100 0.955 2.018 0.097 0.293 0.057
0.375 1.250 1.053 0.750 0.125 0
AC C
EP
TE D
M AN U
SC
RI PT
Ukraine United Kingdom United States Uruguay Venezuela Zambia
ACCEPTED MANUSCRIPT Supplement C. Scatter Plots of "Average Number of Attacks per Election Month" against "Average Number of Attacks per Month"
0
.5
M AN U
0
SC
AveAttacksPerELECTIONMonth .5 1 1.5 2
RI PT
2.5
ITERATE
1
1.5
TE D
AveAttacksPerMonth
0
AC C
EP
AveAttacksPerELECTIONMonth 5 10 15
20
GTD Domestic
0
5
10 AveAttacksPerMonth
15
20