American University



Weapon of the Weak?:Assessing the Effects of State Capacity on TerrorismCullen S. HendrixDepartment of GovernmentCollege of William & Marychendrix@wm.eduandJoseph K. YoungSchool of Public AffairsAmerican Universityjyoung@american.eduAbstractConventional wisdom suggests that dissident groups use terrorism when they face an overwhelming more powerful state. ?Given this core belief, cross-national studies of terrorism find mixed results for how common measures of state capacity influence terrorism. ?We argue that these indeterminate findings are due in part to a partial understanding of both what constitutes state capacity and how different aspects of state strength or weakness relate to the propensity of groups to use terrorism. ?We decompose state capacity into two dimensions that we theorize are particularly relevant to dissident groups: military capacity, or the ability to project conventional military force and bureaucratic/administrative capacity, or the ability to collect and manage information. Our analysis supports the claim that terrorist attacks are more frequently targeted at states with large, technologically sophisticated militaries, but less frequently targeted at states with higher bureaucratic and administrative capacity. States can be capable in different ways, and these various capabilities create differing incentives for using terror as a strategic and tactical tool.KeywordsTerrorism, state capacity, violence, military capacity, bureaucracyIntroduction / ProblemAs Crenshaw (1981, 387) observed decades ago, “[t]errorism is a logical choice when oppositions have such goals and when the power ratio of government to challenger is high. The observation that terrorism is a weapon of the weak is hackneyed but apt.” Crenshaw’s “hackneyed” observation has remained a conventional wisdom for scholars and policy makers. In an Op-Ed to the New York Times, former US President, George W. Bush argued that, Poverty does not transform poor people into terrorists and murderers. Yet, poverty, corruption and repression are a toxic combination in many societies, leading to weak governments that are unable to enforce order or patrol their borders and are vulnerable to terrorist networks and drug cartels. In Bush’s formulation, state weakness allows groups to operate with impunity and thus the factors that make a state weak can influence terrorism both internally and abroad. In contrast to Crenshaw, this view suggests state weakness, rather than state strength, promotes terrorism.Kofi Annan (2006, 15), the former Secretary-General of the United Nations, argued for increasing state capacity to prevent terrorism. Similar to Bush, his concern was related to weak states serving as a base of operations for terrorist groups “to fund, organize, equip and train their recruits, carry out their attacks, and hide from arrest.” If terrorism is a weapon of the weak, then we should expect to see increasing amounts against more capable states. Yet, as Bush and Annan argue, weaker states may be bases and targets of groups as well. The empirical findings linking state capacity to terrorism are similarly mixed. GDP per capita, a measure used in civil war studies to proxy state capacity (Fearon and Laitin 2003, Sambanis 2004, Hegre and Sambanis 2006), is sometimes negatively related to frequency of terrorism (Li 2005), sometimes positively (Findley and Young 2011, Walsh and Piazza 2011) and sometimes the relationship is indeterminate (Young and Dugan 2011). Measures of state material capabilities, indicators that capture actual and latent capacity to wage conventional war, are mostly positively associated with the frequency of terrorism (Li 2005, Koch and Cranmer 2007, Young and Findley 2011, Findley, Piazza, and Young 2012). Lai (2007) finds that weaker states (as measured by low GDP) produce more terrorist attacks that are exported abroad. Taken together, these results are inchoate and often contradictory, suggesting a more complex understanding of the effect of state capacity on terrorism is warranted. In this paper, we discuss different dimensions of state capacity and how they influence the likelihood of terrorism. We also consider how these dimensions of state capacity influence domestic, transnational, and exported terrorism. We argue for dividing the concept of state capacity into two dimensions: military capacity, or the ability to project conventional military force and bureaucratic/administrative capacity, or the ability to collect and manage information. We hypothesize that these separate dimensions have countervailing impacts on both the incentives for and opportunity to engage in terrorism.After developing a consistent set of hypotheses deriving from a multidimensional state capacity approach, we test these claims using data on domestic and transnational terrorism. Consistent with our argument, we find that while military capacity is positively associated with the frequency of terror attacks, bureaucratic/administrative capacity is associated with negatively associated with the frequency of terror attacks. In the conclusion, we discuss the implications for policy makers and for the scholarly study of terrorism. How State Capacity Influences Political Violence How the state structures incentives for dissident violence is a critical portion of the story for why rebellion, and political violence more generally, occurs (Skocpol 1979, Gurr 1988, Goodwin 2001). Given this long tradition of state-centered analyses of political violence (Skocpol 1979, Evans et al. 1985), relatively less attention has been paid to how states help structure incentives for oppositional terror. Some insights from the civil war literature may be useful. Within the civil war literature, state capacity is a key component of political opportunity structure that affects potential rebels’ decisions to fight (Tilly 1978, Hendrix 2010, Sobek 2010). The decision to rebel takes into account the government’s capacity to repress and to accommodate. States with considerable repressive capacity can impose more significant costs on potential dissidents and thus can deter rebellion. States capable of accommodating grievances via redistribution, the granting of autonomy rights, or the incorporation of dissident movements within the party system will be more successful at placating restive groups and less likely to face armed dissent. State capacity, however, is a multifaceted concept (Hendrix 2010, Kocher 2010). In general, military capacity – operationalized as military personnel per capita – is associated with a lower likelihood of onset, higher likelihood of war termination, and shorter war duration (Mason and Fett 1996, Balch-Lindsay and Enterline 2000, DeRouen and Sobek 2004, Buhaug 2010). Likewise, states with greater bureaucratic/administrative capacity – operationalized alternately by level of economic development (Fearon and Laitin 2003), survey measures of bureaucratic quality and expropriation risk (DeRouen and Sobek 2004, Fearon 2005) and indicators of natural resource dependence or revenue-generating capacity (Collier and Hoeffler 2004, Humphreys 2005, Thies 2010, Buhaug 2010, Besley and Persson 2010) – are less likely to experience conflict onset and more likely to endure shorter conflicts. Bureaucratic and military capacities tend to be positively correlated, in part due to the interrelationship between warfare and the development of hierarchical, bureaucratically organized state institutions (Tilly 1975). Some states, however, intermingle low levels of bureaucratic capacity with high levels of military capacity (present-day Russia, Egypt) and comparatively high levels of bureaucratic capacity with low levels of military capacity (Costa Rica, Namibia). In short, these two dimensions of capacity can diverge. Moreover, the two may have differential effects on civil conflict onset. When analyzed together, Hendrix (2011) finds that bureaucratic capacity – operationalized as tax capacity – is negatively associated with conflict onset while larger militaries and more military spending are positively related to civil war onset.How the state structures incentives for violence or non-institutional political participation is part of a larger political opportunity model for behavior (e.g. Eisinger 1973, Tilly 1978, Tarrow 1994, McAdam 1999). The political opportunity model of violent mobilization contends that the decision to mobilize, as well as the subsequent choices of targets and tactics, takes into account the state’s ability to repress and accommodate challenges (Tilly 1978). Thus, state capacity is central. Mann (1984) claims that there are two dimensions of state power: despotic power and infrastructural power. The first is analogous to what previous state theorists might term autonomy from civil society. In Mann’s (1984, 113) words, despotic power is “the range of actions which the elite is empowered to undertake without routine, institutionalized negotiation with civil society groups.” In contrast, Mann (1984, 113) suggests infrastructural power is the capacity of the state “to implement…political decisions throughout the realm.” The difference between these two is illustrated by exploring some cases. For example, the advanced capitalist democracies have strong powers to tax and thus have infrastructural capacity, but they cannot alter fundamental societal rules and thus do not have despotic capacity. Mann’s (1984) two-part distinction serves as an example for a division of state capacity, but there are many others (see Hendrix 2010). Most interpretations of capacity assume that these dimensions are either fused or privilege one dimension over the other. We make some amendments to Mann (1984) as despotic power is a narrower concept than more broad notion many scholars term repressive or military capacity (Goodwin 2001, Hendrix 2010). Similar to Mann’s infrastructural power, bureaucratic power suggests the state has a professional bureaucracy that can see its population (Scott 1998, Hendrix 2010). This can deter or mitigate violence both by channeling dissent as well as providing the state the ability to organize a coherent response to dissent. Moreover, more bureaucratically capable states are better able to credibly negotiate with dissidents because they can actually follow through on their commitments (McBride, Milante and Skaperdas 2011) and thus address one of the common causes of conflict between state and non-state actors (See Lake 2003).Some states seek to deter violent challenges via investments in repressive capacity; others provide institutionalized channels for the expression of grievances, devolve political authority to restive minorities, or incorporate dissident movements into the party system in order to diminish the relative expected gains from violent tactics. Like Goodwin (2001) and other political opportunity theorists, we focus on how the state structures incentives for certain forms of violence. Our focus here is on state repressive capacity and its incentive effects for engaging in terrorism. Terrorism is the threat or the use of violence against noncombatants to influence an audience for political purposes (For detailed discussions of how to define terrorism see: Weinberg et al. 2004, Jongman and Schmid 2005, Hoffman 2006). As policymakers and scholars have suggested, the conventional wisdom is that terrorism is tactic of a weak opponent facing a stronger state.Assuming that a dissident group has decided to use violence, its choice of tactics can range from open armed rebellion to low-level, hit-and-run insurgency and terrorist tactics such as bombings, assassinations, hijackings, and kidnappings. These tactical considerations are informed by dissident beliefs about the viability of these different tactics given the repressive capacity of the state. As Crenshaw (1982) notes,The attractiveness of terrorism to insurgents who lacks means is the reason it is often called the ‘weapon of the weak’ and many strategic models of insurrection situate it as the first phase in the conflict, followed respectively by guerrilla and then conventional warfare as the insurgents grow stronger (387).Terrorism is a relatively more attractive alternative for dissidents facing more militarily capable states. It allows dissidents to avoid direct, costly strikes on government forces, who are typically superior in numbers and weaponry. Dissidents can partially substitute soft targets (undefended civilian populations, infrastructure, transportation and commercial hubs) for hard targets (military and police installations, government buildings) where engagement by state forces would be more likely (Berman and Laitin 2006). If terrorism is a tactical response to preponderant repressive capacity on the part of the state, then states with more repressive capacity should experience more terrorist attacks – this is the standard weapon of the weak hypothesis (See Lake 2002). By contrast, bureaucratic/administrative capacity, or the state’s ability to collect and manage information on potential dissidents may deter or repress these activities via non-militarized means (Goodwin 2001, Hendrix 2010). While military capacity may incentivize terror tactics because it makes other means of violent contestation relatively more costly, bureaucratic/administrative capacity should diminish the use of terror tactics by hampering the ability of terrorist groups to mobilize and conduct attacks. More bureaucratically capable states will be better at identifying and tracking dissidents and more likely to interdict attacks. This multidimensional conceptualization of state capacity suggests two competing effects that state capacity should have on terrorism. At the secondary level, state capacity as divided into military and bureaucratic/administrative capacity will have opposing effects. This leads to the following hypotheses:H1: Indicators of military capacity will be positively related to the number of terrorist attacks.H2: Indicators of bureaucratic/administrative capacity will be negatively related to the number of terrorist attacks.These hypotheses are general and do not distinguish between types of terrorism (domestic vs. transnational, suicide vs. non-suicide). Work by Abadie (2004) and Young and Dugan (2011) suggests that domestic vs. transnational terrorism may have different logics of violence. We will return to this issue in the conclusion. Additionally, countries that export terrorism may also have a more complicated relationship between state capacity and terrorism. In the next section, we discuss how to test these hypotheses derived from a multidimensional conceptualization of state capacity.Research DesignTo test the hypotheses derived from a multidimensional conceptualization of state capacity, we create a time series cross-national dataset that brings together measures of state capacity and terrorist attacks. The temporal domain of our study is 1984-2007. Our critical measures of state capacity with sufficient cross-national coverage begin in the mid 1980s thus limiting the study to this period. Since this period includes some pre-Cold War observations, the rise of Islamic extremist terrorism, and the decline of the Marxist wave of terrorism (Rapaport 1984), we are more confident that our inferences are not necessarily confined to a specific time period and/or type of terrorist organization.Dependent VariableThe annual count of terror attacks committed by dissident groups originating in a particular country is from Enders, Sandler and Gaibulloev (2011), who decompose the Global Terrorism Database into transnational and domestic terror events. See LaFree and Dugan (2007) for a thorough description of the data. As a robustness check, we estimate models using the annual count of transnational terror attacks targeting a country. These data are from the ITERATE project (Mickolus et al. 2003). Because ITERATE omits cases of homegrown terror, the GTD and ITERATE counts of events are only moderately positively correlated (r = 0.43). As our theoretical model does not posit differential effects of state capacity for domestic and transnational terror, as do arguments revolving around domestic political institutions (Young and Dugan 2011), we expect the hypothesized relationships to hold across specifications of the dependent variable. Independent VariablesOur central independent variables are measures of two aspects of state capacity: bureaucratic/administrative capacity and military capacity. Both have been measured in various ways: Hendrix (2010) identifies ten different operationalizations of bureaucratic/administrative and military capacity commonly used in the civil conflict literature, while Van de Walle (2005) identifies five measures of bureaucratic/administrative capacity used in the public administration literature. However, many of these operationalizations are characterized by weak construct validity and/or not being available for a large sample of countries.We construct bureaucratic/administrative capacity from a factor analysis of two variables: the International Country Risk Guide (ICRG) Bureaucracy Quality and Law and Order variables (Copling, O’Leary and Sealy 2007). Bureaucracy Quality is measured on a 0–4 scale and reflects expert assessments of the degree to which the country’s bureaucracy is characterized by (1) regular, meritocratic recruitment and advancement processes, (2) insulation from political pressure, and (3) the ability to provide services during government changes (Knack, 2001). Law and Order is an assessment of (1) the strength and impartiality of the judicial system, and (2) popular observance of the law. The two variables are positively and highly correlated. The resulting variable, bureaucratic/administrative capacity, has a mean of zero and a standard deviation of 0.82. Our second measure is relative political reach (RPR) (Tammen and Kugler, ed. 2012). RPR is the ratio of actual participation in the formal economy – the economy that is taxed and directly supported by public infrastructure – to expected participation in the formal economy, estimated as a linear function of the structure, size, and degree of social spending in the national economy. Given similar structural features, states in which a larger proportion of the population participates in the formal economy will have a greater capacity to monitor and collect information on societal actors. As Arbetman-Rabinowitz et al. (2011) note, “Reach establishes the degree to which the government influences and penetrates into the daily lives of individuals” (2). Thus, the measure is conceptually close to the ability of the state to see its population (Scott 1998). The variable has a mean of one and a standard deviation of 0.29.As Hendrix (2010) notes, some caution is warranted when using expert measures of governance quality to predict political violence. The ICRG measures, which are solicited to generate predictive models of governance crises and the security of foreign investments, will likely be highly sensitive to information about violent unrest, such as terrorist attacks. In the case of Rule of Law, this may be definitional. For these reasons, it will be important to model past political violence explicitly in order to mitigate concerns about endogeneity. To operationalize military capacity, we use both the Correlates of War (COW) Composite Index of National Capability (CINC) and a factor analysis-based composite indicator of military capability. CINC the most widely used measure of a state’s material capacity to wage war. The composite indicator reflects both actual military capacity (military spending, military personnel) and fungible economic and demographic capacity (iron production, energy consumption, and population), and is expressed as the share of power held by country i in year j (Singer 1987). The value ranges from zero to one; we rescale it to range from 0-100 and log it due to the presence of outliers.Despite its widespread use, CINC may not be an ideal operationalization of existing military capacity, which we argue influences the tactical choice to use terror. By including demographic and economic variables, CINC establishes an upper bound of a state’s military capacity assuming total mobilization for war, and not the degree of extant mobilization, which would be the more relevant factor affecting dissident group behavior. Alternately, we use military capacity, which is the result of factor analysis conducted on CINC’s measures of military spending and military personnel. The resulting variable, military capacity, has a mean of 0.05 and a standard deviation of 0.31.Figure 1 plots the scores for bureaucratic/administrative capacity and military capacity for all country-years, 1984-2007. The two measures are positively correlated as the trend line indicates. As all four quadrants are represented, however, there are country-year observations that represent the four possible combinations (high-high, high-low, low-high, low-low). Some states are highly capable both ways (the United Kingdom, USA), while others lack both bureaucratic capacity and the ability to project military force (Nicaragua, Mozambique). The off-diagonal cases are perhaps more theoretically interesting: Costa Rica and Namibia have relatively capable bureaucracies and minimal or nonexistent militaries; Russia, Iran, and Egypt combine relatively weak bureaucratic capacity with strong conventional militaries.[INSERT FIGURE 1 HERE]Table 1 provides some prima facie evidence for the relationships hypothesized in the previous section. The low-high cutoff is zero (≈ mean) for both axes. As hypothesized, country-years with higher military capacity see more terror attacks (country-year mean of 22.4) than those with lower military capacity (6.1, t-test significant at p < 0.001). However, country-years with higher bureaucratic effectiveness see fewer attacks (14.2) than those with lower bureaucratic capacity (25.0, t-test significant at p < 0.001). The lowest average number of attacks is found in country-years that combine high bureaucratic capacity with low military capacity; the highest are found in countries with strong conventional militaries but weak bureaucracies.[TABLE 1 HERE]However, these patterns could be spurious. For instance, more populous countries tend to have larger armed forces, and thus population, rather than military capacity, could explain this relationship. In order to eliminate alternative explanations for these observed relationships, we conduct more rigorous statistical modeling. Since our dependent variable is an overdispersed count of events, we estimate negative binomial models with a full set of control variables.Control VariablesWe include a battery of control variables in order to militate against the possibility that the relationships between the two components of repressive capacity and terror events are spurious. Because present evaluations of bureaucratic/administrative capacity may be sensitive to past levels of terror activity, we include the panel average of terror attacks over the panel duration. We control also for log GDP per capita. Controlling for level of development is standard in conflict studies, though there is disagreement about what the variable proxies. For some, log GDP per capita is a measure of the state’s bureaucratic and military capacity (Fearon and Laitin 2003); for others, it proxies economic grievances and/or opportunity cost to participating in violence (Collier and Hoeffler 2002, 2004, Miguel and Blattman 2010). As we model state capacity more directly, we interpret any significant finding on GDP per capita as evidence of an income-grievance effect. We control also for log population. Controlling for population allows us to mitigate the possibility that our measures of repressive capacity simply capture variation in the population size of states (more people provide more opportunities to repress).We control also for the incidence of domestic armed conflict. One criticism of the GTD is that it captures a diverse set of actors and targets, including acts such as bombings against civilians in Israel, rebel attacks against the government in Colombia, and acts of sabotage against infrastructure by the African National Congress in South Africa. As such, it is a broad measure that captures some violence that may be part of ongoing an ongoing insurgency. Our measure, armed conflict incidence, indicates whether there was an active intrastate war in the previous year; data are from Gleditsch et al. (2002). The inclusion of this control also mitigates concerns about the endogeneity of military capacity to expectations about facing armed resistance, i.e., states that expect to fight insurgents/dissidents will invest more in military capacity. The armed conflict incidence variable controls for this explicitly.We include three controls intended to model regime type and durability. The first two model distinct aspects of democracy: checks and balances and the extent of participation and fortunes of small parties in the electoral arena. The first, constraints on the executive, comes from the Polity IV dataset, and captures the degree to which executive authority is constrained by formal or informal institutional checks (Marshall, Jaggers and Gurr 2011). The second, Vanhanen Democracy Index, is an equally weighted composite of the proportion of votes won by smaller parties and the proportion of the population that actually voted in elections. Past research suggests that these variables should be positively associated with terror attacks (Li 2005, Young and Dugan 2011). Regime durability is the number of years since the most recent regime change (a three-point change in the Polity score over three years or less) or the end of a period of regime instability (such as foreign occupation) (Marshall, Jaggers and Gurr 2011). If bureaucratic/administrative capacity and military capacity both require the investment of significant societal resources over extended periods of time, the confounding effect of regime durability on these factors must be addressed.We include two variables to address temporal issues with the data. We include a linear time trend in order to capture any secular trends either in the reporting of terror attacks or actual prevalence of terror attacks. Lastly, a dummy indicator for the period before/after 1998 is included in the models run on the GTD outcome variable to account for a systematic change in the way that GTD reports terror events from the first to second phase of data collection (see GTD 2011).ResultsTable 2 reports the results of our base models. Models 1-4 use the GTD outcome measure; models 5-8 use ITERATE. Models 1 and 5 include our measures of bureaucratic/administrative capacity and military capacity, models 2 and 6 substitute the log CINC score as a measure of military capacity. Models 3-4 and 7-8 substitute our alternate measure of bureaucratic/administrative capacity, Political Reach (RPR). ----------------TABLE 2 HERETable 2 provides strong support for H2. Countries with higher bureaucratic/administrative capacity experience fewer terror attacks. The coefficients on both measures of bureaucratic/administrative capacity, B/A capacity and Political Reach, are negative and significant across all eight specifications, though Political Reach is only significant at the p < 0.10 level in models 3 and 7.Our findings provide less consistent support for H1: countries with more conventional military capacity experience more terror attacks. The coefficients on military capacity are positive and statistically significant under all four specifications, though they are only significant at the p < 0.10 level in the models including Political Reach. The coefficients on log CINC, however, are negative and statistically significant in models 4 and 8. Thus, the two measures of military capacity do not demonstrate a consistent relationship with terrorist attacks. We point out that on theoretical grounds, our military capacity measure is superior to log CINC as a measure of actual repressive capacity, which we argue influences decisions about the use of terrorism as a strategic and tactical doctrine. Log CINC conflates potential military capacity with actual military capacity and state capacity versus other states.The control variables performed largely as expected. More developed countries with larger populations that previously experienced attacks and are embroiled in civil conflict experience more terrorist attacks. Interestingly, once state capacity is modeled explicitly, the positive relationship between political democracy and terrorist attacks is no longer present. If anything, greater constraints on the executive are associated with fewer attacks. While no linear time trend appears in the GTD models, there are systematically fewer terrorist attacks in the ITERATE data over time.Figure 2 presents the substantive significance of the most robust independent variables. A one standard deviation increase in B/A capacity from the mean, equivalent to going from the Philippines in 2006 to France in 2001, is associated with a 23.5% decrease in the expected count of GTD attacks and a 20.7% decrease in the expected count of ITERATE attacks. The substantive effect of a similar shift in Political Reach is roughly half that magnitude. The largest substantive effects are for military capacity: a similar shift in military capacity (Ethiopia in 2007 to Colombia in 2001) is associated with a 66.2% increase in the expected count of GTD attacks and a 59.9% increase in the expected count of ITERATE attacks.Another way of considering these effects is to focus on particular cases. Peru and Colombia have been among the most terrorism-afflicted countries in the post World War II era. In addition to their above-average populations and long history of civil conflict, Peru and Colombia have had comparatively low levels of bureaucratic/administrative capacity (panel averages for B/A capacity of -0.70 and -0.53, respectively) and, especially since 2000, relatively large militaries (military capacity = 0.18 and 0.36). If Peru and Colombia were to have merely average levels of B/A capacity and military capacity, implying an increase of the former and decrease of the latter, model 1 would predict a 40% and 52% decrease in the expected count of GTD attacks, respectively. RobustnessAs Table 2 indicates, our main findings regarding the state capacity variables are robust across operationalizations of the dependent variable. To further probe the robustness of our results, we estimate a series of models varying the inclusion of temporal controls and World Bank income group indicator variables, as well as additional control variables that have been shown to be robust predictors of terrorism: physical integrity rights and horizontal inequalities (Walsh and Piazza 2010, Moore, Bakker and Hill, Jr. 2011). First, we include a measure of horizontal inequality. Recent research on civil war indicates that horizontal inequalities – inequalities between ascriptively defined groups, such as between the marginalized Fur and the dominant Shaigiya, Ja’Alin and Danagla ethnic groups of the Republic of Sudan – are a source of grievances that spur violent conflict (?stby 2008, Cederman, Weidmann, and Gleditsch 2011). The measure, lineq2, captures income inequalities across ethnic groups; higher values indicate larger degrees of inequality. The data are from Cederman, Weidmann, and Gleditsch (2011). Second, we include a measure of state repression. Respect for physical integrity rights is believed to suppress terrorist activity, as respecting these rights reduces grievances and makes collecting intelligence on extremist groups easier (Walsh and Piazza 2010). We operationalize physical integrity rights using the CIRI physical integrity rights scale (Cingranelli and Richards 2010).The results of our robustness checks are generally consistent with the findings reported in Table 2: while military capacity is associated with more frequent terrorist attacks, bureaucratic/administrative capacity is associated with less. Some of the models using ITERATE as the dependent variable miss conventional levels of statistical significance, especially when controlling for physical integrity rights. The expected directions are always maintained, but the coefficient size is attenuated. Consistent with our expectations, log CINC is the least robust indicator. When we estimate the same models using the zero-inflated estimator, the results are more consistent and robust: bureaucratic/administrative capacity is negatively associated with the frequency of attacks, while military capacity is positively associated with the frequency of attacks. Given the many number of models estimated, and different variables included, the results suggest support for the theoretical disaggregation of state capacity and its competing effects on the number of terrorist attacks. ConclusionsWhile conventional wisdom argues that strong states are the targets of terrorist violence, we find that this relationship is complicated by how we conceptualize state capacity. By fusing research on the state, civil war, and terrorism, and then examining time-series cross-national data, we find that military capacity does seem to encourage terrorism as the weapon of the weak hypothesis would suggest. Additionally, we find that bureaucratic/administrative capacity actually is associated with less terrorism. We are confident in the findings, but there are some important areas for extensions. First, the specific causal mechanisms are still somewhat opaque. Bureaucratic/administrative capacity may depress terrorist attacks due to more effective policing, or due to government capacity to address societal grievances via effective public policies. Military capacity may encourage the use of terror as a tactic due to force preponderance, but large, well-funded militaries may be a source of grievances themselves. Military spending is positively correlated with political corruption (Gupta, de Mello and Sharan 2001), and military spending may be perceived as a form of patronage politics that saps societal resources from other uses (Bueno de Mesquita et al. 2003). Adjudicating between these competing causal mechanisms would constitute an important contribution.Second, we did not fully explore how state capacity relates to domestic vs. transnational terrorism or suicide vs. non-suicide terrorism. As Abadie (2004), Young and Findley (2011) and Findley, Piazza, and Young (2012) argue, there may be a different logic of terrorism between dissidents inside a country contending with that state than between a group residing in a country against another external state. Abadie (2004, 2) suggests:Much of modern-day transnational terrorism seems to generate from grievances against rich countries. In addition, in some cases terrorist groups may decide to attack property or nationals of rich countries in order to gain international publicity. As a result, transnational terrorism may predominantly affect rich countries. The same is not necessarily true for domestic terrorism.The proper research design to test these transnational violence processes may be directed dyads (Young and Findley 2011) or another research design that takes into account multiple actors (see Gelpi and Avdan 2011). A third extension relates to states that export terrorism. Lai (2007) provides an explanation for why states might export terror, and state capacity is a prominent factor. Lai suggests that terrorist organizations seek environments with low operating costs and use these states to export terrorism to countries that have higher costs. Former Secretary of Defense, Robert Gates (2009, 31), argued a similar point: The recent past vividly demonstrated the consequences of failing to address adequately the dangers posed by insurgencies and failing states. Terrorist networks can find a sanctuary within the borders of a weak nation and strength within the chaos of social breakdown. The most likely catastrophic threats to the U.S. homeland, for example, that of a U.S. city being poisoned or reduced to rubble by a terrorist attack, are more likely to emanate from failing states than from aggressor states.Examining different dimensions of state capacity on this interaction could help unpack this process further and test claims from both scholars and policymakers. Finally, we only examine the direct effects that levels of state capacity have on levels of terrorism. Other indirect relationships may explain how opportunities for terror change. Berrebi and Ostwald (2011), for example, examine the effects natural disasters have on terrorism. They find that certain types of disasters increase the frequency of terrorism, but that is effect is concentrated in countries of low to middle GDP per capita. Disasters and other similar exogenous shocks likely influence state capacity and future work should examine whether they influence each dimension of state capacity in similar or different ways and how this might change how capacity and terrorism are related. ReferencesAbadie, Alberto. 2006. “Poverty, Political Freedom, and the Roots of Terrorism.” American Economic Review 96(2): 50-56.Arbetman-Rabinowitz, Marina, Jacek Kugler, Mark Abdollahian, Kristin Johnson, and Kyungkook Kang. 2011. Relative Political Capacity: Definitions & Calculation Procedures. Typescript. Accessed at . Aksoy, Deniz, David Carter, and Joseph Wright. 2012. “Terrorism in Dictatorships.” Journal of Politics. Annan, Kofi. 2006. “Uniting Against Terrorism: Recommendations for a Global Counterterrorism Strategy.” Report of the Secretary General, 27 April 2006, 1-32.Balch-Lindsay, Dylan and Andrew J. Enterline. 2000. “Killing Time: The World Politics of Civil War Duration, 1820-1992.” International Studies Quarterly 44(4): 615-642.Berman, Eli, and David D. Laitin. 2006. “Hard Targets: Theory and Evidence on Suicide Attacks.” NBER Working Paper 11740.Besley, Timothy, and Torsten Persson. 2010. “State Capacity, Conflict, and Development.” Econometrica 78(1): 1-34.Berrebi, Claude and Jordan Ostwald. 2011. “Earthquakes, Hurricanes, and Terrorism: Do Natural Disasters Incite Terror.” Public Choice 149(3/4): 383-403. Blattman, Christopher, and Edward Miguel. 2010. “Civil War.” Journal of Economic Literature 48(1): 3-57.Buhaug, Halvard. 2010. “Dude, Where’s My Conflict? LSG, Relative Strength, and the Location of Civil War.” Conflict Management and Peace Science 27(2): 107-128.Bueno de Mesquita, Bruce, Alastair Smith, Randolph M. Siverson, and James D. Morrow. 2003. The Logic of Political Survival. Cambridge: MIT Press.Cederman, Lars-Erik, Nils B. Weidmann, and Kristian Skrede Gleditsch. 2011. “Horizontal Inequalities and Ethnonationalist Civil War: A Global Comparison.” American Political Science Review 105(3): 478-495.Cingranelli, David L., and David L. Richards. 2010. “The Cingranelli and Richards (CIRI) Human Rights Data Project.” Human Rights Quarterly 32(2): 395-418.Collier, Paul, and Anke Hoeffler. 2004. “Greed and Grievance in Civil War.” Oxford Economic Papers 56: 563–595.Collier, Paul, and Anke Hoeffler. 2002. “On the Incidence of Civil War in Africa.” Journal of Conflict Resolution 46(1): 13–28.Coplin, William, Michael O’Leary, and Tom Sealy. 2007. A Business Guide to Political Risk for International Decisions. Syracuse, NY: PRS Group.Crenshaw, Martha. 1981. “The Causes of Terrorism.” Comparative Politics 13(4): 379-399.DeRouen, Karl R., and David Sobek. 2004. “The Dynamics of Civil War Duration and Outcome.” Journal of Peace Research 41(3): 303-320.Drakos, Konstantinos, and Andreas Gofas. 2006. “In Search of the Average Transnational Terrorist Attack Venue.” Defence and Peace Economics 17(2): 73-93.Enders, Walter, Todd Sandler, and Khusrav Gaibulloev. 2011. “Domestic versus Transnational Terrorism: Data, Decomposition, and Dynamics.” Journal of Peace Research 48(3): 319-337.Eubank, William L., and Leonard B. Weinberg. 1994. “Does Democracy Encourage Terrorism.” Terrorism and Political Violence 6(4): 417-435. Evans, Peter, Dietrich Reuschemeyer, and Theda Skocpol, eds.1985. Bringing the State Back in New York: Cambridge University Press.Fearon, James D., and David D. Laitin. 2003. “Ethnicity, Insurgency, and Civil War.” American Political Science Review 97(1): 75-90.Fearon, James D. 2005. “Primary Commodity Exports and Civil War.” Journal of Conflict Resolution 49(4): 483-507.Findley, Michael G., James A. Piazza, and Joseph K. Young. 2012. “Games Rivals Play: Terrorism in International Rivalries.” Journal of Politics 74(1): 235-248.Findley, Michael G., and Joseph K. Young. 2011. “Terrorism, Democracy, and Credible Commitments.” International Studies Quarterly 55(2): 357-378.Gates, Robert M. 2009. “A Balanced Strategy: Reprogramming the Pentagon for a New Age,” Foreign Affairs (January–February).Gelpi, Christopher and Nazli Avdan. 2011. “Democracy and the Multilateral Flow of Transnational Terrorism, 1968-2007.” Working paper.Gleditsch, Nils Petter, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg, and H?vard Strand. 2002. “Armed Conflict 1946-2001: A New Dataset.” Journal of Peace Research 39(5): 615-637.Goertz, Gary. 2006. Social Science Concepts: A User’s Guide Princeton: Princeton University Press.Goodwin, Jeff. 2001. No Other Way Out: States and Revolutionary Movements, 1945-1991 New York: Cambridge University Press.Gupta, Sanjeev, Luiz de Mello and Raju Sharan. 2001. “Corruption and Military Spending.” European Journal of Political Economy 17(4): 749-777.Hegre, H?vard, and Nicholas Sambanis. 2006. “Sensitivity Analysis of Empirical Results on Civil War Onset.” Journal of Conflict Resolution 50(4): 508-535.Hendrix, Cullen S. 2010. “Measuring State Capacity: Theoretical and Empirical Implications for the Study of Civil Conflict.” Journal of Peace Research 47(3): 273-285.Hendrix, Cullen S. 2011. “Head for the Hills? Rough Terrain, State Capacity, and Civil War Onset.” Civil Wars 13(4): 345-370. Hoffman, Bruce. 2006. Inside Terrorism. New York: Columbia University Press.Humphreys, Macartan. 2005. “Natural Resources, Conflict, and Conflict Resolution.” Journal of Conflict Resolution 49(4): 508-537.Internal Revenue Service. 2010. Jongman, Albert and Alex Schmid. 2005. Political Terrorism: a New Guide to Actors, Authors, Concepts, Data bases, Theories, & Literature Transaction Publishers.Kalyvas, Stathis M. 2006. The Logic of Violence in Civil War. New York: Cambridge University Press.Karl, Terry Lynn. 1990. “Dilemmas of Democratization in Latin America.” Comparative Politics 23(1): 1-21Knack, Stephen. 2001. “Aid Dependence and the Quality of Governance: A Cross Country Empirical Analysis.” Southern Economic Journal 68(4): 310–329.Koch, Michael, and Skyler Cranmer. 2007. “Testing the ‘Dick Cheney’ Hypothesis: Do Governments of the Left Attract More Terrorism than Governments of the Right?” Conflict Management and Peace Science 24(4): 311-326.Kocher, Matthew Adam. 2010. “State Capacity as a Conceptual Variable.” Yale Journal of International Affairs 5(2): 137-145.LaFree, Gary, and Laura Dugan. 2007. “Introducing the Global Terrorism Database.” Terrorism and Political Violence 19(2): 181-204.Lake, David. 2002. “Rational Extremism: Understanding Terrorism in the Twenty-first Century.” Dialogue-IO 1(1): 15-29.Lake, David. 2003. “International Relations Theory and Internal Conflict: Insights from the Interstices,” International Studies Review 5(4): 81-89.Li, Quan. 2005. “Does Democracy Promote or Reduce Transnational Terrorist Incidents?” Journal of Conflict Resolution 49(2): 278-297.Lyall, Jason, and Isaiah Wilson III. 2009. “Rage Against the Machines: Explaining Outcomes in Counterinsurgency Wars.” International Organization 63(1): 67-106.Lyall, Jason. 2009. “Does Indiscriminate Violence Incite Insurgent Attacks? Evidence from Chechnya.” Journal of Conflict Resolution 53(3): 331-362.Mann, Michael. 1984. “The Autonomous Power of the State: Its Origins, Mechanisms, and Results.” European Journal of Sociology 25(2): 185-213.Marshall, Monty J., Keith Jaggers, and Ted Robert Gurr. 2011. Polity IV Project: Political Regime Characteristics and Transitions, 1800–2010. Version p4v2010. College Park, MD: University of Maryland (http:// cidcm.umd.edu/inscr/polity/index.htm).Mason, T. David. and Dale A. Krane. 1989. “The Political Economy of Death Squads: Toward a Theory of the Impact of State-Sanctioned Terror.” International Studies Quarterly 33(2): 175-198.Mason, T. David and Patrick Fett. 1996. “How Civil Wars End: A Rational Choice Approach.” Journal of Conflict Resolution 40(4): 546-568.McAdam, Doug. 1982. Political Process and the Development of Black Insurgency, 1930-1970. Chicago, IL: University of Chicago.McBride, Michael, Gary Milante, and Stergios Skaperdas. 2011. “Peace and War With Endogenous Capacity.” Journal of Conflict Resolution 55(3): 447-468.McClintock, Cynthia. 1998. Revolutionary Movements in Latin America Washington, DC: United States Institute of Peace Press.Mickolus, Edward F., Todd Sandler, Jean M. Murdock, and Peter A. Flemming. 2003. International Terrorism: Attributes of Terrorist Events (ITERATE). Data Codebook.Moore, Will H., Ryan Bakker, and Daniel W. Hill, Jr. 2011. How Much Terror? Dissidents, Governments, Institutions and the Cross-National Study of Terror Attacks. Available at SSRN: , Gerardo and Jay Verkuilen. 2002. “Conceptualizing and Measuring Democracy: Evaluating Alternative Indices.” Comparative Political Studies 35(1): 5-34.?stby, Gudrun. 2008. “Polarization, Horizontal Inequalities and Violent Civil Conflict.” Journal of Peace Research 45(2): 143-162.Rapaport, David C. 2002. “The Four Waves of Revel Terror and September 11.” Anthropoerics 8(1), found online: HYPERLINK "" , Nicholas. 2004. “What is a Civil War? Conceptual and Empirical Complexities of an Operational Definition.” Journal of Conflict Resolution 48(6): 814-858.Sandler, Todd. 1995. “On the Relationship between Democracy and Terrorism.” Terrorism and Political Violence 7(4): 1-9.Sobek, David. 2010. “Masters of their Domains: The Role of State Capacity in Civil Wars.” Journal of Peace Research 47(3): 267-271.Scott, James. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven: Yale University Press.Singer, J. David. 1987. “Reconstructing the Correlates of War Dataset on Material Capabilities of States, 1816–1985.” International Interactions 14(2): 115-132.Tammen, Ronald, and Jacek Kugler, eds. 2012. Performance of Nations. Lanham, MD: Rowman and Littlefield.Thies, Cameron G. 2010. “Of Rulers, Rebels, and Revenue: State Capacity, Civil War Onset and Primary Commodities.” Journal of Peace Research 47(3): 321-332.Tilly, Charles. 1975. The Formation of National States in Western Europe. Princeton, NJ: Princeton University Press.Tilly, Charles. 1978. From Mobilization to Revolution. Reading, MA: Addison-Wesley.Van de Walle, Steven. 2005. “Measuring Bureaucratic Quality in Governance Indicators.” Paper for the 8th Public Management Research Conference, Los Angeles, CA.Walsh, James I., and James A. Piazza. 2011. “Why Respecting Physical Integrity Rights Reduces Terrorism.” Comparative Political Studies 43(5): 551-577.Weinberg, Leonard, and William Eubank. 1998. “Terrorism and Democracy: What recent events disclose.” Terrorism and Political Violence 10(1): 108-118.Weinberg, Leonard, Ami Pedahur, and Sivan Hirsch-Hoefler. 2004. “The Challenge of Conceptualizing Terrorism.” Terrorism and Political Violence 16(4): 777-794.Young, Joseph K. 2008. “Repression, Dissent, and the Onset of Civil War.” PhD dissertation, Florida State University, Tallahassee, FL.Young, Joseph K., and Laura Dugan. 2011. “Veto Players and Terror.” Journal of Peace Research 48(1): 19-33.Young, Joseph K., and Michael G. Findley. 2011. “Promise and Pitfalls of Terrorism Research.” International Studies Review 13(3): 411-431.Table 1: Bureaucratic/administrative capacity, military capacity, and the average number of terror attacks, 1984-2007Military CapacityLowHighTotalLow8.540.125.0Bureaucratic EffectivenessHigh2.117.414.2Total6.122.411.0Table 2: Negative binomial estimates of the effects of state capacity on terrorist attacks, 1984-2007(1)(2)(3)(4)(5)(6)(7)(8)VARIABLESGTDGTDGTDGTDITERATEITERATEITERATEITERATEPast Attacks0.021***0.021***0.021***0.022***0.119***0.119***0.125***0.123***(0.005)(0.005)(0.004)(0.005)(0.022)(0.024)(0.024)(0.024)log Real GDPpc-0.0090.349***0.0850.304***0.164*0.480***0.224**0.416***(0.140)(0.108)(0.110)(0.097)(0.095)(0.094)(0.092)(0.081)B/A Capacity-0.324**-0.479***-0.280**-0.394***(0.143)(0.170)(0.123)(0.149)Political Reach-0.574*-0.998***-0.388*-0.849***(0.301)(0.343)(0.218)(0.320)Military Capacity1.575**0.973*1.456***0.860*(0.633)(0.576)(0.464)(0.509)log CINC-0.353-0.589**-0.287-0.487**(0.255)(0.286)(0.241)(0.246)log Population0.346***0.639***0.428***0.704***0.1300.411***0.194**0.461***(0.091)(0.107)(0.083)(0.097)(0.080)(0.113)(0.086)(0.111)Exec. Constraints-0.005-0.017***-0.006**-0.011***-0.007*-0.019***-0.008***-0.015***(0.003)(0.006)(0.002)(0.003)(0.004)(0.006)(0.003)(0.004)VH Democ. Index0.018*0.0130.0110.004-0.000-0.004-0.002-0.005(0.009)(0.009)(0.009)(0.009)(0.006)(0.007)(0.006)(0.007)Regime Durability-0.008***-0.004-0.012***-0.009***-0.0030.000-0.005***-0.003*(0.003)(0.003)(0.003)(0.003)(0.002)(0.002)(0.002)(0.002)Internal Conflict1.535***1.545***1.644***1.610***0.814***0.735***0.836***0.753***(0.214)(0.215)(0.206)(0.203)(0.176)(0.197)(0.166)(0.167)After 1998-1.095***-1.058***-1.116***-1.156***(0.225)(0.249)(0.202)(0.211)Year-0.029-0.022-0.021-0.014-0.084***-0.078***-0.077***-0.077***(0.019)(0.019)(0.017)(0.017)(0.010)(0.010)(0.008)(0.008)Constant55.07435.92639.86822.039165.205***147.569***149.598***145.528***(38.054)(38.266)(34.062)(33.362)(20.605)(20.733)(16.752)(15.689)Observations2,3442,4212,8772,9902,5642,6653,2523,437Robust standard errors, clustered on countries, in parentheses*** p<0.01, ** p<0.05, * p<0.1Table 3: Robustness checks of the effects of state capacity on terror attacks with different estimators and controlsDependent VariableAdditional ControlsB/A Capacity Coefficient w/Military Capacity(Standard Error)B/A Capacity Coefficient w/log CINC(Standard Error)RPR Coefficient w/Military Capacity(Standard Error)RPR Coefficient w/log CINC(Standard Error)Military Capacity Coefficientw/ B/A Capacity(Standard Error)Military Capacity Coefficient w/RPR(Standard Error)log CINC Coefficient w/B/A Capacity(Standard Error)log CINC Coefficient(Standard Error)GTDNo temporal controls-0.374**(0.150)-0.447***(0.151)-0.410(0.364)-0.647*(0.342)1.371**(0.613)0.856(0.605)-0.157(0.274)-0.421(0.308)ITERATENo temporal controls-0.285**(0.121)-0.349***(0.132)-0.109(0.224)-0.453(0.289)1.103**(0.471)0.563(0.543)-0.134(0.215)-0.315(0.235)GTDHorizontal Inequality-0.384***(0.139)--0.557***(0.168)-0.585*(0.314)-1.083***(0.367)1.632**(0.673)1.126*(0.625)-0.571**(0.251)-0.804***(0.308)ITERATEHorizontal Inequality-0.298**(0.133)-0.444***(0.158)-0.396*(0.225)-0.969***(0.342)1.087**(0.466)0.705(0.528)-0.466**(0.229)-0.722***(0.243)GTDWB Income Group dummies-0.284*(0.159)-0.469***(0.178)-0.531*(0.305)-0.949***(0.333)1.637**(0.666)1.452**(0.648)-0.434(0.267)-0.593**(0.286)ITERATEWB Income Group dummies-0.256*(0.135)-0.405***(0.151)-0.325(0.234)-0.827***(0.295)1.374***(0.519)1.000*(0.526)-0.327(0.241)-0.582***(0.213)GTDPhysical Integrity Rights-0.206(0.151)-0.284*(0.154)-0.367(0.277)-0.599**(0.285)1.351**(0.635)1.015*(0.589)-0.204(0.283)-0.270(0.304)ITERATEPhysical Integrity Rights-0.230*(0.128)-0.201(0.126)-0.267(0.217)-0.349(0.230)1.554***(0.489)1.177**(0.551)-0.268(0.233)-0.290(0.247)Figure 1: Bureaucratic/Administrative Capacity and Military Capacity, 1984-2007 Figure 2: Expected Increase in Terrorist Events Based on Changes in Bureaucratic/Administrative Capacity and Military Capacity MeasuresThe results are based on one standard deviation changes from the mean or changes from the 25th to the 75th percentile for the variables of interest. The dependent variable is either from the Global Terrorism Database (GTD) as adjusted by Enders, Sandler, and Gaibulloev (2011) or from the International Terrorism: Attributes of Terrorist Events (ITERATE) database. The measures for Bureaucratic/Administrative capacity are the factor score made from ICRG variables (BA) or the Relative Political Capacity measure (RPR). The Military Capacity measure (MilCap) is the factor score from military personnel and military spending. ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download