Weapon of the Weak?:Assessing the Effects of State Capacity on Terrorism1

Cullen S. HendrixDepartment of GovernmentCollege of William & Mary

chendrix@wm.edu

and

Joseph K. YoungSchool of Public Affairs

American Universityjyoung@american.edu

Abstract

Conventional 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.

Keywords

Terrorism, state capacity, violence, military capacity, bureaucracy

1 A version of this paper was presented at the 2012 Midwest Political Science Association Annual Meeting, Chicago, IL. The authors would like to thank Chris Gelpi, Andrew Boutton, and Mike Findley for helpful comments and Alex Testa for research assistance.

Introduction / Problem

As 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.2

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

2 George W. Bush, “Securing Freedom’s Triumph.” New York Times Op-ed, September 11, 2002.

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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 dimensions3 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.

3 Goertz (2006, 4) states that “good concept[s] draw distinctions that are important in the behavior of an object.” What he refers to as the basic level of the concept, or the main definition, is the noun to which we attach adjectives. State capacity is the noun where we will add modifiers, such as bureaucratic or military. In Goertz (2006) verbiage, these secondary level dimensions then offer causal mechanisms or how a multidimensional concept relates to other concepts. Munck and Verkuilen (2002) offer a similar schema but refer to a concept (Goertz’s basic level) and its attributes (Goertz’s secondary level). We adopt Goertz’s language to explain the multidimensional nature of the concept of state capacity.

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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.4 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

4 The one area that has received extensive attention is the study of regime type and terrorism (e.g. Eubank and Weinberg 1994, Sandler 1995, Weinberg and Eubank 1998, Li 2005, Young and Dugan 2011, Aksoy, Carter and Wright 2012).

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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

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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:

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Weinberg et al. 2004, Jongman and Schmid 2005, Hoffman 2006).5 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,6 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.7 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). 5 We are examining oppositional terror in this study. State terror is both real and understudied (Stohl 1983). Unfortunately, there is insufficient cross-national events data to examine the relationship between state terror and state capacity.6 For a recent study of the use of nonviolence as a dissident strategy, see Chenoweth and Stephan (2011).7 Against guerrilla fighters, the typical advantages of large, mechanized fighting forces are diminished due to guerrilla’s avoidance of direct assault and the tactical environments, such as mountains or closed terrain, in which guerrillas prefer to operate (Hendrix 2011). Lyall and Wilson III (2009) find that mechanized armies are less likely to defeat insurgencies.

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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

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discuss how to test these hypotheses derived from a multidimensional conceptualization of state

capacity.

Research Design

To 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 Variable

The 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

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Dugan 2011), we expect the hypothesized relationships to hold across specifications of the

dependent variable.

Independent Variables

Our 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

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participation in the formal economy, estimated as a linear function of the structure, size, and

degree of social spending in the national economy.8 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

8 RPR is based on the calculation of activity rate as follows: Activity Rate/Population = α + β1+(time) + β2 (bureaucracy) + β3 (Education) + β4 (Pop Age) + β5 (Social Security) + β6 (Urbanization) + β7 (GDPpercap) +β8(Unemployment)+ε (Arbetman-Rabinowicz et al. 2011). RPR is the actual value for activity rate/predicted value generated by this equation.

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(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.9 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]

9 Hence the inclusion of population, urban population, and steel/iron production, which reflect the types of fungible assets that were/are key to military power in the age of mechanized warfare. Another limitation of CINC is that it is a proportion of the state’s power in the international system. Since we are more interested in the military ability of a state vs. dissidents rather than other states, it is not the optimal measure.

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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.10

Control Variables

We 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

10 Because there may be systematic underreporting of terrorist activity in highly authoritarian regimes, we also estimate zero-inflated negative binomial models with a measure of democracy (the Polity2 scale) in the logistic regression stage that corrects for over-reporting of zeroes (Drakos and Gofas 2006, Findley and Young 2012). Vuong tests confirm that the zero-inflated models are a better fit for the models (Vuong 1989). The findings, reported in Table A1 in the appendix, are similar to those reported here.

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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.11 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.

11 As a robustness check, we estimate all models using the one-year lag of terror attacks, the number of active terror organizations, and the panel average of active terror organizations. Results did not deviate materially from those shown here.

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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).

Results

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Table 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 HERE

Table 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.

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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).12

12 This is partly a legacy of the historical strength of military institutions in recently established democracies (see McClintock 1998, 43). In many Latin American states, transitions to democracy required a special acquiescence to military privileges (Karl 1990).

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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.

Robustness

As 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 (Østby 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).

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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.13 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.14 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.

Conclusions

While 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

13 The inclusion of physical integrity rights reduces the statistical significance of bureaucratic/administrative capacity. This is due in part to the two variables being strongly positively correlated (0.58). However, there are theoretical reasons to believe that the physical integrity measure may mediate some of the effect of bureaucratic/administrative capacity: previous studies suggest that regimes lacking in bureaucratic/administrative capacity respond to both terror campaigns and insurgency with more widespread repression. These regimes’ inability to identify and target dissidents directly causes them to resort to more collective forms of punishment, inciting violence by dissidents (Mason and Krane 1989, Kalyvas 2006, Lyall 2009).14 Results reported in Appendix A1.

19

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

20

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.

21

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Table 1: Bureaucratic/administrative capacity, military capacity, and the average number of terror attacks, 1984-2007

Military CapacityLow High Total

Low 8.5 40.1 25.0Bureaucratic Effectiveness High 2.1 17.4 14.2

Total 6.1 22.4 11.0

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Table 2: Negative binomial estimates of the effects of state capacity on terrorist attacks, 1984-2007(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES GTD GTD GTD GTD ITERATE ITERATE ITERATE ITERATE

Past Attacks 0.021*** 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 -0.009 0.349*** 0.085 0.304*** 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 Capacity 1.575** 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 Population 0.346*** 0.639*** 0.428*** 0.704*** 0.130 0.411*** 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. Index 0.018* 0.013 0.011 0.004 -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*** -0.003 0.000 -0.005*** -0.003*

(0.003) (0.003) (0.003) (0.003) (0.002) (0.002) (0.002) (0.002)Internal Conflict 1.535*** 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)Constant 55.074 35.926 39.868 22.039 165.205*** 147.569*** 149.598*** 145.528***

(38.054) (38.266) (34.062) (33.362) (20.605) (20.733) (16.752) (15.689)Observations 2,344 2,421 2,877 2,990 2,564 2,665 3,252 3,437

Robust standard errors, clustered on countries, in parentheses*** p<0.01, ** p<0.05, * p<0.1

29

Table 3: Robustness checks of the effects of state capacity on terror attacks with different estimators and controlsDependent Variable

Additional Controls

B/A Capacity Coefficient w/Military Capacity(Standard

Error)

B/A Capacity

Coefficient w/log CINC

(Standard Error)

RPR Coefficien

t 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)

GTD No 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)

ITERATE No 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)

GTD Horizontal 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)

ITERATE Horizontal 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)

GTD WB 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)

ITERATE WB 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)

GTD Physical 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)

ITERATE Physical 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)

30

Figure 1: Bureaucratic/Administrative Capacity and Military Capacity, 1984-2007

-2 -1.5 -1 -0.5 0 0.5 1 1.5

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

R² = 0.252260043950688

Bureaucratic/Administrative Capacity

Mili

tary

Cap

acity

31

Figure 2: Expected Increase in Terrorist Events Based on Changes in Bureaucratic/Administrative Capacity and Military Capacity Measures

GTD/BA

ITERATE/BA

GTD/RPR

ITERATE/RPR

GTD/MilCap

ITERATE/MilCap-40.00%

-20.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

+1 SD from Mean25th – 75th Percentile Shift

The 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.

32