US foreign aid,interstate rivalry,and incentives for counterterrorism cooperation

Abstract

A common strategy pursued by states targeted by international terrorism is to provide economic and military assistance to the states that host this activity. This is thought to increase their willingness and capacity to crack down on terrorism, but very little work to date has looked at whether this strategy actually leads to desirable outcomes. This article offers an explanation for why a strategy of foreign aid-for-counterterrorism can be successful in some contexts, but counterproductive in situations in which recipients have more pressing strategic priorities. Specifically, I argue that host states receiving US foreign aid that are involved in an ongoing interstate rivalry will use the aid to arm against their rival, rather than to undertake counterterrorism. These states thus have an incentive not to disarm terrorist groups, but rather to play-up the threat from terrorism in order to continue receiving aid concessions. Using data on US foreign aid and terrorist activity in recipient countries, I employ a series of duration and count models to demonstrate that, while US foreign aid can help to decrease terrorist activity in non-rivalrous states, the opposite is true in states with at least one rival.

Keywords

counterterrorism foreign aid rivalry

Introduction

Since the terrorist attacks of 11 September 2001, the United States has provided Pakistan with over $8 billion in total foreign assistance, including more than $2.4 billion in military aid. This assistance is designed largely to encourage the Pakistani government to combat the Taliban and other terrorist groups within Pakistan, and to bolster its capacity to do so. However, Pakistan has significantly different strategic priorities in South Asia than does the United States. The Pakistani government’s primary concern since independence in 1947 has been achieving parity with its more powerful neighbor and rival, India (Haqqani, 2005; Paul, 2014). According to some observers, Pakistan has used a sizeable portion of US military aid not to defeat terrorist groups, but rather to engage in nuclear and conventional arms buildups in anticipation of a possible future conflict with India (Rashid, 2008; Haqqani, 2005). The Pakistani intelligence service, the ISI, has also been accused of supporting the Haqqani network as a tool to counter Indian political influence in Afghanistan. Furthermore, others have speculated that the Pakistani regime fears the possibility of losing US support if the threat from terrorism is reduced and the region becomes stable (Siddiqa, 2009). From the perspective of Pakistan, prolonging and amplifying the threat of terrorism within its borders has the potential to yield financial and strategic benefits into the future. Despite billions of dollars in aid over the past 15 years, Pakistan remains host to many terrorist groups and is still quite unstable (START, 2012). This may be due to the fact that much of the aid sent to Pakistan has not been put toward counterterrorism, police, and economic development as the United States desired, but rather toward attaining a more favorable balance of power with India (BBC, 2009).

This is one example of what I demonstrate is a systematic trend of aid recipients manipulating US interests in order to continue receiving aid. In this article, I offer an explanation for why this happens in some cases. Specifically, I argue that sending foreign assistance to a country for the purpose of combating terrorism signals a donor’s strategic interest in the recipient. Because the donor’s national interests are at stake, the recipient is confident that aid will not be withdrawn, even if it suspects that a large portion of it is misspent. Here, I argue that a recipient that is part of an ongoing interstate rivalry with a third state will divert foreign assistance and other resources toward efforts to build capacity against its rival, rather than toward counterterrorism as the donor would prefer. An important implication of this argument is that in some cases, the presence of a domestic security threat in a country can be a strategic benefit for a government. This runs contrary to the more intuitive assumption that the dominant strategy for governments is always to decrease levels of terrorist activity and instability. Rather, I contend that foreign aid can alter the incentives of recipient states, making those with rivalries less cooperative counterterrorism partners.

The following section summarizes previous attempts to understand the effectiveness of aid at eliciting cooperation. Next, I present a theory of counterterrorism aid, beginning with the problem of aid fungibility, which is further compounded by interstate rivalry and informational asymmetries. In the third section, I test two implications from the argument empirically. Results indicate that higher levels of US foreign aid are associated with a significant increase in the number of anti-American terrorist attacks, as well as with longer terrorist campaigns in recipient countries. Aid to non-rivalrous countries decreases the length of terrorist campaigns, but has no effect on the number of attacks.

Security motivations for aid allocation

Several recent empirical studies have examined the effectiveness of aid in eliciting cooperation from the recipient state. In research on democracy aid to sub-Saharan African countries from 1975 to 1997, Dunning (2004) argues that due to the threat of Soviet influence in Africa during the Cold War, the conditions placed by Western donors on aid disbursements had little credibility. African countries were aware of the strategic motives driving Western aid allocation, and knew that the threat to withdraw aid if they did not comply was usually an empty one. As a result, they promised to implement democratic reforms, accepted the aid, and then reneged on their promises.

Bearce & Tirone (2010) tell a similar credible commitment story to explain the effect of aid on economic performance in recipient countries from 1965 to 2001. Using economic reforms and growth as their dependent variables, they show that levels of OECD aid had no effect during the Cold War, but a positive and significant impact on both reforms and growth after 1991.

Tessman, Sullivan & Li (2011) use a scale of cooperative–conflictive actions to measure the effect of military aid on cooperation. Surprisingly, they find that higher levels of US military aid are associated with less cooperation from the recipient. They also find that uncooperative recipient actions tend to be followed by an increase in military aid in the following period, while periods of cooperation lead to aid cuts. Thus, for a recipient, non-cooperation pays, while cooperation is punished. Stone (2004) makes a similar argument about IMF lending in Africa. IMF program recipients who are ‘strategically important’ to IMF lenders violate conditions more often, as they know that the lenders will intervene to prevent strict enforcement of conditions. Girod & Tobin (2014) finds that, even though conditionality may be unenforceable, recipients will comply with World Bank loan conditions if they are heavily dependent on foreign aid and do not have access to other sources of revenue. Finally, Bapat (2011) argues that US counterterrorism aid creates perverse incentives for host states to maintain terrorist activity at low levels so as to continue receiving assistance to combat it. At the same time, his model demonstrates that it is still rational for the United States to continue giving aid in order to prevent the host government from collapsing or striking a deal with terrorist groups.

The present study fits well within the framework of previous research on aid and recipient cooperation. In addition to the donor’s credible commitment problem, my argument introduces rivalry to the analysis as a way to capture how recipient priorities condition foreign aid effectiveness. One additional contribution made by this article is that, while the studies discussed above use the credible commitment explanation to account for variation in democratic reforms (Dunning, 2004) and economic performance (Bearce & Tirone, 2010), with the exception of Bapat (2011), no attempt has yet been made to apply this framework to explain the effectiveness of aid for counterterrorism, a problem that is particularly relevant to the current international environment. This study builds upon Bapat’s (2011) perverse incentives argument, but also argues that an aid recipient’s strategic priorities – in this case, interstate rivalry – are important determinants of how effective foreign aid can be at eliciting counterterrorism cooperation. I also build upon extant research on the link between rivalry and terrorism. Both Conrad (2011) and Findley, Piazza & Young (2012) find that terrorism occurs with greater frequency in rivalrous dyads; I show that this is not always the case, and that rivalry increases terrorism primarily when foreign aid is involved. My findings even suggest that states involved in rivalries sometimes experience shorter terrorism campaigns.

Additionally, past studies of foreign aid effectiveness have not given due consideration to recipient incentives and government counterterrorism behavior. This is a significant shortcoming, as foreign aid is often allocated with the understanding that the host government will cooperate toward achieving the donor’s counterterrorism goals. With a couple of exceptions (i.e. Bandyopadhyay, Sandler & Younas, 2010; Bapat, 2011), previous examinations of the aid–terrorism relationship have focused on the indirect effects of foreign aid on terrorism, either by asking how foreign aid decreases terrorism through increases in education (Azam & Thelen, 2008) or through allocations to other sectors, such as health and civil society (Young & Findley, 2011). While it is not my goal to find fault with these studies, I argue that a primary theoretical focus of any study of the effects of foreign aid on terrorism should be on the incentives and behavior of the recipient government.

A theory of rivalry and foreign aid

Fungibility

A substantial body of empirical research on the question of aid fungibility has concluded that most aid is fungible, and has pointed to this as a serious impediment to aid effectiveness. Fungibility, as defined by Hagen (2006), means that it is possible for the recipient to divert aid away from the donor’s intended purpose. In an analysis on the effects of foreign aid on expenditure in the Dominican Republic, Pack & Pack (1993) find evidence of substantial diversion of foreign assistance away from its intended purposes. Feyzioglu, Swaroop & Zhu (1998) actually find reductions in recipient spending in sectors for which aid is intended. Devarajan & Swaroop (2004) approach the problem from a slightly different angle, pointing out that the link between public spending and aid is not straightforward. Some aid may be targeted for specific programs in which the recipient government would have invested even in the absence of aid. In this case, foreign aid simply relaxes the recipient’s budget constraints, freeing up funds to be used in other areas.

This is likely to be the case for counterterrorism aid: the recipient government would probably have invested some of its own resources in combating violent groups anyway. As a result, the government may see counterterrorism aid as either money to be used for other projects or, at worst, as an opportunity for rent-seeking. For example, the US Embassy in Islamabad reports that as much as 70% of $5.4 billion in US aid to Pakistan in 2007 went unaccounted for (Rohde et al., 2007; Walsh, 2008). In addition, Pakistan has also allegedly used a substantial portion of this money to boost its military capacity to fight India, its long-time rival.

Aid diversion and interstate rivalry

Counterterrorism campaigns can require a vast commitment of political, military, and financial resources (Kilcullen, 2010). If a recipient has more pressing strategic imperatives, it will have little motivation to devote its own men and materiel to fighting a costly war on behalf of another country at the expense of its other interests. Aid fungibility is unlikely to matter if the recipient views counterterrorism as a top priority (Hagen, 2006). In that case, the recipient would theoretically want to channel all of the aid received toward the sector for which it was intended by the donor. However, this is not always the case, as recipients of US aid often have different priorities, such as the pursuit of interstate rivalries.

Interstate rivalries are characterized by competition between two states who view one another as a security threat. Importantly, these states see one another as enemies not just in the moment, but into the foreseeable future as well (Thompson, 2001; Thompson & Dreyer, 2011). Given this indefinite time horizon, rivals will want to build up their militaries now in order to hedge against power shifts and threats from their adversary in the future. Previous research finds that long-term rivalry dyads are disproportionately likely to engage in arms races (Rider, Findley & Diehl, 2011; Rider, 2009). In the context of this study, rivalrous states that also receive large amounts of foreign aid enjoy an advantage in that they are partially freed from having to raise taxes and manufacture equipment to finance their own military buildup. Aid donors like the United States often supply equipment directly to the military or provide the recipient with grants with which it can then purchase equipment. This enables the recipient state to engage in arms buildups while avoiding the costs typically associated with increased military expenditures such as rising deficits, inflationary pressures, decreases in investment capital, and unrest stemming from cuts in social spending (Rider, 2009).

External rivals can also serve as a focal point for nationalist sentiments. Politicians can benefit politically by rallying constituents around a common external threat that can also serve as a convenient scapegoat to distract attention from domestic problems. Thus, politicians in recipient states are able to continue the politically expedient rivalry at less cost. On the other hand, a state with no rivals has less incentive to secure future aid concessions and thus sees little value in prolonging a potentially destabilizing terrorist campaign simply to receive aid in the future. Donors may attempt to attach to aid packages conditions which must be met in order for aid to continue, but for reasons that I expand upon below, these conditions tend to be non-credible and tend to be violated with impunity.

Donor commitment problems

Donor states attach conditions on aid packages which the recipient must fulfill if it wishes to continue receiving aid. These conditions often specify reforms to be undertaken, a level of effort that must be exerted, or simply that certain outcomes must be achieved, with the implicit threat that aid will be withdrawn or cut absent these reforms or outcomes. For the donor, this is the most attractive strategy to induce recipient cooperation, as it entails the fewest costs. Indeed, many believe that credible conditionality is the only way for aid to be effective at inducing policy change (Killick, Gunatilaka & Marr, 1998; Easterly, 2001; Dunning, 2004; Stone, 2004; Collier, 2007; Riddell, 2007; Bearce & Tirone, 2010). However, for conditionality to serve as an effective persuasive mechanism, threats to withdraw aid must be viewed by the recipient as credible. In other words, the recipient must fear an aid reduction if it does not comply with conditions set by the donor. In this section, I discuss why issuing credible threats to withhold strategic counterterrorism aid is quite problematic for donors like the United States.

Aid conditionality is often not credible for a number of reasons. First, for conditions to be credible, recipient non-compliance must be observable to the donor (Buchanan, 2000; Gibson et al., 2005). However, counterterrorism effort is often subtle and unobservable, and the information requirements for such monitoring are prohibitive.

An additional barrier to credible aid conditionality, and perhaps the most important for my argument, is the strategic interest that prompted the aid in the first place. Suspending counterterrorism aid simply might not be considered a viable option for donors unwilling to risk the effects of an aid cutoff to a ‘partner’ country. US officials are often under considerable domestic political pressure to ‘do something’ about threats to US interests. The public careers of US politicians may be at risk if voters or lobbies perceive the withdrawal of military and economic support for these countries as unwise in the face of threats to the country. But because military action to counter these threats is so costly, the USA will almost always opt to delegate the fight by bolstering the capacity of these states through the provision of aid (United States Congress, 2008; Boutton & Carter, 2014). Thus, even if it is verifiable that the host state is non-compliant, cutting aid may constitute an unacceptable level of risk for the United States.

Riddell (2007) observes that although conditions have been attached to almost all aid flows, aid is rarely withdrawn if the conditions are not met. Even in those cases where aid is withdrawn, this suspension is usually temporary. Importantly, this observation was made about aid designed to encourage economic reforms rather than aid for counterterrorism. If punishment is so rarely enacted in the case of the former, it is even less likely in the latter case because the donor’s commitment problem is intensified (Bapat, 2011). As mentioned earlier, Tessman, Sullivan & Li (2011) find that defiant recipient states are often treated to increases in aid following non-cooperation, since the issue for which aid is given remains unresolved.

The United States and other donors have refrained from placing conditions on aid packages to strategically important countries for fear of creating a backlash and thus a more defiant partner. Specific conditions attached to aid packages can be viewed by recipients as violations of sovereignty, which often create political upheaval in the recipient country (Hagen, 2004). In the wake of an uproar within the Pakistani elite over the initial terms of a recent US aid package, the United States softened the conditions contained in the 2009 Enhanced Partnership with Pakistan Act to state that Pakistan must simply ‘show evidence’ that it is combating terrorism in order to continue receiving aid (Perlez, 2009; United States Congress, 2009).

The story I tell here is that when a donor such as the USA sends foreign aid to a country in which violent threats to its interests are based, the recipient country’s strategic priorities determine whether the aid will be effective in achieving counterterrorism cooperation. When the attention and resources of a recipient state are not focused on the pursuit of an interstate rivalry, foreign aid has the potential to be effective. However, when the recipient is part of a rivalry, the aid will be counterproductive for the reasons described above. Because of the donor’s informational disadvantage and commitment problem, the recipient feels free to do as it pleases with the aid, and the donor has limited ability to prevent this.

Hypotheses

The argument outlined above points to expectations about the quality and/or quantity of counterterrorism effort undertaken by a recipient of US foreign aid. A direct test of these arguments would necessitate very detailed data on the counterterrorism behavior and capacities of US aid recipients. Unfortunately, the concept of ‘counterterrorism’ remains ill-defined by scholars, spans across numerous policy areas, and can be quite difficult to observe. Counterterrorism ‘effort’ could include overt activities such as military and police raids on terrorist hideouts, arrests, assassinations, or passage of antiterrorism legislation. However, more covert, less observable actions, such as secret negotiations with a terrorist group, interrogation, or the freezing of financial assets are also relevant. Therefore, any measure of counterterrorism that relies on observable indicators will inevitably be incomplete. Furthermore, observing a government’s attempts to deliberately manipulate the level of terrorism present – which is an important part of my theoretical argument – is very difficult. Perhaps jailbreaks would occur more frequently, as we have seen in Iraq, Yemen, and Pakistan recently (Reuters, 2013; Johnsen, 2013; Foreign Policy, 2013), although again, systematic data on these events are sparse.

While systematic measures of these concepts are currently unavailable, some implications of this argument can be evaluated empirically. The argument I discuss regarding aid diversion and interstate rivalry points to two implications about terrorism we should expect to observe in US aid recipients.

First, given a greater emphasis on counterterrorism in non-rivalrous states, higher levels of foreign aid should lead to better outcomes for the United States. We should therefore expect terrorist groups based in these states to have shorter lifespans, as their hosts engage in counterterrorism with greater urgency. However, I expect that host states involved in interstate rivalries will pursue the rivalry instead of US counterterrorism goals, and prolong the threat from terrorism as a source of leverage in order to keep receiving aid from the USA. Thus, groups in these states should be less likely to ‘fail’ in a given year. This leads to my first two hypotheses:

Hypothesis 1a: As US aid to state i increases, groups based in that state will be more likely to fail in year t if that state has no rivals.

Hypothesis 1b: As US aid to state i increases, groups based in that state will be less likely to fail in year t if that state is involved in a rivalry.

The second set of hypotheses concerns how rivalry will condition the effect of foreign aid on the magnitude of terrorism in recipient countries. It is possible that the goal of US aid may not be for recipient states to completely destroy the groups, but rather to enable the regime to prevent attacks against US citizens and interests. As long as its interests remain safe, the United States may be indifferent to the prolonged presence of terrorist groups in recipient countries.

Given their desire for continued US assistance to bolster their capabilities against their rival, recipient states may seek further assurance of future aid. Fearful that the United States would devote its aid resources elsewhere once it feels its interests are safe from attack, recipients may turn a blind eye specifically toward attacks against US interests, and may even encourage such activity. Therefore, in addition to longer-lasting groups, we might also expect more terrorist attacks against US interests in aid-receiving states with rivals. This leads to the following hypotheses:

Hypothesis 2a: States that are not involved in an interstate rivalry will experience fewer anti-US attacks in a given year as US aid increases.

Hypothesis 2b: States that are involved in an interstate rivalry will experience more anti-US attacks in a given year as US aid increases.

In the following section, I outline the data, research design, and methodology used to assess these hypotheses.

Data and research design

Dependent variables

To test my expectations about the effect of US aid on a recipient state’s incentive to combat terrorism, I rely on two types of data. First, I use a modified version of the data used in the Jones & Libicki (2006) study on how terrorist groups end in order to test my hypothesis about group duration. For the second set of hypotheses, in which terrorist attacks are the quantity of interest, I take data from the Global Terrorism Database (GTD). These variables are described in detail below.

Group duration

The Jones & Libicki (2006) data identify 648 violent non-state groups that exist or have existed throughout the world between 1968 and 2006. Of these groups, 45 were based inside the United States and are thus not included in the present study. This leaves 603 internationally hosted, non-state actors during this time period. I converted the format into binary time-series cross-sectional format, with one observation per country, per year that the group was in existence. This type of arrangement is best to analyze the factors that cause a group to fail or endure, which is what I seek to explain here.

In a few cases in the data, groups had legitimate bases in more than one country. When this was the case, I sought confirmation of multiple bases from US Congressional Research reports, the Global Terrorism Database (START, 2012), and histories of the groups themselves when available. Where appropriate, I coded a group’s presence in multiple countries from the date that these bases were established. These groups typically began operations in the multiple countries more or less simultaneously. For example, Jemaah Islamiyah began operating in Malaysia in 1993, but quickly established bases in Indonesia and the Philipinnes. This group is thus coded in all three countries from 1993. Similarly, the Salafist Group for Call and Combat has carried out attacks in Morocco, Algeria, Mali, and Mauritania. This coding strategy is appropriate because the United States recognizes that these groups often traverse freely between countries, and its aid patterns reflect this (Ellis, 2004; Boutton & Carter, 2014).

Because I am trying to explain action taken by the recipient state against these groups, my dependent variable is the failure of a group in year t due to actions taken by the state. The group duration data contain five different types of group failure: accomplishment of group goals; entry into politics; defeat by the military forces of an outside state; defeat by police action; and dissolution/splintering. Only the latter two are well suited to my purposes, since these are the result of actions taken by the host state, which is what the USA has in mind when providing foreign aid to these states. Therefore, I combine police failures with splintering to form my dependent variable. This variable takes a value of 1 if the group fails in year t, and 0 otherwise:

y_{i, t} = \{\begin{matrix} 0 & i f g r o u p i s a c t i v e i n y e a r t o r ` e n d s' f o r r e a s o n s u n r e l a t e d t o h o s t s t a t e a c t i o n \\ 1 & i f g r o u p f a i l s i n y e a r t d u e t o i n t e r n a l d i s s o l u t i o n o r p o l i c e f o r c e \end{matrix}

When a group ‘fails’ by splintering or police force, it leaves the data and is not included in any subsequent years; there are 206 failures in the data. Groups which fail for any of the other three reasons remain in the data until they fail according to the Jones & Libicki (2006) data, at which point they drop out of the data without ever having ‘failed’ in the model.

Attacks

Data on terrorist attacks are taken from the Global Terrorism Database (START, 2012), which contains information on the location, year, and primary target of over 80,000 terrorist attacks from 1970 to 2012. I construct three separate dependent variables based on the primary target of each attack: (1) a count of the number of attacks directed at US interests in a host country in a given year; (2) attacks directed at non-US targets; and (3) total attacks in a host country. An important point is that GTD classifies terrorist attacks according to the country in which they take place, and not according to the nationality or origin of the perpetrator(s) (START, 2012). For example, a cross-border attack on the US embassy in Nairobi by the Somali group Al-Shabaab would be associated with Kenya, not Somalia. This is appropriate for my purposes, since the USA responds to terrorism primarily by allocating foreign aid to countries in which terrorist activity takes place (Boutton & Carter, 2014).

Independent variables

US foreign aid

The theory outlined above specifies that levels of US foreign aid provided should influence the likelihood of a group failing in year t, conditional upon the presence or absence of interstate rivalry in the recipient. I construct four separate explanatory aid variables, one each for military, economic, Defense Department security aid,¹ and total aid (the first three categories added together),² all of which are taken from the US Agency for International Development’s Greenbook (United States Agency for International Development, 2013) and measured in constant 2009 US dollars.

I make several adjustments to these variables. First, raw aid variables are highly right-skewed. In my data, US aid ranges from $0 to over $17 billion, with a mean of $754 million. To correct for this, I take the natural log of (1 + aid), which smooths out the distribution of the aid variables by reining in outliers. Taking the natural log of aid also accounts for the decreasing marginal returns to aid. Finally, I lag each aid variable by one year in order to account for the fact that aid received in year t is unlikely to affect the fate of a given group in year t. It is more likely that aid given to a state in year t − 1 will be the relevant factor, as aid may take time to have an impact in the recipient country. Each of the aid variables is also measured per capita to normalize the effect of aid by the population of the recipient country.

Interstate rivalry

For interstate rivalry data, I use Thompson & Dreyer’s (2011) work on ‘strategic rivalries’. Making use of information and descriptions contained in diplomatic and political histories, the authors classify states as rivals if key decisionmakers within both states ‘regard each other as (a) competitors, (b) the source of actual or latent threats that pose some possibility of becoming militarized, and (c) enemies’ (Thompson, 2001: 560). This approach to measuring rivalry differs in important ways from the ‘dispute density’ approach favored by some (i.e. Goertz & Diehl, 1993; Klein, Goertz & Diehl, 2006, among others). The latter method classifies two states as rivals if they engage in a specified number of militarized disputes over a certain period of time.³ However, while the presence of competition is necessary for a dyad to be called a rivalry in both measures, the dispute density approach – by definition – includes only highly militarized dyads. As Thompson (2001) points out, this can result in dyads being misclassified in a number of ways. First, two states may anticipate future conflict even if they have not yet engaged in a militarized dispute, meaning that the actual rivalry may begin well before the dyad crosses the dispute density threshold. Furthermore, the dispute density approach may be capturing cases in which major powers use military force repeatedly to coerce smaller powers. Such states are not likely to view one another as rivals, but would be classified as such by dispute density approaches. The strategic rivalry approach avoids this by limiting rivalries to dyads in which the states view one another as ‘peer competitors’. In short, I use the strategic rivalry approach here because it is less biased toward rivalries that are already militarized, and because it is the most theoretically appropriate for this particular research question.^4,5 I use a dummy variable equal to 1 if a recipient state was involved in at least one interstate rivalry in a given year, and 0 if not.⁶

To evaluate the hypothesis that the effect of aid on terrorist activity is conditional upon the presence of a rivalry, I multiply the rivalry indicator by each aid variable to create an interaction term.

I also include a set of covariates that reflect findings in extant quantitative terrorism literature. These include a natural log of GDP per capita (Maddison, 2012), natural log of country population (World Bank, 2013), democracy and military regime indicators (Geddes, Wright & Frantz, forthcoming), Cold War and post-September 11 indicators, an ongoing civil war indicator (Themnér & Wallensteen, 2012), state sponsorship (Carter, 2012), the number of terrorist groups in country, group orientation variables (Jones & Libicki, 2006), and cubic time polynomials (Carter & Signorino, 2010).

In the duration data, there are 6,522 group-years across 92 countries and 603 groups between 1968 and 2006. Dropped observations from countries in which there is no variation on the dependent variable leave 6,227 observations in the full models. The attack-count data contain 5,202 country-year observations, extending from 1970 to 2010.

Endogeneity

Aid cannot reasonably be believed to be exogenous; it is likely that the United States allocates more aid to countries that experience more terrorism, and that any estimate of the effect of aid on terrorist activity will suffer from bias as a result. Azam & Delacroix (2006) report a positive relationship between aid and terrorism, not because aid has a perverse influence on terrorist activity, but because donor countries allocate more aid to countries that experience high levels of terrorism and which are likely to continue experiencing elevated levels of terrorist activity in the future. Addressing the issue of endogeneity is therefore critical to conducting a proper empirical analysis.

In the past, scholars have relied on instrumental variables (IV) and two-stage regressions in order to achieve a plausible claim of exogeneity (Sovey & Geen, 2011). However, despite the difficulty of finding an instrument that satisfies the assumptions of IV estimation, Cohen & Easterly (2009) lament that the IV technique began to be used as a quick fix by economists and political scientists to generate ‘causal’ results. Identifying a variable that predicts aid levels but is uncorrelated with terrorism is particularly difficult. Furthermore, IV estimation can generate biased estimates in non-linear models (Terza, Basu & Rathouz, 2008).

Rather than attempt to instrument for US foreign aid, I draw upon Terza, Basu & Rathouz (2008) and Young & Findley (2011) for two methods to address endogeneity. First, I use the two-stage residual inclusion (2SRI) method described in (Terza, Basu & Rathouz, 2008). This involves estimating a series of linear regressions in which a set of exogenous explanatory variables are used to explain levels of aid.⁷ The residuals from these reduced-form equations are then included as independent variables in the equations for the attack count models as a test for endogeneity bias.

The second solution I use is to estimate a series of system generalized method of moments (GMM) models accounting for endogenous regressors in dynamic panel data based on Arellano & Bond (1991). This method is designed for situations in which the number of time periods is small relative to the number of groups (countries, in this case), and in which the independent variables may not be exogenous. This technique has been used in foreign aid studies by Young & Findley (2011) and Dreher et al. (2008). The findings for the main independent variables in the US attacks model do not change, and are reported in the online appendix along with a more detailed explanation of the system GMM approach.

Results

Aid, rivalry, and group duration

To analyze the conditional effect of US aid on group duration, I estimate a series of grouped duration models in which the outcome variable is either failure by host state police force or internal dissolution (= 1), or not (= 0). The model is represented by the following equation:

P (F a i l = 1) = F (β_{1} l n {(A i d)}_{i t - 1} + β_{2} R i v a l r y_{i t - 1} + β_{3} l n {(A i d)}_{i t - 1} * R i v a l r y_{i t - 1} + β_{4} X_{i t - 1} + γ_{i} + ε),

where i indicates the country, t is the year, $F (\cdot)$ is the cdf of the logistic distribution, X is a vector of control variables, γ is the set of country fixed-effects,⁸ and ε is an error term. All time-varying covariates are lagged. The estimation gives the effect of the independent variables on the probability of group failure in a given year, so a negative (positive) coefficient means that variable makes group failure less (more) likely. Further, the fixed-effects models estimate within-country variation to ensure that the results are not simply a reflection of cross-national variation. This is important, since the theoretical expectation is that as a country involved in a rivalry receives more aid, it will have increasingly less incentive to disarm terrorist groups. The results of these models are shown in Table I.^9,10

Columns 2–4 contain estimates from the logit models treating military, economic, and defense aid separately; the model in column 5 groups them together as total aid. The sign on the coefficient for GDP per capita is statistically significant in the expected direction in all four models: as a state grows wealthier, the likelihood of a group being eliminated in that country increases.

Groups were more likely to fail at any given time during the Cold War, which is surprising given the norm of state sponsorship during this time (Pillar, 2004). The estimates for total number of groups in a country are positive, meaning that as the number of other groups based in a country increases, group i is more likely to fail. It is not clear why

Table I.

The conditional effects of rivalry and US aid on group duration

Type of aid	Military	Economic	Defense	Total	Military p.c.	Economic p.c.	Total p.c.
${l n (A i d)}_{t - 1}$	0.11*	0.07*	0.17*	0.08*	0.12*	0.07*	0.08*
	[0.04]	[0.03]	[0.06]	[0.03]	[0.04]	[0.03]	[0.03]
${R i v a l r y}_{t - 1}$	1.14	0.89	0.48	1.47*	−1.60	0.96	−1.04
	[0.63]	[0.62]	[1.20]	[0.69]	[0.64]	[0.56]	[0.56]
${A i d^{*} R i v a l r y}_{t - 1}$	−0.16*	−0.11*	−0.18*	−0.15*	−0.16*	−0.11*	−0.15*
	[0.05]	[0.04]	[0.07]	[0.04]	[0.05]	[0.04]	[0.04]
${l n (G D P}_{t - 1})$	0.84*	0.80*	1.45*	0.72*	0.85*	0.81*	0.72*
	[0.26]	[0.26]	[0.54]	[0.26]	[0.26]	[0.26]	[0.26]
$C o l d W a r$	0.70*	0.62*	–	0.63*	0.70*	0.62*	0.62*
	[0.30]	[0.29]		[0.30]	[0.30]	[0.30]	[0.30]
${N u m b e r o f g r o u p s}_{t - 1}$	0.05	0.05	0.13*	0.06*	0.05	0.05	0.06
	[0.03]	[0.03]	[0.05]	[0.03]	[0.03]	[0.03]	[0.03]
$l n (P o p u l a t i o n)$	2.02*	1.75*	−1.40	1.95*	1.98*	1.72	1.89*
	[0.92]	[0.94]	[2.61]	[0.94]	[0.92]	[0.93]	[0.93]
${D e m o c r a c y}_{t - 1}$	−0.51	−0.54	−2.11*	−0.54	−0.50	−0.53	−0.53
	[0.45]	[0.45]	[0.94]	[0.45]	[0.45]	[0.45]	[0.45]
${M i l i t a r y r e g i m e}_{t - 1}$	−1.10	−1.07	−1.93	−1.12	−1.08	−1.05	−1.10
	[0.66]	[0.66]	[1.34]	[0.66]	[0.66]	[0.66]	[0.66]
$S p o n s o r e d$	−0.25	−0.22	0.20	−0.23	−0.25	−0.21	−0.23
	[0.22]	[0.26]	[0.40]	[0.26]	[0.26]	[0.26]	[0.26]
$T e r r i t o r i a l$	−0.20	−0.21	0.39	−0.21	−0.20	−0.21	−0.21
	[0.25]	[0.25]	[0.37]	[0.26]	[0.25]	[0.25]	[0.26]
$R e g i m e c h a n g e$	0.07	0.06	0.39	0.06	0.07	0.06	0.06
	[0.22]	[0.22]	[0.34]	[0.22]	[0.22]	[0.22]	[0.22]
$S t a t u s q u o$	−0.14	−0.13	−1.06	−0.12	−0.14	−0.14	−0.12
	[0.52]	[0.52]	[0.87]	[0.52]	[0.52]	[0.52]	[0.52]
Log likelihood	−566.19	−569.62	−305.83	−547.85	−565.67	−569.30	−547.53
$χ^{2}$	165.13	158.52	94.04	163.97	166.18	159.16	164.60
N	6,227	6,227	3,213	6,227	6,227	6,227	6,227

*indicates significance at $p < 0.05$ . Numbers in brackets are standard errors.

this might be the case; one possible explanation is that proliferation of groups in a country increases fighting between groups, causing them to die out or splinter more quickly.

The population estimate is significant and positive in three of the four models, meaning that groups in more populous countries tend not to last as long. The coefficient for the democracy variable is negative and significant in the models containing observations only from 1993 and later, but is insignificant in all other models. The military regime coefficient is negative and significant only in the military and total aid models (columns 2 and 5). Consistent with past empirical research on sponsorship (e.g. Carter, 2012), groups that are sponsored by outside states do not last significantly longer than groups that are not sponsored. None of the group orientation variables (territorial, regime change, or status quo) reach statistical significance in any of the models.

Turning to my variables of interest, we see that the levels of US military, economic, defense, and total aid (i.e. the coefficients for $l n {(A i d)}_{t - 1}$ in each model) are significant and positive. This should be interpreted as the effect of increasing levels of aid when the recipient is not involved in a rivalry. The positive coefficients in all four models lend support to Hypothesis 1a, which predicted that, absent a rivalry, foreign aid should lead states to defeat terrorist groups more quickly.

The coefficient on the aid–rivalry interaction term, which estimates the effect of aid on group duration in rivalrous recipients, is negative and significant in all models. This indicates that increasing US aid to a state with an interstate rival is associated with longer-lasting terrorist campaigns. This suggests support for Hypothesis 1b.

Interestingly, the estimated effect of rivalry when aid = 0 (i.e. the coefficient estimate on $R i v a l r y_{t - 1}$ ) is positive and significant in column 5, meaning that groups based in recipient states receiving no aid and involved in a rivalry are more likely to fail in year t. This runs slightly counter to the intuition in Findley, Piazza & Young (2012) and Conrad (2011), who find increased terrorist activity between interstate rivals.

Figure 1.

Probability of group failure across values of military aid per capita in non-rivalrous states

Figure 2.

Probability of group failure across values of military aid per capita in rivalrous states

Columns 6–8 contain results of duration models in which the natural log of aid per capita is used. The findings – particularly for the main independent variables – do not change. Increases in aid per capita make group failure more likely in the absence of rivalry, and less likely when the recipient is involved in one or more rivalries. This provides additional support both parts of Hypothesis 1.

Since it is difficult to interpret the effects of these variables on Y simply by looking at the logit coefficients, Figures 1 and 2 display graphically the substantive effects of increases in military aid in both the presence and absence of rivalry. Each figure shows changes in predicted probabilities of group failure as military aid per capita increases over its full range (from $0 to $2,777 per capita). The rug plot at the bottom of each of the figures represents the distribution of the aid variable in each scenario. In Figure 1, we see that when a host state is not involved in a rivalry, the probability of a terrorist campaign in that country is strictly increasing as it receives more military aid per capita. The opposite is true when the host is involved in an interstate rivalry, as shown in Figure 2. In this case, predicted probability of group failure is strictly decreasing in military aid. These effects are large and statistically significant, providing further support for Hypotheses 1a and 1b.

Aid, rivalry, and terrorist attacks

The findings in the previous section suggest that US foreign aid may in some cases lead recipient governments to deliberately prolong terrorist campaigns. However, a possible counter-argument is argue that the aim of US aid to these governments may not be the complete elimination of these groups. Perhaps the goal is more narrow: to prevent US interests in the recipient country from being attacked. In order to maintain a level of threat, recipient states may seek either to facilitate attacks against US interests, or simply turn a blind eye toward such activity as a method to ensure the continued flow of US foreign aid to finance a rivalry. In this section, I present the results of the count models assessing Hypotheses 2a and 2b.

Columns 2–4 in Table II contain estimates from negative binomial regressions, with terrorism-relevant covariates, in which the dependent variables are, respectively, total terrorist attacks in a country in a given year, attacks against non-US targets, and attacks against US targets.¹¹

In all three models, the coefficient on the aid variable¹² is positive but not statistically significant, meaning that states receiving foreign aid, but which are not involved in a rivalry, do not experience higher or lower levels of terrorism than those that are. Rivalry alone is also insignificant; states involved in a rivalry and receiving no aid are no more susceptible to terrorism than those that receive aid.

Turning to the other value of interest, the positive coefficient on the interaction term in the US attacks model (column 4) indicates that total US aid has a positive effect on anti-American attacks in states engaged in an interstate rivalry. This effect loses significance when total attacks and

Table II.

Aid, rivalry, and terrorist attacks: Negative binomial regression

	All attacks	Non-US attacks	Anti-US attacks
${l n (T o t a l a i d)}_{t - 1}$	0.03	0.03	0.05
	[0.03]	[0.03]	[0.04]
${R i v a l r y}_{t - 1}$	0.49	0.50	−0.46
	[0.50]	[0.51]	[0.48]
${l n (A i d)^{*} R i v a l r y}_{t - 1}$	0.01	0.01	0.10*
	[0.03]	[0.03]	[0.03]
${l n (G D P}_{t - 1})$	0.57*	0.56*	0.80*
	[0.13]	[0.13]	[0.14]
$C o l d W a r$	−0.85*	−0.88*	−0.22
	[0.12]	[0.20]	[0.19]
$P o s t - S e p t e m b e r 11$	−0.89*	−0.88*	−0.99*
	[0.20]	[0.12]	[0.36]
$l n (P o p u l a t i o n)$	0.65*	0.66*	0.48*
	[0.07]	[0.07]	[0.09]
${D e m o c r a c y}_{t - 1}$	0.14	0.13	0.00
	[0.24]	[0.25]	[0.26]
${M i l i t a r y r e g i m e}_{t - 1}$	0.94*	0.96*	0.53
	[0.48]	[0.50]	[0.34]
$C i v i l w a r$	2.65*	2.70*	1.45*
	[0.21]	[0.21]	[0.21]
$E n d o g e n e i t y b i a s$	0.02	0.02	−0.03
	[0.02]	[0.02]	[0.03]
$C o n s t a n t$	−14.52*	−14.52*	−17.36*
	[1.62]	[1.64]	[1.52]
$α$	5.41	5.58	8.03
Log likelihood	−14,287.38	−13,961.79	−3,513.29
$χ^{2}$	498.60	517.55	263.24
N	5,202	5,202	5,202

*indicates significance at $p stretchy="false">) < 0.05$ . Numbers in brackets are standard errors clustered by country. Dispersion parameters greater than 0 suggest that the negative binomial out-performs the Poisson estimation.

non-US attacks are modeled (columns 2 and 3), suggesting that recipient states may be turning a selective blind eye toward terrorism specifically directed at US interests in order to continue receiving US assistance. Hypothesis 2b is supported, but only as it relates to anti-US attacks; Hypothesis 2a does not receive support.¹³

The Cold War and post-September 11 indicators are negative, suggesting that terrorism against any target was less prevalent during the Cold War and after 2001 than during the 1990s. A country involved in a civil war is also likely to experience more terrorist attacks of both types. The effects of both population and GDP per capita are positive and large, which comports with previous findings in the terrorism literature. Democracies (as measured by Geddes, Wright & Frantz, forthcoming) do not experience significantly different amounts of terrorism in these models, but military regimes suffer from more terrorist attacks than other regime types. Military regimes are limited in their institutional capacity, and thus their ability to allow for the peaceful settlement of grievances, and rely heavily on coercion to quell dissent (Geddes, 2003). This finding also corroborates previous research on the link between regime type and terrorism (Conrad, Conrad & Young, 2014; Piazza & Wilson, 2013).

The main findings do not attenuate when the residuals from the aid regressions are included in the models, as indicated by the insignificant coefficient for endogeneity bias. This suggests that endogeneity is not a significant problem in the fixed-effects count models.

Conclusion

This article has offered a possible explanation for why US foreign aid is effective at eliciting counterterrorism cooperation from some states, but may be associated with less compliance in other instances. Applying arguments from the foreign aid literature regarding preference divergence, I argue that when a recipient/host regime is engaged in a rivalry with another state, it will want to build up its capacity to hedge against future threats from its rival. Foreign aid provides these states with a way to build capacity at significantly less cost. Therefore, if a state with a rival is also receiving foreign aid from the United States to fight terrorism, it will have little incentive to decrease terrorism, but will rather play up the threat in order to continue receiving aid windfalls with which to arm against its rival.

The findings presented in this article have implications for the study of terrorism and foreign aid, a field which remains relatively underdeveloped. My findings suggest that while such aid may be effective in some cases, it may actually be achieving the opposite of its goal in others. A look at the top recipients of US foreign assistance can serve to corroborate this. Terrorist campaigns in countries such as Israel, Pakistan, and the Philippines persist despite high levels of aid, yet the USA continues and often increases its allocation to these countries. However, aid to countries such as Bangladesh and Indonesia has been relatively effective. These findings suggest that the United States should be more cautious when allocating aid to these countries, perhaps with more attention paid to the security priorities of the recipient.

The present analysis reveals several avenues for future inquiry. First, while these findings are interesting and counterintuitive, they are only suggestive. More work needs to be done to specify and test causal mechanisms in the relationship between aid and counterterrorism cooperation. In order to test the argument more directly, more precise measures of this concept are needed.

Second, information about the conditions attached to aid packages – and perhaps more importantly, the credibility of those conditions – would be useful. Although aid is fungible and can be used as the recipient pleases, the presence of credible conditions should minimize aid diversion, as the recipient could face the prospect of punishment if caught misusing aid.

Finally, there are undoubtedly sources of preference divergence other than interstate rivalry that could give states incentives to prolong terrorist campaigns in order to continue receiving aid. Differing strategic priorities between donor and recipient is a critical determinant of whether foreign aid will be effective, and thinking about other major sources of preference divergence (i.e. internal political rivals) is a potentially rewarding path for future foreign aid research.

Footnotes

Replication data

The online appendix, datasets, and command files for the analyses in this article can be found at http://www.prio.no/jpr/datasets and .

Acknowledgements

Many thanks to Navin Bapat, Claude Berrebi, David Carter, Mike Kenwick, Bethany Lacina, Doug Lemke, Jim Piazza, Joe Wright, Joe Young, the editor and three anonymous reviewers, and participants at EITM 2011 for their helpful comments and suggestions on earlier drafts, and to Bill Thompson for sharing his rivalry data. All remaining mistakes are mine.

Notes

References

Arellano

Manuel

Bond

Stephen

(1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58 (2): 277–297.

Azam

Jean-Paul

Delacroix

Alexandra

(2006) Aid and the delegated fight against terrorism. Review of Development Economics 10 (2): 330–344.

Azam

Jean-Paul

Thelen

Véronique

(2008) The roles of foreign aid and education in the War on Terror. Public Choice 135 (3/4): 375–397.

Bandyopadhyay

Subhayu

Sandler

Todd

Younas

Javed

(2010) Foreign aid as counterterrorism policy. Federal Reserve Bank of St Louis, Working Paper 2009–021B (http://research.stlouisfed.org/wp/2009/2009-021.pdf).

Bapat

Navin A

(2011) Transnational terrorism, US military aid, and the incentive to misrepresent. Journal of Peace Research 48 (3): 303–318.

BBC (2009) Musharraf admits aid diverted. 14 September (http://news.bbc.co.uk/2/hi/8254360.stm).

Bearce

David

Tirone

Daniel C

(2010) Foreign aid effectiveness and the strategic goals of donor governments. Journal of Politics 72 (3): 837–851.

Boutton

Andrew

Carter

David B

(2014) Fair weather allies: Terrorism and the selective allocation of US foreign aid. Journal of Conflict Resolution doi:10.1177/0022002713492649.

Buchanan

James M

(2000) Collected Works of James Buchanan, Volume 7: The Limits of Liberty. Indianapolis, IN: Liberty Fund.

10.

Carter

David B

(2012) Blessing or curse? State support for terrorist groups. International Organization 66 (1): 129–151.

11.

Carter

David B

Signorino

Curtis S

(2010) Back to the future: Modeling temporal dependence in binary data. Political Analysis 18 (3): 271–292.

12.

Cohen

Jessica

Easterly

William

(2009) What Works in Development? Thinking Big and Thinking Small. Washington, DC: Brookings Institution Press.

13.

Collier

Paul

(2007) The Bottom Billion: Why the Poorest Countries Are Failing and What Can Be Done About It. Oxford: Oxford University Press.

14.

Collier

Paul

Hoeffler

Anke

(2007) Unintended consequences: Does aid promote arms races? Oxford Bulletin of Economics and Statistics 69 (1): 1–27.

15.

Conrad

Courtenay

Conrad

Justin

Young

Joseph K

(2014) Tyrants and terrorism: Why some autocrats are terrorized while others are not. International Studies Quarterly doi: 10.1111/isqu.12120.

16.

Conrad

Justin

(2011) Interstate rivalry and terrorism: An unprobed link. Journal of Conflict Resolution 55 (4): 529–555.

17.

Devarajan

Shantayanan

Swaroop

Vinya

(2004) The implications of foreign aid fungibility for development assistance. In: Gilbert

Christopher L

Vines

David

(eds) The World Bank: Structure and Policies. Cambridge: Cambridge University Press, 196–209.

18.

Dreher

Axel

Klasen

Stephen

Vreeland

James R

Werker

Eric

(2008) Does aid for education educate children? Evidence from panel data. World Bank Economic Review 22 (2): 291–314.

19.

Dunning

Thad

(2004) Conditioning the effects of aid: Cold War politics, donor credibility, and democracy in Africa. International Organization 58 (2): 409–423.

20.

Easterly

William

(2001) The Elusive Quest for Growth: Economists’ Adventures and Misadventures in the Tropics. Cambridge, MA: MIT Press.

21.

Ellis

Stephen

(2004) Briefing: The pan-Sahel initiative. African Affairs 103 (412): 459–464.

22.

Feyzioglu

Tarhan

Swaroop

Vinya

Zhu

Min

(1998) A panel data analysis of the fungibility of foreign aid. World Bank Economic Review 12 (1): 29–58.

23.

Findley

Michael

Piazza

James A

Young

Joseph K

(2012) Games rivals play: Terrorism in international rivalries. Journal of Politics 74 (1): 235–248.

24.

Foreign Policy (2013) The great escape: How al Qaeda broke hundreds of bad guys out of the world’s most notorious jail – and what it means for America. 5 August (http://www.foreignpolicy.com/articles/2013/08/05/the_great_escape_abu_ghraib_prison_al_qaeda).

25.

Geddes

Barbara

(2003) Paradigms and Sandcastles: Theory Building and Research Design in Comparative Politics. Ann Arbor, MI: University of Michigan Press.

26.

Geddes

Barbara

Wright

Joseph

Frantz

Erica

(forthcoming) Autocratic breakdown and regime transitions: A new data set. Perspectives on Politics.

27.

Gibson

Clark

Andersson

Krister

Ostrom

Elinor

Shivakumar

Sujai

(2005) The Samaritan’s Dilemma: The Political Economy of Development Aid. New York: Oxford University Press.

28.

Girod

Desha M

Tobin

Jennifer

(2014) Take the money and run: The determinants of compliance with aid agreements. Manuscript (https://ncgg.princeton.edu/IPES/2011/papers/F1150_rm3.pdf).

29.

Goertz

Gary

Diehl

Paul F

(1993) Enduring rivalries: Theoretical constructs and empirical patterns. International Studies Quarterly 37 (2): 147–171.

30.

Hagen

Rune J

(2004) Foreign aid and domestic politics: Implications for selectivity. CAE Working Paper 04–12 (http://ideas.repec.org/p/ecl/corcae/04-12.html).

31.

Hagen

Rune J

(2006) Buying influence: Aid fungibility in a strategic perspective. Review of Development Economics 10 (2): 267–284.

32.

Haqqani

Hussein

(2005) Pakistan: Between Mosque and Military. Washington, DC: Carnegie Endowment for International Peace.

33.

Johnsen

Gregory D

(2013) The Last Refuge: Yemen, al-Qaeda, and America’s War in Arabia. New York: WW Norton.

34.

Jones

Seth G

Libicki

Martin C

(2006) How Terrorist Groups End: Lessons for Countering al Qaeda. Washington, DC: RAND Corporation.

35.

Kilcullen

David

(2010) Accidental Guerilla: Fighting Small Wars in the Midst of a Big One. Oxford: Oxford University Press.

36.

Killick

Tony

Gunatilaka

Ramani

Marr

Ana

(1998) Aid and the Political Economy of Policy Change. New York: Routledge.

37.

Klein

James

Goertz

Gary

Diehl

Paul F

(2006) The New Rivalry Dataset: Procedures and patterns. Journal of Peace Research 43 (3): 331–348.

38.

Maddison

Angus

(2012). Statistics on world population, GDP and per capita GDP, 1-2008 AD (http://www.ggdc.net/MADDISON/oriindex.htm).

39.

Nordhaus

William

Oneal

John

Russett

Bruce

(2012) The effects of the international security environment on national military expenditures: A multicountry study. International Organization 66 (3): 491–513.

40.

Pack

Howard

Pack

Janet R

(1993) Foreign aid and the question of fungibility. Review of Economics and Statistics 75 (2): 258–265.

41.

Paul

(2014) The Warrior State: Pakistan in the Contemporary World. New York: Oxford University Press.

42.

Perlez

Jane

(2009) Pakistan aid places us in the midst of a divide. New York Times, 13 October: A4 (http://www.nytimes.com/2009/10/13/world/asia/13islambad.html?_r=0).

43.

Piazza

James A

Wilson

Matthew

(2013) Autocracies and terrorism: Conditioning effects of authoritarian regime type on terrorism. American Journal of Political Science 57 (4): 941–955.

44.

Pillar

Paul R

(2004) Terrorism and U.S. Foreign Policy. Washington, DC: Brookings Institution Press.

45.

Rashid

Ahmed

(2008) Descent into Chaos: The United States and the Failure of Nation Building in Afghanistan, Pakistan, and Central Asia. New York: Viking.

46.

Reuters (2013) Mass jail break in Pakistan as Taliban gunmen storm prison. 30 July (http://www.reuters.com/article/2013/07/30/us-pakistan-prison-attack-idUSBRE96S0WG20130730).

47.

Riddell

Roger C

(2007) Does Foreign Aid Really Work? Oxford: Oxford University Press.

48.

Rider

Toby J

(2009) Understanding arms race onset: Rivalry, threat, and territorial competition. Journal of Politics 71 (2): 693–703.

49.

Rider

Toby J

Findley

Michael G

Diehl

Paul F

(2011) Just part of the game? Arms races, rivalry, and war. Journal of Peace Research 48 (1): 111–118.

50.

Rohde

David

Gall

Carlotta

Sanger

David E

Schmitt

Eric

(2007) U.S. officials see waste in billions sent to Pakistan. New York Times, 24 December (http://www.nytimes.com/2007/12/24/world/asia/24military.html?pagewanted=all).

51.

Siddiqa

Ayesha

(2009) Turf wars: Why Pakistan’s military hates America’s new aid package. Foreign Policy, 14 October (http://www.foreignpolicy.com/articles/2009/10/14/turf_wars).

52.

Sovey

Allison

Green

Donald P

(2011) Instrumental variables in political science: A reader’s guide. American Journal of Political Science 55 (1): 188–200.

53.

START (2012) Global terrorism database (http://www.start.umd.edu/start/).

54.

Stone

Randall W

(2004) The political economy of IMF lending in Africa. American Political Science Review 98 (4): 577–591.

55.

Terza

Joseph V

Basu

Anirban

Rathouz

Paul J

(2008) Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling. Journal of Health Economics 27 (3): 531–543.

56.

Tessman

Brock

Sullivan

Patricia

Xiaojun

(2011) US military aid and recipient state cooperation. Foreign Policy Analysis 7 (3): 275–294.

57.

Themnér

Lotta

Wallensteen

Peter

(2012) Armed conflict 1946–2011. Journal of Peace Research 49 (4): 565–575.

58.

Thompson

William R

(2001) Identifying rivals and rivalries in world politics. International Studies Quarterly 45 (4): 557–586.

59.

Thompson

William R

Dreyer

David

(2011) Handbook of Interstate Rivalry, 1494–2010. Washington, DC: Congressional Quarterly Press.

60.

United States Agency for International Development (2013) U.S. overseas loans and grants: Foreign assistance data (http://gbk.eads.usaidallnet.gov/data/).

61.

United States Congress (2008) Foreign aid and the fight against terrorism and proliferation: Leveraging foreign aid to achieve U.S. policy goals. 110th Congress, 2nd session, serial no. 110–225 (http://www.gpo.gov/fdsys/pkg/CHRG-110hhrg43840/pdf/CHRG-110hhrg43840.pdf).

62.

United States Congress (2009) Enhanced Partnership with Pakistan Act of 2009. 111th Congress, 1st session, S.1707 (http://www.gpo.gov/fdsys/pkg/BILLS-111s1707enr/pdf/BILLS-111s1707enr.pdf).

63.

Walsh

Declan

(2008) Up to 70% of US aid to Pakistan misspent. Guardian, 27 February (http://www.theguardian.com/world/2008/feb/27/pakistan.usa).

64.

World Bank (2013) World Development Indicators database (http://data.worldbank.org/data-catalog/world-development-indicators).

65.

Young

Joseph K

Findley

Michael

(2011) Can peace be purchased? A sectoral-level analysis of aid’s influence on transnational terrorism. Public Choice 149 (3/4): 365–381.