Bribes and Bombs: The Effect of Corruption on Terrorism

Meierrieks, Daniel; Auer, Daniel Article — Published Version Bribes and Bombs: The Effect of Corruption on Terrorism American Political Science Review American Political Science Review Provided in Cooperation with: WZB Berlin Social Science Center Suggested Citation: Meierrieks, Daniel; Auer, Daniel (2024) : Bribes and Bombs: The Effect of Corruption on Terrorism, American Political Science Review American Political Science Review, ISSN 1537-5943, Cambridge University Press, Cambridge, Vol. 119, Iss. 2, pp. 670-686, https://doi.org/10.1017/S0003055424000418 This Version is available at: https://hdl.handle.net/10419/312586.2 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. Bribes and Bombs: The Effect of Corruption on Terrorism DANIEL MEIERRIEKS WZB Berlin, Germany DANIEL AUER Collegio Carlo Alberto, Italy We leverage plausibly exogenous variation in regional exposure to corruption to provide causal estimates of the impact of local political corruption on terrorist activity for a sample of 175 countries between 1970 and 2018. We find that higher levels of corruption lead to more terrorism. This result is robust to a variety of empirical modifications, including various ways in which we probe the validity of our instrumental variables approach. We also show that corruption adversely affects the provision of public goods and undermines counter-terrorism capacity. Thus, our empirical findings are consistent with predictions from a game-theoretical representation of terrorism, according to which corruption makes terrorism relatively more attractive compared to peaceful contestation, while also decreasing the costs of organizing and carrying out terrorist attacks. INTRODUCTION Political corruption is “the use of public office for private gains”(Bardhan 1997, 1321). It involves activities in which public officials, legislators, and politicians “use powers delegated to them by the public to further their own economic interests at the expense of the common good”(Jain 2001, 73). Usually, these activities are illegal or—when permissible— entail strong public disapproval; for instance, they include taking bribes and kickbacks, engaging in embezzlement, and the looting of public coffers as well as resorting to various forms of nepotism, cronyism, and patronage (e.g., by securing government contracts for friends, family, or political supporters). Corruption is an ancient phenomenon. It was already described in various religious texts, such as the Bible or the Quran, and discussed by political philosophers throughout history (including Plato, Aristotle, Machiavelli, and Montesquieu; see, e.g., Bardhan 1997; Jain 2001; Miller 2018). Unjust enrichment and the exploitation of power bothers citizens. As such, corruption is generally understood as a driver of social and political unrest (e.g., Nye 1967; Rose-Ackerman and Palifka 2016). In this article, we study the effect of corruption on terrorism. 1 Drawing on rational choice and game theory, we argue that corruption can fuel terrorism by (1) adversely affecting the provision of public goods, which, in turn, aggravates economic grievances, (2) facilitating political dissatisfaction, and (3) undermining counter-terrorism capabilities. Through its empirical focus, our article contributes to the discussion of the corruption–terrorism nexus in two main ways. First, we use data for a sample of 175 countries between 1970 and 2018 and an experimental identification strategy that allows for causal statements. Second, we provide correlational evidence for the mechanisms underlying the corruption–terrorism nexus, examining the impact of corruption on public goods provision, political dissatisfaction, and counter-terrorism capacity. We also contribute to the broader literature on the role of corruption in conflict (e.g., Dyrstad and Hillesund 2020; Fjelde 2009; Le Billon 2003). Here, existing research focuses on the impact of corruption on largescale forms of political instability and violence such as ethnic wars, insurgencies, and civil wars, neglecting the corruption–terrorism relationship. Yet studying the effect of corruption on terrorism is important for three reasons. In contrast to large-scale political violence, terrorism (1) also affects richer economies located in, for example, Northern America and Western Europe, (2) has also become—in contrast to large-scale conflict —more relevant in less developed countries especially in Africa and Asia, and (3) often has an international dimension, for example, as terrorist groups cross borders to attack in other countries or as terrorist violence in one part of the world inspires radicalization and extremism in others (e.g., Gaibulloev and Sandler 2019). Moreover, as there are no existing empirical studies on the effect of corruption on terrorism, our study also adds to the literature on the determinants of terrorism (e.g., Abadie 2006; Auer and Meierrieks 2021; Campos and Gassebner 2013; Jetter and Stadelmann 2019; Lai 2007; Piazza 2006; for overviews of this literature, see Gaibulloev and Sandler 2019; Krieger and Meierrieks Daniel Meierrieks , Senior Researcher, Department for Migration, Integration, and Transnationalization, WZB Berlin, Germany, [email protected]. Corresponding author: Daniel Auer , Assistant Professor, Collegio Carlo Alberto, Italy, [email protected]. Received: February 23, 2022; revised: November 08, 2022; accepted: March 27, 2024. First published online: May 28, 2024. 1 Terrorism is “the premeditated use or threat to use violence by individuals or subnational groups against noncombatants in order to obtain a political or social objective through the intimidation of a large audience beyond that of the immediate victims”(Enders, Sandler, and Gaibulloev 2011, 321). 670 2011; Sandler 2018; Schneider, Brück, and Meierrieks 2015). In particular, we add to this literature by providing causal estimates of corruption on terrorism via an instrumental variables (IV) approach. This approach is warranted given the potential for endogeneity, especially due to concerns about feedback. To account for potential endogeneity, we instrument a country’s level of corruption by its exposure to corruption in geographically and economically proximate countries. In so doing, we build on earlier evidence that corruption in proximate countries has predictive power in explaining local corruption (e.g., Becker, Egger, and Seidel 2009; Borsky and Kalkschmied 2019; Correa, Jetter, and Agudelo 2016) due to a region-specific demand for corruption control. We probe the soundness of our IV in various ways, for example, by using placebo IVs. In addition, we address concerns about the validity of the exclusion restriction in a number of ways, for example, by accounting for time-variant economic, politico-institutional, and demographic factors at the regional level. Moreover, we apply the plausibly exogenous framework of Conley, Hansen, and Rossi (2012) and beyond plausibly exogenous framework of van Kippersluis and Rietveld (2018) that allows us to explicitly relax the exclusion restriction and study how instrument invalidity affects our empirical conclusions. Leveraging plausibly exogenous variation in the exposure to corruption in geographically and economically proximate countries to provide causal estimates of the effect of corruption on terrorism, we show that political corruption leads to more terrorist activity in the country of interest. Further investigating likely transmission channels, we provide evidence that corruption unfavorably affects the provision of public goods and undermines local military capacity, which, in turn, correlate with increased terrorist activity. In contrast, we find no evidence that corruption contributes to political dissatisfaction and nonviolent political protest. While our article has primarily empirical focus, our findings have immediate policy implications, implying that anticorruption measures—be they a consequence of domestic policy change or initiatives financed by foreign aid— could also have pacifying effects. THEORETICAL FRAMEWORK The role of corruption in terrorism can be understood by considering the basic dynamics and outcomes of a game between a government and its citizenry, which we sketch below. Game-theoretical approaches to terrorism have been used in the literature to explain related phenomena, such as terrorist hostage-takings or counter-terrorism policies (e.g., Bapat 2006; Jacobson and Kaplan 2007; Lapan and Sandler 1988; for overviews, see Sandler and Enders 2004; Sandler and Siqueira 2009). As shown in Figure 1, we consider a sequential game, where the government makes the first move, deciding whether to be corrupt (c)orhonest(h); this reflects the idea that corruption is a determinant of terrorism. The choice in favor of or against corruption is due to the governments’utility-maximization calculus, where utility from the spoils of corruption πc(e.g., rents) is compared to the utility from non-corrupt behavior πh (e.g., in the form of political approval), subject to certain constraints and costs of being corrupt δc(e.g., criminal punishment). 2 The costs of non-corrupt behavior δh could take the form of opportunity costs (e.g., forgone personal rents diverted from public investments) or be FIGURE 1. Game-Theoretical Approach to the Corruption–Terrorism Nexus Note: Game between a government and its citizens, where the government’s decision whether to be corrupt is a utility function πof the benefits Uand the cost δof (not) being corrupt. In a subsequent round, citizens decide on whether they remain peaceful or retaliate with terrorism, where they behave rationally according to a utility function φof the benefits Vand the cost θof (non)violence. 2 Accounting for corruption costs allows us to explain why some governments (e.g., in societies with high levels of transparency) do not opt for corruption. Bribes and Bombs 671 more indirect (e.g., greater difficulties to implement a political program by not resorting to corruption). A government will thus turn to corruption when πc>πh. In response, the citizens decide whether to engage in terrorism (t) as an extra-institutional form of (violent) contestation or to remain peaceful (p). That is, they compare the utility of terrorism, φt, and peaceful contestation, φp, under government corruption or honesty, respectively, choosing the utility-maximizing response. The idea that(potential) terrorists consider the costs and benefits of terrorism is rooted in the rational-economic model of terrorism, as applied in Landes (1978), Sandler, Tschirhart, and Cauley (1983), and Enders and Sandler (1993); more recent discussions can be found in, for example, Sandler and Enders (2004), Caplan (2006), and Schneider, Brück, and Meierrieks (2015). This model assumes that terrorists are rational actors who “maximize expected utility or net payoffs subject to constraints,”as argued by Sandler and Enders (2004, 311). This utility maximization implies that—ceteris paribus—terrorist activity will (1) decrease as the material costs of terrorism increase, (2) increase as the benefits from terrorism grow, and (3) increase as the opportunity costs of terrorism decrease, that is, as alternatives to terrorism become less attractive. Importantly, the government’s choice for or against corruption affects how citizens respond by influencing the respective (opportunity) costs and benefits of terrorism and peaceful contestation (i.e., of participation in the ordinary political process); in Figure 1, subscripts associated with the payoffs, costs, and benefits of terrorism and corruption point to their relationship with the previous government choice for corruption or honesty. In relation to the citizens’response, we make the following arguments concerning the citizens’calculus: 1. Corruption reduces a country’scounter-terrorism capacity and thus lowers the costs of terrorism, θtc. For instance, higher levels of corruption may imply that fewer public resources are available to finance a country’s police or military. Also, corruption may allow terrorist groups to pay off border guards, the police, judges, or prison guards, which is consequently expected to facilitate terrorist attacks and the cross-border transfer of materiel (arms, explosives, etc.), hamper the legal prosecution of terrorists, or aid the escape of captured terrorists from prison (Rotberg 2009; Shelley 2014; Thachuk 2005). An example are the 2004 Russian aircraft bombings (which caused 90 fatalities) conducted by “female suicide bombers who bribed their ways onto the planes”(Thachuk 2005, 147). By lowering the costs related to financing and organizing terrorism as well as the risk of capture and punishment, corruption may thus lead to more terrorism. 3 This prediction speaks to the empirical evidence that countries with low levels of counter-terrorism capacity are more likely to face terrorist activity within their borders (e.g., George 2018; Lai 2007; Piazza 2008). 2. Corruption may also matter to the benefits of terrorism, Vtc. The prevalence of corruption points to the existence of rents that could be captured in the wake of a terrorist success. For instance, the model by Kirk (1983) alludes to the role of rents as a motivating factor in terrorism. What is more, terrorist groups tend to generate income from different sources (e.g., smuggling and kidnapping) that could become more profitable as corruption increases (e.g., Thachuk 2005). For instance, bribing border guards and customs officials may allow terrorist groups to smuggle contraband (e.g., cigarettes and narcotics). An increase in terrorists’income, in turn, will increase terrorist attacks (Enders and Sandler 1993). Importantly, while corruption is expected to increase the benefits of terrorism in such manners, it does not affect the benefits of peaceful political contestation (i.e., Vpc) in the same way. 3. Corruption curtails political participation and reduces public trust in and the legitimacy of political institutions (e.g., Anderson and Tverdova 2003; Chang and Chu 2006; Dimant and Tosato 2018). That is, corruption makes it less viable for citizens to achieve their goals through the ordinary political process (meaning a higher θpcÞ:Conversely, terrorism becomes a more attractive alternative to participating in this process. This argument indeed underlines that political grievances may be relevant to terrorism (e.g., Abadie 2006; Gaibulloev and Sandler 2019; Krieger and Meierrieks 2011; Piazza 2006), while also speaking to earlier contributions that link political dissatisfaction, protest, and low levels of political legitimization to higher levels of terrorist activity (e.g., Campos and Gassebner 2013; Masters and Hoen 2012). 4. Corruption also adversely affects the provision of public goods. For instance, corrupt politicians may favor public spending that facilitates rent-seeking (e.g., large infrastructure projects; Croix and Delavallade 2009). This, in turn, means that public spending for which rent-seeking cannot be easily concealed (e.g., public education) is not prioritized. 4 There is ample evidence that corruption adversely influences the quantity and quality of public goods (e.g., Bose, Capasso, and Murshid 2008;Mauro1998; Rajkumar and Swaroop 2008). An inadequate provision of public goods (e.g., by 3 As suggested by a referee, it is also possible that corrupt governments fear removal and thus increase their counter-terrorism spending, making their security infrastructure less susceptible to corruption. On balance, this may point to a conundrum for corrupt leaders who want to maximize their gains from corruption (which would mean lower counter-terrorism spending) and militarily protect their position, leaving the eventual relationship between corruption and counter-terrorism capability an empirical question. We return to this question in the Exploration of Mechanisms section. 4 Besides directly affecting investment decisions, corruption could also reduce the quality and quantity of public goods through its adverse impact on economic activity (e.g., Aidt 2009; Croix and Delavallade 2009; Gründler and Potrafke 2019; Mauro 1995; see also Bardhan 1997; Ugur 2014), which means that less tax income is available to finance those public goods. Daniel Meierrieks and Daniel Auer 672 denying parts of the population access to education and health) is, in turn, expected to make terrorism more likely by fueling economic grievances related to the access to and distribution of (public) resources. For one, the availability of fewer public goods makes it more difficult for related economic grievances to be addressed politically, lowering the overall utility of peaceful contestation (φpjh). This is tantamount to reduced opportunity costs of terrorism as an alternative way to accommodate such grievances. Accordingly, the inadequate provision of public goods incentivizes terrorist activity to address grievances through the use of force, for example, by violently appropriating and redistributing (public) resources, thus increasing the potential benefits of terrorism, Vtc. Arguing that an unfavorable provision of public goods does not allow for accommodating distributional economic grievances also speaks to the finding that terrorism is associated with economic inequality and exclusion (e.g., Ezcurra and Palacios 2016;Kriegerand Meierrieks 2019;Piazza2011;seealso,e.g.,Dyrstad and Hillesund 2020; Muller and Weede 1994). It is also consistent with the notion that some terrorist organizations provide public goods (e.g., health services) to grow popular support (e.g., Berman and Laitin 2008; Hilsenrath 2005). In sum, this discussion leads us to two predictions. First, when the government is corrupt, the citizens’utility of terrorism is relatively greater than the utility of (nonviolent) peaceful contestation, that is, φtjc>φpjc. Second, the utility of peaceful contestation is relatively greater than that of terrorism, that is, φpjh>φtjh,when the government is honest. Thus, consistent with gametheoretical representations that are associated with rational choice theory, our main hypothesis is as follows: Higher levels of corruption lead to more terrorist activity. Figure 2 summarizes our theoretical arguments in more detail, alluding to additional testable hypotheses associated with the mechanisms underlying the corruption–terrorism nexus (see the Exploration of Mechanisms section). For one, political corruption unfavorably affects the provision of public goods, which, in turn, fuels economic grievances related to, for example, economic inequality and exclusion. At the same time, corruption makes nonviolent political contestation less viable. For instance, corruption may make citizens less trustworthy in political institutions, leading them to perceive the political process as less useful to accommodate their demands in comparison to terrorism. The unfavorable distribution of public goods and associated economic grievances as well as political dissatisfaction, in turn, motivate terrorism. Moreover, corruption affects a country’s capacity to counter terrorism and the income from terrorism (e.g., associated with the capturing of rents), which may also influence terrorist activity. Finally, Figure 2 points to two further aspects of the corruption–terrorism nexus that warrant a brief discussion. First, there is the possibility that corruption could actually discourage terrorism. According to this “corruption buys peace”hypothesis (Le Billon 2003), the government may use corruption to buy off a potentially violent opposition. This de-escalating effect of corruption does not conflict with φpjh>φpjc(because citizens are constrained by the government’s decision in the first step of the game) but would instead imply that φpjc>φtjc. Buying off the opposition could mean that the government shares the spoils of corruption, while the potential terrorists accept these spoils to satisfy their greed and accommodate their grievances. Le Billon (2003)argues that many developing countries in Africa and Asia used corruption to buy internal peace after having gained independence (see also Fjelde 2009). However, Le Billon (2003) also stresses that such arrangements did not prove to be stable in the long run. Consequently, corruption may not be able to buy off terrorist opposition. For instance, the economic costs of terrorism tend to be rather small (e.g., Gaibulloev and Sandler 2019), which makes it less likely that the government will share the spoils of corruption to prevent terrorism. FIGURE 2. The Corruption–Terrorism Relationship Bribes and Bombs 673 Second, Figure 2 highlights potential feedback between terrorism and political corruption. It is possible that corruption does not only influence terrorism but that terrorism also affects political corruption. For instance, terrorists may use corruption to gain access to government counter-terrorism information, infiltrate prisons that house terrorist operatives, influence judicial decisions concerning terrorist offenders, or smuggle weaponry across the border (e.g., Shelley 2014). At the same time, terrorist groups can raise large amounts of money from various illegal activities (e.g., kidnapping, smuggling, drug trafficking, and extortion), meaning that they also have access to the financial means to engage in corruption in the first place (e.g., Freeman 2011). The possibility of reverse causation is the main motivation for our IV approach described below. DATA AND EMPIRICAL APPROACH We test our hypothesis for a sample of 175 countries between 1970 and 2018. A list of countries and the summary statistics are provided in Tables A.1 and A.2 in Appendix A of the Supplementary Material. Measuring Terrorism Our main dependent variable is the number of terrorist attacks per country-year observation. We apply the inverse hyperbolic sine transformation to this variable to accommodate the influence of outliers; importantly, and in contrast to the log transformation, the inverse hyperbolic sine transformation is also defined for country-year observations with no terrorist activity (e.g., Burbidge, Magee, and Robb 1988). 5 The terrorism data are drawn from the Global Terrorism Database (GTD) described in LaFree and Dugan (2007). 6 The GTD collects information on terrorist activity from reputable media outlets. For a terrorist event to be recorded, it must be documented by at least one high-quality media source and meet the following three criteria: it must (1) be intentional, (2) entail some level of violence or threat of violence, and (3) be committed by non-state actors, meaning that violence by state actors is excluded. Furthermore, the incident must meet at least two of the following three criteria: (1) it must be carried out to achieve a political, economic, religious, or social goal, (2) there must be evidence of an intention to coerce, intimidate, or convey some other message to a larger audience than the immediate victims, and/or (3) the incident must be outside the context of conventional warfare (LaFree and Dugan 2007). Measuring Corruption Our measure of corruption is the political corruption index from the Varieties of Democracy Dataset (VDEM; Coppedge et al. 2019). Higher values of this index correspond to higher levels of political corruption. This political corruption index is the arithmetic mean of four variables measuring corruption in the (1) executive, (2) legislature, (3) judiciary, and (4) public sector. It covers corruption in the various branches of government and at various levels of government. 7 The corruption index accounts for corruption aimed at influencing policyand law-making as well as the implementation of these policies and laws. Finally, it covers different forms of corruption, accounting for both “passive”corruption (such as taking bribes) and “active”corruption, for example, in the form of the embezzlement of public resources by public officials and politicians. VDEM relies on countryand subject-based expert opinion. For instance, to evaluate the extent of legislative corruption, experts are asked to assess to what extent members of the legislature abuse their position for financial gain. To arrive at representative values of political corruption per country-year observation that can also be compared between countries, VDEM then applies item response theory and subjects the individual expert opinion data to other forms of statistical scrutiny to minimize uncertainty and bias (Coppedge et al. 2019). Empirical Model To examine the effect of corruption on terrorist activity, we estimate the following model: terrorismi,t¼β×corruptioni,t−1 þδXi,t−1þαiþτtþεi,t,(1) where the (inverse hyperbolic sine transformed) number of terrorist incidents (terrorism) in country iand year tis a function of the country’s political corruption (corruption) in the previous year (t−1), a set of controls Xas well as countryand year-fixed effects (αand τ, respectively) to account for time-invariant factors (e.g., culture and norms that affect corruption and/or terrorism) and global time trends. Below, we consider both a parsimonious and different covariate-adjusted models. Here, the choice of the baseline controls follows the literature on the determinants of terrorism (e.g., Piazza 2008; Krieger and Meierrieks 2011; Campos and Gassebner 2013; Enders, Hoover, and Sandler 2016; Gaibulloev, Piazza, and Sandler 2017; Gaibulloev and Sandler 2019). We include controls for population size and (inflationadjusted) per capita income. Data on these variables come from the World Development Indicators (WDI; World Bank 2019); both variables are also inverse 5 As part of our robustness checks, we consider alternative ways to operationalize terrorism. 6 Note that the original GTD data for the year 1993 are incomplete (LaFree and Dugan 2007, 186). We therefore follow the crosschecked imputation approach of Enders, Sandler, and Gaibulloev (2011) to recover the missing values for 1993. 7 Below, we also examine how terrorism responds to the individual components of the corruption index. Daniel Meierrieks and Daniel Auer 674 hyberbolic sine transformed to account for skewness. We also control for democracy, using data from Gründler and Krieger (2016). 8 Finally, we employ an index of state failure from the Political Instability Task Force (PITF 2019) that indicates the extent of largescale civil warfare and other forms of political instability (e.g., coup d’états). Instrumental Variables Approach The estimates from Equation 1 might be affected by endogeneity bias due to measurement error in the corruption variable, the omission of relevant variables in our empirical model or feedback/reverse causation. To address these endogeneity concerns, we leverage a two-stage least squares (2SLS) IV model of the following form: corruptioni,t¼β1×regcorruptioni,t þδ1×Xi,tþα1,iþτ1,tþε1,i,t,(2) terrori,t¼β2×d corruptioni,t−1þδ2×Xi,t−1 þα2,iþτ2,tþε2,i,t,(3) where the first-stage regression (Equation 2) predicts potentially endogenous country-year corruption levels using our IV, regcorruption. The predicted countryspecific corruption levels are then used in the second stage to explain terrorism (Equation 3). Importantly, the panel structure of our data and the inclusion of countryand year-specific effects can already ameliorate some concerns about a lack of causal identification. Country-fixed effects can account for certain unobserved factors that correlate with our instrument and increase terrorism through means other than increasing national corruption. Year-fixed effects can factor in the possibility that there are changes over time that are spuriously correlated with both the instrument and terrorism. Construction of Instrument The instrument regcorruption measures a country’s exposure to regional corruption. That is, similar to other examples in the literature (e.g., Gründler and Potrafke 2019), our instrument is defined as the mean level of political corruption (using the VDEM political corruption index introduced above) in countries that are geographically and economically proximate to the country of interest proxi:regcorruptioni,t¼ 1 nPn prox¼1proxi¼prox1þprox2þ⋯þproxn n. Geographical proximity involves those countries that are located in the same world region as the country of interest. We use the following six United Nations world regions: the Americas; East Asia and the Pacific; Europe and Central Asia; the Middle East and Northern Africa; South Asia; and Sub-Saharan Africa. 9 Economic proximity means that only those countries within a specific world region are considered for our IV if they are also in the same income group as the country of interest. We differentiate between low-, middle-, and high-income countries using WDI data. To give an example, France is a high-income country located in the United Nations (UN) world region Europe and Central Asia. We thus consider the mean level of political corruption in all countries in Europe and Central Asia that are also high-income economies. For instance, this includes Germany and Spain but excludes the United States (a high-income country outside Europe and Central Asia) or Bulgaria (which is located in Europe and Central Asia but is not a highincome country). Instrument Relevance and Exclusion Restriction For our IV (the mean level of political corruption in geographically and economically proximate countries) to be valid, it should be sufficiently strong. In our case, regional exposure to corruption should predict political corruption. Indeed, considerable empirical evidence suggests that corruption in proximate countries has predictive power in explaining local corruption (e.g., Becker, Egger, and Seidel 2009; Borsky and Kalkschmied 2019; Correa, Jetter, and Agudelo 2016; Dimant and Tosato 2018; see also the related literature on the spatial contagion of economic reforms such as Gassebner, Gaston, and Lamla 2011; Simmons and Elkins 2004). We argue that corruption levels ought to correlate across space to a common demand for corruption control (or a common tolerance for corruption) that is specific to geographically and economically proximate countries (but differs between geographically and economically diverse countries). Why do we expect this demand for corruption control to be is similar across proximate countries? First, geographical proximity is expected to coincide with common political histories and cultures. For instance, countries that are geographically close tend to have similar religious histories. Religion may, in turn, affect corruption, for example, by shaping how strongly religious dogma affects government policy with respect to measures that punish immoral (corrupt) behavior (e.g., Dimant and Tosato 2018; La Porta et al. 1999; North, Orman, and Gwin 2013). Second, the economic component of the instrument ought to reflect similarities in production, economic needs, and preferences. For instance, economically proximate countries share a similar demand for internationally mobile factors of production (i.e., physical and human capital). Given that capital and talent are attracted to low levels of corruption (e.g., Dimant, Krieger, and Meierrieks 2013; Poprawe 2015; Wei 2000), this may explain why 8 Gründler and Krieger (2016) use machine learning techniques for pattern recognition to construct a democracy index that is less susceptible to methodological issues that plague alternative democracy measures. 9 Note that we combine North and South America to the Americas due to North America only consisting of two countries. Bribes and Bombs 675 industries in economically proximate countries demand similar levels of corruption control. Finally, both geographical and economic proximity make it more likely that tolerance for corruption aligns, for example, due to close informational ties and low information and transaction costs. Figure 3 shows that the levels of corruption across countries are not independent of each other (Map A). For instance, corruption tends to be much higher in Sub-Saharan Africa as compared to Western Europe. As expected, this interdependence is also reflected in the IV we construct (Map B). What is more, comparing both parts of Figure 3 strongly suggests that exposure to regional corruption ought to be predictive of local corruption levels. Indeed, the simple pairwise correlation between both corruption variables is r¼0:70 (p<0:01 ) for the largest possible sample (see also Figure B.1 in the Supplementary Material). In line with Figure 3, we expect our IV to positively predict local corruption. We assess the strength of our instrument by means of the first-stage F-statistic. The usual rule of thumb to indicate instrument strength (F> 10) has received some criticism for being anticonservative, meaning that instruments may be weak even if F>10(Leeetal.2021). Thus, we also report results for the Anderson–Rubin test that is robust to arbitrarily weak instruments (Anderson and Rubin 1949; Lee et al. 2021). A rejection of the Anderson– Rubin test null hypothesis indicates that the coefficient of the endogenous regressor in the structural equation equals zero, which would support the IV estimates. We also report the Anderson–Rubin confidence set (which inverts the Anderson–Rubin test) to further illustrate the trustworthiness of our IV approach in terms of statistical and economic significance(seeStock,Wright,andYogo2002 for a further discussion). Figure 3 also speaks to our idea that there is a variable, the demand for corruption control, that is similar across countries that are geographically and economically proximate. However, the corruption control demand variable itself is not observed. Rather, we employ our IV (the mean level of political corruption in geographically and economically proximate countries) to use differences in regional corruption to draw conclusions about differences in this underlying “hidden” FIGURE 3. Corruption across Countries and Exposure to Corruption as Instrument Note:Map A shows the average level of corruption per country, categorized into quartiles. Map B shows the respective countries’regional exposure in quartiles, that is, the average level of corruption in economically and geographically proximate countries. Daniel Meierrieks and Daniel Auer 676 variable. 10 In arguing that there is an unobserved variable measuring demand for corruption control, we can address the criticism of “spatial instruments”by Betz, Cook, and Hollenbach (2018). They argue that instruments that use realizations of endogenous variables in other spatial units are not valid because of simultaneity in the first-stage equation; in our case, local corruption would affect regional corruption and vice versa. However, as the regionally clustered demand for corruption control is causally prior to regional and local levels of political corruption (in that political demand for corruption control induces policy changes related to corruption control), this simultaneity issue does not emerge. For our IV approach to be valid and causally estimate the effect of corruption on terrorism, the instrument should only affect terrorism via its effect on local corruption. However, as pointed out by Betz, Cook, and Hollenbach (2018), there may be various economic, political, and demographic spillovers that could constitute alternative pathways from the instrument to terrorism. For instance, economic downturns in countries that are geographically and economically proximate to the country of interest are expected to correlate with regional levels of corruption (our IV). At the same time, such downturns could spill-over to the country of interest, affecting both local corruption and terrorism by influencing the opportunity costs of non-corrupt and nonviolent economic activities. Such an alternative pathway from regional corruption to terrorism would violate the exclusion restriction. To address this concern, we implement two additional robustness checks. For one, we use the plausibly exogenous framework of Conley, Hansen, and Rossi (2012) and developed further by van Kippersluis and Rietveld (2018). This method allows us to directly examine how plausible violations of the exclusion restriction matter to causal inference. Allowing for violations of the exclusion restriction and still finding that corruption matters to terrorism would raise confidence in our IV approach. For another, we control for a series of observable time-varying shocks that are correlated across countries that are both geographically close and economically similar. For instance, this includes regional levels of economic growth, political instability, and institutional quality. Finding that corruption (instrumented by regional exposure to corruption) affects terrorism even after accounting for factors that might correlate with our IV (and thus potentially account for further transmission channels from our IV to terrorism) would provide evidence in favor of the exclusion restriction. EMPIRICAL RESULTS Main Results The main empirical results presented in Table 1 can be summarized as follows. First, the OLS models (specifications 1–3) show a positive and statistically significant association between political corruption and terrorism. Second, the effect of corruption on terrorism is more pronounced in our preferred IV models (specifications 4–6). Here, the impact of regional exposure on corruption in the first stage has the expected effect on local corruption and is sufficiently strong, as indicated by the first-stage F-statistic. The additional IV diagnostics are also sound. Third, introducing the baseline controls to the model does not affect our main empirical conclusion that political corruption encourages terrorism. Concerning these controls, terrorism positively correlates with population size, state failure, democracy, and economic development. These associations are also reported in other studies (e.g., Piazza 2008; Krieger and Meierrieks 2011; Campos and Gassebner 2013; Enders, Hoover, and Sandler 2016; Gaibulloev and Sandler 2019). However, due to the lack of an identification strategy associated with estimating these associations, they cannot be given a causal interpretation (Keele, Stevenson, and Elwert 2020). Table 1 also reports some diagnostics and initial robustness checks. For one, there may be concerns about the presence of cross-sectional dependence in the regression residuals, which may affect the validity of statistical inference (e.g., Sarafidis and Wansbeek 2012). 11 The results of a test for cross-sectional independence of the residuals (Pesaran 2015) show that for some specifications, cross-sectional dependence is indeed present in the residuals, pointing to a potential violation of the assumption of spatial independence of observations. Therefore, we also run a variant of our baseline model using standard errors proposed by Driscoll and Kraay (1998), which are robust to heteroskedasticity and autocorrelation, but also to general forms of cross-sectional dependence. As shown in specification 7, accounting for residual cross-sectional dependence in this manner produces even smaller standard error estimates. This suggests that our choice of standard errors (i.e., cluster-robust standard errors) produces rather conservative standard error estimates, so that type I errors are less likely to occur. As another way to address the issue of cross-sectional dependence, we also run a common correlated effects regression within a GMM framework following Pesaran (2006). As shown in Table C.1 in the Supplementary Material, 10 Our argument for our instrumental variable mimics the one by Acemoglu et al. (2019) who instrument local democratic institutions via regional democratization to estimate the causal effect of local democracy on economic growth. They argue that regional democratization reflects “the demand for democracy …across countries within a region, which tend to have similar histories, political cultures, practical problems, and close informational ties”(Acemoglu et al. 2019, 80). Similarly, we argue that tolerance for corruption is similar across geographically and economically proximate countries, where we can approximate this unobserved variable via regional corruption levels. 11 For a discussion of the issue of cross-sectional dependence in terrorism research, see Gaibulloev, Sandler, and Sul (2014). Bribes and Bombs 677 foreign aid may ultimately also deter terrorism in aidreceiving countries through its favorable effect on local corruption. This may be especially interesting to donor countries due to the international dimension of terrorism, where terrorism in one part of the world can easily motivate radicalization and extremism in others. SUPPLEMENTARY MATERIAL To view supplementary material for this article, please visit https://doi.org/10.1017/S0003055424000418. DATA AVAILABILITY STATEMENT Research documentation and data that support the findings of this study are openly available at the American Political Science Review Dataverse: https://doi. org/10.7910/DVN/ZTVXNL. ACKNOWLEDGMENTS We thank the editors at APSR and four anonymous reviewers for their thoughtful comments and guidance throughout the review process. Their feedback has greatly improved this study. We are also grateful to Monika Bauhr, Patricia Calca, Krzysztof Krakowski, Nils Ringe, Max Schaub, the panelists at the 2022 Annual CES Conference in Lisbon and the 2022 Annual Meeting of the German Economic Association, as well as seminar participants at the University of Regensburg, the University of Essex, and the Helmut Schmidt University Hamburg for valuable feedback on previous versions of the article. 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