metadata: authors: 0: author: Bodea,Ana Maria Cristina
1: author: Higashijima,Masaaki
2: author: Singh, Raju Jan
count: Haiti
docna: 0: docna: Oil and civil conflict : can public spending
            have a mitigation effect ?
docty: Policy Research Working Paper
subtopic: Conflict and Fragile States,Public Sector Economics,Educational Sciences,Social Conflict and Violence,Public Financial Management,Social Cohesion,Public Finance Decentralization and
            Poverty Reduction
teratopic: Education,Rural Development,Environment,Conflict and Development,Macroeconomics and Economic Growth,Public Sector Development
abstracts: cdata!: This paper explores the conditions under
            which public spending could minimize violent conflict
            related to oil wealth. Previous work suggests that oil can
            lead to violent conflict because it increases the value of
            the state as a prize or because it undermines the
            state's bureaucratic penetration. Yet, little has been
            said on how oil wealth could be used to prevent the onset of
            violent conflict through public spending by buying off
            citizens and elites, increasing state legitimacy by
            providing basic services, or strengthening the military and
            security apparatus. The empirical analysis (148 countries
            over 1960-2009) shows that higher levels of military
            spending are associated with lower risk of small- and
            large-scale conflict onset in countries rich in oil and gas.
            By contrast, in economies with little natural resources,
            increases in military spending are associated with a higher
            risk of conflict. Welfare expenditure is associated with
            lower risk of small-scale conflict, irrespective of the
            level of oil revenue. However, general government spending
            does not appear to have any robust mitigating effects.
geo_reg_key: 119228,517206,517191
repnme: repnme: Oil and civil conflict : can public
            spending have a mitigation effect ?
display_title: Oil and civil conflict : can public
            spending have a mitigation effect
listing_relative_url: /research/2014/11/20366887/oil-civil-conflict-can-public-spending-mitigation-effect
topicv3: Public Finance Management m1326316 716,Political Violence and Civil War m1326942 739,Education m1327048 470,Conflict and Violence m1327549 2433,Military m1505651 3036
publishtoextweb_dt: 2014-11-01T00:00:00Z
historic_topic: Education,Rural Development,Environment,Conflict and Development,Macroeconomics and Economic Growth,Public Sector Development
no_of_pages: 32
author_keys: 0: author_key: 888893
1: author_key: 1240679
model_extractions: WPS7100 Policy Research Working Paper 7100 Oil and Civil Conflict Can Public Spending Have a Mitigation Effect?

_Raju Jan Singh Cristina Bodea Masaaki Higashijima_ Latin America and the Caribbean Region Haiti Country Management Unit November 2014
Policy Research Working Paper 7100

# **Abstract**

This paper explores the conditions under which public spending could minimize violent conflict related to oil wealth. Previous work suggests that oil can lead to violent conflict because it increases the value of the state as a prize or because it undermines the state’s bureaucratic penetration. Yet, little has been said on how oil wealth could be used to prevent the onset of violent conflict through public spending by buying off citizens and elites, increasing state legitimacy by providing basic services, or strengthening the military and security apparatus. The empirical analysis (148 countries over 1960–2009) shows that higher levels of military spending are associated with lower risk of small- and large-scale conflict onset in countries rich in oil and gas. By contrast, in economies with little natural resources, increases in military spending are associated with a higher risk of conflict. Welfare expenditure is associated with lower risk of small-scale conflict, irrespective of the level of oil revenue. However, general government spending does not appear to have any robust mitigating effects.

This paper is a product of the Haiti Country Management Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rsingh9@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Oil and Civil Conflict: Can Public Spending Have a Mitigation Effect?**<sup>**1**</sup>

**Raju Jan Singh**<sup>**2**</sup> _<u>rsingh9@worldbank.org</u>_

**Cristina Bodea**<sup>**3**</sup> _<u>bodeaana@msu.edu</u>_

**Masaaki Higashijima**<sup>**4**</sup> _<u>higashij@msu.edu</u>_

JEL: C23, D74, H51, H52, H56, O11, O13, Q34, Q35, Q38, Q43 Keywords: Civil War, Conflict, Natural Resources, Oil, Public Spending, Resource Curse, Social Spending, State Capacity, Welfare Spending, Panel Data.

> 1 The authors are grateful to Massimiliano Cali, Richard Damania, Shantayan Devarajan, Francisco Ferreira, Maëlan Le Goff, Philip Keefer, Auguste Kouame, Bryan Land, Magda Lovei, Anand Rajaram, and Carolina Renteria for their helpful comments, as well as to the participants of the Workshop on Conflict at the Department of Political Science at Michigan State University.

> 2 Program Leader and Lead Economist at the World Bank.

> 3 Assistant Professor of Political Science at Michigan State University.

> 4 PhD Candidate of Political Science at Michigan State University.
# **INTRODUCTION**

The last decade has seen significant oil and gas discoveries. Ross (2012), for instance, reports that between 1998 and 2006 19 new countries, mostly low and middle-income, have become oil and gas exporters. At the same time, many of these regions have been plagued by political violence, in particular ravaging civil wars. This link between natural resources and conflict outbreak has been confirmed by numerous studies (Fearon and Laitin, 2003; Collier and Hoeffler, 2004; Humphreys, 2005; Lujala et al., 2005; Fearon, 2005; Ross, 2006; Lujala, 2010). Ross (2012) warns that the inflow of oil revenue into mainly poor nations is likely to spread further the oil curse in the form of lack of democracy and civil conflict. While other causal mechanisms have been suggested (Humphreys, 2005; Ross, 2006), such negative outcomes can also be linked to features of oil revenue - non-tax based, unstable and secretive - that limit the ability and incentive of governments to spend such revenue productively (Ross, 2006).<sup>5</sup> In this paper, we study the conditions under which the patterns of public spending may mitigate the risk of violent domestic conflict. Some recent research suggests that more government spending either in general or specifically for welfare and the military may reduce the risk of civil conflict onset (Hegre and Sambanis, 2006; Basedau and Lay, 2009; Fjelde and de Soysa, 2009; Taydas and Peksen, 2012). While oil wealth has begun to be considered in the study of civil conflict as an important source of revenue for governments, there has not been a systematic analysis of whether oil-rich countries can increase public spending or alter the particular allocation of such spending to social sectors or the military as a way to mitigate the risk of conflict. This paper brings together the literature linking public spending and conflict, on the one hand, and that connecting natural resources and conflict, on the other.

We use time-series cross section data (148 countries, 1960-2009) to test the hypothesis that oil has a conditional effect on civil conflict depending on the size of government expenditure and the allocation of government spending. Our dependent variable is the onset of small and large civil conflict (Gleditch et al., 2002). The empirical estimations show that small and large conflicts alike are less likely when military spending is high in oil and gas rich economies. By contrast, in economies with little natural resources, increases in military spending are associated with a higher risk of conflict. Increased spending in education, health or social security is associated with lower risk of small-scale conflict, irrespective of the level of oil revenue. On the other hand, higher levels of general government expenditure do not appear to have any robust mitigating effects.

The paper proceeds as follows: Section I reviews work on natural resources and conflict; Section II discusses the literature on public spending and conflict; Section III presents our approach,

> 5 <mark>Gylfason (2001) links low economic growth in resource rich countries to low investment in education. Cotet and Tsui (2013) find that oil-rich countries tend to spend more on their military.</mark>

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derives testable hypotheses, and presents the data; Section IV describes the results; and the final section concludes.

# **I. Natural Resources and Conflict**

A significant amount of research has investigated why some countries experience violent civil conflict. Previous work points to rebel motivation coming from grievance and injustice (Gurr, 1970; Wimmer et al., 2009; Cederman et al., 2011) or economic opportunity and greed (Collier and Hoffler, 1998, 2004). Other parts of the literature emphasize the characteristics of the state and the different facets of state capacity (Fearon and Laitin, 2003; Buhaug, 2006; Hendrix, 2010; Thies, 2010). A key aspect of state capacity is having the material resources to govern and using fiscal policy to advance government’s priorities.

Natural resources have been argued to influence conflict through these same channels. A large body of literature on civil war emphasizes that natural resources fuel violent conflict (de Soysa, 2002; Fearon and Laitin, 2003; Ross, 2004a; de Soysa and Neumayer, 2005; Dixon, 2009).<sup>6</sup> Generally, natural resources are argued to promote violence through three major causal mechanisms (see Ross, 2004b; Humphreys, 2005; Le Billon, 2008): (1) motivation to take up arms may result from grievances, such as ecological damage or the withholding of resource revenues; the costs and benefits related to resources are here the driving forces of conflict; (2) resources also provide the opportunity for conflict by making rebellion or warfare financially feasible; (3) resources may have a detrimental influence on state institutions (the ‘weak state’) and socio-economic development which in turn makes civil war more likely.

Two groundbreaking papers, Collier and Hoeffler (2004) and Fearon and Laitin (2003), both show that wealth in natural resources increases the probability of civil war onset. Collier and Hoeffler (2004) suggest that natural resources finance rebel groups and thus lower opportunity costs for rebellion. On the other hand, Fearon and Laitin (2003) emphasize the fact that oil producers tend to have weaker state apparatuses, which makes it difficult for governments to sustain efficient conflict prevention.<sup>7</sup> In addition, Fearon and Laitin (2003), Englebert and Ron (2004), Fearon (2005), and Besley and Persson (2009) argue that natural resources swell the state’s coffers and thus increase the value of the state, which is then more likely to induce conflicts over the state as a “prize”.

Ross (2004a) observes, however, that this negative effect may be related only to oil and not to natural resources in general. Reviewing 14 quantitative studies of the resource–conflict link, he concludes that primary commodities as a whole cannot be robustly linked to either civil war onset or duration. Only oil-exporting countries seem to be particularly prone to civil war onset.

> 6 For a more exhaustive literature review, see Ross (2006a).

> 7 Alternatively, Bulte and Bruunnschweiler (2009) and Cotet and Tsui (2013) find no statistically significant association between natural resource wealth and risk of conflict, while Ross (2012) continues to find this effect.

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This finding is supported by another meta-analysis conducted by Dixon (2009). Furthermore, Lujala (2010) finds that the mode of resource extraction and its location matter: oil is only linked to civil war onset when produced onshore; offshore oil production is unrelated to the onset of civil war.

On the other hand, research has long suggested that oil might provide states with resources to deliver public or private goods and stabilize political regimes, be they democracies or dictatorships. Considerable work suggests that natural resource rents can, in fact, bring stability to the state-society relationship (Mahdavy, 1970; Beblawi and Luciani, 1987; Smith, 2004; Morrison, 2009; Basedau and Lay, 2009; Fjelde, 2009; Morrison, 2010; Ross, 2012). Bueno de Mesquita et al. (2003) point out that government spending decisions are strategic responses aimed at maintaining power. Regimes can offset oil-related or other conflict risks by generous and large-scale distributional policies, and as a result grievances are less likely to emerge. A large security sector, financed by oil money, also helps to render rebellion more difficult.

More specifically, Ross (2001) argues that oil wealth has two mechanisms through which governments provide goods that reduce social pressures against the government. First, natural resource wealth permits governments to buy off citizens using low tax rates and patronage (a “rentier effect”). The second is a “repression effect”: natural resources allow governments to strengthen the military and security forces to maintain social order.<sup>8</sup> Along the same lines, Smith (2004) and Morrison (2009) both demonstrate that natural resources or non-tax revenues tend to increase political stability by prolonging regime durability. Ulfelder (2007) and, more recently, Wright, Franz and Geddes (2014) also show that autocracy and individual autocratic leaders are more durable in natural resource rich countries.

The above discussion suggests a potential conflict between the “rentier state” argument and the resource curse argument, and research has been seeking background conditions that may condition the effect of oil on civil conflict. For instance, Humphreys (2005) finds that the presence of oil production significantly increases the likelihood of civil war in weak states and may lower conflict risk in strong states. Morrison (2010) finds similar results. Fjelde (2010) finds that oil wealth tends to mitigate civil war risk if political corruption is high enough to help buy off oppositions and placate restive groups by providing patronage in exchange for political loyalty.

> 8 Haber and Menaldo (2011) contest these claims, which are reinforced in Andersen and Ross (2013), particularly after the 1970s.

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# **II. Public Spending and Conflict**

Research points out the importance of government spending in preventing violent conflict. This work emphasizes specific types of spending including (1) general government spending, (2) military spending or (3) social spending such as education, health and social security.

# **_General spending_**

High levels of government expenditures could signal that the political leadership is willing to use its resources to reward its supporters, rather than spending them for personal enrichment. A strategic allocation of state patronage, in the form of government construction contracts, public employment, or economic transfers, might buy support for the government from powerful segments of the population and placate restive groups (Bratton and Van de Walle, 1997; Acemoglu et al., 2004). Fjelde and de Soysa (2009) provide evidence indicating that higher government expenditure enables governments to buy off effectively opposition and increase the welfare of marginalized groups.<sup>9</sup> This has the potential to increase the status-quo stakes of key social actors, as well as reduce grievance and inequity, thus reducing the appeal of violent challenges to power from both elites and the broader population.

In oil-rich countries, the signaling function and public nature of spending commitments could be even more important than in oil-poor countries. Because oil resources are non-tax revenue, it is generally difficult for citizens and rebels to figure out how much oil resources the government actually has and how much the ruling elites can siphon out from the public coffers (Ross, 2012). In this context, rebels may overestimate the value of the state. Signaling via large-scale provision of public goods could thus lower the rebels’ perception about the value of the state as a prize going to small elites and their expectations that they will be able to grab a high state prize pending successful insurgency. This should result in fewer incentives to take up arms, and more readiness to cooperate with the government.

# **_Military spending_**

Collier, Hoeffler and Rohner (2009) argue that the financial and military feasibility (and thus the coercive and administration capacity of a state) play an important role in explaining the occurrence or not of rebellion. Weak states (i.e. ones that do not have sufficient bureaucratic presence across their national territory, police, and military forces) will face difficulties in enforcing laws, imposing order, and maintaining peace. Similarly, Fearon and Laitin (2003)

> 9 Hypotheses are tested for 141 countries, 1965 - 2006. The onset of violent conflict is measured using the UCDP/PRIO armed conflict dataset, with both the 25 casualties and the 1000 death toll threshold to define conflict onset. Government expenditure as percent of GDP (World Development Indicators) excludes government military expenditures that are part of government capital formation. Statistical results consistently and statistically significant show that countries with large government expenditure ratios to GDP are less likely to experience violent conflict. Thies (2010), however, uses an instrumental variable approach and finds no effect between the share of government spending and large civil wars (1000 death toll threshold).

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suggest that “most important for the prospects of a nascent insurgency are the government’s police and military capabilities and the reach of government institutions into rural areas.”<sup>10</sup> Military spending would be an important aspect of state capacity to pacify violent conflict via coercion and policing (Hendrix, 2010).

In consolidated democracies, where there is significant oversight over the budget, increasing military expenditure provides public goods for citizens in the form of domestic and international security. In mixed regimes and autocracies, on the other hand, military spending is primarily used for repression, patronage to regime loyalists, or direct rents to the military to prevent possible coup attempts (Collier and Hoeffler, 2007).

In this regard, Herbert et al. (2013) point out that the emergence of rebel groups in the Central African Republic partly stems from the state’s inability to protect its citizens from violence and poverty. The state capacity and authority in many core state functions is too weak. Along the same lines, Hegre and Sambanis (2006) show that the size of military personnel tends to be negatively and robustly correlated with civil war onset.

The opposite could also be suggested, however. Increasing military spending could provide potential military opposition with the needed equipment to overthrow a regime. Larger military budgets may not generate efficiency and loyalty, but may be a result of leaders bribing the military to keep them from staging a coup (Belkin and Schofer, 2003; Acemoglu et al., 2009; Keefer, 2010). Many countries at risk of civil war face other risks (Bodea and Elbadawi 2007; Svolik, 2009; Roessler 2011). Stacking the military with loyalists or ethnic group members, by shuffling, arresting or executing high ranking officers, or by creating multiple and overlapping units suspicious of each other could undermine the quality of the military forces and weaken further vulnerable regimes.

Furthermore, Henderson and Singer (2000) argue that because higher military expenditure crowds out social spending, economic growth and investment, subsequent citizen grievance is more likely to be linked with insurgency and thus fuel violent conflict. Collier and Hoeffler (2007) find that military spending is at best ineffective in reducing the risk of internal conflict. Similarly, the recent work by Taydas and Peksen (2012) find no effect of military spending on civil war onset.

> 10 Previous studies have mainly focused on the duration of violent conflict, relying on military personnel per capita taken from the Correlated of War (COW) Material Capabilities dataset (Hendricks 2010). A key argument is that because the strong military can defeat rebel groups more easily, it contributes to shortening the duration of conflict (Mason and Fett, 1996; Mason et al. 1999; DeRouen and Sobek, 2004). There is also evidence to the contrary (Balch-Lindsey and Enterline, 2000).

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# **_Social spending_**

Azam (2001) observes that “the occurrence of civil conflict is intimately related to the failure of government to deliver the type of public expenditure that people want; i.e. with a strong redistributive component such as health and education”. In earlier work, he uses a game theoretical model that explicitly links redistributive policy adopted by states with domestic peace (Azam, 1995). Governments could thus also foster their legitimacy, and thereby reduce the risk of conflict, by signaling that they care about their population. This could be done in a variety of ways, such as increased spending for water sanitation, securing basic health needs, or providing a strong system of education (Stasavage, 2005).

Taydas and Peksen (2012) argue that spending resources in the form of social welfare policies leads to more loyalty and support from citizens, which increases the difficulty of rebel recruitment.<sup>11</sup> Through welfare policies that influence positively the living standards of citizens, governments can outspend the opposition, helping gain support from a broad segment of the population, co-opt political opposition, and decrease the incentives for organizing a rebellion. Social safety nets, transfers and investment in public goods show people that the state cares, and prevents disadvantaged members of society from falling below a certain level of poverty and experiencing absolute desperation. It has been argued that this strategy, namely government commitment to redistribution in favor of the poor, had been used by rulers in the West in the nineteenth century to increase their legitimacy and prevent revolutions (Acemoglu and Robinson, 2000).

Robins (2012) mentions that studies of the attitudes and priorities of conflict-affected people have concluded that the degree to which a state meets its citizens’ every day needs is an important component of their subjective assessment of its legitimacy. From this perspective, positive encounters with frontline service officials is argued to provide a source of legitimacy for the state, particularly in fragile and conflict-affected situations where the state was previously mistrusted, or outright feared (Brinkerhof, Wetterberg, and Dunn, 2012). Similarly, in his study in Medellin, Colombia, Guerrero (2011) finds that a quick upgrading of basic services (infrastructure, health, education) in the city’s less favored districts improved political support for and trust in government.

Taydas and Peksen (2012) also argue that social spending promotes economic development, thereby raising further the opportunity cost of rebellion. Government investment in human capital has a positive influence on social mobility, labor opportunities, and the distribution of

> 11 Hypotheses are tested using a dataset for 153 countries, 1975 - 2005. The same dependent variable is used as in Fjelde and de Soysa (2009). The independent variable is welfare spending which combines three sources of welfare (education, health and social security spending, as percentage of GDP). Statistical results with imputed data consistently show that welfare spending is negatively related to the onset of both armed conflict (25 battle deaths) as well as major civil war (1000 battle deaths).

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earning capacities (Korpi and Palme, 1998; Chu, Davoodi and Gupta, 2000; Justino, 2003; Rudra, 2004; Thyn, 2006). It leads to better skilled and more productive workers, enhancing the international competitiveness of the economy (Schultz, 1963; Van de Walle, 1996; King, 1998; Gupta and Verhoeven, 2001; Brown and Hunter, 2004; Avelino, Brown and Hunter, 2005; Burgoon, 2006). As social policies improve the quality of life in society and promote wealth, the likelihood of domestic violence will decrease.

Based on the existing quantitative evidence, there seems to be an emerging consensus in the literature that education in particular has a general pacifying effect on conflict (Østby and Urdal, 2010).<sup>12</sup> As discussed above, Thyne (2006) argues that by investing state resources in education governments send a strong signal toward citizens that they are trying to improve their lives.<sup>13</sup> Education creates opportunities for economic development and social equality, each of which lowers grievances that lead to civil war. Education is also argued to increase the opportunity costs for the youth to take up arms (Collier and Hoeffler, 2004). Barakat and Urdal (2009) provide evidence how higher levels of educational attainment increase the opportunity cost for male youth (the key suppliers of rebel labor) to engage in violent conflict and reduce the risk that large male youth bulges are associated with violent conflict.<sup>14</sup> At the micro level, the failure of the Sudanese government to provide educational resources adequately for the southern region has been cited as a major grievance leading to a 22-year-long civil war (Glickman, 2000; Deng, 2001; Breidlid, 2005). Similarly, Richards (2003) places the failure of the government to provide an adequate education at the center of rebel decisions to go to war in Sierra Leone. Brett and Specht (2004), who have been conducting interviews with young soldiers, have found strong micro-level support for the expectation that lack of schooling in addition to poverty, and low alternative income opportunities are important reasons for joining a rebel group. Oyefusi (2008) also finds that low educational attainment significantly increased the willingness of young people to participate in armed struggle for local resource control in the oil-rich Niger Delta in Nigeria.

Furthermore, Thyne (2006) suggests that education promotes social cohesion by encouraging students to cultivate interpersonal skills and thus learn how to resolve disputes peacefully. Such social cohesion enables the country to achieve economic and social stability, which lessens the

> 12 See Østby and Urdal (2010) for an extensive literature review.

> 13 Empirical tests are carried on 160 countries, 1980 - 1999. The dependent variable, civil war onset, is based on Fearon and Laitin (2003). Five variables measure education: total government expenditures allocated to education as a percentage of GDP, primary enrollment rates, secondary enrollment rates, postsecondary enrollment rates, and adult literacy rates. Results show a moderate to strong association between all the education variables and lower conflict risk, with the exception of postsecondary education that does not have any significant effect.

> 14 They use a new global dataset (120 countries, 1970 – 2000) measuring cohort specific educational attainment rates. Violent conflict is operationalized by the UCDP/PRIO low intensity armed conflict. The key finding is that the effect of large young male cohorts on conflict is mitigated by higher secondary education attainment especially in low-middle income societies.

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incidence of violent conflicts. Lipset (1959) argues that education broadens one’s outlook, increases tolerance for other members of society, restrains extremist activity and doctrines, and increases a person’s ability to pursue their interests using the political process. Similarly, Brady, Verba, and Schlozman (1995) and Heyneman (2003) argue that schools are one of several agents of socialization that provide people the means by which to pursue their needs in a peaceful manner. Education provides civic skills to people, ultimately leading to increased political participation and social stability.

Education, however, may not always lead to civil peace. Rapid expansion of education in developing countries may increase the number of educated people to the level where the market cannot absorb them, so that it fuels violent conflict (Huntington 1968; Oyefusi 2010). Urdal (2006), for example, shows that the impact of large youth cohorts on terrorism is magnified in countries experiencing a rapid expansion of education. Lange and Dawson (2010) and Lange (2012) also argue that education is more likely to fuel violent conflict, especially ethnic violence in countries with ethnic divisions, ineffective political institutions and/or low incomes. Reasons include the fact that education is often provided in a segregated manner with biased curricula and tends to deepens ethnic antagonisms. Also, schools provide diverse resources that can be exploited to organize collective movements, so that education intensifies citizens’ mobilization power, triggering ethnic violence.

# **III. Data and Methodology**

The manner in which the government uses natural resource revenue could affect significantly rebels’ view of rewards and costs from violent conflict. In a paper most closely related to ours, Basedau and Lay (2009) find that oil wealth (when controlling for dependence on oil revenue) reduces the chances for civil war onset. Their work on a small sample of countries with high average dependence on oil revenues shows that, comparatively, the countries rich in oil can maintain peace because they engage in larger scale distribution and spend more on the military. The analysis is based, however, on a simple comparison of country values to sample medians for 27 oil dependent countries (after 1990) and may overlook important differences among countries. A more rigorous approach taking into account the possible non-linearities mentioned by Basedau and Lay (2009) and the endogeneity issues that plague many of the existing studies would be needed to understand the possible mitigation effect of public spending on the risk of conflict onset related to oil.

# **_Method and Hypotheses_**

Based on our literature review, we derive the following three hypotheses:

- H1 (general public spending): A larger size of public spending in oil rich countries is associated with a lower risk of violent conflict onset.

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- H2 (military spending): More military spending in oil rich countries is associated with a lower risk of violent conflict onset.

- H3 (social spending): More social spending (education, health and social security) in oil rich countries is associated with a lower risk of violent conflict onset.

Our dependent variable is the dichotomous indicator for conflict onset and empirical estimations are logit models with robust standard errors, that include the duration of peace years (or years since independence) and cubic splines to control for time dependence (Beck et al., 1998). To test our conditional hypotheses, we use interaction terms between the measure of oil value and measures of broad public spending and the specific spending items described above. Similar to previous work, all time varying variables are lagged one year.

# **_Dependent Variable_**

Several data sources have been used for conflicts, explaining part of the different results found in the literature. There are four major datasets: (1) Armed Conflict Dataset (ACD) from the Uppsala Conflict Data Program PRIO (Gleditsch et al., 2002); (2) Fearon and Laitin (2003); (3) Sambanis (2004); and (4) the Correlates of War, or COW (Sarkees and Wayman, 2010). All four use a threshold of 1000 battle deaths to define a civil war. The UCDP/PRIO dataset also codes smaller civil conflicts of at least 25 battle deaths per year, leading to three common UCDP/PRIO conflict constructions: low intensity (25 deaths) conflict years, cumulatively high-intensity (1000 death) years, and high-intensity years only. These competing measures differ in relation to when to code the start, what counts as a war, and how to treat breaks in violence.

To measure the onset of violent conflict, we use in this paper the Armed Conflict Dataset (ACD) for years 1960-2009 (Gleditch et al. 2002, Version 4.1.) to generate a dichotomous variable that takes the value of one in years with a new conflict onset and zero otherwise. This dataset has become the standard reference for cross-country analyses of conflict determinants. The majority of the recent studies have tended to use this source. Furthermore, along with major civil conflicts, PRIO also codes the onset of minor conflicts as those above 25 battle deaths per year. These smaller conflicts provide a relevant complement to the rarer full blown civil conflicts. Adding these smaller conflicts to define conflict onset should assist in estimating the determinants of rare events such as conflict onset.

Following Ross (2012), we regard a conflict as a new conflict onset if it occurs two years after the previous conflict. The ACD defines violent conflict as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths” (UCDP/PRIO 2012). As mentioned above, in addition to the low intensity violence indicator, we also use the 1000 battle-related deaths threshold to measure the onset of major civil conflict.

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# **_Key Independent Variables: Natural Resources and Public Spending_**

**Oil revenue:** to operationalize oil revenue, we follow Ross (2012) and use his oil-gas value per capita<sup>15</sup> . The data measures the value of oil and gas production per capita, multiplying the amount of oil gas production in a country with current oil and gas prices.<sup>16</sup>

**General government spending:** we use general government final consumption expenditure relative to GDP (WDI). This includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes expenditure on national defense and security, but excludes government military expenditures.

**Military spending:** the Correlates of War Project (COW) provides information on total military expenditure that we scale by total population.<sup>17</sup>

**Welfare spending:** this includes total health, social security and education expenditures. Total health expenditure (public and private) is based on the WDI, complemented with data from the IMF and Kugler et al. (2000). Social security expenditure includes public pensions and unemployment benefits. We use for this variable the data provided by Kugler et al. (2000) and Burgoon (2006). The education spending variable is the total public expenditure (current and capital) on education expressed as a percentage of GDP in a given year. We use education spending from the IMF to complement the WDI data. Following Burgoon (2006) and Taydas and Peksen (2012), we create a welfare spending variable by combining the health, social security, and education spending indicators to see the impact of aggregate social spending and oil on conflict.

Missing observations in the welfare spending variable, especially in developing countries, is an important issue that may pose difficulties in interpreting results.<sup>18</sup> To address this issue and for the purpose of comparison and replication, we use the same approach as in Taydas and Peksen (2012) and use Stata’s ICE multiple imputation procedure. The procedure specifically imputes

> 15 The data is available online at http://dvn.iq.harvard.edu/dvn/dv/mlross (accessed on March 14th, 2013). Previous work shows that even when looking at a wider range of natural resources, it is hydrocarbons that affect civil war onset (Fearon, 2005; de Soysa and Neumeyer, 2008).

> 16 This data has advantages over other measures that scale oil production to GDP or exports, both of which can be driven down by risk of civil war. More importantly, it gives a sense of the size of the oil wealth available to the government for spending. We use both logged and non-logged oil variables in our analyses. For models using nonlogged oil variable, one may suspect that results are driven by a handful of outliers. Yet, this is not the case for our results. Excluding outliers that have more than 10,000 dollars oil-gas value per capita from the models did not change the main results. In Tables 1 and 2, we report models using the non-logged oil variable without excluding outliers. In the Appendix, we show results using the logged oil variable, including replications of the results of Ross (2012).

> 17 We prefer scaling military spending by total population because the GDP data from sources other than the COW are not in the same metric. For comparison, in our summary Table 1 we scale military spending with GDP data from the Penn World Table 7.1.

> 18 Education spending, for instance, has significantly more missing values than health and social security spending.

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missing observations based on the pattern of the observed values of the non-missing variables in the same row and creates a completed dataset. In the new completed dataset, the observed values are the same, while the missing observations are filled in with the imputed data.

Table 1 shows descriptive statistics on oil revenue and public spending based on data for all countries except western developed democracies. We group oil-rich countries in two categories: _oil abundant_ countries (more than 996 dollars of oil-gas value per capita, or above the 90th percentile for Ross’ (2012) oil-gas value per capita in the sample of non-western countries); and o _il producing_ countries (oil gas value per capita is between 109 and 996 dollars, or the range between 75<sup>th</sup> and 90<sup>th</sup> percentiles). We further classify countries according to whether they are low or high on various categories of public spending (the cut point is the mean). The classification of countries in the tables is based on national means, which are computed by taking within-county means over time.

**Table 1: Variations in Spending Among Oil-Rich Countries: Average Spending**

|||Oil Abundant|Oil Producer|
|---|---|---|---|
|Total Government Spending|High|21.29% (8.17)|18.2% (6.0)|
||Low|12.3% (6)|12.12% (3.11)|
|Military Spending|High|7.85% (9.09)|4.69% (4.27)|
||Low|0.97% (1.12)|1.09% (0.76)|
|Education Spending|High|5.51% (2.11)|5.52% (1.57)|
||Low|3.44% (1.08)|3.15% (1.43)|
|Health Spending|High|2.69% (1.05)|3.32% (1.32)|
||Low|1.86% (0.92)|1.37% (0.82)|
|Social Security Spending|High|4.78% (2.48)|3.6% (2.08)|
||Low|0.55%(0.62)|1.0%(0.97)|

Note: Countries are defined as oil abundant if oil-gas value per capita (Ross, 2012) ranges above the 90 percentile (more than 996 dollars). Countries are oil producers if oil-gas value per capita ranges between the 75 and 90 percentiles (between 109 and 996 dollars). Each spending item is regarded as high if it is above the mean.

There is a remarkable variation in the pattern of government spending in countries with significant oil revenue. No clear pattern appears to emerge. Oil abundant countries can have high ratios of government spending to GDP (21.29 percent: Russia, Equatorial Guinea, Saudi Arabia, Kuwait, Bahrain, Qatar, Oman, Brunei) or more modest ones (12.3 percent: Trinidad, República Bolivariana de Venezuela, Gabon, United Arab Emirates, Turkmenistan). Similarly, resources in oil abundant countries can be directed to the military (average of 7.85 percent of military spending to GDP: Bahrain, Brunei, Iraq, Kuwait, Libya, Oman, Qatar, Russia, Saudi Arabia, United Arab Emirates) or military spending can be much less prominent (0.97 percent: Equatorial Guinea, Gabon, Trinidad, Turkmenistan, República Bolivariana de Venezuela). The average difference between high and low spenders on welfare is less impressive but it exists. Oil abundant countries that allocate significant resources to social security spend on average 4.7 percent of GDP (Kuwait), while those that allocate little spend on average 0.55 percent of GDP (Bahrain, Equatorial Guinea, Gabon, Iraq, Libya, Oman, Qatar, Saudi Arabia, Trinidad, United Arab Emirates, República Bolivariana de Venezuela). Education spending shows similar 12
patterns, with some oil abundant countries spending above the mean for developing countries (5.51 percent: Brunei, Kuwait, Saudi Arabia, Trinidad, República Bolivariana de Venezuela), while other spend below that mean (3.44 percent: Bahrain, Gabon, Iraq, Libya, Oman, Qatar, Russia, United Arab Emirates).

# **_Control Variables_**

Along the lines of Fearon and Laitin (2003), our model also includes a range of control variables, capturing the variety of factors possibly explaining conflict. All time-varying variables are lagged one year. All models include the following variables:

- The logged income per capita to control for the “state’s overall financial, administrative, police, and military capabilities.” (Fearon and Laitin, 2003). This variable also captures the argument that there exist fewer grievances in a society with higher levels of income;

- The log of the country’s population to control for “the number of potential recruits to an insurgency at a given level of income.” (Fearon and Laitin, 2003);

- Noncontiguous territory, because if a country has a territorial base separated from the state’s center due to geography, rebel groups could find it easier to take up arms;

- A country’s democracy score: a more democratic country should be less likely to experience violent conflict because political rights and civil liberties are guaranteed in democracies so that people find it easier to resolve conflict through non-violent means;<sup>19</sup>

- Political regime instability: unstable regimes signal a weakened state capacity, leading to an increase in the risk of violent challenges to government power;

- Ethnic and religious fractionalization captures the notion that civil wars are more frequent in heavily heterogeneous societies. Ethnically or religiously divided societies may face strong grievances, which increases the risk of violent conflict;

- A dummy variable for ongoing war: if a country is in a violent conflict with another group, other rebel groups may find is easier to challenge the government.<sup>20</sup>

> 19 Following Vreeland (2008), we also recode Polity IV score to avoid possible endogeneity issues.

> 20 Income per capita comes from Penn World Tables and World Development Indicators, supplemented with per capita energy consumption. Population size is based on World Bank World Development Indicators. We do not control for recent independence because for some of our specifications data is only available post 1960.  Democracy is based on Polity IV (-10 to 10). For political instability, we follow Fearon and Laitin (2003) and use a dummy variable for country/years that had a three or greater change on the Polity IV regime index in any of the three years prior to the country-year in question.

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# **_Econometric Issues_**

While previous studies have been influential, the cross-country empirics they are based on have not always taken fully account of cross-country heterogeneity or the likely endogeneity between conflict and its determinants. The endogeneity bias may arise from measurement errors, omitted variables or potential reverse causality between the dependent variable, conflict onset, and our variable of interest, public spending. It is possible, for instance, that education enrollment drops as a civil war approaches because people flee their homes in anticipation of fighting. Likewise, expenditures may drop before an insurgency as the government diverts resources from social expenditures to the military in order to defend itself. Studies of conflict onset typically address this problem by lagging the explanatory variables so that conflict onsets in a given year are explained by the values on the explanatory variables in the previous year. However, it is possible that problems with reverse causality appear before t-1.

To take account of unmeasured regional effects, all estimations include regional dummies as in Fearon and Laitin (2003) for Asia, the Americas, Africa, and Europe (the Middle East being the reference region). We also use random effects logit models. Since the dependent variable is binary, fixed effects would drop all the countries that do not experience any conflict over the period of study. Yet, at the same time, it may be the case that the effect of interactions may differ systematically due to country specific conditions, even after the inclusion of our control variables. Therefore, as an alternative, we include country-level random effects to account for the likelihood that each country may have different effects with regard to the interaction term.

Finally, to test our results for military spending – as shown below, these are our most statistically significant results – we also employ instrumental variable (IV) estimations. It is not hard to imagine that increases in military spending could be driven by high risk of civil war. To mitigate the risk of reverse causality, we use IV estimations in which we first predict military spending with models including instrumental variables that are not clearly determined by the risk of civil war. Following Collier and Hoeffler (2007), we use two instrumental variables: neighboring countries’ military spending relative to home country’s GDP and a Cold War dummy.<sup>21</sup> From the first stage model we predict values of military spending per capita and subsequently estimate logit models of civil conflict onset that include: oil-gas value per capita, predicted military spending per capita, and the predicted interaction between military spending and oil wealth.<sup>22</sup>

> 21 We also include neighboring countries’ military spending relative to their own GDP and our results do not change. 22 Besides the exogenous instruments, we include the following independent variables in the first stage model: a country’s coup history and the years since the last coup, an indicator variable for military regimes, international war duration, and one year lagged values for Polity IV, per capita GDP, trade openness, oil-gas value per capita. To avoid the Wooldridge (2002) forbidden regression the predicted interaction between military spending and oil wealth comes from a similar model that also includes the interaction of predicted military spending and oil wealth.

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# **IV. Results**

Tables 2 and 3 report our key results. We show the results for both small and large conflict onset. The coefficients on the control variables have broadly the expected signs. Political instability increases the risk of both small and large conflict. More populous countries also face a higher chance of civil conflict. The estimated coefficient for states with noncontiguous territory is positive and consistent with Fearon and Laitin (2003). Ethno-linguistic fractionalization (ELF) increases the risk of small scale war, while it does not appear to be associated with larger scale conflicts. Democracy reduces the risk of large scale conflict, while it has an ambiguous effect on small scale war. Finally, economic development, operationalized as income per capita is not predicting civil conflict in our models.

**Table 2 Conditional Effect of Oil Wealth on Conflict Onset**

||Model 1<br>Small conflict<br>onset|<br>Model 2<br>Large conflict<br>onset|<br>Model 3<br>Small conflict<br>onset|<br>Model 4<br>Large conflict<br>onset|Model 5<br>Small conflict<br>onset|<br>Model 6<br>Small conflict<br>onset|
|---|---|---|---|---|---|---|
|Oil-Gas Value per capita|0.17|-0.52|0.2|-0.08|-0.08|-0.51|
||(0.124)|(0.381)|(0.149)|(0.236)|(0.166)|(0.3570)|
|Government Expenditure|0.016<br>|0.004<br>|||||
||(0.01)|(0.02)|||||
|Gov Expenditure*Oil|-0.013*|0.01|||||
||(0.008)|(0.016)|||||
|Military Expenditure per capita|||0.0012***<br>(0.00)|0.002***<br>(0.00)|||
|Military Expenditure*Oil|||-0.001***|-0.001***|||
||||(0.0004)|(0.0003)|||
|Welfare Expenditure|||||-0.109***|-0.008|
||||||(0.0340)|(0.046)|
|Welfare Expenditure*Oil|||||(0.0003)|0.02|
||||||(0.0175)|(0.019)|
|ELF|1.609***|0.508|1.508***|0.517|1.484***|0.257|
||(0.48)|(0.95)|(0.44)|(0.93)|(0.51)|(0.915)|
|Religious Fractionalization|-0.704|0.672|-0.23|0.603|-0.3|0.778|
||(0.46)|(0.71)|(0.50)|(0.81)|(0.54)|(0.817)|
|Political Instability|0.686***|0.715**|0.741***|0.739***|0.792***|0.716**|
||(0.19)|(0.30)|(0.17)|(0.29)|(0.22)|(0.29)|
|Non Contiguity|1.166***|1.286*|1.171***|1.325**|1.293***|1.678***|
||(0.38)|(0.68)|(0.33)|(0.60)|(0.28)|(0.54)|
|Polity IV|0.038|-0.115**|0.026|-0.127***|0.0529*|-0.107**|
||(0.03)|(0.05)|(0.02)|(0.04)|(0.03)|(0.05)|
|Logged Population|0.362***|0.417***|0.348***|0.397***|0.219***|0.318***|
||(0.07)|(0.11)|(0.06)|(0.09)|(0.07)|(0.10)|
|Logged GDP per capita|-0.009|0.155|-0.091|0.025|-0.286*|-0.211|
||(0.09)|(0.12)|(0.10)|(0.13)|(0.16)|(0.23)|
|Constant|-12.43***|-14.46***|-11.61***|-13.59***|-6.015***|-10.27***|
||(1.54)|(2.33)|(1.30)|(2.00)|(2.107)|(2.90)|
|Psedo R squared|0.126|0.114|0.123|0.118|0.14|0.12|
|Log pseudolikelihood|-611.51|-253.83|-692.3|-288.59|-503.73|-254.78|
|Wald Chi-squared|355.51***|98.17***|414.25***|125.25***|175.81***|96.23***|
|Observations|5,190|5,190|5,618|5,618|4,108|4,226|

Note: ***p<0.01, **p<0.05, *p<0.1. The estimation method is logistic regression. Models 5 and 6: Welfare spending is imputed using Taydas and Peksen (2012) data. Regional dummies, half decade dummies, peace years and cubic splines are included in all models. Country clustered standard errors in parentheses.

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# **Table 3: Additional Analysis: Logit Models of Conflict Onset**

||Model 7<br>Small Conflict<br>onset|Model 8<br>Large conflict<br>onset|Model 9<br>Small conflict<br>onset|<br>Model 10<br>Large conflict<br>onset|<br>Model 11<br>Small conflict<br>onset|<br>Model 12<br>Large conflict<br>onset|Model 13<br>Small Conflict<br>onset|<br>Model 14<br>Large conflict<br>onset|
|---|---|---|---|---|---|---|---|---|
||Random|effects|Random|effects|Random effects /|Imputed spending|Instrumen|tal variable|
|Oil-Gas Value per capita|0.165|-0.476|0.16|-0.02|-0.138|0.231|0.00|0.00|
||(0.220)|(0.988)|(0.182)|(0.41)|(0.388)|(0.734)|(0.000)|(0.000)|
|Government Expenditure|<br>0.016|<br>0.021|||||||
||(0.016)|(0.032)|||||||
|Gov Expenditure*Oil|<br>-0.013<br>(0.015)|<br>0.001<br>(0.043)|||||||
|Military Expenditure per capita|||0.001**|0.002**|||0.001***|0.001***|
||||(0.0005)|(0.00)|||(0.0004)|(0.0005)|
|Military Expenditure*Oil|||<br>-0.001*|<br>-0.001*|||<br>-0.001**|<br>-0.001*|
||||(0.0006)|(0.00)|||(0.0004)|(0.0004)|
|Welfare Expenditure|||||-0.119***<br>(0.037)|-0.008<br>(0.050)|||
|Welfare Expenditure*Oil|||||<br>0.001|<br>-0.073|||
||||||(0.047)|(0.116)|||
|ELF|1.609***|0.951|1.547***|0.825|1.482***|0.214|1.770***|0.361|
||(0.429)|(0.922)|(0.465)|(0.87)|(0.551)|(0.841)|(0.516)|(0.927)|
|Religious Fractionalization|<br>-0.704|<br>1.117|<br>-0.178|<br>0.865|<br>-0.202|<br>0.492|<br>-0.50|<br>0.44|
||(0.493)|(1.280)|(0.529)|(1.18)|(0.625)|(1.158)|(0.516)|(0.916)|
|Political Instability|<br>0.686***|<br>0.730**|<br>0.791***|<br>0.793**|<br>0.862***|<br>0.530|<br>0.689***|<br>0.779***|
||(0.192)|(0.352)|(0.19)|(0.33)|(0.224)|(0.339)|(0.18)|(0.28)|
|Non Contiguity|<br>1.166***|<br>1.272**|<br>1.179***|<br>1.310**|<br>1.368***|<br>1.875***|<br>1.202***|<br>1.703**|
||(0.310)|(0.640)|(0.33)|(0.61)|(0.403)|(0.661)|(0.40)|(0.82)|
|Polity IV|<br>0.038|<br>-0.112**|<br>0.0292|<br>-0.123***|<br>0.061*|<br>-0.093**|<br>0.030|<br>-0.126***|
||(0.028)|(0.045)|(0.03)|(0.04)|(0.032)|(0.046)|(0.03)|(0.05)|
|Logged Population|0.362***|0.543***|0.354***|0.521***|0.209**|0.471***|0.310***|0.323**|
||(0.070)|(0.175)|(0.07)|(0.16)|(0.086)|(0.164)|(0.07)|(0.13)|
|Logged GDP per capita|<br>-0.009|<br>0.036|<br>-0.0757|<br>-0.0865|<br>-0.304**|<br>-0.354|<br>0.000|<br>0.0803|
||(0.096)|(0.204)|(0.10)|(0.20)|(0.151)|(0.259)|(0.00)|(0.14)|
|Constant|<br>-12.433***|<br>-17.687***|<br>-12.18***|<br>-16.66***|<br>-6.321***|<br>-12.557***|<br>-11.33***|<br>-13.45***|
||(1.604)|(3.731)|(1.64)|(3.435)|(2.244)|(3.815)|(1.26)|(2.59)|
|Log likelihood|-611.5|-250.6|-691.60|-284.82|-502.7|-267.9|-576.84|-246.59|
|Wald Chi2|153.81***|36.27***|121.44***|121.85***|100.2***|38.5 ***|413.88***|137.38***|
|Observations|5190|5190|5,618|5,618|4,108|4,108|4,836|4,836|

Note: ***p<0.01, **p<0.05, *p<0.1. Models 9-14: Random effect models. Models 11 and 12: Welfare spending is imputed using Taydas and Peksen’s (2012) data. Models 13 and 14: Military spending per capita is instrumented. Regional dummies, half decade dummies, peace years and cubic splines are included.

18
General government spending is not robustly associated with a lower risk of conflict. Models 1 and 2 present the estimation results for oil revenue interacted with general government spending. While the results in Table 1 indicate a negative and statistically significant coefficient (at the 10 percent level) in the case of small-scale conflicts, this result does not hold once random effects are included (Table 3). Hypothesis 1 does not seem to hold, whereby governments are able to contain the increased risk of conflict onset related to oil revenue by signaling their willingness to use this revenue not just for their own enrichment, but also to reward their supporters.

Military spending, by contrast, appears to have a mitigating effect both on large- and small-scale conflicts. Models 3 and 4 report coefficients from estimations that include the interaction between oil-gas value per capita and military spending per capita. The negative and statistically significant coefficients on the interaction terms show that higher levels of spending on the military are associated with lower risk of both small and large civil conflict. These results remain unchanged even when random effects are included (Table 3) and support our Hypothesis 2.

In logit models the substantive effect of a particular independent variable depends on the levels of all other variables. Therefore, to facilitate the substantive interpretation of the result, Figure 1 shows the predicted probability of small conflict at low and high oil-gas value per capita for increasing levels of military spending using estimation results from Model 3. These predicted probabilities range between 0 and 1 by definition, with all the variables kept at their observed mean with the exception of the two that are varying (military spending and oil/gas production).

**Figure 1: The Effect of Military Spending on Conflict at Different Levels of Oil-Gas Value**

<!-- Start of picture text --> Predicted<br>probability<br>of conflict<br>onset<br>0 500 1000 1500<br>Military Spending per capita (USD)<br>Oil-Gas Value per capita (0 USD per capita)<br>Oil-Gas Value per capita (2,500 USD = the 95th percentile)<br>.2<br>.15<br>.1<br>.05<br>0<br><!-- End of picture text --> Note: Predicted probability of small conflict onset using Model 3. The vertical lines show the 90 percent confidence interval around the predicted probabilities of conflict.

At low levels of military spending per capita, the production of oil and gas tends to increase the risk of violent conflict. The dotted line (no oil/gas production) is below the continuous line (high oil/gas production). In oil and gas rich economies, however, this increase in risk of small-scale 18
conflict can be mitigated and even reversed with higher levels of military spending. Moving along the military spending per capita scale (from zero to 1,500 dollars), the predicted probability of conflict onset not only returns to its initial level (when both oil/gas production and military expenditure were set at zero), but falls below it and tends to zero. For economies not endowed with oil or gas, by contrast, a similar increase in military spending is associated with continuously higher risks of conflict. For large conflicts, a similar pattern emerges. In this case, however, the downward slope for oil and gas rich economies is less pronounced (Appendix B).

These non-linearities may explain at least in part the diverging results observed in the literature. For oil-poor countries with limited resources, military spending could be perceived as crowding out more important spending, fueling citizen grievance and possibly leading to higher risk of conflict. As countries become richer in oil and can tap more resources, they may be less constrained and military spending can take place without crowding out other expenses. Furthermore, as these governments grow richer they may represent more attractive prizes, and would need larger military budgets to fend off unwanted claims on their treasury, either through patronage or military action.

In both cases, signaling and patronage seem to prevail over any possible loss of efficiency in the military apparatus. The results presented here would suggest that, in oil and gas rich economies, spending on repression, patronage to regime loyalists, or direct rents to the military to prevent possible coup attempts offsets any undermining in the quality of the armed forces. These governments seem to have been able to spend on military and contain the increased risk of conflict onset related to oil resources, while not becoming more vulnerable to other threats.

Finally, higher welfare spending seems to be associated with lower risk of small-scale conflict. Results from Models 5 and 6 indicate a negative and statistically significant coefficient for the level of welfare spending. The coefficient of the interaction term with the oil-gas value per capita is, however, not statistically significant. These results remain unchanged once random effects are included (Table 3). This would suggest that the reduction in conflict risk associated with higher levels of spending on education, health, and social protection is independent from the oil-gas value. The relation is here linear and the effect on conflict higher than military spending for low levels of oil revenue.

# **_Robustness Tests_**

The use of instrumental variables for military spending does not alter the results. Models 13 and 14 in Table 3 show estimations using instrumented values for military spending per capita and indicate that the earlier results remain robust. Interaction terms remain negative and statistically significant and their magnitude changes little (large conflict) or not at all (small conflict).

19
We also investigate the robustness of the results by imposing various sample restrictions that previous work suggest may be relevant. In all cases, the results reported above remain unchanged.

- A first restriction is whether a country is having another violent conflict or not and only observations that do not have an ongoing war were kept in the sample;

- We also restrict our sample by only focusing on non-democracies (autocracies and anocracies);<sup>23</sup>

- We also exclude non-oil producing countries and include only country-year observations that produce more than 106 dollars per capita of oil and gas;

- The sample was restricted to very poor countries (the bottom 25 percentile based on logged GDP per capita), selecting only the post-Cold War period (1990-2009);

- We include both military and welfare spending (and their interacted terms with oil gas value per capita) in the same model and the results for small conflict are very similar to the ones reported so far.

- Finally, we use logged oil gas value per capita to replicate the findings in Ross (2012). The results are shown in Models 15-19 in Appendix C and remain broadly unchanged.

# **CONCLUSION**

If natural resources increase the risk of violence in a country, are there ways for the government to change this course of events? Contributing to this ongoing debate in the study of civil war, our paper sheds light on one key aspect of governments’ political decisions: the allocation of natural resource wealth, in particular oil wealth, for various public spending items. This pacifying outcome may be due to a signaling effect of spending, showing potential rebels the size of the coalition they would be facing; a reduction of grievance; or a deterrent effect due to a greater security apparatus. By doing this, the paper brings together the literature on public spending and conflict, on one hand, and that on natural resources and conflict, on the other.

The statistical analysis presented in this paper confirms that the association between natural resources and conflict is not a fatality. The pattern of public spending could play a role. Higher levels of military spending in oil-rich countries are associated with lower risks of both small- and large-scale conflict. This mitigating effect is all the more potent when oil revenue grows larger.

> 23 We consider countries as autocracies or anocracies if a combined score of xrconst, xrcomp and xropen in Polity IV is no more than 11 (the maximum score is 14).

20
As these resources grow in importance, larger military budgets may be called for either for repression, patronage to regime loyalists, or direct rents to the military to prevent possible coup attempts. At low levels of oil revenue, however, the opportunity costs of military expenditure in terms of foregone alternative spending may fuel grievance. Higher levels of military expenditure are in this case associated with higher risk of conflict.

Higher spending in education, health, and social protection is associated with lower risk of small scale conflict. This effect is independent from the level of oil revenue and could be more effective than military spending in reducing conflict risk for countries not well-endowed with natural resources. While unable to prevent full-scale wars, higher spending on public services for which the citizens care could be enough to reduce grievance, foster a government’s legitimacy, and reduce the risk of small-scale conflict. Finally, higher levels of general government spending are not robustly associated with lower risk of conflict.

These results uncover interesting non-linearities in the relations between conflict, natural resources (oil and gas in this case), and public spending that could explain partly the divergent results presented in the literature and call for further investigation. First, while general government spending does not seem to have any effect on conflict risk, patronage usually goes through construction contracts or civil service employment. Examining the association between higher investment budgets or higher wage bills and the risk of conflict may yield different results.

Furthermore, citizens may care more about actual improvements in public service delivery than higher public spending. Signaling without delivering the actual service may backfire and fuel grievance. Examining the possible role of improving education or health outcomes in reducing grievance and conflict risk could also be a fruitful venue for future research.

21