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Oil, Conflict, and Stability

Kevin M. Morrison Assistant Professor

Graduate School of Public and International Affairs University of Pittsburgh

Email: morrison@gspia.pitt.edu

First draft: April 2009 This draft: July 2012

Abstract: According to existing literature, the presence of oil leads simultaneously to increased

risk of civil conflict and exceptional regime stability. The seemingly contradictory nature of

these findings—oil leading both to instability and stability—has never been systematically

addressed. Analyzing the causal mechanisms underlying both relationships, I argue that oil's

tendency to spur civil conflict should disappear in the context of strong state capacity, and that

oil's tendency to stabilize political regimes should disappear in the context of weak capacity.

Employing data for all available countries and years from 1960 to 2000, and using several

different measures of state strength, I find evidence supporting this argument, with results that

are robust to the inclusion of country fixed effects and instrumental variables analysis. The

argument and findings bridge the two major—but until now, largely separate—approaches to the

political resource curse.

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Oil, Conflict, and Stability I. Introduction In 1985, Mexico produced $573 of oil per capita. The authoritarian Institutional

Revolutionary Party, or PRI by its Spanish initials, had been in power since the 1920s, ruling

through a combination of repression and benefits distributed via the party’s highly

institutionalized organization throughout the country (Magaloni 2006). Although the 1960s and

1970s had seen some moderate opening in the political system (Brachet-Márquez 1994),

evidenced by an important electoral reform in 1963, the period between 1977 and 1994 would

see no major democratic electoral reform. The first part of this period saw oil receipts spike to

levels far greater than had ever been seen in Mexico. By 1985, they had fallen back, but they

were still higher than any level seen since the 1920s (Haber and Menaldo 2011). The PRI was

able to funnel the revenues obtained from oil into social programs that kept violent dissent

against the regime at a low level (Trejo 2004). It was only in the late 1980s and early 1990s,

when oil receipts had fallen lower, that the PRI would begin to lose its power, ending in the loss

of control of the Chamber of Deputies in 1994 and the loss of the presidency in 2000.

In 1997, the Republic of Congo (Congo-Brazzaville) produced almost the same amount

of oil as Mexico in 1985—$556 per capita.1 Instead of stabilizing the regime, however, by most

accounts oil contributed to the country’s collapse into a devastating civil war. Congo’s recent

democratization “had disrupted the ancien regime’s patronage networks” (Englebert and Ron

2004, 65), and important groups in society were unhappy with or insecure about the benefits they

1 The values in this paragraph and the previous one are in constant (2000) $US, using Ross’

(2008) data.

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were receiving from Congo’s oil. In addition, the government was unable to control various

militias in the country, who were able to secure financing from oil firms willing to bet that they

could take over the country (Ross 2004). The result was a war that resulted in the deaths of

about 10,000 people.

What can account for these two very different outcomes attributed to the presence of the

same amount of oil? Knowing when oil produces civil war or the stability of a political regime is

obviously of deep importance, yet very little has been written on this question, despite the

voluminous literature that has arisen documenting various aspects of the “oil curse”—the

apparent tendency for countries rich in oil to perform worse along economic and political

dimensions than one would otherwise expect.2 In fact, these two cases are examples of the two

most prominent findings with regard to the political side of this curse. The first is that the

presence of oil tends to make dictatorships and democracies last longer than they otherwise

would (Ross 2001; Dunning 2008; Morrison 2009). The second is that the presence of oil tends

to increase countries’ risk of civil conflict (Collier and Hoeffler 2000; Fearon and Laitin 2003;

Ross 2004; Humphreys 2005). In other words, the two major strands of literature regarding the

political oil curse can be summarized in the following statement: The more oil a country has, the

higher the risk of an abnormally long-lived political regime… and civil war.3

2 An important exception is Boix (2008). Space constraints prevent a full discussion, but

because of that paper’s relation to his previous work (Boix 2003), discussed in detail below, its

model suggests that oil should only be associated with increased civil conflict at high levels of

state capacity, the opposite of my Hypothesis 1 below.

3 It should be noted that some skeptics exist on both sides of this debate. On the civil war side,

see Smith (2004); on the regime stability side, see Haber and Menaldo (2011).

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If these two findings are not inherently contradictory, they at least seem to call for further

investigation. To the extent that civil wars and regime transitions are examples of political

instability, these two findings suggest that oil simultaneously leads to instability and stability.

Indeed, controlling for other factors, a statistically significant negative correlation between

political regime stability and civil war onset has been found by a multitude of studies (e.g. Hegre

and others 2001; Fearon and Laitin 2003). In fact, in their sensitivity analysis of the empirical

results regarding the correlates of civil war onset, Hegre and Sambanis (2006) find regime

instability to be one of the most robust variables in the literature.4

With this negative correlation between regime stability and civil war in mind, this paper

seeks a more integrated understanding of the political effects of oil, attempting to reconcile these

two major findings of the literature. In particular, I develop a theoretical argument about the

conditioning effect of what has been referred to as “the degree of government” (Huntington

1968) or, more often, “state capacity” (e.g. Geddes 1994). In Tilly’s words, the theoretical

concept is “the extent to which the governmental agents control resources, activities, and

populations within the government’s territory” (Tilly 2003, 41). In Mann’s terms, it is “the

capacity to organize and control people, materials, and territories” (Mann 1986b, 2-3). At high

levels of state capacity, “If the Politburo, the Cabinet, or the President make a decision, the

probability is high that it will be implemented through the government machinery” (Huntington

1968, 1). In other words, the government has a “bureaucracy able and willing to enforce

any…policy” throughout the territory (Besley and Persson 2009). At lower levels of state

capacity, the state is weaker and less coherent, and “officials cannot carry out the policies they

choose” (Geddes 1994, 14).

4 This significant negative correlation is found again below (Table 2).

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While some scholars have underlined the importance of state capacity for development

outcomes (e.g. Haggard 1990), and other scholars have analyzed its determinants (e.g. Tilly

1990; Besley and Persson 2009), less work has focused on how state capacity underlies causal

mechanisms in existing theories in a variety of areas. In fact, any theory that relies on action by

the state—such as taxation, redistribution, or nation-building—implicitly assumes a certain level

of state capacity.5 Causal mechanisms like these are unlikely to apply in states with low levels

of capacity.

In this vein, I will demonstrate here that the concept of state capacity is necessary for

understanding the effects of oil. The reason is that the causal mechanisms linking oil to regime

stability and civil war in the literature make implicit—and different—assumptions about the

state’s capacity to capture and effectively channel oil rents. Little attention has been paid to

these assumptions in the literature, but revealing them yields novel predictions about where oil

will have certain effects and, just as important, where it will not.

The paper proceeds as follows. In the next section, I develop my theoretical approach by

analyzing in detail the various causal mechanisms that have been proposed to link oil to either

regime stability or civil war. I demonstrate that the mechanisms linking oil to regime stability

assume relatively strong state capacity—strong enough that the government can control oil

revenues and spend the resulting funds effectively—whereas the mechanisms linking oil and

civil war assume relatively weak state capacity. Viewing the literatures in this way not only

5 Recent examples include works analyzing the role of redistributional conflicts on political

regime transitions (Boix 2003; Acemoglu and Robinson 2006) and the effect of institutions on

ethnic conflict (Elkins and Sides 2007). See Soifer (2009) for a discussion of the implications

for the literature on redistributional conflicts and regime transitions.

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serves to unify two bodies of work that have largely existed in parallel, but also generates an

important hypothesis: oil only leads to regime stability in the context of strong state capacity,

whereas oil leads to a higher probability of civil war only in the context of weak capacity.

The third section moves to empirical evaluation of this hypothesis, based on regression

analysis of all countries for which data are available from 1960 to 2000. While existing

indicators of state capacity are imperfect, I argue that one is particularly appropriate for my

purposes. I find suggestive support for my hypothesis using that indicator and then demonstrate

that the support is not reliant on utilizing that particular measure—the findings are robust to the

use of several other widely employed measures of state capacity. The results are also robust to a

variety of different specifications, including the use of country fixed effects and instrumental

variables analysis.

The argument and findings have important theoretical implications, including delineating

the correct empirical domain for these two significant bodies of work, as well as more precisely

specifying the causal mechanisms linking oil to civil war and regime stability. A fifth section

concludes by assessing these implications for the existing literature and future research.

II. Synthesizing theoretical perspectives on the effects of oil Cracking the puzzle of why oil leads to civil war in some countries and regime stability in

others requires careful attention to causal mechanisms. And indeed, both strands of the political

resource curse literature have been characterized by careful attention to the mechanisms

underlying their hypothesized relationship. In the context of the relationship between oil and

regime stability, this is largely due to the substantial literature consisting of case studies (e.g.

Beblawi and Luciani 1987; Karl 1997) that preceded Ross’s (2001) landmark statistical work. In

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the context of the relationship between oil and civil war, the focus on mechanisms has intensified

in the wake of Collier and Hoeffler’s highly publicized finding of a correlation between

commodity exports and civil war.6 This section reviews the most prominent mechanisms in both

literatures and examines the extent to which they require strong state capacity. I begin with the

links between oil and civil conflict and then address the links between oil and regime stability. It

should be noted that my goal here is not to evaluate the evidence in favor of (or against) these

mechanisms. Rather, I want to enumerate them and demonstrate how they relate to state

capacity, with the objective of developing original hypotheses.

There are two main sets of mechanisms linking oil to civil conflict in the literature. The

largest set revolves around grievance. Oil’s benefits are often not distributed evenly within

countries, which creates marginalized populations willing to fight for better access to those

benefits. However, within this general dynamic, the specific natures of the salient grievances

vary widely. Often the grievances will be defined geographically. For example, the local

population in the area where the resource is located may have had land appropriated, or been

exposed to environmental hazards, or subject to forced migration, and so forth (e.g. Klare 2001).

In addition, areas that are rich with natural resources may protest against the benefits of those

resources being spread elsewhere in the country and develop separatist tendencies (Le Billon

2001; Humphreys 2005). Alternatively, unrest may arise in areas of the country that are not

benefiting from natural resource riches in other parts. At other times, as Humphreys notes (2005,

511), the marginalized population may not be so geographically defined, as when poorer groups

in resource-rich countries experience “transitory inequality as part of the development process,”

or when groups suffer from the terms of trade shocks that resource-dependent countries often

6 For discussion and relevant citations, see Fearon (2005).

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experience. Though they did not go into detail, I believe grievance dynamics are what Fearon

and Laitin (2003, 81) had in mind when they said that oil revenues raise the value of the “prize”

of controlling state power, and for this reason foster civil conflict. After all, if a group did not

have a grievance—if, at the extreme, it were receiving all the state’s resources, for example—

there would be little prize for starting a civil war.

The second major mechanism linking oil and civil war concentrates on the increased

funding that rebels can capture as a result of the presence of oil. There are two variants of this

line of argument. In the first, rebels actually control the resources and sell them in order to gain

additional funds.7 In the second, external actors—other countries or international corporations—

have an increased interest in the political outcome and therefore support various rebel groups.

Both of these revolve around the fixed (as opposed to mobile) nature of oil. As Ross (2004, 40)

says, for example, “If rebels try to loot or extort money from manufacturing firms, the firms will

relocate to a safe area or be forced out of business; but if rebels extort money from resource

firms, the firms cannot relocate and can often make payments to rebels and still turn a profit.”

This lack of mobility is also what motivates external actors to become involved in the country.

Both of these sets of mechanisms linking oil to civil war assume a weak state. To see this

more clearly, it is useful to compare them to the two principal sets of mechanisms in the

literature linking oil to regime stability. The most well known of these is the rentier effect,

caused by governments using oil revenues to relieve social pressures. As Ross (2001) notes, this

effect can be caused by governments using the revenues either to reduce taxation or to increase

government spending of various kinds.8 It is essentially a government finance effect. Ross

7 This was Collier and Hoeffler’s (2000) “greed” hypothesis.

8 Also see Jensen and Wantchekon (2004).

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discusses two other mechanisms that I believe are best considered part of this rentier effect. The

first is his “group formation effect,” caused by the government’s ability to “use its largesse to

prevent the formation of social groups that are independent from the state” (Ross 2001, 334).

The second is a “repression effect,” by which governments “spend more on internal security”

(Ross 2001, 335). Since both of these have to do with how governments spend their increased

resources, they are really part of the rentier effect as conceived here. Similarly, recent works

that have shown how both democratic and authoritarian regimes can use these revenues to

respond to threats are essentially studying variants of the rentier effect (Morrison 2007; Dunning

2008; Goldberg, Wibbels, and Mvukiyehe 2008; Smith 2008; Morrison 2009).

The second major mechanism is the asset specificity argument that has been advanced by

Boix (2003). Boix argues that because many democratic transitions involve conflicts between

richer elites and poorer citizens interested in redistributing the wealth of those elites, one of the

key factors in regime transitions is how mobile the assets of the country are. The more fixed are

the assets—oil being a primary example—the greater the ability of the state to tax those assets,

since their owners cannot take them out of the country. “As a result, capital will invest

considerable effort in blocking democracy, especially since the costs to capital of not doing so

are high” (Boix 2003, 39). While this argument closely relates to the rentier effect because of its

link to government finance, there is an important difference. While the rentier effect particularly

concerns the effect of oil on the ability of the state to respond to societal pressures, Boix’s

argument principally concerns how asset specificity changes the preferences of social groups

about political regimes.9 To use an analogy present in the civil war literature, the rentier

9 Readers familiar with Boix’s (2003) argument may note that while I am applying his theory to

political stability in general, his argument is that asset specificity leads to more stable

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argument is about oil presenting the “opportunity” to stabilize regimes, and the asset specificity

argument is about oil presenting the “motive.”10

For the purposes of this paper, the critical point is that both the rentier and asset

specificity mechanisms require a high level of state capacity—high enough that the government

can control oil revenues and spend the resulting funds effectively. While the rentier mechanism

assumes that oil production translates into government revenue, this is not necessarily true.

Examining countries whose measure of state capacity is above or below the median value in my

sample, the correlation between oil production per capita and total revenue per capita varies

dramatically. In regimes above the median of state capacity, the correlation is 0.23, with a p-

value of 0.0000. In regimes below the median, the correlation is -0.065, with a p-value of

authoritarian regimes and less stable democratic ones. While space constraints prevent a

thorough exposition, I believe Boix’s conclusion results from his theoretical model only focusing

on transitions from authoritarian regimes (Chapter 1). If the elites value authoritarian regimes

more in the context of asset specificity, as Boix argues, then it seems reasonable to expect that

citizens should also value democracy more, leading them also to “invest considerable effort” in

blocking the overthrow of democracies. In fact, Boix’s own results seem to indicate that asset

specificity stabilizes both dictatorships and democracies (for example, Table 2.5, p. 87). If I am

wrong in my interpretation, oil’s effect on regime stability should probably be insignificant in the

analysis below (due to contrasting effects in democracies and dictatorships).

10 In the civil war literature, the funding mechanism provides the opportunity, while the

grievance mechanism provides the motive (for example, Collier and Hoeffler 2000).

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0.2001.11 In other words, in contrast to high-capacity regimes, in low-capacity regimes there is

no significant correlation between oil and government revenue, the hallmark of rentier theories.

High state capacity also underpins Boix’s argument. An important aspect of Boix’s

theory is that it is centered on how taxable an asset is. While Boix’s exposition of his argument

primarily focuses on whether the asset is fixed or not, the key characteristic of an asset is

whether it can be taxed easily (that is, whether it is easily moved out of the country, whether it is

easily hidden from tax inspectors, and so forth). As he says, “Individuals with assets that are not

extremely mobile may still be able to avoid taxes without any risk of getting caught. A change

in the extent to which an asset can be monitored and taxed has the same consequences as a shift

in the degree of mobility” (Boix 2003, 25). This perspective on his argument highlights that its

relevance depends on the state being sufficiently capable of actually administering and collecting

taxes, something that should not be taken for granted (Soifer 2009). In fact, a state’s ability to

collect taxes is often used precisely as an indicator of state capacity (Lieberman 2002).

The state capacity inherent in the mechanisms linking oil to regime stability stands in

stark contrast to that in the mechanisms linking oil to civil conflict.12 For the funding

11 These calculations are performed using telephone lines per 100 people as the measure of state

capacity (discussed below). The difference in the correlations is even stronger if one uses state-

owned enterprise revenue instead of total revenue. In low-capacity (below the median) regimes,

the correlation between oil production per capita and state-owned enterprise revenue is -0.0603

with a p-value of 0.2357. In high capacity regimes, it is 0.8706 with a p-value of 0.0000.

12 In his important examination of many potential mechanisms linking oil to civil war,

Humphreys (2005) notes (p. 521) that state capacity might mediate the relationship between oil

and conflict, but he does not discuss why this might be. He also presents this as an alternative

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mechanism to provide the link between oil and civil war, the state must be essentially absent

from relevant parts of the country, unable to gain control over the natural resources or to prevent

direct interference by other governments or countries. Harkening back to the quotation from

Tilly above, the state must not “control resources…within the government’s territory” (Tilly

2003, 41). By contrast, in the asset specificity mechanism linking oil to regime stability, it is the

state itself that is receiving the funding, by virtue of its ability to control and gain revenue from

the oil production. Similarly, while the rentier argument linking oil to regime stability holds that

states distribute the benefits of oil resources to satisfy the necessary groups, the grievance

argument linking oil to civil war argues that this is exactly what the state is doing poorly, so that

the risk of civil war increases.

The comparison at the beginning of this paper between Mexico in 1985 and Congo in

1997 illustrates the different dynamics that oil causes in states with different levels of capacity.

Over decades of rule in Mexico, the PRI had created what Mario Vargas Llosa once referred to

as the “perfect dictatorship,” because the party gave the impression of democracy due to its

institutionalized changing of leaders every six years, while never losing office as a party.

Through an extensive apparatus that stretched throughout the country, the PRI largely

maintained power by selectively rewarding and punishing citizens through the use of federal

funding (Diaz-Cayeros, Magaloni, and Weingast 2006; Greene 2007). Not surprisingly, oil in

hypothesis to the funding and grievance mechanisms, rather than integral to them as I argue here.

In his empirical evaluation of several hypotheses, he presents evidence supporting the sort of

interaction effect argued here, but we differ in the measures of state capacity used as well as the

subjection of the results to robustness checks such as fixed effects and instrumental variables

analysis.

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this context had an effect mainly through government finance—a rentier effect—in which it

funded social programs and other spending to deter unrest.

The regime in the Congo in 1997 was quite different (Clark 1997, 2002). When the

country had democratized in 1992, and the authoritarian leader Denis Sassou-Nguesso had lost

the election to Pascal Lissouba, it quickly became apparent how much of the previous

government’s capacity relied on the personal connections of Sassou-Nguesso.

“[I]t became clear that possession of Congo’s presidential palace did not

guarantee ownership over Sassou’s former networks of allies, patrons, and

clients. Lissouba had won the vote, but some senior…army officers remained

loyal to Sassou. Sassou also enjoyed warm relations with foreign allies, such as

France’s prime minister, Jacques Chiraq, Gabon president Omar Bongo

(married to Sassou’s daughter), and Angolan president Eduardo dos

Santos….Distrustful of the army and worried by Sassou’s defection, Lissouba

created a personal militia to bolster his rule.” (Englebert and Ron 2004, 65)

In this environment, with a government that had lost much of its ability to “control resources,

activities, and populations with the…territory” (Tilly 2003, 41), oil had both grievance and

funding effects. Important regions feared they would not benefit under the new regime, and

militias were able to use oil to access funding for their activities (Clark 1997; Englebert and Ron

2004; Ross 2004). Oil in this context pushed the country toward civil war.

In sum, the principal mechanisms linking oil to civil conflict assume weak state capacity,

and those that link oil to regime stability assume strong state capacity. Considering all of these

mechanisms together reveals a striking parallel between them, which I summarize in Table 1.

The causal mechanisms all focus on one of two aspects of oil production, and how those aspects

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translate into regime stability or conflict. The rentier and grievance mechanisms center on the

fact that oil produces vast amounts of money: in the rentier scenario, the state is able to distribute

those resources effectively to stay in power for exceptionally long periods of time; in the

grievance scenario, in contrast, the state is unable to control the resources or distribute them

effectively, generating unrest. The funding and asset specificity mechanisms center on a

different aspect of oil production: the fact that it is immobile. In the funding scenario, oil’s fixed

nature gives rebels a way to raise money by exploiting the owners of those resources. In the

asset specificity scenario, by contrast, it is the state that can exploit the owners.

This table summarizes the novel theoretical insight of this paper. It indicates that the

differing predictions regarding the effects of oil on civil war onset and regime stability are not

due to the literatures focusing on different aspects of oil production. In fact, they have focused

on the same aspects and come to very different conclusions about their effects on political

stability. Instead, the differences reflect variations in the assumptions about state capacity in oil-

producing countries. In sum, the argument I have made results in two hypotheses:

H1: Higher oil income should lead to a higher probability of civil war in states

with weak state capacity but not in states with strong state capacity.

H2: Higher oil income should diminish the probability of a regime transition in

states with strong state capacity but not in states with weak state capacity.

The next section turns to the empirical analysis of these hypotheses.

III. Empirical approach and results

In order to test the hypotheses developed in the previous section, my goal is to analyze

statistically the effects of oil on civil conflict and regime stability, conditional on state capacity,

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for all countries and years for which the necessary data are available between 1960 and 2000. In

the regressions that follow, the two dependent variables examined are, of course, civil war onset

and political regime stability.13 The data on civil war onset were collected by Fearon and Laitin

(2003, 76), and civil wars were coded as occurring when:

(1) They involved fighting between agents of (or claimants to) a state and

organized, nonstate groups who sought either to take control of a government, to

take power in a region, or to use violence to change government policies. (2) The

conflict killed at least 1,000 over its course, with a yearly average of at least 100.

(3) At least 100 were killed on both sides (including civilians attacked by rebels).

The last condition is intended to rule out massacres where there is no organized or

effective opposition.14

Civil war onset is simply measured as a dichotomous variable that equals “1” in the first year of

a civil war and zero otherwise.

The data on political regime stability come from Przeworski and his colleagues (2000),

updated by Cheibub, Gandhi, and Vreeland (2010) who code all countries as either democracies

or dictatorships (that is, a dichotomous coding). Specifically, a regime is coded as democratic if

the chief executive is elected, the legislature is elected, there is more than one party, and

13 These phenomena have a negative correlation, as discussed above, but they are certainly

separable phenomena. Many regime transitions happen without civil wars, and vice versa.

14 The authors note that their criteria are broadly similar to the Correlates of War project (Doyle

and Sambanis 2000) and several others.

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incumbents lose elections. If all of these characteristics are not present, the regime is a

dictatorship. A regime change is simply a change from one type of regime to the other.15

There are two key independent variables. The first is oil income per capita. I employ the

per capita value of oil production in a country-year, using the most complete data of which I

know, those collected by Ross (2008) using data on oil prices from the BP Statistical Review and

data on oil production from the World Bank and US Geological Survey Mineral Yearbooks.16

The second key independent variable is state capacity, and it presents the largest

challenge to the empirical analysis in this paper. Scholars have used a variety of measures for

state capacity, and all of them are imperfect. It is not my goal here to improve upon these

measures, but rather to show that the results do not change in important ways if one uses a

particular measure as opposed to another. I therefore test the robustness of the results to several

different measures of state capacity widely used in the literature. To the extent that the measures

of capacity in the literature are flawed, one should not treat the empirical results here as

conclusive. Nevertheless, the robustness of the results to several of these measures will

hopefully be considered strongly suggestive.

15 Since the theoretical literature on oil and regime stability is about transitions between

democracies and dictatorships (for example, Dunning 2008; Ross 2001), this indicator accurately

captures the concept of interest (Cheibub, Gandhi, and Vreeland 2010). However, this

dichotomous variable does miss more “fine-grained” instability, and it is helpful to know if the

argument here applies to such instability. As such, below I show that the regime change results

are robust to the use of the Polity IV measure of regime change (Marshall and Jaggers 2003),

which measures a change of three or more on Polity’s 21-point measure of democracy/autocracy.

16 The value is calculated in constant (2000) $US.

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I believe the measure that best captures the concept of state capacity used in this paper is

telephone lines per 100 people. This directly reflects “infrastructural power,” which Mann has

used to address the “capacity of the state…to implement logistically political decisions

throughout the realm” (Mann 1986a, 113).17 Using an analogy from Alice in Wonderland, Mann

writes, “This [capacity] was comparatively weak in the historical societies…; once you were out

of sight of the Red Queen, she had difficulty in getting at you. But it is powerfully developed in

all industrial societies…. [F]rom Alaska to Florida, from the Shetlands to Cornwall, there is no

hiding place from the infrastructural reach of the modern state” (Mann 1986a, 113-4). It is in

exactly these latter societies that I would expect oil to have a stabilizing effect on political

regimes, as states are able to control revenue from the oil and distribute it to the necessary

groups. By contrast, as I have discussed above, there are many states in developing countries

that do not have this capacity, and it is in these states that I would expect the presence of oil to

spur civil conflict. Support for this measure of state capacity is gained from the fact that, as

reported earlier, oil production is significantly correlated with government revenue in states with

telephone lines per 100 people above the median, but not in states below the median. Data for

this variable come from the World Bank’s World Development Indicators.

With the key independent variables in hand, it is useful to consider whether there may be

a relationship between oil production and state capacity, as a strong relationship might lead to a

collinearity problem in the statistical analyses below. There is disagreement in the literature

about what the nature of that relationship might be. Some scholars have suggested that oil-

17 While indicators of other types of infrastructure (such as roads) were considered, data on

telephone lines is available for a longer time period and greater cross-section of countries than

any other.

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producing states may systematically have lower state capacity (e.g. Karl 1997; Fearon and Laitin

2003), while other scholars have argued that oil-producing states might systematically have

higher state capacity (e.g. Smith 2004; Thies 2010). Still others have argued that there is no

relationship between state capacity and oil (e.g. Jones Luong and Weinthal 2010). The minimal

correlation between my state capacity variable and oil income per capita (-0.02) does not suggest

a relationship strong enough to cause problems.18

In order to examine the effect of oil on civil conflict and regime change in the context of

varying institutional environments, I include in my regressions an interaction term between oil

and telephone lines per 100 people. The standard set-up of the statistical model (in matrix

notation) is

Yi,t = OILi,t-1β1 + OILi,t-1*CAP,t-1 β2 + CAPi,t-1β3 + Xi,t-1β4 + εi,t ,

where Yi,t is the dependent variable, which varies both by country i and year t, OIL is the key

independent variable, CAP is the measure of state capacity, X is a matrix of control variables

(including an intercept), and ε is an error term. Since both sets of regressions have dichotomous

dependent variables, all of the regressions are performed using logistic analysis with errors

clustered by country.19

In order to facilitate understanding of the results, it is useful to address briefly the

interpretation of interaction terms. As various authors have discussed, regression models such as

18 The correlation between oil income per capita and my other state capacity variables (discussed

below) are as follows: GDP per capita 0.56; income tax/GDP -0.02; bureaucratic quality 0.04.

The correlation between telephone lines per 100 people and my instrument for oil (discussed

below) is -0.10.

19 The regressions were performed using Stata.

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these examine conditional hypotheses, and therefore the coefficients and standard errors (and by

implication statistical significance) of interacted variables must be calculated using both the

coefficient of a variable by itself and the coefficient of the interaction term including that

variable (Braumoeller 2004; Brambor, Clark, and Golder 2006; Kam and Franzese 2007). For

example, regressions in this paper will be examining the effect of oil conditional on a certain

level of state strength. The significance or lack thereof of oil in different contexts of state

strength cannot be read easily from the initial regression results. For example, if the interaction

term is insignificant, it does not mean that oil’s effect remains constant across all levels of

institutional strength. To simplify interpretation, two sets of regression results will be presented

for each dependent variable: one with telephone lines per 100 people centered at its 20th

percentile value, and one with telephone lines per 100 people centered at its 80th percentile

value.20 These adjustments are necessary for my purposes, since I am interested in the effect of

oil in the context of weak and strong capacity. Respectively, the coefficient on oil by itself—

β1—in these regressions will represent the effect of oil when the measure of state strength is low

(specifically, at the 20th percentile value) and high (80th percentile), echoing the hypotheses

outlined above (H1 and H2).21

20 To give an example, I subtract the 20th percentile value of telephone lines per 100 people from

all observations of telephone lines per 100 people. This results in “zero” being equal to the 20th

percentile value of telephone lines per 100 people.

21 If this adjustment were not made, the coefficient on the oil variable would represent the effect

of oil when the capacity variable was at zero, which is an unobserved (and therefore rather

meaningless) value for most capacity variables. For example, using telephone lines per 100

20

Civil conflict

In the multivariate analysis of the determinants of civil conflict—in which the dependent

variable is coded as a one if there is conflict and zero if not—I include a standard set of controls

to avoid omitted variable bias. I first control for whether or not there was a distinct civil war in

the previous year (that is, different from that captured by the dependent variable) and whether

there were any regime changes in the previous three years (instability), each of which affect the

underlying tendency toward unrest in a state.22 Second, I control for a country’s GDP growth

and GDP per capita, as richer countries are generally thought to be less prone to civil war.

Third, I control for the level of population, as larger populations may be more difficult for a

central government to control and may also provide more potential recruits to rebels. Fourth, I

control for the proportion of a country that is mountainous, as rough terrain should favor

insurgency and civil war. For all of these I use the data from Fearon and Laitin (2003), who

discuss the relevant literature and sources.23

Table 2 presents the results. As mentioned above, the first column presents the results

with telephone lines per 100 people centered at its 20th percentile, and the second column

people as the capacity variable, the coefficient on the oil variable would represent the effect of

oil in a country where there exist no telephone lines.

22 As the reader will see, in the subsequent regressions on regime change, I include a variable

denoting whether the onset of a civil war occurred in the previous year. These are included so

that the results for oil’s effect on, for example, civil war, capture its effect net of any effects it

might have on regime stability. The results do not change if these variables are excluded.

23 These data are also used in, among others, Humphreys (2005), Cederman and Girardin (2007),

and Fearon, Kisara, and Laitin (2007).

21

presents the results with that variable centered at its 80th percentile. Because of the interaction

term between this variable and oil, the coefficient on oil income per capita is the effect of oil

when telephone lines per 100 people is at the relevant percentile. Because this is a simple linear

transformation of the variable, the only coefficient (and significance level) that changes in the

regressions is the value for oil income per capita, with which telephone lines is interacted. In the

“low capacity” regression, the coefficient on oil income per capita by itself is significant and

positive. The interaction term, however, is negatively signed, and accordingly the effect of oil

weakens as states become stronger, eventually becoming statistically insignificant. This can be

seen in the “high capacity” regression, at which telephone lines per 100 people is centered at its

80th percentile.

In addition to the variables listed in Table 2, I checked the robustness of the results to the

inclusion of a variety of other variables, including decade dummies, year dummies, and country

dummies.24 I also experimented with the inclusion of ethnolinguistic fractionalization, a Middle

East dummy variable, military spending, a country’s neighbors’ median polity score, different

sources of nontax revenue, a country’s time at peace since the last civil war, whether or not the

country was a new state or an inconsistent polity, whether or not the country had a neighbor at

war, and whether the year occurred during the Cold War.25 In addition, to account for the

possible endogeneity of oil to civil conflict, I employed instrumental variables analysis, using the

world price of oil (lagged one period) as the instrument, as this is the most commonly used

instrument for oil revenue in the literature, having been employed to study both civil war and

regime transition (Collier and Hoeffler 2009; Brückner and Ciccone 2010; Dube and Vargas

24 With country effects, the slowly changing variable of percent mountainous was excluded.

25 Many of these variables come from the dataset of Hegre and Sambanis (2006).

22

2010).26 Details and results of this analysis are in an on-line appendix. None of these robustness

checks had an effect on the substantive results.

In addition, I tested the robustness of the results to three other measures of state capacity

used in the literature. The first of these variables was GDP per capita (in log form), which

Fearon and Laitin use as “a proxy for a state’s overall financial, administrative, police, and

military capabilities” (Fearon and Laitin 2003, 80). Higher income, they say, “will mark more

developed countries with terrain more ‘disciplined’ by roads and rural society more penetrated

by central administration” (Fearon and Laitin 2003, 80). The second variable was income tax as

a share of GDP, as a large literature has examined the “extractive capacity” of the state as an

indicator of state strength, and Lieberman argues in his review of this literature that direct taxes

like income tax are better measures of extractive capacity than indirect taxes (Lieberman 2002).

Data for these two variables came from the World Bank’s World Development Indicators.27 The

third variable was bureaucratic quality, from the Political Risk Services Group’s International

Country Risk Guide. This measure uses expert surveys to capture the degree to which the

country’s bureaucracy is characterized by “(1) regular meritocratic recruitment and advancement

processes, (2) insulation from political pressure, and (3) the ability to provide services during

26 Until relatively recently, no scholar used instrumental variables analysis to study these

relationships.

27 The data for income tax as a share of GDP are the IMF’s previous coding of it (International

Monetary Fund (IMF) 1986), available 1973-2001.

23

government changes.”28 The results with all of these measures were substantively similar to the

result with telephone lines per 100 people.29

Regime change

Like the regressions analyzing civil war onset, the regressions analyzing the determinants

of regime change—in which the dependent variable is coded as a one if there is a change and

zero if not—include important covariates drawn from recent relevant work (e.g. Smith 2004), in

order to avoid omitted variable bias. To account for the underlying instability in the regime, I

include the number of previous regime changes in the sample for a given country-year (past

changes), the age of the regime, and whether there was a civil war in the previous year.30 I also

account for GDP per capita, growth in GDP per capita, the urban population growth rate, and

population density. These four variables come from the World Bank’s World Development

Indicators. Finally, to account for “waves” of regime transitions (Huntington 1991), I include

Przeworski and his colleagues’ (2000) annual measure of the percent of the world’s countries

that are democratic.

28 The quotation is from Hendrix (2010), citing Knack (2001). These data are also used by

DeRouen and Sobek (2004), among others. They are available starting in 1984.

29 Similar results were also attained using a state capacity variable created by factor analysis

(Skrondal and Rabe-Hesketh 2004) of telephone lines per 100 people, GDP per capita, and

income tax as a share of GDP.

30 The use of cubic splines of the age of the regime, as recommended by Beck, Katz, and Tucker

(1998), made little difference (an F-test of their joint significance failed to reject the null

hypothesis), so I used the simpler operationalization instead.

24

As with Table 2, Table 3 presents two regressions, the first with telephone lines per 100

people centered at its 20th percentile and the second with that variable centered at its 80th

percentile. Oil does not have a significant stabilizing effect in the “low capacity” regression.

However, the interaction term is negatively signed, indicating that oil’s stabilizing effect

becomes stronger for political regimes when states have higher institutional capacity. This can

be seen in the “high capacity” regression, where oil’s effect is statistically significant. The

results again support the framework advanced above: oil’s effect in stabilizing political regimes

only holds in the context of strong institutional capacities.

In addition to the variables listed in Table 3, I explored the robustness of the results in

five different ways. First I ran the regressions including decade dummies, year dummies,

and country dummies. 31 Second, I experimented with the inclusion of variables for

ethnolinguistic fractionalization, a Middle East dummy variable, other sources of nontax

revenue, and the Cold War. Third, to account for the possible endogeneity of oil to civil conflict,

I employed instrumental variables analysis, using the world price of oil (lagged one period) as

the instrument, as this is the most commonly used instrument for oil revenue in the literature,

having been employed to study both civil war and regime transition (Collier and Hoeffler 2009;

Brückner and Ciccone 2010; Dube and Vargas 2010).32 Details and results of this analysis are in

an on-line appendix. Fourth, I employed different measures of state capacity, as with the civil

war regressions above, using GDP per capita, income tax as a share of GDP, bureaucratic

31 With country dummies, the slowly changing variable of past regime changes was excluded, as

fixed effects are highly correlated with slowly changing variables.

32 Until relatively recently, no scholar used instrumental variables analysis to study these

relationships.

25

quality, and the state capacity variable generated by factor analysis (see footnote 29). Fifth, I

tested whether it made a difference to use Polity IV’s measure of regime change instead of that

of Przeworski and his co-authors (2000).33 The results were robust to all of these alterations.

Finally, in order to test my interpretation of Boix’s work, discussed earlier, I ran the regression in

Table 3 with an interaction term between oil and regime type (as well as regime type on its own).

Oil significantly stabilized both democracies and dictatorships in the context of strong state

capacity and did not stabilize either regime type in the context of weak state capacity.

V. Conclusion

Ross (2001, 325) once said that “political scientists believe that oil has very odd

properties.” And the two principal findings in the literature regarding oil’s political effects—that

it leads to increased civil conflict and enhanced regime stability—seem very odd indeed when

placed beside one another. As discussed above, these outcomes are negatively correlated with

one another at a statistically significant level when controlling for other relevant factors. How

can oil simultaneously lead to instability and stability?

I have argued that the answer to this puzzle does not lie in the possibility that these

literatures have focused on different aspects of oil—in fact, they have focused on exactly the

same aspects of oil (Table 1). Rather, the answer lies in the capacity that states have to control

and distribute proceeds from oil. While scholars linking oil to civil war have focused on

grievance and funding mechanisms, they have not realized how integral the lack of state capacity

is to these mechanisms. Similarly, while scholars linking oil to regime stability have focused on

the rentier and asset specificity mechanisms, they have not realized how integral the presence of

state capacity is to these mechanisms. Synthesizing the two literatures, I have hypothesized that

33 See Footnote 15.

26

oil should lead to civil war in weak states but not strong ones, and that oil should lead to regime

stability in strong states but not weak ones.

A variety of empirical operationalizations have supported my theoretical approach.

Using several different measures of state capacity widely employed in the literature, I have

shown that oil’s tendency to spur civil conflict disappears in the context of strong capacity, and

that oil’s tendency to stabilize dictatorships and democracies disappears in the context of weak

capacity. I have also shown that these results are robust to a variety of different specifications,

including country fixed effects and instrumental variables analysis.

The findings here have important implications for at least four bodies of theoretical work.

The first is the literature on state capacity. As noted in the introduction, work on state capacity

has largely focused on its determinants, perhaps because its effects are taken for granted. As this

paper has shown, however, theories that do not explicitly address state capacity may in fact be

assuming some level of it. It may be that the kinds of interaction effects demonstrated in this

paper hold for other kinds of relationships as well. Any theory whose key independent variable

requires some sort of complex action on the part of the state—be it taxation, redistribution, co-

optation, or something else—is probably assuming at least some minimum level of state

capacity. A re-examination of other theoretical works may usefully refine the domain under

which they apply.

The second and third bodies of work are those on civil war and regime stability. While

some scholars have linked the occurrence of these two phenomena (e.g. Snyder 2000), few

works have simultaneously addressed how one independent variable affects them both. This

paper suggests there may be some returns to this approach. For example, economic growth has

been linked to the probabilities of civil war and regime instability, but predictions often vary.

27

Growth may lead to more or less civil war, and growth may stabilize or (as the modernization

hypothesis holds) destabilize political regimes. Similar to this paper, future research might

explore whether the relationship between growth and these outcomes depends on state capacity.

Finally, of course, there is the body of work on oil, which has spiked in recent years

along with oil prices. This paper has both empirical and theoretical implications for this

literature. On the theoretical side, the paper suggests there may be more to learn by

systematically comparing the surprisingly disconnected literatures relating oil to civil war and

regime stability. And on the empirical side, the theoretical approach suggests important

modifications to analysis in these two areas of research. For example, there have recently

appeared several sophisticated empirical papers debating the existence of rentier effects in oil-

rich authoritarian regimes (e.g. Andersen and Ross 2011; Haber and Menaldo 2011). While

these papers test this relationship in all countries, the theoretical analysis here has suggested that

there is no reason in the literature to expect oil to have rentier effects in weak states. This would

mean that these empirical works are testing the theory in an improper domain. Similar

implications would hold for empirical work on civil wars. Certainly, despite the voluminous

literature on oil and politics, there is still much to learn about this relationship.

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34

Table 1: A framework for understanding the current literature on oil, conflict, and stability

Aspect of oil Effect in weak state Effect in strong state

Generates

lots of

money

Grievance. Oil generates civil

conflict by creating grievances

among groups who feel they are

not benefitting enough (e.g.

Fearon and Laitin 2003)

Rentier. Oil generates regime stability by

giving the state the ability to satisfy necessary

groups in society with various kinds of

spending including repression (e.g. Ross

2001)

Fixed in the

ground

Funding. Oil generates civil

conflict by giving rebels the

opportunity to fund themselves

by exploiting resource owners

who cannot leave (e.g. Collier

and Hoeffler 2000)

Asset specificity. Oil’s fixed nature heightens

the redistributional stakes inherent in the

choice of political regime, leading to greater

regime stability because those in power invest

more effort in defending status quo (Boix

2003)

35

Table 2: Oil's effect on civil war onset Dependent variable: Civil war (1) or not (0)

Independent variables Low capacity High capacity Oil income per capita (t-1) 0.000241** -0.000761 (0.000112) (0.00140) Telephone lines per 100 (t-1) -0.0620 -0.0620 (0.116) (0.116) Oil*telephone lines (t-1) -3.85e-05 -3.85e-05 (5.61e-05) (5.61e-05) Civil war (t-1) -0.753* -0.753* (0.411) (0.411) Population (ln, t-1) 0.602*** 0.602*** (0.148) (0.148) GDP per capita (ln, t-1) -0.201 -0.201 (0.240) (0.240) GDP growth -0.0709*** -0.0709*** (0.0192) (0.0192) Percent mountainous (ln) 0.178 0.178 (0.171) (0.171) Instability 1.498*** 1.498*** (0.389) (0.389) Constant -10.28*** -11.89*** (1.704) (2.959) Log pseudolikelihood -92.274 Observations 1751 Countries 118

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

Note: In the “low capacity” regression, telephone lines per 100 people is centered at its 20th percentile value. In the “high capacity” regression, telephone lines per 100 people is centered at its 80th percentile value.

36

Table 3: Oil's effect on regime change

Dependent variable: Regime change (1) or not (0)

Independent variables Low capacity High capacity Oil income per capita (t-1) -0.000730 -0.0203** (0.000926) (0.0101) Telephone lines per 100 (t-1) -0.198** -0.198** (0.0795) (0.0795) Oil*telephone lines (t-1) -0.000708* -0.000708* (0.000389) (0.000389) Past changes 0.302*** 0.302*** (0.101) (0.101) GDP growth -0.0749*** -0.0749*** (0.0180) (0.0180) GDP per capita (ln, t-1) 0.330* 0.330* (0.182) (0.182) Urban pop. growth (t-1) -0.143* -0.143* (0.0816) (0.0816) Population density (t-1) 0.000370 0.000370 (0.000350) (0.000350) Percent democracy, world -2.161 -2.161 (1.835) (1.835) Age of regime -0.00459 -0.00459 (0.00880) (0.00880) Civil war onset (t-1) -0.301 -0.301 (0.289) (0.289) Constant -2.114** -7.583*** (1.036) (2.702) Log pseudolikelihood -147.636 Observations 1664 Countries 116

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10

Note: In the “low capacity” regression, telephone lines per 100 people is centered at its 20th percentile value. In the “high capacity” regression, telephone lines per 100 people is centered at its 80th percentile value.