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Oil, Conflict, and Stability
Kevin M. Morrison Assistant Professor
Graduate School of Public and International Affairs University of Pittsburgh
Email: [email protected]
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.
2
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.
3
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).
4
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).
5
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.
6
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
7
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).
8
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).
9
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
10
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).
11
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
12
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.
13
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
14
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,
15
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.
16
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.
17
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.
18
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.
19
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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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.