Measuring the Resource Curse: Revisiting the Politics of Oil Wealth1

Benjamin Smith Associate Professor of Political Science

University of Florida Box 117325 Anderson Hall Gainesville, FL 32611 USA

bbsmith@ufl.edu

Abstract The last decade has seen the study of the “resource curse” grow from a niche question to a major research agenda with multiple outcomes of interest—regime type, regime stability, civil conflict and economic growth to take a few. However, the proliferation of different measurement choices without careful consideration of their fit with current concepts and theories has hamstrung the potential for real knowledge accumulation. In this essay I review the two main theorized mechanisms linking fuel wealth and politics—weak institutions and rent patronage—and suggest focusing on just the latter. I also outline the bases for and problems with current measures. The essay then presents a new measure—rent leverage—to capture the share of individuals’ relative buying power that accrues directly from fuel rents. This measure is both conceptually closer to current theory and in accord with current best practices in development economics. Initial analysis of cross-national data from 1960 to 2009 suggests no systematic relationship between fuel wealth and regime stability. This in turn suggests that prior measures may have been strongly biasing results against developing countries by failing to account for purchasing power parity effects.

DRAFT: Please do not cite or quote without contacting the author first. Please do send comments.

1 Thanks to Graeme Blair, Victor Menaldo, Brian Min, Michael Ross and participants in the “How Autocracies Work” conference, April 15-16, 2011, at the Weiser Center for Emerging Democracies at the University of Michigan for thoughtful comments on previous versions of the essay.

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Introduction

In the late 20th century, oil wealth was found to have a strong positive effect on the

durability of political regimes. This held regardless of regime type (Smith 2004) and appeared to be

true for lower-level manifestations of stability as well--anti-regime collective action and civil conflict.

The last of these stood in contrast with the then-growing greed thesis (Collier et al various years)

positing that resource wealth made one form of instability--civil war—significantly more likely. The

findings on this topic generally rested on a common measure of oil wealth--the value of fuel exports

as a share of a country's gross domestic product (GDP). Subsequent research largely confirmed the

stability-inducing effects of oil wealth (Morrison 2009), but at the same time accumulated evidence

that while oil enhanced the tenure of rulers it also made civil war more likely. As Kevin Morrison

(2012) has noted, these are contradictory findings—it seems difficult to imagine how oil wealth

could simultaneously enhance stability on one dimension while undercutting it in another.2 In the

last decade, however, there have emerged a number of lines of argument aimed at explaining the

variant effects of resource wealth. They hinge on the starting assumption that the effects of oil rents,

like any other revenue source, are likely to be conditional and therefore shaped by other factors (see

for example Dunning 2005, 2008; Jones Luong and Weinthal 2006, 2010; Lowi 2010; Morrison

2012; Smith 2006, 2007).

This essay engages this seeming paradox, building on the initial observation that it seems

strange oil would cause both stability and instability. It does so, first, by introducing a new

comparatively and conceptually better measure for fuel wealth and, second, by bringing the time

period as close to the present as is currently possible. There are a number of reasons for revisiting

those initial findings. First, there are now a sizeable number of new oil exporters in the world,

2 This is different than simply stating that some oil producers are stable and some are not, which implies no necessary relationship between oil and stability. Morrison argues, and I concur, that oil in fact can shape both.

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raising the likelihood that a more disparate group no longer dominated by Middle East exporters

might reveal different dynamics.3 Second, the now 20-year period of independence for resource-rich

former Soviet republics (Azerbaijan, Kazakhstan, Russia, Turkmenistan, Ukraine and Uzbekistan)

provides a long enough time series to begin to explore the dynamics of oil wealth in those states.

Third, the emergence of two major new oil producing regions in the developing world--West Africa

and the Andes--provides two other sets of cases with which to explore the question of how resource

wealth manifests politically. Fourth, the events of the Arab Spring of 2011 appear to have singled

out oil exporting countries in the Middle East and North Africa, even as many of their oil-rich as

well as oil-poor counterparts seemed to sail through that tumultuous period politically unscathed.4

Finally, scholars of the "resource curse" have refined measures and theories in the last five years,

making it possible to conduct aggregate analyses with measures that are much closer to the concepts

undergirding the theories themselves.

With all this in mind, I endeavor in this essay to build on the progress that scholars of the

resource curse have made in the last decade or so, with an eye to conducting some initial empirical

forays into the links between oil and political stability and to exploring the linking mechanisms that

appear to connect the two. To be clear, in this essay I do not engage the issue of oil’s impact on

regime type, merely its impact on the likelihood that regimes will come to a variety of different ends.

As I discuss below, I also proceed without any assumption that the ends of autocracies and

democracies are likely to be caused by the same factors. Rather, I proceed inductively, asking

whether it is the case and then seeing whether the results suggest it is or not. In an era characterized

3 A number of works have highlighted the paucity of regime transitions in the Middle East even as they proliferated elsewhere. See Brownlee (2002). 4 Egypt, Libya, Tunisia, and Yemen were all oil exporters as of 2009, as measured by fuel rents per capita of at least US$100. See Ross (2012), 20. However, note that with the exception of Libya, these were not the ultra-rich exporting states in which rulers could leverage more than $2000 per citizen in oil rents. I discuss the concept and measurement of “rent leverage” more below.

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by large numbers of hybrid regimes employing elements of both classic types, it is to my mind a

better starting point to proceed without preconceptions.

I explore these dynamics using data from all countries in the world between 1960 and 2009.

Using a measure that accounts for the relative political weight of oil production value in different

countries, I ask whether the rent leverage that rulers can deploy increases or decreases their long-term

durability in power. This measure, as I describe below, brings the measurement of rentierism and

resource wealth into line with best practices in development economics, in particular with the theory

of purchasing power parity (PPP). I then employ subsets of it to gain closer purchase on the Arab

spring: first, using a sample drawn from the Arab world and then including just oil producing

nations.5 These two sets of analyses are plausibility probes, aimed at situating the regime changes in

Egypt, Libya, Tunisia and Yemen in both a set of Arab nations prone to diffusion effects and in a

set of oil producers with arguably similar oil-shaped polities

To foreshadow what I find below, once we account for the relative importance of fuel

wealth in rich versus poor countries, there appears to be no systematic effect on regime stability.

This finding is robust, and in stark contrast with measures either of fuel income per capita or of a

straight measure of fuel income as a share of GDP per capita. And this conclusion appears to be a

function of introducing a measure that accounts for the bias inherent in prior per capita measures.

Employing a new measure of rent leverage, adjusted to take account of purchasing power parity, I

suggest that past results may have been driven by improper measurements in a way that exaggerated

the effects of oil, particularly in developing countries.6 This is, to be clear, an initial exploratory

probe, and among other things it should not be taken as holding firm implications for links between

fuel wealth and either democracy or civil conflict.

5 For both replicability and accumulation reasons I use the same cutoff as in Ross (2012, 20): whether a country in 2009 received at least US$100 in oil and gas rents in selecting into the “oil producer” sample. 6 I hope it will be clear here that this is not an exercise in outward finger-pointing since my own work (2004, 2007) fell guilty to this misstep.

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Oil and Stability: Concepts and Theories

There are two main conceptual approaches to thinking about how oil wealth shapes politics.

The first is more structural, and centers on the long-term impact of oil revenues on state institutions

and broader issues of state capacity. This line of argument suggests that oil export dependence

produces a variety of durable political-institutional effects that are partially or largely out of the

hands of political leaders. The second is more agency-oriented, and has to do with what we

hypothesize oil revenues allow rulers to do vis-à-vis their citizens: in other words what leverage

those revenues provide to rulers that they would not have otherwise to shape their citizens’

decisions and actions. These two conceptual tracks both stem from oil export dependence, but the

time frames and measurement implications are different. The structural-institutional hypothesis

suggests a longer-term time horizon for the effects to manifest. Ross (2001) found five-year lagged

oil effects on regime type; Smith (2007) found effects manifesting as long as two decades later in

analyzing the impact of oil wealth on regime durability. The agency or leader hypothesis, on the

other hand, is shorter; basically it leads us to suspect nearly immediate effects. Moreover, these

effects can run in both regime-stabilizing and destabilizing directions, since broad rent distribution

(resembling public goods provision) might buy acquiescence while narrower exclusive distribution

(closer to private goods) might provoke revolt by those left out. This dynamic is intuitively and

empirically plausible, especially when we look back at how the Gulf monarchies in 2011 made

massive spending promises to short-circuit popular uprisings. They, of course, could bring to bear

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many times the rent leverage that their less rent-rich counterparts in countries such as Egypt, Syria

and Tunisia could muster.7 I return to this point below.

Common current measures

How do scholars measure oil wealth or rentierism? The answer is unfortunately disparate. In

reviewing econometric work on the resource curse over the last decade or so I tracked down at least

a dozen different means of measuring the effect of oil income as an independent variable in

quantitative studies (see Table 1). Looking across the field of resource wealth politics, one is struck

by the large number of measures for what seems to be a fairly straightforward concept. There are a

number of dummy variables used as proxies for rentierism: OPEC membership (Fish 2002, 2005),

dependence on oil exports for more than half of total exports (Gandhi and Przeworski 2007, 1285;

2006). These dummies have multiple, easily solvable, problems. The first is that today there are more

non-OPEC major exporters than OPEC members, leaving out many of the producing countries

arbitrarily. Furthermore, there is no good reason to believe that OPEC exporters are inherently

different than their more numerous exporting counterparts. Another is that there is no good

theoretical rationale for supposing that at 49.9% and below oil revenues are politically irrelevant or

that conversely the effects manifest at 50.1%. Given the ease of acquiring continuous data, it is at

best a questionable choice to employ dummy variables.

Yet even some continuous measures are problematic too. One common strategy—taking oil

exports’ share of total exports (see Jensen and Wantchekon 2004)—leaves us wondering how to

assess the importance of exports in a country’s economy. Two countries in which oil exports

comprised 50% of total exports might respectively depend on exports for 80% of GDP (in a small

country with limited domestic market, for example) and only 20% of GDP (a larger, more

7 Among others Brownlee et al (forthcoming) argue that Libya’s trajectory is fundamentally a function of external intervention.

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prosperous country with a substantial domestic market). And of course the issue of export

diversification is in substantial part a policy choice, raising the question of reverse causality or

endogeneity (see below).

Table 1 about here

Another such measure, as mentioned above the standard for several prominent works, is oil

export revenues as a share of GDP (Morrison 2009; Ross 2001; Smith 2004, 2007). This measure at

least has the virtue of capturing the relative importance of export earnings in the economy writ large.

Yet it misses two things: how much patronage or coercive boost it gives rulers vis-à-vis each citizen

and how much revenue is derived from oil that is consumed at home (see also Ross 2012, 14-17).

Both of these are important questions. The former question is crucial when we compare countries

like Nigeria—with 100 million citizens—with Equatorial Guinea, which has only 600,000. The

difference in what each regime can pay into affecting citizens’ political behavior, by whatever means,

is massive. The latter issue is equally important when it comes to thinking about countries—Russia,

Brazil and Indonesia among them—that by virtue of substantial non-oil sectors in diversified

economies consume a sizable portion of what they produce. I would point, too, to Tunisia and

Egypt, two countries that rarely are described as rentier states or even included in groups of “oil

states” because they export relatively little. The reason is that they consume much of what they

produce and import still more. Still a third problem with this measure is that it can be endogenous

to oil exports: poorer countries consume less of their oil and gas, thereby inflating the export figure,

and Dutch Disease effects can negatively affect non-oil sectors.

The sheer number of measures employed to try to capture oil wealth may be behind the

widely varying findings on its effects. For example, some authors conclude it makes civil war more

likely (Collier and Hoeffler 1995; Ross 2006; de Soysa and Neumayer 2007) while others conclude it

either has no effect or makes internal conflict less likely (Smith 2004). Some conclude it has a net

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positive impact on political stability (Smith 2004) while others (Karl 1999, 1997) find the opposite.

And while new studies highlighting the conditionality of oil’s impact are welcome innovations in our

theoretical understandings, contradictory findings continue to accumulate. At the very least, the use

of a standard measure closer to the concepts we wish to explore could make us confident that it is

not measurement differences producing such disparity in conclusions.

The original statements relating oil to stability came from classic rentier state theory

(Mahdavy 1971; Delacroix 1980; Beblawi and Luciani 1987; Karl 1997). The earliest versions

provided mostly inferential implications, suggesting on one hand that rents could substitute for

representation but on the other that the shallow consent engendered, when combined with the

volatility of the market for oil, might be destabilizing. These were the foundational points of

reference. Perhaps unsurprisingly, the research programs that grew from them—focused respectively

on regime maintenance and civil conflict—pointed in two different and seemingly contradictory

directions.8

Despite differing hypothetical expectations, both predictions of the resource curse relied on

two mechanisms—rentierism and weak institutions. The first postulated that it was the capacity of

rulers to rely on rents rather than on taxes that drove subsequent fiscal state-society relations and

allowed rulers to “substitute spending for statecraft” (Karl 1997). Whether for patronage or

coercion, the flexibility of spending made possible by fuel revenues remains a core insight of all

theories of oil and politics. The agent-centered variant of this thesis suggests that rulers’ freedom to

dole out fuel rents to important social recipients can prolong their rule, either by creating rent-

funded social contracts or by defusing crises through one-time payoffs. The second suggests that

fuel wealth weakens state institutions, making instability and civil conflict more likely (see among

others Fearon and Laitin 2003). In recent years this mechanism has come into serious doubt.

8 Here I address the extant literature only on the regime stability end of resource curse theory. I revisit the civil conflict variant in another paper currently underway.

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Brunnschweiler (2008) finds that the causal arrows run counter to it and that in fact it is weak

institutions that cause fuel export dependency, not the other way around. And, Ross (2012, chapter

6) and I (Smith 2012) have found in global and Southeast Asian samples, respectively, that fuel

wealth is positively related to institutional quality. These results come amid a host of other studies

that find no relationship between fuel wealth and stateness. As a result I focus exclusively below on

the agent-centered mechanism both in terms of measurement and of theory, suggesting in the

conclusion that it is the one standing mechanism with both theoretical logic and empirical support

behind it.

The stability theory of oil politics, based as it was on observing countries that were mostly

non-democracies, grew into a “yes” answer to the question, “Does Oil Hinder Democracy?” (Ross

2001). In that first exploration Ross suggested some partially confirming findings linking oil and

autocracy via stunted modernization, repression and rent patronage. Note that these are effectively

stability mechanisms: given a set of prior expectations that under ordinary conditions regimes might

become more democratic over time, oil allowed rulers to prevent such moves.

In work subsequent to that (Smith 2004, 2006, 2007) I confirmed that conclusion more

broadly, showing that the more visible episodes of dramatic political change in oil exporting

countries were outliers, but also finding that oil’s impact was conditional on the timing and

circumstances surrounding its entry into a political economy. Morrison (2009) found the same

stability-enhancing effect. We both found these effects accruing independently of the kind of

regime. Meanwhile Wantchekon and Jensen (2004) found a stabilizing effect for oil among the

autocracies of Africa. Tsui (2010) finds a long-term democracy-hindering effect for oil discoveries,

but one that is dependent on measurement choices.

Ulfelder (2007, 996) suggests that fundamentally different factors drive the destabilization of

autocracies and democracies, respectively. Blair (2012) suggests something similar, specific to oil

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exporters. These conclusions follows the seminal, and appropriate observation by O’Donnell and

Schmitter (1986, 18) that the factors that bring democracy are not the same ones that bring it down.

Blair (2011) finds suggestive evidence for this difference, concluding that oil’s effects morph

depending on where regimes sit on a continuum. Still, it remains quite plausible that political regimes

rise and fall in line with a baseline set of permissive or constraining factors and that oil wealth might

be one of them. On balance, these questions essentially remain open ones, both in terms of effects

and in terms of measurement choice. I turn to these in the next section.

The Resource Curse Revisited: Oil and Regime Durability, 1960-2009

Data, Methods and Models

In this section I present new data for all countries with populations of more than 1,000,000

between 1960 and 2009. It builds on publicly available data used in Ross (2012) as well as the World

Development Indicators and Net Savings data projects at the World Bank.9 I employ the data to

explore the relationship between various measures of oil wealth/dependence and regime durability.

Independent Variables: Measuring Oil Wealth

The original benchmark measure—oil export revenues over GDP—was simple and easy to

calculate from the World Development Indicators, and a better measure than early alternatives such

as oil exports as share of total exports or dummy variables for OPEC membership. This measure

was meaningful in the sense that it allowed us to see the relative importance played by the oil sector

in a large number of economies, and to infer that the dynamics would be stronger as a result. Yet it

9 Ross’s data are publicly available at http://dvn.iq.harvard.edu/dvn/dv/mlross. I would issue a note of collective gratitude for this gesture and wholeheartedly echo his thoughts on scholarly transparency (Ross 2012, 23). While I suggest an improvement to Ross’s measure of choice, it would be impossible without both his exhaustive data collection efforts and his willingness to push for such a high standard of transparency and professional generosity.

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contained a couple of major problems. First, it gave us no relative understanding of what that figure

meant for rulers and their relationships to individuals. Rulers in two countries with identical oil

export revenues and similar GDPs, but with very different population sizes, would have equally

different rent capacities to be employed for patronage or coercion (or modernization). Thus, this

measure masked differences in the rentierism capacity of states. Second, the measure left oil wealth

endogenous to the size of a country’s economy. Poorer countries looked more oil-rich simply

because their economies were less developed than others, not because they produced more oil.

Furthermore, recent research has suggested that oil dependence is itself endogenous to the quality of

political institutions. Brunnschweiler (2008) found that it is weak institutions—and subsequently

poor economic policy—that leads some countries to be more dependent on resource exports over

time. Third, the measure focused on oil export revenues distorted the revenue picture by neglecting

entirely the oil that is consumed domestically. This was arguably less problematic 30 years or more

ago when few oil exporting countries were highly developed: it is much less the case now, especially

when we think of two members of the “BRIC” group, Brazil and Russia, both of which are

substantial oil exporters.

In the last few years a new measure has become predominant: fuel rents per capita, which

takes the total value of oil and natural production (including domestic consumption) and divides it

by a country’s population. As noted in Ross (2012, 15-17), this measure gets us substantially closer

to the concept we aim to explore: the ability granted by fuel rents to allow rulers to shape their

relationship with society. Yet it too is imperfect: production capacity is of course endogenous to

politics and in particular to political stability, as the recent examples of Iraq, Libya and South Sudan

have highlighted so starkly.10

10 To take one causally complex example from Iraq, the ouster of Saddam Hussain’s Ba’ath Party regime during the U.S.-British invasion in 2003 led to a regime change that among other things threw Arab-Kurdish relations into disarray. One offshoot of that was a substantial drop in production (fuel rents per capita) in Iraqi Kurdistan for some years, followed

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It is also the case, the more that natural gas has assumed a central role in the global

economy, its lack of a world market price leaves some difficult measurement questions to resolve.

Unlike oil, which is easy to transport and whose different spot prices are closely related, natural gas

is largely transported through pipelines, whose owners and transit states have a kind of lock on

supply that is not the case with oil. As a result, there is wide variation across regions. Second, natural

gas production (annualized) is converted from volume to British Thermal Units (BTUs) and

multiplied either by a standard price OR by the market price in which that country’s natural gas is

mostly sold.11 Haber and Menaldo (2011), by virtue of compiling data going back more than a

century and prioritizing historical time-series breadth, used the Well Head Price for Natural Gas in

the United States, extending from 1922 to 2008. This price statistic is the most temporally complete

available. Another consistent price is the Henry Hub price for the United States. Finally, Hamilton

and Clemens (1999, 2002) take the weighted average of all available measures in each year of their

time-series coverage, and their data are available from 1970 from the World Bank.12

It is important to note here that even within a single country like the United States prices can

vary substantially, sometimes by as much as 30 per cent. It is also true that annual averages can mask

large monthly fluctuations, just as they can in the more globally coherent oil market.13 Absent a truly

herculean effort to collect intricate sub-national or monthly price data, and to track each exporter to

such micro-market prices, scholars are going to have to recognize that there are tradeoffs involved

whether taking a single annual price (versus monthly averages) or multiple regional market prices

(versus one price for the entire globe). I simply want to suggest here that it is important to be aware

by a return to production capacity that has since been subject to center-region politics that halted production entirely for several months in early 2012. The point is simply that recursive causality between production and politics is ubiquitous and inescapable. 11 See for example Hamilton and Clemens (1999), Section II pp. 339-40. 12 Available at http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/ENVIRONMENT/EXTEEI/0,,contentMDK:20502388~menuPK:1187778~pagePK:148956~piPK:216618~theSitePK:408050,00.html 13 Thanks to Brian Min for his thoughts on this issue.

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of those tradeoffs and to state explicitly which measurement choice is taken, and why, for the

purpose of comparing findings.

There is little reason to be confident that gas rents per capita in Russia, or Iran, or Qatar,

have any consistent relationship to the Henry Hub price. So scholars need to be aware of the

tradeoffs inherent in striving for global consistency. For datasets focused on particular regions (i.e.

Latin America, Asia, etc.) it is more feasible to select a market price by most common destination.

To take just one example, in an article focused on Southeast Asian producers I simply used the

Japanese unit price, since nearly 80% of the region’s gas exports are to that market (Smith 2012). For

scholars who are focusing on a tighter time period and/or on a particular region of the world, it is

feasible to employ regional market prices, which can vary substantially across the major markets (US,

EU, Canadian, British and Japanese, for example). Market prices can be found, among other

locations, in the “Gas Prices” spreadsheet in BP’s Statistical Review of World Energy.

Another theoretical wrinkle is that these data, even in maximally precise form, still do not tell

us what share of a country’s gas or oil production is state-owned or controlled. Since many of the

theorized mechanisms linking fuel wealth and political outcomes—rentierism, repression, to take

just two—are fundamentally about either characteristics or actions of governments, it would be ideal

to know this. But at this point we simply do not have this information on a cross-national scale. The

takeaway message here is simply that there will be tradeoffs in choosing a price for natural gas and

scholars should be cognizant of them.

Rent Leverage: A New, Better Measure

Another issue, and one that has not yet been addressed, is that even the better measure of

fuel rents per capita does not allow us to get at the extent to which a regime’s ability to deploy fuel

rents leverages individual citizens’ incomes. The logic of thinking about the concept this way is that

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it allows us to measure the importance of rents, and of state leaders’ ability to marshal them in

different ways, in citizen’s everyday economic lives. The data analyses below, therefore, centrally

employ a measure of rent leverage, which I take as the ratio of fuel rents per capita to GDP per capita

(the latter of which is corrected for purchasing power parity). This measure is both empirically

sounder and conceptually closer to the phenomenon on which the rentier thesis is focused: the

ability of rulers to use rents to sway their citizens’ political decisions and subsequent actions. The

measure presumes, and accounts for, both the overall share of the average citizen’s income that

derives from rents and how that share relates to her buying power in a given country.

Rent leverage gets us analytically closest to the empirical phenomenon we actually wish to

explore: how much of each individual’s livelihood is on average a function of a regime’s ability to

allocate fuel rents? This measure allows us to explore cases in which fuel rents per capita might be

similar but in which the former regime’s leverage is much greater over citizen’s economic lives. It

also accounts for the established bias in absolute GDP per capita numbers that tend to deflate real

buying power in poorer countries and inflate it in richer ones. The logic of oil politics suggests that

this is much more likely the best measure when we are exploring macro-regime outcomes:

transitions between democracy and autocracy, less wholesale regime changes (i.e. changes of at least

3 points on the Polity scale but not necessarily full transitions), and coups d’état. During the crises

that can threaten changes of these sorts, I would argue that a regime’s capacity to leverage rents

against dissent is most important, more so than an absolute rent figure or the “dependence”

captured by oil rents as a share of GDP. Moreover, the correlation between poverty and various

forms of political instability also suggests that we need to account for any bias against poor

countries.

The rent leverage measure ties this research agenda into a long vein of inquiry drawing from

modernization theory. Just as modernization scholars posited that a certain level of income would

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likely induce changes in citizens’ political engagement and behavior, the logic of a PPP-corrected

rent leverage measure suggests that the influence state-deployed rents will have on those citizens’

political behavior will depend not so much on the absolute value of the rents but rather on the

relative buying power they would supply. Uncorrected GDP per capita scores have long been shown

to exaggerate the buying power differences between citizens of poor and rich countries,14 and the

results I report below suggest that this may be the case for fuel rents too. Because instability is more

common in poor than in rich countries, and because fuel rents generally comprise a larger share of

per head income in poor than in rich countries, the effect of fuel rent abundance may appear to be

more significant than it actually is. Below I illustrate how different the relationship between stability

and fuel rent abundance looks when using absolute GDP per capita versus PPP-corrected data as

the denominator for the fuel rents per capita/GDP per capita calculation.

This measure also allows us to think of the political benefit to rulers both in terms of what

Ulfelder (2007, 997-98) refers to as the “supply” and “demand” dynamics of oil wealth. Whether

fuel rents allow rulers to spend without taxing, thereby undercutting extant calls for greater

accountability, or whether they allow rulers to placate important social forces during times of crisis,

this measure gets to the heart of what we typically think is important about them: namely that they

are an unusually fungible source of money for rulers. Their gravity, of course, depends on how many

dollars per citizen rulers can dole out, either during hard or easy times, hence the focus here on the

per-head value. It depends equally heavily on the buying capacity that a set number of fuel rents

engender, hence the correction for purchasing power parity.

As with the original oil export revenues to GDP measure, there are endogeneity issues since

of course GDP per capita may be related to oil production in a number of economic and political

14 See for example Asea and Corden 1994; Balassa 1964; Froot and Rogoff 1994; Kim 1990; Rogoff 1996. Some scholars have shown that the PPP effect has manifested more recently—as of roughly 1970—but this would actually accord with what Ross (2012) has demonstration, namely that the political effects of oil began about then with the wave of oil sector nationalizations in exporting countries.

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ways. Here I employ three other measures—oil rents as share of GDP, fuel rents per capita and fuel

rents per capita as a share of GDP per capita (uncorrected)—in addition to my preferred measure of

fuel rents as share of GDP per capita (PPP), noting as I proceed how and when they differ.

The Question of Using Instrumental Variables for Oil Wealth

Endogeneity concerns have raised the question whether trying to find instrumental variables

for oil wealth would be a superior strategy. Humphreys (2005) and Ramsay (2011) have respectively

used oil reserves and natural disasters to proxy for oil wealth in efforts to get around such concerns.

With regards to oil reserves, Lehèrre (2003) has shown that they are highly politicized and in the case

of OPEC apparently manipulated by producing states in order to maximize their quotas within the

cartel’s production allocation. With regards to natural disasters in producing states (an instrument

for prices), the capacity of Saudi Arabia to make up for shortfalls in other producing countries

introduces a wrinkle: Ramsay (2011) finds that the results change when the Middle East is included

in versus excluded from the data sample. Tsui (2010) uses oil discoveries, but finds that only

absolute rather than per capita discoveries are significant predictors of long-term regime trends. This

issue of discovery is part of a broader research program on foreign investment as well, which

economists (see for example Collier 2010) have found to be strongly linked to prior stability and

institutional quality. In short, there appear to be no easy solutions to endogeneity, not for a

commodity so intrinsically tied up with the global political economy. For these reasons, I would

argue that we cannot avoid the possibility of endogeneity: we simply need to recognize its

implications and try to account for them.

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Dependent Variables

The first outcome I explore here is regime change. In the interests of knowledge

accumulation and comparability of findings I use the same measure employed in Smith (2004, 2007)

and Morrison (2009, 2012), namely regime shifts that cause a change of 3 or more points on the

Polity scale. Realizing that there are both regime changes of greater magnitude (i.e. full regime shifts

between democracy and autocracy) and of lesser (i.e. a coup that might change only leadership but

not the regime), I also estimate the same models with measures for greater and lesser magnitude of

change: respectively transitions back and forth between democracy and autocracy (drawn from the

Cheibub and Gandhi data updated in 2009) and coups (drawn from the Polity project).15 As I detail

below there are some important differences in the results across all three measures. All of these

variables are binary, taking a value of “1” in years where they occur and “0” otherwise. Although in

earlier work (Smith 2004) I also explored the impact of oil wealth on civil war, that subject, like the

oil-autocracy nexus, has become a weighty enough research program on its own that I limit myself

here to a single family of regime failure phenomena, noting in the concluding section the plausibility

that the corrective presented might apply more broadly to other outcomes.

Control variables

While I included a larger number of control variables in earlier model specifications, here I

include only those that are stably significant across at least two of the three stability/instability

measures. The first is regime coherence, simply the square of the Polity2 measure (ranging from -10

to 10), the logic being that highly consolidated regimes at either end of the spectrum are more likely

to persist than those that are less consolidated or that combine attributes of both pure types. The 15 Respectively available at https://netfiles.uiuc.edu/cheibub/www/DD_page.html and http://www.systemicpeace.org/inscr/inscr.htm. Here I take only successful coups, i.e. those that on the Polity coup scale of 1-4 are coded “1.” Readers who are familiar with my 2004 paper will note the lack here of Banks’ updated Cross-National Time Series Data on anti-regime mobilization. Credit for that absence goes to the new, prohibitive corporate price of those data.

17

second is the Polity2 variable itself. The third is a dummy variable for Eastern European countries:

this was the only regional dummy that remained consistently significant across all model

specifications. Fourth, I include the natural logarithm of GDP per capita, drawn from the World

Development Indicators (World Bank 2012). Finally, I include a time dummy and three cubic

splines, constructed as recommended by Beck, Katz and Tucker (1998). The latter four are always

estimated but not reported.

I also estimated results from two separate time series. The first is 1960-2009, the entire

period for which data are available; this set is reported here. The second is 1960-1999, the time

period from which the results in Smith (2004, 2007) are derived.16 Countries that became

independent after 1960 enter the dataset in the year in which they became sovereign states. The total

possible number of country year observations is 7,481; with the variables included in these models

the maximum sample is 5,706 country years. Because all of the dependent variables are binary, I use

logit regression to estimate these models. I also cluster by country and report robust standard errors.

Results

Before imposing regression assumptions on the data, I took simple bivariate correlations

between the four measures used here. They are presented in Table 2. If the measures were close

enough in terms of variation, we might reasonably just choose one for maximal data availability and

move forward. Here, however, we see substantial difference. Rent leverage correlates at only 57%

with what I would take to be the next best measure, Ross’s fuel rents per capita. Rent leverage and

fuel rents per capita as a share of uncorrected GDP per capita correlate at only 41%, worse still, and

at a very low level of 10% with oil and gas rents as a share of GDP. These simple descriptive

16 I use this second, shorter time period as a way of gauging the replicability of the findings from those earlier works employing newer measures. Results were substantively similar and are not reported here, but are available from the author.

18

observation statistics suggest that the low shared variance might result in substantively different

results for the measures.

Table 2 about here

Figures 1 and 2 present two-way scatterplots of rent leverage versus fuel rents/GDP and

fuel rents per capita, respectively. One thing is visually apparent in comparing rent leverage to both

alternatives: the number of observations close to the y-axis that array the alternative measures widely

but reveal little change in rent leverage. This in turn also suggests that there may be substantively

(and statistically) important differences in the measures depending on whether or not we account for

relative buying power across countries.

Figures 1 and 2 about here

That in mind, Table 3 presents results for the regime change models estimated using each

measure of oil wealth (rent leverage, fuel rents per capita, fuel rents per capita as a share of

uncorrected GDP per capita, and oil and gas rents as a share of GDP, respectively). In this initial set

of analyses I use absolute figures for each of these measures rather than taking the natural logarithm

to calculate straightforward predicted likelihoods with different values.17 As mentioned above, I

opted to include a set of control variables small enough to remain stably consistent across all four

models while still accounting for key variables in the literature.18 These are: regime coherence,

democracy, the Eastern Europe dummy, and GDP per capita. Model 1 includes my rent leverage

measure alongside these controls, the time dummy and three cubic splines. Here rent leverage is

insignificant, and was so even under much less stringent model specifications and without controls.

17 I also estimated these models using the natural logarithms of the variables with little substantive change in results. Regarding predicted likelihoods, I used the “predcalc” function in Stata 10 to calculate predicted likelihoods setting all right-side variables at their means and then adjusting each oil measure by one standard deviation from the mean. 18 In part this strategy draws its inspiration from Achen (2002).

19

What is interesting is that both the fuel rents per capita and fuel rents per capita/absolute

GDP per capita measures are highly significant, positive predictors of regime failure.19 Keeping these

differences in mind, I estimated predicted likelihoods of regime failure using each of the four

measures, holding all other right-side variables at their mean values and adjusting each oil wealth

measure upward by one standard deviation. The variations in statistical significance masked relatively

minor changes in the substantive relevance of oil, regardless of how it was measured. At their

means, the four measures yield predicted chances of regime failure in a very small window, ranging

between 2.2 and 2.8%. Even given the rarity of regime failure, such a tight range suggests a lessening

role for oil from older findings, or perhaps a more dominantly conditional role. In any case, once we

account for the relative buying power that a set amount of rent revenue provides in any particular

country, its unmediated impact appears to wash out.

Implications: Conditional or NO relationship?

There are three plausible explanations for the null result using the updated data here. The

first has to do with measures: it might be the case that a conceptually better indicator reveals what

was masked before by less-good ones. The second has to do with the bigger oil exporting

neighborhood in the world: the arrival on the scene of blocks of significant exporters in West Africa,

South America, and the former Soviet Union might well expand the universe of exporting,

developing countries enough to reveal a possible regional bias (i.e. Middle East and more stable

regimes) in prior samples. Where, for example, I found in data to 1999 22 oil exporters for the

sample in Smith (2007), Ross (2012) finds 49 when the time period comes to 2009. The third may be

historically time-related. Including another dozen years of historical record might introduce new

19 Ross (2012 and forthcoming with Andersen 2014) shows that the wave of oil sector nationalizations in the 1970s changed the politics of oil wealth fundamentally. I tried to accommodate this reality by including a dummy variable for post-1980 years and re-estimating the models. Results were unchanged and the dummy was insignificant.

20

dynamics and trends into our array of country-year data. In the latter two cases, it is quite plausible

that the higher price of oil since 1999 has both sparked new exploration (including by direct foreign

investment) into countries that, while less stable, were suddenly worth the perceived risk with oil at

US$100 or more per barrel as opposed to US$30 or less. That, in turn, may have dampened the

generally stability-seeking bias of investors in the oil sector of developing countries. Among others

Collier (2010) has noted cogently that it is not at all the case that in the 20th century oil companies

somehow rushed into poor countries seeking unfair advantage relative to their positions in rich ones.

On the contrary: those companies overwhelmingly choose to explore, and exploit, in richer, more

stable countries. However, price increases such as those of the post-1999 period might spur a

willingness to invest in previously unattractive countries. If this is reflective of the last decade, we

might simply be seeing the political “mean” shift toward the unstable end of the spectrum as prices

remain high for years at a time. At the same time, those countries might in fact become more stable

over time as a function of rising oil and gas revenues, effectively boosting regimes’ rent leverage. Or,

prior patterns of social or ethnic exclusion might be magnified with an infusion of new fuel rents,

potentially provoking domestic conflict as was the case in Bahrain in 2011.

Exploring the Arab Spring Through the Lens of the Resource Curse

The events of 2011 in the Arab world provide a more focused window into the role of fuel

wealth in catalyzing regime change. A simple look at the countries that have either undergone regime

change (Egypt, Libya, Tunisia), ousted a ruler (Yemen) or erupted into civil war (Syria) reveals that

all are oil producers, but with the exception of Libya ones whose rulers were less endowed in rent

patronage than the Gulf monarchies. Here I present a brief look at the years preceding the uprisings

of 2011 through the lens of rent leverage.

21

Two recent analyses form the basic starting point for my (mostly speculative) discussion

here. The first, by Kevin Morrison (2012), finds a conditional relationship between fuel wealth and

state power and suggests that the interaction between the two explains the contradictory effects of

oil on stability—bolstering regimes but making civil war more likely. This finding highlights the

state/regime dynamic of oil politics, namely how fuel wealth shapes the infrastructural and rent

resources available to rulers. The second, by Filipe Campante and Davin Chor (2012) captures a part

of the social effect—the impact that fuel wealth has on both longer-term social changes and on the

shorter-term motivations that affect the prospects of anti-regime mobilization. Campante and Chor

suggest that the Arab World was “poised for revolution” as a result of mismatch between education

levels and economic opportunity.

One immediate observation of the countries whose rulers were ousted or overthrown in

2011—Egypt, Libya, Tunisia, and Yemen—is that Libya is an outlier in terms of both fuel rents per

capita and rent leverage. Fuel rents per capita in 2009 ranged between US$240 and US$275 in Egypt,

Tunisia and Yemen but came to $6420 in Libya. Moreover, where regimes in the other three states

could leverage just one-quarter to one-third of their citizen’s buying power in 2009 (though in all

cases less than half what had been the case a year earlier), Moammar Qaddafi could leverage nearly

all of Libyans’ in the same year. But where he fell dramatically short of his counterparts was in

infrastructural reach (here initially proxied by telephone lines per 100 people).20 Figure 1 provides a

region-wide account of the rent leverage-state reach nexus in the Arab World. Notably, four of the

small monarchies of the Persian Gulf (Kuwait, Oman, Qatar and the UAE) rank well above the rest

and none of the four experienced any serious uprisings in 2011. Bahrain, the fifth, did of course, to

the point that the ruling family invited Saudi military intervention. The Bahraini exception points to

communal exclusion as a promising line for future inquiry that could bridge research programs on

20 I am at work on this line of inquiry and the subsequent analysis will employ more comprehensive measures for state reach, including factor analysis of multiple indicators.

22

oil politics and the dynamics of ethnic exclusion (for example Cedermann et al 2010). Libya, despite

its massive advantage in rent leverage, had by all accounts (and continues to have) a persistently

weak state. This manifested in an inability to deal effectively with incipient insurrection in eastern

Libya during the first half of 2011, such that a huge reserve of oil revenues (allegedly paid in cash to

an increasingly mercenary army) could not ultimately keep Qaddafi in power against an externally

supported insurrection.

Figure 3 about here

I do not explore empirically the second side of the equation—the social effects of fuel

wealth—except to point to two common dynamics that emerged during the Arab Spring uprisings

and that are in part likely to have been a function of that fuel wealth. The first is systematic

exclusion of ethnic or communal minorities from access to rent patronage. Despite his regime’s still

powerful army, one reason that Bashar Assad has been unable to regain control of Syria is that the

opposition has increasingly turned sectarian in narrative, pitching the civil war as one between the

oppressed and excluded Sunni majority and the dominant Alawi minority. This tracks closely with

the dynamics of the Bahraini uprisings. The second is oil-induced distortions in the economy. Ross

(2008) has found that among other things oil wealth tends to depress the light manufacturing sector,

a common first entry point for women in developing countries. Oil has also been heavily invested in

national education in the Middle East, though that educational expansion has been of questionable

quality and has also not been accompanied by expanding employment opportunities. The

simultaneous expansion of college graduate numbers and constriction of non-oil employment is an

under-explored dynamic of fuel wealth that also might shed light on the variation across oil

exporting countries. Tom Pepinsky21 has noted the vast difference in unemployment numbers as a

cautionary note against using Indonesia’s transition to justify a rosy expectation for Tunisia and

21 http://blogs.cornell.edu/indolaysia/2012/07/09/unemployment-developmental-legacies-and-the-arab-spring/. Accessed August 26, 2012.

23

Egypt. This is probably accurate, but misses the element of expectation stemming from greater

access to education.

Conclusion

I have endeavored to make two points here. The first is that by accounting for relative

buying power, as development economics has pushed for, it is possible to construct a better measure

of the political effect of fuel wealth—what I term rent leverage. The measure accounts for numerous

dynamics, is sensitive to what different levels of fuel wealth mean in different settings, and is

conceptually closer to the theoretical state of the art than prior measures. It is also easy to calculate

and widely available, qualities whose importance Ross (2012) has to my mind rightly noted. It also

focuses our attention on the theorized mechanism linking oil wealth to politics that has substantially

stronger support in the extant literature, unlike the weak states thesis that has come under wide

criticism.

In addition, employing the rent leverage measure appears to yield different conclusions.

Analysis of at least the relationship between rent leverage and regime change suggests no systematic

relationship. However, the second main conclusion here is not that there is no relationship between

fuel wealth and political outcomes, but rather that to my mind the relationship is almost certainly

conditional.22 In suggesting this I draw on a tradition originating in modernization theory that linked

demographic and social change to political trajectories. I also draw on a tradition of scholarship on

state-society relations (Mann 1993; Migdal 1988, 2001) In essence, the two research programs

together suggest that the capacity of regimes both to shape their societies in a power-maintaining

fashion and to ride out periodic crises are at least in part a function of economic change. In the case

22 Here I owe thanks to David Waldner. In the course of co-authoring an essay on rentier states (Walder and Smith 2012) we have had what has been to me an enlightening discussion-debate between what David describes as a heterodox oil scholar (me) and a heretic one (himself).

24

of fuel wealth, it first shapes the rent leverage available to rulers, whether they use the rents for

coercion or for patronage. Second, it shapes the structure of society, the grievances (motives) that

particular groups may have for challenging the state, and in interaction with state reach the ability of

rulers subsequently to deal with social dissent. By removing the barricades separating oil exporting

states from other states (especially in the post-colonial world), we gain the capacity to put resource

wealth in a more normalized place in comparative inquiry, where resource wealth is influential but

not entirely constitutive.

25

Table 1. Measuring the Resource Curse:

Problems with Current Measures

Measure Conceptual/Empirical Shortcomings

Endogeneity Problems

OPEC membership (dummy):

Omits all exporting non-members; no way to account for varying export dependence, population size

Oil exports > 50% exports (dummy):

No good analytical reason to start at 50.01%; no way to account for varying export dependence, population size

Oil exports/total exports: No way to know how important in overall economy or to account for population

Oil export revenues/GDP No way to account for revenues per capita

Poorer countries bias the measure upward.

Oil discovery per capita Oil exploration conditioned by political variables often used as DVs

Oil discovery to date Oil exploration conditioned by political variables often used as DVs

Oil export revenues/GNI No way to account for revenues per capita

Energy resource depletion/GNI

No way to account for revenues per capita

Oil&gas income/population (oil income per capita)

Measure is dependent on production, which can depend on political variables used as DVs.

Oil deposits per capita See Oil Discovery per capita above

Oil value per capita See Oil Discovery per capita above

26

Table 2. Correlating Different Measures of Oil Wealth

Rent Leverage (fuel rents per capita/GDP per capita(PPP)

Fuel rents per capita .57

Oil & Gas Rents/GDP .10

Fuel rents per capita/GDP per capita (uncorrected)

.41

27

Figure 1. Rent Leverage vs. Fuel rents as a share of GDP

28

Figure 2. Rent Leverage vs. Fuel Rents per capita

29

Table 3. Oil Wealth and Regime Change (DV = Polity IV Regime Change)

Independent Variable Model 1 Model 2 Model 3 Model 4

Rent Leverage -.028 (.028)

-- -- --

Fuel rents per capita -- .0000359 (.00000938)**

-- --

Oil & Gas Rents/GDP

-- -- -.004 (.004)

--

Fuel Rents per capita/GDP per

capita(uncorr.)

-- -- -- .313 (.097)**

Regime Coherence -.027 (.003)**

-.0280 (.003)**

-.034 (.003)**

-.031 (.003)**

Democracy .060 (.015)**

.067 (.015)**

.079 (.019)**

.075 (.017)**

Eastern European .517 (.137)**

.532 (.128)**

.697 (.175)**

.747 (.149)**

GDP per capita -.189 (.075)*

-.208 (.072)**

-.160 (.102)

-.249 (.078)**

Observations 5397 5706 3421 5143

Prob > chi2 .000 .000 .000 .000

Pseudo R2 .237 .237 .252 .238

Analysis is by logistic regression. Robust (Huber-White) standard errors in parentheses. Coefficients for time dummy, cubic splines and constant estimated but not reported. * p<.05; ** p<.01

30

Figure 3. State Reach and Rent Leverage in the Arab World, 2009.

0

200

400

600

800

sta

ten

essxo

il

0 5 10 15 20countrynumber

Kuwait

Oman

Qatar UAE

31

Appendix I: Calculations 1. Calculating fuel income per capita Oil Income Per Capita

Daily oil production data are widely available, from the sources indicated below. One simply multiplies daily production figures by 365 to annualize, and then by the average price each year to compute annual income. Dividing by yearly population produces oil income per capita.

Gas Income Per Capita

This statistic is somewhat more complicated for two reasons. First, all production figures are in volume (such as billion cubic feet or bcf), whereas prices are in British Thermal Units (BTUs). Second, there is no standard global market price for natural gas as there is for oil. As a result, scholars will by necessity need to recognize the tradeoffs in using pricing strategies and minimize the error accordingly.

Price calculation strategies:

Use a standard price and treat it “as if” it applies globally. In calculating the fuel rents per capita data that I subsequently use to calculate rent leverage, Ross (2012) used the Henry Hub US price (available in the BP data below). Haber and Menaldo (2011) took the Well Head price of Natural Gas from the United States, available from the IE at http://tonto.eia.doe.gov/dnav/ng/hist/n9190us3a.htm.

Use regional market prices. The BP data supply market prices for Canada, the United States (multiple measures), the EU, Britain, and Japan. One of us (Smith 2011) took Japanese prices in calculating gas income for Southeast Asia, since the region’s gas exports overwhelmingly go to that market.

As with oil income, I used World Bank population data as the denominator.

Volume to Energy conversions

A fairly simple metric is that 1 cubic foot of natural gas provides approximately 1,000 BTUs of energy (www.engineeringtoolbox.com) If one takes annualized gas production in billion cubic feet and multiplies it first by price per million BTUS, then by 1,000 to convert to billions, one obtains gas income per year The equation: (annual production in bcf) X (price per million BTUs) X (1,000) = gas income.

32

Example: Russia in 1990 produced 20,837 billion cubic feet (i.e. 20.837 trillion) of natural gas. That, multiplied by 1.64 (1990’s Henry Hub price per million BTUs) and by 1,000 to convert to billions, results in national gas income of $34,173,008,000. Dividing by Russia’s 1990 population of 148,292,000 produces natural gas income per capita of $230.44.

Here in the interests of replicability I use the data calculated by Ross (2012).

2. Calculating Rent Leverage a. I take fuel rents per capita (from Ross 2012) here and divide it by GDP per capita (PPP). I use GDP per capita/PPP data from the Penn World Tables (http://pwt.econ.upenn.edu/php_site/pwt_index.php). Accessed on August 12, 2012. 3. Sources I have found the annual statistical manuals released by British Petroleum an immensely good starting point for compiling data. Moreover, the BP data provide production, market price and year-on-year change figures, accomplishing much of what one needs to produce reliable cross-national data. However, there are some countries missing from the BP data, which can be supplemented from the following sources below: British Petroleum, Statistical Review of Energy. http://www.bp.com/productlanding.do?categoryId=6929&contentId=7044622. United States Energy Information Administration: http://tonto.eia.doe.gov/country/country_energy_data.cfm?fips=TT. United States Geological Survey, International Minerals Statistics and Information http://minerals.usgs.gov/minerals/pubs/country/index.html#pubs.

33

References Achen, Christopher. 2002. “Toward a New Political Methodology.” Annual Review of Political Science (5), pp.

423-450. Asea, Patrick and W. Max Corden 1994. “The Balassa-Samuelson Model: An Overview,” Review of International

Economics. Balassa, Bela. 1964. “The Purchasing-Power Parity Doctrine: A Reappraisal,” Journal of Political Economy 72, 6:

584-96. Beck, Katz and Tucker. 1998. “Taking Time Seriously in Binary Time-Series Cross-Section Analysis." American Journal

of Political Science 42(4):1260-1288. Bergin, Paul, Reuven Glick and Alan Taylor. 2004. “Productivity, Tradability and the Long-Run Price Puzzle.

Working Paper, UC Davis.” Blair, Graeme. 2012. “Crude Distribution: Where Oil Money Goes in the State and the Consequences for

Civil Conflict.” Manuscript, Princeton University. __________. 2010. Causal Identification in the Study of Oil and Conflict. Manuscript, Princeton University. Brownlee, Jason. 2002. “…And Yet They Persist: Explaining Survival and Transition in Neopatrimonial

Regimes,” Studies in Comparative International Development 37, 3: 35-63. Brunnschweiler, Christa. 2008. Cursing the Blessings? Natural Resource Abundance, Institutions, and

Economic Growth. World Development 36, 3, 399–419. Collier, Paul. 2010. The Plundered Planet. New York: Oxford University Press. Collier, Paul and Benedikt Goderis. 2009. Structural Policies for Shock-Prone Developing Countries. Center

for the Study of African Economies Working Paper Series #2009-03. Collier, Paul and Anke Hoeffler. 1998. On Economic Causes of Civil War. Oxford Economic Papers 50: 563-573. Delacroix, Jacques. 1980. “The Distributive State in the World System,” Studies in Comparative International

Development 15: 3-20. Thad Dunning. 2008. Crude Democracy. New York: Cambridge University Press. __________. 2005. “Resource Dependence, Economic Performance, and Political Stability,” Journal of Conflict

Resolution, Vol. 49 No. 4, August 2005 451-482. Fearon, James and David Laitin. 2003. Ethnicity, Insurgency and Civil War. American Political Science Review, 97,

1: pp. 75-90. Fish, M. Steven. 2005. Democracy Derailed. New York: Cambridge University Press. __________. 2002. Islam and Authoritarianism. World Politics 55, 1: 4-37.

34

Froot, Kenneth and Kenneth Rogoff. 1994. “Perspectives on PPP and Long-Run Real Exchange Rates.”

NBER Working Paper 4952. Gandhi, Jennifer and Adam Przeworski. 2006. Cooperation, Cooptation and Rebellion Under Dictatorships.

Economics and Politics 18, 1: 1-26. __________. 2007. Authoritarian Institutions and the Survival of Autocrats. Comparative Political Studies 40, 11:

1279-1301. Haber, Stephen and Victor Menaldo. 2011. Do Natural Resources Fuel Authoritarianism? A Reappraisal of

the Resource Curse. American Political Science Review 105, 1: 1-26. Humpreys, Macartan. 2005. Natural Resources, Conflict, and Conflict Resolution: Uncovering the

Mechanisms. Journal of Conflict Resolution 49, 4: 508-37. Jensen, Nathan and Leonard Wantchekon. 2004. Resource Wealth and Political Regimes in Africa. Comparative

Political Studies 37, 7: 816-41. Jones Luong, Pauline and Erika Weinthal. 2010. Oil is Not a Curse. New York: Cambridge University Press. __________. 2006. “Combating the Resource Curse: An Alternative Solution to Managing Mineral Wealth,”

Perspectives on Politics 4, 1: 35-53. Karl, Terry L. 1999. The Perils of the Petro-State: Reflections on the Paradox of Plenty. Journal of International

Affairs 53, 1: 31-48. __________. 1997. The Paradox of Plenty: Oil Booms and Petro-States. Berkeley: University of California Press. Kim 1990. “Purchasing Power Parity in the Long Run: A Cointegration Approach,” Journal of Money, Credit and

Banking 22, 4: 491-503. Lahérre, Jean. 2003. “Future of Oil Supplies.” Presented at the Seminar of Energy Conversion, Zurich. Lowi, Miriam. 2010. Oil Wealth and the Poverty of Politics. New York: Cambridge University Press. Mahdavy, Hossein. 1970. The Patterns and Problems of Economic Development in Rentier States: The Case

of Iran. In M.A. Cook ed. Studies in the Economic History of the Middle East. Oxford: Oxford University Press, 428-67.

Morrison, Kevin. 2012. Oil, Conflict and Stability. Working paper, University of Pittsburgh. Morrison, Kevin. 2009. Oil, Nontax Revenue, and the Redistributional Foundations of Regime Stability.

International Organization 63, 1: 107-38. Ramsay, Kristopher. 2011. “Revisiting the Resource Curse: Natural Disasters, the Price of Oil, and

Democracy,” International Organization 65, Summer: 507–29 Rogoff, Kenneth. 1996. “The Purchasing Power Parity Puzzle,” Journal of Economic Literature, 34, 2: 647-68. Ross, Michael. 2012. The Oil Curse: How Petroleum Wealth Shapes the Development of Nations. Princeton: Princeton

University Press.

35

__________. 2006. “A Closer Look at Oil, Diamonds and Civil War,” Annual Reviews of Political Science 9: 265-

300. __________. 2001. “Does Oil Hinder Democracy?” World Politics 53, 3: 325-61. Smith, Benjamin. 2012. “Oil Wealth and Political Power in Southeast Asia.” In Robert E. Looney ed.

Handbook of Oil Politics. New York: Routledge, __________. 2007. Hard Times in the Lands of Plenty. Ithaca, NY: Cornell University Press. __________. 2006. “The Wrong Kind of Crisis.” Studies in Comparative International Development 40, 4: 55-76. __________. 2004. “Oil Wealth and Regime Durability in the Developing World, 1960-1999,” American

Journal of Political Science 48, 2: 232-46. De Soysa, Indra and Eric Neumayer 2007. Resource Wealth and the Risk of Civil War Onset: Results from a

New Dataset of Natural Resource Rents, 1970-1999. Conflict Management and Peace Science 24:201–218. Tsui, Kevin. 2010. More Oil, Less Democracy: Evidence From Worldwide Crude Oil Discoveries. Economic

Journal 121: 89-115. Ulfelder, Jay. 2007. Natural-Resource Wealth and the Survival of Autocracy. Comparative Political Studies 40, 8:

995-1018.