Oil and Autocratic Regime Survival · autocratic survival by lowering the chances of...

Oil and Autocratic Regime Survival

Joseph Wright, Erica Frantz, and Barbara Geddes∗

Pennsylvania State University

Bridgewater State University

UCLA

Word count: 7,931

(10, 561 including footnotes and references)

May 20, 2013

Abstract

This paper uncovers a new mechanism linking oil wealth to autocratic regime survival: increasesin oil income lower the risk of ouster by groups that establish new autocratic regimes, notby reducing the likelihood of democratization. We investigate whether oil wealth influencesautocratic survival by lowering the chances of democratization, reducing the risk of transitionto subsequent dictatorship, or both. Using a new measure of autocratic durability shows thatonce we model unit effects, oil wealth promotes autocratic survival by lowering their risk ofouster by rival autocratic groups. Evidence also indicates that oil income increases militaryspending in dictatorships, which suggests that increasing oil wealth may deter coups that cancause regime collapse.

∗We are grateful to Jørgen Andersen, Ben Bagozzi, Xun Cao, Matt Golder, James Honaker,Michael Ross, John Zaller, and Chris Zorn for helpful conversations. We thank an editor andthree anonymous reviewers for excellent feedback. Finally, we thank Victor Menaldo for sharingdata and replication code. All errors remain our own. This research is supported by NSF-BCS#0904463 and NSF-BCS #0904478. Emails: [email protected], [email protected],and [email protected] (corresponding author).

Conventional wisdom about the resource curse holds that oil rich autocracies can use their wealth

to co-opt their citizens, buy security forces to repress them, or both. By these means, oil rich

autocracies remain in power longer than dictatorships that lack these resources. Most research on

the political implications of oil wealth examines only one form of regime change, the replacement

of autocracy by democracy. Yet democratization is not the only way autocracies end. In fact, as

Figure 1 shows, fewer than half of autocratic regimes that ended after World War II democratized.1

Most have been replaced by another autocracy. This paper examines how oil income influences both

types of transition: to democracy and to new autocracy.

Though often overlooked in the political science literature, autocracy-to-autocracy transitions

involve much more than substituting one dictator for another. Often violent, they can lead to major

social upheavals as well as reversal of fortunes for supporters of the ousted dictatorship. In 1959, for

example, an insurgency led by Fidel Castro ousted the dictatorship of Fulgencio Batista in Cuba,

leading to mass expropriations, the flight into exile of much of the Cuban upper and middle classes,

and the wholesale replacement of the military by the insurgent army. In a completely different

setting, a military coup in 1962 ended the Yemeni imamate and the sayyid caste’s birthright to

rule; members of the Hamid al-Din extended family and their supporters were executed, jailed, or

exiled, and their property was confiscated.2 Many autocratic transitions are similarly destructive

to supporters of the old regime. Regime leaders would thus be expected to guard against such

ousters as assiduously as they guard against democratization.

By ‘regime change’ we mean fundamental changes in the formal and informal rules that identify

the group from which leaders can be chosen and determine who can influence policy (the leadership

group). Autocratic regimes can and often do include multiple dictators, particularly when the

1Geddes, Wright and Frantz 2013.2Burrowes 1987, 22.

1

0

10

20

30

40

Nu

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er

of

reg

ime

tra

nsitio

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1940s 1950s 1960s 1970s 1980s 1990s 2000s

Democratic transitions (102) Autocratic transitions (112)

Figure 1: Number of democratic and autocratic regime transitions, by decade. Regimefailures that end in a foreign occupation are excluded.

regime has an institutionalized mechanism for rotating leadership. In Mexico, for example, the

ruling Institutional Revolutionary Party (PRI) selected a new president every six years (prior to

2000). In these cases, leaders leave office but the fundamental rules of the regime do not change.

Indeed, many leadership changes in dictatorships, particularly the regular rotation of leaders at the

expiration of an executive term limit, reflect a stabilizing mechanism for keeping the incumbent

ruling elite in power.

While transitions from one autocratic regime to another are a common feature of the real world,

the resource curse literature has yet to consider them. With new data that identify autocracy-to-

autocracy transitions, we investigate whether oil resources increase autocratic regime survival by

reducing the likelihood of transitions to new dictatorship, transitions to democracy, or both. We

use an empirical approach that assesses the influence of both cross-country differences in oil wealth

and changes in the level of oil wealth within countries.3

We find support for the resource curse claim that higher oil wealth increases autocratic regime

3Haber and Menaldo 2011.

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survival.4 The mechanism through which this occurs, however, is not deterrence of the forces

of democratization, as suggested in much of the literature. Like the recent study by Haber and

Menaldo (2011), we find little evidence that decreasing oil income makes autocratic regimes more

likely to democratize. Instead, we find that as oil wealth rises, autocracies are increasingly able to

prevent ouster by groups that would initiate new dictatorships if they were able to defeat the old

one. Further, we find evidence that oil income increases spending on the military, suggesting one

avenue through which oil wealth helps autocratic regimes survive.

These findings suggest that increasing oil rents stabilize dictatorships by suppressing challenges

from future autocrats, rather than by quelling democratic opposition movements. Our study thus

supports a basic idea in the resource curse argument – that oil bolsters autocratic regimes – and

identifies a new mechanism through which it does so. We first discuss the resource curse literature

and, specifically, what it says about oil and autocratic survival. We then outline our empirical

strategy, present the results, and close by discussing the implications of this research.

Theoretical Background

The idea of the oil curse is rooted in the rentier state theory.5 Drawing from experience in the

Middle East, the theory claims that governments reliant on external revenue (such as oil income)

can operate autonomously from societal interests, making them unaccountable to citizens and thus

4Oil wealth comes primarily from state ownership of the oil industry or taxation of foreignowners. The government’s dependence on such revenues is reflected in the contribution of oilexports to the total economy. Resource wealth typically refers to resource export income percapita, resource dependency to natural resource exports as a share of GDP, and rentierism tothe percentage of external rents, such as (but not restricted to) resource income, that make upgovernment revenues (Herb 2005; Dunning 2008; Basedau and Lay 2009).

5The argument that oil is a curse for democracy is part of the larger ‘natural resource curse’literature, which finds that abundant natural resources have a variety of pernicious effects, whichinclude reducing growth and increasing internal violence (Sachs and Warner 1995; Karl 1997; Auty2001).

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more likely to be autocratic.6 Because the revenue accrues either from the profits of state-owned

resources or from taxing foreign resource owners, it does not depend on the cooperation of citizen

tax payers, leaving rulers free to act as they please with few repercussions.7 Extensive revenues

drawn from oil wealth allow rulers to protect themselves from overthrow by providing benefits to

citizens and/or by building coercive capacity.

All dictatorships maintain power through a combination of two strategies: repression and co-

optation.8 Oil wealth makes both of these strategies easier to pursue. With state coffers full,

autocratic governments have the financial capacity to beef up the security apparatus, rooting out

potential threats to their control.9 They can also use the state’s oil wealth to co-opt potential

opponents, by giving them jobs, lucrative government contracts, business subsidies, high wages,

and low-priced fuel. They can discourage plotting and elite calls for political change by distributing

more rents to discontented regime insiders, leaders of the opposition, and/or the security forces,

while limiting popular demands for accountability by reducing taxes and/or offering subsidies. In

these ways, autocracies can purchase elite cooperation and popular acquiescence, if not exactly

support, and thus extend their time in office. In sum, because rentier autocratic regimes can use oil

revenues to buy regime survival, they should be more resistant to collapse than other autocracies.

This could occur because oil dampens pressure for democratization, prevents new autocratic groups

from seizing power, or both.

The bulk of existing studies examine the first possibility, exploring whether oil prevents de-

mocratization. Building on Ross (2001), these studies investigate the cross-national relationship

between oil wealth and incremental measures of democracy and, by and large, find that oil lowers

6Mahdavy 1970; Beblawi and Luciani 1987.7Anderson 1991; Crystal 1995; Luciani 1990; Vandewalle 1998.8Wintrobe 1998.9Ross 2001.

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levels of democracy.10 This relationship is confirmed by studies showing that oil-rich autocracies

are less likely to democratize than autocratic countries with few oil resources.11

A few recent studies, however, use unique empirical approaches to challenges these findings.

Herb (2005), for example, compares the level of development in resource-poor countries with

resource-rich countries exclusive of their resource rents. He finds that natural resources do not

deter democracy, concluding that there is little reason to believe that the absence of resource

wealth in oil-producing countries would have made any difference to their political futures. Dun-

ning (2008) reconsiders the oil curse by exploring whether the relationship is context dependent.

He finds that oil rents encourage democracy in Latin America, but discourage it elsewhere, because

the political impact of oil wealth depends on other factors, such as inequality. Haber and Menaldo

(2011) also use a new strategy to evaluate the oil curse. They argue that examining how oil influ-

ences democratization requires modeling a process that unfolds over time, though most empirical

strategies used to test it do not use time-series centric methods or counterfactuals (Herb is an

exception). To address this problem, Haber and Menaldo analyze fluctuations in a country’s oil

wealth over time and find little evidence that increases in oil wealth reduce the level of democracy,

as measured by Polity.12

Despite the many studies of how oil affects democratization, few have looked at its impact on

autocratic regime survival more generally.13 Yet the argument that oil wealth can buy political

10Boix 2003; Jensen and Wantchekon 2004; Smith 2004; Ulfelder 2007; Basedau and Lay 2009;Ross 2009; 2012.

11Ulfelder 2007; Ross 2009; 2012.12Haber and Menaldo (2011) also test the relationship between oil wealth and transitions to

democracy; they use conditional fixed effects logit regressions to show that increases in oil wealthover time make autocracies more vulnerable to democratization. In Appendix B, we address thesefindings.

13Bueno de Mesquita and Smith (2010) and Cuaresma, Oberhofer and Raschky (2011) examineautocratic leader survival, while Andersen and Aslaksen (2013) model leader survival for autocraciesthat lack ruling parties. This latter strategy treats rotations among members of military juntas and

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support or repress opposition also implies that regimes shaped and led by particular leadership

groups should persist longer in oil producing countries, not just that some form of autocratic rule

should prevail in oil-rich places. Oil wealth could potentially increase autocratic regime survival by

deterring democratization, transitions to a new autocracy, or both. In this study we consider all

these possibilities. We do so using new data that capture transitions from autocracy to autocracy,

in addition to transitions from autocracy to democracy, a subject to which we now turn.

Measuring Regimes

Much of the literature on the oil curse uses data that identify the duration of non-democratic

periods or levels of democracy, but not the beginnings and ends of autocratic regimes (unless they

precede and follow democracy). This is appropriate if the subject of interest is democratization,

but for those interested in oil’s impact on autocratic regime survival, such data will omit about

half of all autocratic regime collapses because autocracies are often replaced by new autocracies.

For example, Iran was autocratic in the 1970s when the Shah was ousted as well as in the 1980s

under Ayatollah Ruhollah Khomeini, but this is not a period of regime continuity. By standard

definitions of the word regime, the 1979 Revolution was a regime change. Evaluating the effect of

oil on autocratic regime survival, then, requires data that identify not only democratizations but

also transitions to new autocracies.

Older data, however, do not capture this latter kind of transition. The Polity index, for example,

which measures characteristics of political systems, is often interpreted as an indicator of how

hereditary successions in monarchies as instances of regime change. Smith (2004) and Morrison(2009) employ the Polity Durable variable as a proxy for institutional instability. We discuss thismeasure below and in Appendix C to show that the durability of institutions as measured by PolityDurable does not accurately capture autocratic regime durability. McFaul (2002) and Gleditschand Choung (2004) examine transitions both to democracy and to subsequent dictatorship, butneither addresses the resource curse.

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democratic a system is and widely used to measure transitions to and from democracy. Polity

identifies changes in the exclusiveness of political systems and the level of constraint faced by

executives, but it does not attempt to identify regime changes, other than those between democracy

and dictatorships.

Autocracy-to-autocracy transitions may not involve changes in executive constraint or popular

participation so they cannot be captured by Polity scores.14 When Mobutu Sese Seko’s regime in

the former Zaire collapsed in 1997, for example, it was replaced by another dictatorship, led by

former rebel commander Laurent Kabila. The combined Polity score15 for that country did not

change when Mobutu’s regime was ousted because the level of democracy did not change.

When used as a proxy for regime change, the Polity index not only ignores transitions between

dictatorships, but can also conflate periods of relaxation or liberalization in ongoing dictatorships

with democratic transitions.16 The combined Polity score for former Zaire, for example, increases

eight points (on a 21-point scale) from 1991 to 1992 due to the legalization of opposition political

parties at this time, even though Mobutu’s regime never held multiparty elections and remained

in power for another five years. This increase in Zaire’s combined Polity score is comparable to

the increase in Chile’s combined Polity score from 1988 to 1989 (nine points), when that country

14Indeed, the difficulties of interpreting the middle ranges of Polity scores are well known, asis the absence of substantive criteria for determining what point or difference in the scale wouldindicate regime change. See Gleditsch and Ward (1997), Treier and Jackman (2008), and Pemstein,Meserve and Mellon (2010).

15Combined Polity scores aggregate autocratic and democratic Polity scores, resulting in a 21-point scale that ranges from -10 to 10. For more on this measure, see Polity IV (2010).

16Ulfelder (2007) also criticizes the use of Polity scores in studies of the oil curse, arguing thatthere is little reason to expect oil wealth to make a country that is democratic even more democratic,or a country that is autocratic even more autocratic. He uses a configuration of Polity scores togroup observations into two categories – dictatorships and democracies – counting changes fromone category to another as instances of regime change. Though this strategy is an improvement,it still ignores some transitions from one dictatorship to another. Studies that attempt to assessexogenous variation in oil wealth, such as Ramsay (2011) and Tsui (2011), also employ the Polityscale.

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democratized. In one case the large increase in combined Polity score is associated with the survival

of a dictatorship (Zaire 1992) and in the other it is associated with regime collapse and democratic

transition (Chile 1989).17

The Polity Durable variable does not solve this problem.18 This variable codes increases or

decreases of three points or more (over three years) in the combined Polity score as instances of

‘regime change’. Using this measure, however, Iran’s 1979 revolution, Mobutu’s 1992 legalization of

opposition parties, and Chile’s 1989 democratic transition are identified as identical ‘regime change’

events because their combined Polity scores each increase by more than three points.19 As these

examples illustrate, studies that use this variable in effect treat instances of democratization (Chile

1989), autocracy-to-autocracy transition (Iran 1979), and a wily dictator’s cosmetic changes leading

to regime persistence (Zaire 1992) as equivalent events. In contrast, the data used in this paper

code autocracy-to-democracy transitions (Chile 1989) and autocracy-to-autocracy transitions (Iran

1979) as distinct types of regime collapse while treating periods of regime persistence (Zaire 1992)

as non-transitions.

The other data source often used to measure democratization, Cheibub, Gandhi and Vreeland’s

(CGV) (2010) dichotomous regime type measure, also ignores autocracy-to-autocracy transitions.

The 1968 Iraq transition from rule by a military faction that had forced most Ba’thist officers into

retirement to the Ba’th-dominated regime that gave Saddam Hussein his start shows the weaknesses

17The substantive difference in these two political events is captured by the level of the Polityscale: Zaire 1992 receives a score of 0 (on a scale from -10 to +10) while Chile 1989 receives a scoreof +9. A linear model, such as that used in Ross (2001) and Haber and Menaldo (2011), whichemploys Polity as a quasi-continuous variable treats these two events as roughly equivalent becausethe change in the Polity score is roughly equivalent.

18Appendix C shows how results using the Durable variable differ from those using data fromGeddes, Wright and Frantz (2013).

19Further, the Polity Durable variable does not mark some cases of regime collapse, such asMobutu’s ouster in 1997, when the combined Polity score does not change.

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of both measures. With the CGV data, no transition is observed.20 With the Polity data, Iraq’s

combined Polity score decreases two points, which is insufficient to be considered regime change,

as most scholars use the data. Indeed, Polity’s Durable variable shows no change in Iraq from

independence to 2002, though several regime changes occurred during this period, including the

ouster of the monarchy in 1958. This is not to say these measures are not useful for many purposes,

but they are ill-suited for studying autocratic regime survival.

In this study, we use autocratic regime data from Geddes, Wright and Frantz (2013). These

data code the start and end dates of autocratic regimes, defined as the set of basic formal and

informal rules that identify the group from which leaders can come and the rules through which

leaders and policies are chosen.21 Regimes thus often span multiple leaders; and one autocratic

regime can follow another during a period of non-democratic rule. An autocratic regime ends, by

this definition, when the identity of the leadership group and other basic rules of the political game

change, even if the succeeding government is autocratic. The data identify regime beginnings and

ends to enable assessments of autocratic regime survival independent of whether democratization

followed regime breakdown.

Empirical Approach

In this study, we examine the relationship between oil wealth and autocratic regime survival and

therefore restrict the sample to autocratic cases. We use data on autocratic regimes from Geddes,

20Cheibub, Gandhi and Vreeland also have a measure that codes different types of dictatorshipby characteristics of their leaders (civilian, monarch, or military). However, this measure also failsto identify changes in which the outgoing dictatorship and its successor are led by individuals inthe same category. In the example from Iraq, because both the first and second regimes were led byofficers, both are coded as military dictatorships, and the period is viewed as a single, continuousmilitary regime.

21For a more detailed definition of regime and discussion of how regime change is coded in thedata set, see Geddes, Wright and Frantz (2013).

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Wright and Frantz (2013), which codes each regime failure and the subsequent government as dic-

tatorship or democracy. We first test models that group all autocratic regime collapses together

and then test models that estimate transitions to democracy and transitions to subsequent dicta-

torship separately. This enables us to identify whether oil affects autocratic survival by: alleviating

pressures for democratization, deterring new autocratic challenges, or both. When we test the

democratic transition model, we treat transitions to a subsequent dictatorship as right-censored

(and vice versa).22 In the main sample used below, with 261 distinct autocratic regimes in 114

countries from 1947-2007, there are more transitions to subsequent dictatorship (103) than tran-

sitions to democracy (93). This means that studies that include only democratic transitions will

miss more than half of the events that end autocratic regimes.

To measure oil, we use data on total oil income per capita from Haber and Menaldo (2011) and

thus test arguments about oil wealth, as opposed to oil dependence. This variable is the level of

crude oil production multiplied by the world oil price and then divided by population size.23 We

prefer to follow other recent studies such as Haber and Menaldo (2011) and Ross (2012), which use

a measure of oil wealth that does not include GDP in the denominator, because large fluctuations

in GDP can independently influence autocratic survival.24 This variable is highly skewed, so we

calculate its natural log (plus one).

Because autocracies with oil may be richer than other dictatorships, we include a control for

wealth, logged GDP per capita from the most recent version of Maddison (2010). There is also a

large literature linking natural resource wealth to civil conflict (e.g., Ross 2006). To avoid conflating

22Using a multinomial model, or competing risks approach, which excludes the other type ofregime collapse from the comparison category, yields results similar to those reported below.

23See the Appendix to Haber and Menaldo (2011) for information on oil data sources.24The main results reported below from Table 1, column 6 remain when we use: Oil

GDP ; the natural

log of OilGDP ; (log) oil per capita from Ross (2008); and (log) total fuel income per capita from Haber

and Menaldo (2011).

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the effects of oil with those caused by conflict, we include a binary indicator of civil war (lagged one

year) from the updated Gleditsch et al. (2002) data. Finally, we include a measure of neighboring

country democratic transitions in the prior year to make our analysis as comparable to Haber and

Menaldo’s as possible.25

Our analysis distinguishes between-country effects from within-country effects. Between-country

effects measure whether different average levels of oil wealth bear on regime survival (i.e. whether

autocratic regimes in countries like oil-rich Iraq last longer than those in countries like oil-poor

Tunisia). Most previous research focuses on how differences in levels of oil wealth among countries

affect politics. Within-country effects measure whether changes in amount of oil wealth in a single

country alter its prospects for regime change. Haber and Menaldo focus on whether within-country

changes in oil wealth affect levels of democracy. There is little consensus in the literature about

whether theories of the resource curse imply between-country differences or within-country ones.26

Both effects are of interest to academics and policy makers. The within-country effect better cap-

tures the idea that oil discovery sets nations on a different path of development, as proposed in

the resource curse literature and emphasized by Haber and Menaldo, but both relationships are

important for understanding of how oil affects autocratic durability.

Given the nature of our data, we estimate a non-linear model where the outcome variable

is a binary indicator of regime change, and account for duration dependence with polynomials of

duration time.27 This model estimates the probability of transition to a different regime (democracy

or subsequent autocracy) in time t, given autocratic rule in t-1.

25Neighboring countries are defined as those with capital cities within 4000 km of the targetcountry. The variable takes a value of 1 if one democratic transition takes place (20% of thesample), and 2 if more than one transition occurs (7% of the sample) in neighboring countries.

26Ross 2001; Haber and Menaldo 2011. Dunning (2008) also references this idea in his casestudies.

27Carter and Signorino 2010.

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Most of the empirical literature on the resource curse does not separate cross-national correla-

tions (between-country effects) from variation within countries (within-country effects) (e.g., Ross

2001; Smith 2004; Jensen and Wantchekon 2004; Ulfelder 2007; Morrison 2009; Bueno de Mesquita

and Smith 2010). To compare our results with theirs, we begin with this specification:

Pr(Yt = 1|Yt−1 = 0) = α0 + β1Ot−1 + β2Xt−1 + β3ϑi,t + β4ζt + µi,t (1)

In this equation, the measure of Oil (O) and the control variables (X) are lagged one year; (ζt) is

a calendar time trend to account for time-varying common shocks; and ϑi,t is a vector of duration

time polynomials to account for duration dependence.28

Yet, as Haber and Menaldo (2011) point out, this specification does not account for possible unit

effects. Their approach is to model unit heterogeneity directly by including country fixed effects,

thus isolating the within-country variation. With a quasi-continuous dependent variable (e.g., the

Polity scale) a fixed effects model is straightforward.29 With a binary dependent variable, however,

researchers typically use a conditional logit model, which accounts for factors that vary by country

and may be correlated with both the level of oil rents and the latent propensity for regime change.30

This is the method used by Haber and Menaldo in their estimation of the within-country effects

of changes in oil wealth on a binary measure of regime change (for more on this, see the online

Appendix that supplements their study). The second model we estimate is thus a conditional logit.

28This measure of duration time is not the same as the time elapsed since the onset of autocracy(i.e. the last year of democracy) but the time since the onset of the autocratic regime. See FigureA-1 in the Appendix for a discussion of non-proportional hazards.

29However, see Miller (2012) who argues that even with a continuous variable, fixed effects caninduce instability bias.

30Katz (2001) shows that a conditional logit model does not suffer from an incidental parametersproblem when T is larger than 18. In the sample, the maximum T is 61 and the average T in theunbalanced panels is 36.

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This strategy, however, drops countries from the analysis that do not experience regime change.

Haber and Menaldo’s analysis of regime change drops the 64 autocratic countries that did not

democratize during the period under study; the dropped cases include the Gulf monarchies and

other autocratic oil producers like Angola and Libya. In the model that includes both types of

regime collapse, a conditional logit drops 26 countries; in the democratic transition model, 51

countries; and in the model of transitions from autocracy to autocracy, 57 countries. Dropping

countries that do not experience regime change may bias estimates downward by selecting only

those where regime change has occurred in the sample period, particularly if those stable political

systems have high oil wealth. Below, we investigate the possibility that this restriction on the

sample induces selection bias.

To account for unit heterogeneity and isolate the influence of differences in oil wealth within

countries without dropping those that never experience regime change, we use an approach that

simultaneously models both between- and within-country effects.31 To do this, we employ a model

specification that conditions the marginal effects of the covariates on the country means for ex-

planatory variables, a strategy suggested by Mundlak (1978) and Chamberlain (1982). The logic of

this estimation procedure is to decompose the independent variables into their cross-country and

31An alternative approach for modeling unit heterogeneity is to include random effects. Thisassumes that Cov(Xi,t, αi) = 0. The results reported in Table 1 suggest this may not be the casebecause including Yi in the equation changes the estimates for both the mean level of and thedeviations in oil wealth, suggesting that αi is correlated with oil. Given our data, simultaneouslyestimating the time-varying and cross-country variation while including Yi also yields superiormodel information statistics (e.g. BIC) than a random effects model. A further alternative is aCox survival model with country strata. Similar to a conditional logit, this approach pools within-country variation. The main results for the within-country effects reported in Table 1 are similarto those using a stratified Cox model.

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within-country effects.32 This technique yields the following specification:

Pr(Yt = 1|Yt−1 = 0) = α0+β1(Oi,t−1−Oi)+β2(Xi,t−1−Xi)+β3Oi+β4Xi+β5ϑi,t+β6ζt+µi,t (2)

In equation (2), Oi and Xi are the country-means of Oil and the vector of control variables (used

to capture between-country effects), while (Oi,t−1 − Oi) and (Xi,t−1 − Xi) are the deviations from

the country means with which we measure within-country differences.33 Again we include calendar

time trend (ζt) and duration time polynomials (ϑi,t).

While this approach estimates the effect of within-country changes in oil wealth independently

of cross-country differences, it does not account for varying intercepts. In a non-linear model,

excluding the country-specific intercepts may be problematic because the estimated marginal effect

of covariates can be influenced by other covariates in the model, including the intercept, which is

constrained to be the same for all countries in (2). Therefore, we also test models that include

the country mean of the regime change variable on the right-hand side of the equation, further

accounting for unit heterogeneity by allowing the ‘intercept’ to vary by country.34 Unobserved

factors that vary by country and may also be correlated with the level of oil rents and the latent

32Zorn 2001.33In the table below, we refer to within-country effects as XDev or deviations. To estimate the

within-country effects, we compute the deviations using the lagged value so the change in thelevel of oil income within countries precedes the regime collapse event chronologically. Results aresimilar, though slightly stronger, when we use current year observations to calculate deviations.We do not decompose civil war and neighbor democracy into between and within effects becausethese variables contain mostly time-varying information.

34Adding the unit mean of the dependent variable as an explanatory variable also occurs whenevera linear model includes unit-fixed effects.

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propensity for regime collapse should be captured in the unit means for regime change.

Pr(Yt = 1|Yt−1 = 0) =α0 + β1(Oi,t−1 − Oi) + β2(Xi,t−1 − Xi)+

β3Oi + β4Xi + β5ϑi,t + β6ζt + β7Yi + µi,t

(3)

The only difference between (2) and (3) is the inclusion of Yi on the right side of (3).35

Because the data transformations in (3) are an attempt to mimic a conditional logit without

dropping countries that do not experience transition during the sample period, we interpret the

coefficient for the lagged deviations, β1, as we would a coefficient from a conditional logit model:

the marginal effect of changes in the level of oil within countries. In our tables, O refers to between-

country effects, or how variation in average levels of oil wealth across countries affects autocratic

survival; OilDev refers to within-country effects, or how changes in oil wealth within a country

affects survival. OilDev measures how different the current year’s oil wealth is from the country’s

average oil wealth for the whole period, not year-on-year changes. It thus captures the effects of

both new discoveries and international price increases.

Finally, we test equations (2) and (3) both on the full sample of autocracies and on the restricted

sample of those that experienced regime change to see whether omitting the most stable cases

changes the results. The size of the restricted sample varies, depending on which type of regime

change we examine. For all regime changes, the restricted sample includes 88 countries (compared

to the full sample of 114); for democratic transitions, it includes 63 countries; and for transitions

to a subsequent dictatorship, it includes 57 countries. Comparing the results from the restricted

sample with those from the full sample helps us understand the extent to which sample restrictions

35Equation (3) is not a dynamic model as defined by including differenced explanatory variableson the RHS. We tested for the possibility that the differenced deviations should be included in themodel, and a Wald-test indicates that they are not jointly significant (De Boef and Keele 2008).

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necessitated by the use of conditional logit change estimates of the effects of oil on regime survival.

Results

The first column of Table 1 reports the results of an ordinary logit model with lagged explanatory

variables, the approach used in much of the literature; the second column reports the results of a

conditional logit model, showing the effect of accounting for unit effects while dropping countries

with stable authoritarianism; the third and fourth columns report ordinary logit models, but es-

timate effects of between- and within-country differences in the explanatory variables separately

(column 3 uses the restricted sample and column 4 the full sample); last, the fifth and sixth columns

do the same, but include the mean of the dependent variable in the specification. The top panel (A)

reports the results of these six models for all regime changes regardless of what kind of government

followed the initial autocracy. The middle panel (B) shows results for autocratic collapses that

resulted in democratization, and the bottom panel (C) for transitions to subsequent dictatorship.

Looking at all types of regime change (Panel A), the results indicate that oil resources are

associated with a reduced likelihood of autocratic breakdown. This relationship holds regardless

of the model used. Leaving out the most stable autocracies increases the effect of oil wealth in

models that combine between- and within-country effects (column 1 and 2), but has little effect in

the models that estimate them separately. Estimates for differences in oil income within individual

countries are only statistically significant at conventional levels when the mean of the dependent

variable is included (columns 5 and 6), but the sign is the same in the specifications that do

not include it. These tests suggest that greater oil wealth is associated with greater autocratic

durability, both across countries and across differences in oil wealth within countries. Dictatorships

in oil-rich countries are more able to resist ouster than dictatorships lacking these resources (a

Table 1: Oil income and autocratic regime survival

(a) All Regime Failures

Sample Full Restricted Restricted Full Restricted FullInclude DV No No No Yes Yes

(1) (2) (3) (4) (5) (6)

Oilt−1 -0.121** -0.265**(0.04) (0.10)

Oili -0.092* -0.111** -0.053+ -0.071*(0.04) (0.04) (0.03) (0.03)

OilDev -0.196 -0.166 -0.300* -0.290*(0.12) (0.13) (0.14) (0.13)

¯Faili 13.212** 14.901**(1.15) (1.37)

Area under ROC 0.671 0.631 0.672 0.748 0.799Observations 4138 3176 3176 4138 3176 4138Countries 114 88 88 114 88 114

(b) Democratic Transitions


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.153** -0.002(0.05) (0.17)

Oili -0.113+ -0.224** -0.089+ -0.169**(0.07) (0.07) (0.05) (0.06)

OilDev 0.032 0.107 -0.055 -0.038(0.20) (0.16) (0.21) (0.19)

¯Demi 17.634** 24.107**(2.16) (3.55)


(c) Autocratic Transitions


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.055 -0.325*(0.05) (0.13)

Oili -0.097+ -0.014 -0.075+ -0.023(0.05) (0.05) (0.04) (0.05)

OilDev -0.263* -0.280* -0.324* -0.347*(0.12) (0.14) (0.13) (0.14)

¯Dicti 14.344** 19.877**(1.74) (2.71)


+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Conditional logit in column 2. Ordinary logit witherrors clustered on country in all other columns. Time dependence polynomials (3); cal-endar time polynomials (3); and control variables (GDP per capita, Civil War, NeighborDemocracy) included in all models but not reported. Years: 1947-2007.

between-country effect); and increases in a dictatorship’s oil rents, whether from new discoveries

or price hikes, also make it more resilient to regime collapse (a within-country effect).

We next separate democratic transitions from autocratic transitions, which reveals a more

nuanced picture. Looking only at democratic transitions (Panel B), as nearly all prior research

has done, the ordinary logit model (column 1) that includes all cases and lumps between- and

within-country differences together yields a negative coefficient for oil, consistent with much of

the literature.36 The conditional logit model (column 2) that excludes all the cases that never

democratized – one of the models used by Haber and Menaldo (2011) – shows no effect of oil

wealth on the likelihood of democratization. Eliminating the most stable cases while focusing on

within-country variation prevents us from seeing the relationship. It should not be surprising that

if about 45 percent of the most stable autocracies are excluded from the sample, a different estimate

results.

When the between- and within-country effects are disaggregated in columns 3 and 4, we find that

exclusion of the most stable autocracies from the sample changes the estimate of the cross-country

differences but not the estimate of within-country differences.37 The estimate for the full sample

indicates a stronger negative cross-country correlation between oil wealth and democratization.

Changes in oil wealth within countries, however, have no effect on the likelihood of democratization,

regardless of whether all cases are included in the analysis (as in columns 4 and 6) or not (as in

columns 3 and 5). The negative cross-country correlation is consistent with the bulk of the

literature on the oil curse, while the absence of a within-country association is consistent with the

main finding in Haber and Menaldo (2011). The results from Panel B suggest that the negative

36Ross 2009; 2012; Ulfelder 2007.37See Appendix Table B-2 for related discussion of this issue using the Haber and Menaldo (2011)

model.

18

relationship between oil and the likelihood of democratization picked up in the ordinary logit model

without unit effects (column 1) and revealed in much of the prior literature is due to the cross-

country variation in average levels of oil wealth. Below we discuss possible interpretations of this

finding.

Since more than half of autocratic regime collapses are followed by new dictatorships, we also

investigate the effect of oil wealth on the likelihood of collapse that is not followed by democratiza-

tion in Panel C. We find that increases in oil wealth reduce the likelihood of autocracy-to-autocracy

regime changes. This finding shows up in the conditional logit model in column 2 and is replicated

in all models and samples that look separately at between- and within-country effects (columns

3-6). Including the average of the dependent variable, ¯Dictatorship, as a control strengthens this

result but excluding the most stable countries has little effect on the estimate. Finally, we find

little evidence that cross-country differences in oil wealth deter autocracy-to-autocracy transitions,

either in the ordinary logit model (column 1) or in the simultaneous models (as indicated by the

coefficient for Oili in columns 3-6). The only models in which these differences approach statistical

significance use the restricted sample, which omits the most resilient half of the cases. The main

result in this panel, then, is that increasing oil wealth within dictatorships decreases the likelihood

of transitions to new dictatorship; but cross-country differences in oil wealth do not affect the

likelihood of such transitions.

Excluding cases that have not experienced transitions has less effect on estimates when the

outcome is autocratic transition (Panel C) than when it is democratization (Panel B). That is,

countries that have never democratized are more different from countries that have experienced at

least one democratization than countries that have never experienced an autocracy-to-autocracy

transition are from other countries that have been stably autocratic.

19

Comparing the estimates of within-country effects shown in Panel B with those shown in Panel

C indicates that higher levels of oil wealth relative to the country average prolong autocratic survival

(as also shown in Panel A for the combined sample that includes both kinds of transition) even if

they do not deter democratization. In short, increases in oil wealth fuel dictatorships by preventing

ouster by groups that organize new dictatorships, not by undermining efforts to democratize.

Figure 2 shows the substantive effect of the main results.38 The upper left figure shows that

moving the average level of oil income from the 10th percentile to the 90th percentile lowers the

predicted probability of democratic transition from 2.6% to 0.9% (the effect of between-country

differences). For autocracy-to-autocracy transitions, however, the between-country effect is negli-

gible. The lower right panel shows that moving the change in oil income from the 10th percentile

to the 90th percentile lowers the predicted probability of autocracy-to-autocracy transition from

2.3% to 0.7% (the within-country effect). That is, decreases in oil income are associated with a

predicted risk of ouster by an autocratic challenger more than three times as large as the predicted

risk when oil income in a country rises. Changes in oil income (within-country differences) have

little influence on the likelihood of democratization.

Robustness tests

To understand how sensitive the results are to geographic variation, we test the main finding for

each type of regime collapse while excluding one geographic region at a time (A-1).39 The results

38Simulations conducted use the results reported in Table 1, B.6 and C.6. Regime duration isset at the median value and calendar year is set to 1991. GDP per capita (mean and deviation),Civil War, Neighbor Democracy, and mean democracy set to their respective in-sample means.

39The regions are: Middle East and North Africa, Latin America, Asia, Sub-Saharan Africa, andthe European Union. Using the specification in column 6 from Table 1, we add a binary variablefor the ‘excluded’ region and interactions between region and Oil and between region and OilDev.The coefficient estimates for Oil and OilDev then capture the marginal effect of these variables forall regions except for the ‘excluded’ region. We report the Wald test for the interaction terms,

20

Democratic Transition Autocratic Transition

0

.01

.02

.03

.04

Pr(

De

mo

cra

tic T

ran

s.)

0 1 2 3 4 5 6 7

Country-means for oil rents per capita (log)

0

.01

.02

.03

.04

Pr(

Au

tocra

tic T

ran

s.)

0 1 2 3 4 5 6 7

Country-means for oil rents per capita (log)

0

.01

.02

.03

.04

Pr(

De

mo

cra

tic T

ran

s.)

-1.5 -1 -.5 0 .5 1 1.5

Deviations from country-means

0

.01

.02

.03

.04

Pr(

Au

tocra

tic T

ran

s.)

-1.5 -1 -.5 0 .5 1 1.5

Deviations from country-means

Figure 2: Between and within effects of oil income, by type of regime failure. Top paneldisplays the simulated predicted probability of regime transition across a range of values for theaverage level of oil income, with deviations set at zero (the median value of deviations for countrieswith a mean oil income value greater than zero). The lower panel depicts the predicted probabilityof transition across a range of values for deviation in oil income within countries with mean levelsgreater than zero. Mean oil is set at the median above zero, or 3.18 log units. Dotted lines depict90% confidence intervals. See footnote 28 for details on the simulations.

21

indicate that the main findings are robust to the exclusion of any one region. The only notable

change that arises occurs when we exclude the Middle East and North Africa, which produces

a positive but non-significant coefficient for OilDev, the measure of within-country differences, in

the model of democratization. This suggests that studies using models that exclude much of this

region (e.g., conditional fixed effects models that exclude countries that have not democratized)

may produce upwardly biased estimates of the effect of within-country oil wealth on the likelihood

of democratization.

The two main empirical results – the negative cross-country effect of average oil wealth on

democratic transition and the negative within-country effect of increases in oil wealth on transitions

to subsequent dictatorship – are robust to different measures of oil: Ross’s (2008) oil and gas rents

(A-2) and Haber and Menaldo’s (2011) total fuel income (A-3). Further, Tables A-4 and A-5

illustrate that the negative cross-country correlation with democracy persists in a purely cross-

sectional regression of unit means, and that the negative within-country result for transitions to

subsequent dictatorship persists in a linear probability model with country and year fixed effects.40

We replicate the negative cross-country correlation using the CGV data on democratic transitions,

which is the measure of regime change used by Haber and Menaldo in their conditional logit model

(A-6). The results persist when we use year fixed effects and when we include a time-varying control

for world oil price (A-7). These tests are presented in the Appendix.

Andersen and Ross (2014) point out that a wave of oil industry nationalizations in the 1960s and

1970s may have changed the way oil wealth influences political development in autocracies by vastly

increasing the revenues they could use. They argue that the most appropriate test of the oil curse

which estimates whether these interaction terms are jointly significant.40We acknowledge concerns with a linear probability model (heteroskedasticity and unbounded

predicted values) but provide this evidence to facilitate comparison with studies that employ linearmodels of democratic transition. See, for example, Brukner, Ciccone and Tesei (2012).

22

should therefore focus on the period after 1979. Following their strategy of interacting the main

oil variables with a time period dummy, we find that the main results are indeed stronger for the

period from 1980-2007 than from 1947-1979. The effect of increases in oil income on the likelihood

of transition to a new autocracy, however, remains statistically and substantively significant in the

earlier period (A-8).41

Endogenous oil income

A final robustness test addresses the potentially endogenous relationship between oil income and

regime collapse. While we have directly modeled unobserved unit heterogeneity, time-varying

expectations about regime survival may still influence oil investment and production. If autocratic

elites believe opponents are likely to gain power in the future, they may reduce investment in the

oil sector so opponents will not reap the benefits of this investment down the road.42 Alternatively,

an unstable regime with a short time horizon may maximize current production to capture as much

rent income as possible to survive an imminent crisis. Thus the bias introduced by potentially

endogenous oil rents could work in either direction.

To address this issue, we use known oil reserves with a five-year lag as an instrument for oil

income.43 Oil reserves, especially with a longer lag, are less likely than oil income to be influenced

by expectations about near-term political survival. Further, although reserves are not distributed

41The cross-country correlation between average level of oil wealth and democratization, however,is negative and statistically significant only in the later period. Andersen and Ross (2014) also arguethat the effect of oil occurs over periods greater than a year because many oil-rich countries havesovereign wealth funds or other mechanisms for smoothing short-term fluctuations in oil income.To investigate this, we use longer lagged moving averages of oil income to calculate the deviationvariable. The main result for within-country changes in oil wealth remains, though it becomesslightly weaker as the moving average increases over more lagged years. See Figure A-2.

42Dunning 2010.43Data on oil reserves are from Haber and Menaldo (2011).

23

randomly across the world, we can model time-invariant geological factors linked to both reserves

and long-term political outcomes with unit fixed effects. Employing this instrument conditional on

unit effects accounts for non-oil channels – such as factor endowments, the geographic determinants

of trade, and long-term economic development – through which cross-sectional variation in reserves

may influence politics.

Appendix (D) includes graphs depicting the de-meaned values of logged oil income per capita

(1-year lag) and logged oil reserves per capita (5-year lag) for dictatorships in five countries: Angola,

China, Egypt, Malaysia, and Mexico. These graphs illustrate that while over-time changes in oil

reserves are correlated with oil income, many of the year-to-year fluctuations in oil income that may

reflect political expectations do not correspond to known reserves. For example, in Angola lagged

reserves were stagnant after 2000 but oil income increased steadily after the death of the long-time

rebel leader, Jonas Savimbi, in 2002 (and the increase in international oil prices). Expectations

about increased political stability after Savimbi’s death may have contributed to both the rise in oil

income and regime longevity. In this case, while oil reserves are correlated with oil income over a

30-year period, the former are not correlated with the potentially endogenous increase in oil income

after 2002.

We test the specification in (3) with two estimators.44 First we use a two-stage probit model

with ReservesDev as an excluded instrument for OilDev. Second we employ a two-stage linear

probability model. F-tests from the first stage indicate a strong instrument; the partial R2 for oil

reserves is 0.46. Because the sample is restricted to observations with non-missing values for lagged

reserves, we also report naive models with the same sample for comparison.

The results are consistent with the findings in Table 1: a positive, though substantively small,

44For these tests, we treat Oili as a proxy for unit effects and thus interpret it as a controlvariable.

24

relationship between differences in oil income and the likelihood of democratic transition, but a

negative and substantively strong effect on the risk of transition to a new autocracy. With both

estimators, the IV result is slightly weaker than the naive result and only statistically significant

at the 0.14 level or lower. These results suggest that if we believe lagged oil reserves are plausibly

exogenous to short-term expectations about political survival, then once we account for time-

invariant unit effects these correlations can be interpreted as causal.

Oil income and military spending

The evidence presented here indicates that oil bolsters autocratic survival by reducing the likelihood

of successful grabs for power by groups intent on displacing the current elite and initiating new

forms of autocracy. In this section, we suggest a potential mechanism through which this could

occur. Regime ousters that result in new dictatorships are usually brought about by military

coups.45 Officers can respond to decreased wages (and other problems that might be caused by

declines in oil revenue) by ousting the regime more quickly than civilians can. Because officers’ cost

of organizing is typically lower than that of civilians, coups can be carried out by small factions of

the military. A few officers with control of a few hundred troops and weapons have toppled many

governments. Civilian opponents of dictatorships, in contrast, typically have to organize large

numbers to succeed. It may also be easier for autocratic regimes to use increasing oil wealth to co-

opt challengers in the officer corps by buying new weapons, raising military wages, and providing

other benefits than to develop the kinds of institutions needed to reach masses of citizens with

sufficient benefits to deter demands for democracy. For these reasons, fluctuations in oil revenues

may have more effect on potential challenges that come primarily from the military than those that

45Geddes 2003.

25

require widespread civilian support to be effective, as democratization usually does.

If increasing oil income reduces the threat from military insiders, we would expect oil wealth to

be correlated with military spending. We test this proposition with an error-correction approach

to model the influence of oil income on both short- and long-term patterns of military spending.46

4Spend = α0 + β1Spendt−1 + β24Oil + β3Oilt−1 + β44X + β5Xt−1 + β6ζt + µi,t (4)

We use military spending data from the Correlates of War project. The dependent variable

is logged real military spending. The specification in (4) includes a common (quadratic) calendar

time trend, ζt, and controls for civil and interstate conflict and population size in X. β2 estimates

the short-term influence of oil income on military spending, while β3 estimates the long-term effect.

The first column of Table 2 does not include country fixed effects, while the second does. The

third column adds country-specific time trends; and the fourth adds GDP per capita, neighbor

democracy, and regime duration. In all specifications, estimates of β2 and β3 are positive and

statistically different from zero.47 The substantive effect in model (2) is large: a one-standard

deviation increase in oil income is associated with a roughly 12 percent short-term increase in

military spending while the same change in oil rents is associated with a 6 percent long-run increase

in spending. The estimate of the long-run multiplier suggests that this increase in oil income is

associated with a 25 percent boost to the equilibrium level of military spending. In short, there is

a strong correlation between oil income and military spending, lending credence to the proposition

46De Boef and Keele 2008.47Appendix E describes robustness tests; offers a table with the summary statistics; and provides

graphs that: (1) depict the distribution of military spending in the sample, and (2) show thecross-country correlation between oil income and military spending.

26

Table 2: Oil income and military spending

Model (1) (2) (3) (4)

4 Oil 0.038+ 0.041+ 0.077** 0.059*(0.02) (0.02) (0.03) (0.03)

Oil 0.022** 0.024+ 0.068** 0.033+(0.01) (0.01) (0.02) (0.02)

4 Civil war -0.004 0.020 0.014 0.014(0.03) (0.03) (0.03) (0.03)

Civil wart−1 0.004 0.067* 0.061 0.070+(0.02) (0.03) (0.04) (0.04)

4 International war 0.074* 0.123** 0.145** 0.150**(0.04) (0.04) (0.05) (0.05)

International wart−1 -0.020 0.082 0.152+ 0.171*(0.03) (0.06) (0.08) (0.08)

4 Population 0.118 0.131 -0.278 -0.442+(0.30) (0.23) (0.21) (0.24)

Populationt−1 0.035** 0.294** -0.204 0.000(0.01) (0.09) (0.30) (0.31)

4 Neighbor democracy -0.020(0.01)

Neighbor democracyt−1 -0.037*(0.02)

4 GDP per capita 0.369**(0.13)

GDP per capitat−1 0.422**(0.10)

4 Duration -0.003(0.00)

Durationt−1 -0.003(0.00)

Military spendingt−1 -0.054** -0.257** -0.467** -0.467**(0.01) (0.04) (0.06) (0.06)

(Intercept) 0.519** 0.791 6.684* 1.329(0.08) (0.66) (2.84) (3.06)

R2 0.048 0.188 0.337 0.321n 3288 3288 3288 3244

time trend Y Y Y Ycountry FE N Y Y Ycountry-specific time trend N N Y Y

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variable is 4Spending.OLS with clustered standard errors. Country effects and quadratictime trend not reported.

27

that oil wealth increases the survival of autocratic regimes by enabling them to buy the military’s

support and lower the risk of coups.

Discussion

Our findings suggest that increases in oil wealth stabilize autocratic regimes, not by deterring de-

mocratization, but by reducing the vulnerability of dictatorships to ouster by groups that establish

subsequent dictatorships. This finding implies that increases in oil rents reinforce the durability of

regimes like Iran’s, whereas falling oil prices or the exhaustion of reserves destabilizes them. Thus

we find that oil makes dictatorships more resilient, but not via the means suggested by the existing

literature. We find little evidence that increases in oil wealth prop up dictatorships by mitigating

the threat of democratization. Rather, the evidence indicates that oil bolsters autocratic survival

by reducing the likelihood of successful seizures of power by groups intent on displacing the current

elite and initiating new forms of autocracy.

Other theories linking oil wealth to autocratic stability argue that groups outside the regime

elite, such as mobilized poor citizens and revolutionary movements, are the key threats to regime

leaders.48 Our findings point to a different mechanism: increasing oil income over time reduces

the risk that the military will topple the regime. This explanation is consistent with the general

pattern of autocratic leader ousters, as well. Svolik (2009), for example, shows that regime insiders,

including the military, cause more than two-thirds of non-constitutional leader exits in dictatorships,

not opposition outsiders.

We also find support for the existence of a negative cross-country correlation between average

oil wealth and democratization. It may be that the average oil wealth for each country reflects

48Morrison 2009; Bueno de Mesquita and Smith 2010.

28

the development of institutional configurations in many oil-producing countries – for example, the

development of the administrative networks needed to deliver health care and other benefits to

most citizens – and thus should be interpreted as confirming the resource curse argument. If we

believe that such institutions take time to develop, as Andersen and Ross (2014) have argued, and

are characterized by substantial inertia, we would not expect institutional changes in short time

periods. Such institutional differences might be better captured by the country averages than by

within-country fluctuations in oil wealth. We would not expect institutions such as legislatures

that organize patronage distribution, administrative networks for the delivery of subsidies to large

numbers of citizens, or the size of well-armed and trained security services to be much changed by

short-term price changes, even if they were quite large. We would, however, expect the incremental

development of such institutions if oil revenues are high on average and autocratic regimes can

increase their chances of survival by spending oil revenue on co-optation and repression.

However, cross-country differences in oil wealth may simply reflect differences in state capacity

or something else that predates the discovery of oil. Haber and Menaldo (2011) argue that such

preexisting conditions might themselves be the main cause of subsequent resistance to democra-

tization, making the oft-found correlation spurious, a point that must be taken seriously. The

specification used in Table 1 cannot arbitrate between these possibilities (nor can most of those

used in other empirical analyses of the oil curse). We see understanding whether the cross-country

correlation reflects characteristic and relatively stable institutional changes caused by oil wealth or

simply identifies preexisting conditions as a next step in this research agenda.

29

Conclusion

The resource curse theory implies that autocratic regimes with oil wealth should survive longer

than those lacking oil. Most studies have explored whether this occurs because oil-rich regimes

are able to withstand pressures for democratization. As a result, previous research has focused on

whether oil wealth is correlated with measures of democracy or ‘democraticness,’ not the effect of oil

on autocratic survival. Yet oil may also prolong dictatorships by protecting them from seizures of

power by new autocratic groups. Earlier studies have largely ignored autocratic regime breakdowns

not followed by democratization, though these constitute more than half of all autocratic regime

collapses. We examined oil’s impact on autocratic regime survival, exploring whether oil makes

democratic transitions, as well as autocracy-to-autocracy transitions, more or less likely.

Most prior research also lumps cross-country differences in oil wealth with within-country

changes, making it impossible to be sure that the differences identified do not predate the discovery

of oil. Haber and Menaldo (2011) overcome this problem, but their approach49 eliminates the most

resilient autocracies, including a number of the countries that produce much of the world’s oil from

the analysis. We used a different approach to separate cross-national effects from within-country

effects while still incorporating information from the most stable autocracies.

We find that increases in oil income stabilize dictatorships, not by limiting prospects for democ-

ratization, but by helping to avert regime collapses that lead to subsequent dictatorship. These

results indicate that upswings in oil wealth within particular countries stabilize autocracies by re-

ducing the risk of ouster followed by a new dictatorship, but that changes in oil wealth do not affect

49Here we reference the conditional logit models presented in their Appendix, where the de-pendent variable is a binary measure of regime change. The bulk of the models they employ usecombined Polity scores as the dependent variable instead, which is also problematic (as discussedearlier), but for different reasons.

30

the chances a dictatorship will democratize. When oil wealth increases, autocratic regimes become

more resilient to the threat of a new dictatorship, perhaps by enabling the regime to purchase the

continued support of the military. Consistent with this interpretation, we find evidence that oil

income increases spending on the military in dictatorships.

We also find evidence that higher average oil wealth is associated with reduced prospects for

democratization, consistent with much of the literature. We are cautious about interpreting this

finding, however. It may be that so long as oil-rich autocracies ‘share the wealth,’ the quality of

life for their citizens will be high enough to make democratization less appealing, as Ross (2009)

suggests, and that higher average levels of oil wealth have made possible the long-term development

of the institutions required for sharing it. We cannot, however, rule out the possibility that the

cross-country differences reflect differences that predate the discovery of oil in many countries.

Until future research disentangles whether preexisting conditions affect later regime stability, we

cannot be sure that the apparent relationship is not spurious.

The central message of our study is that increases in oil wealth over time help dictatorships to

hang onto power. This does not, however, imply that oil is a curse for citizens in countries endowed

with it. Although such citizens might be better off in a democracy, they might be worse off in a

country in which one dictatorship follows another in rapid, often bloody succession.

References

Andersen, Jørgen Juel and Michael L. Ross. 2014. “The Big Oil Change: A Closer Look at the

Haber-Menaldo Data.” Comparative Political Studies forthcoming.

Andersen, Jørgen Juel and Silje Aslaksen. 2013. “Oil and Political Survival.” Journal of Develop-

31

ment Economics 100:89–106.

Anderson, Lisa. 1991. “The State in the Middle East and North Africa.” Comparative Politics

20(1):1–18.

Auty, Richard M. 2001. Resource Abundance and Economic Development. Oxford: Oxford Univ.

Press.

Basedau, Matthias and Jann Lay. 2009. “Resource Curse or Rentier Peace? The Ambiguous Effects

of Oil Wealth and Oil Dependence on Violent Conflict.” Journal of Peace Research 46(6):757–776.

Beblawi, Hazem and Giacomo Luciani. 1987. The Rentier State. New York: Croom Helm.

Boix, Carles. 2003. Democracy and Redistribution. New York: Cambridge University Press.

Brukner, Markus, Antonio Ciccone and Andrea Tesei. 2012. “Oil Price Shocks, Income, and Democ-

racy.” Rev. of Economics and Statistics 94:389–399.

Bueno de Mesquita, Bruce and Alastair Smith. 2010. “Leader Survival, Revolutions, and the Nature

of Government Finance.” American Journal of Political Science 54(4):936–950.

Burrowes, Robert D. 1987. The Yemen Arab Republic: The Politics of Development, 1962-1986.

Boulder: Westview Press.

Carter, David and Curt Signorino. 2010. “Back to the Future: Modeling Time Dependence in

Binary Data.” Political Analysis 18(3):271–292.

Chamberlain, Gary. 1982. “Multivariate Regression Models for Panel Data.” Journal of Economet-

rics 18(1):5–46.

32

Cheibub, Jose Antonio, Jennifer Gandhi and James Raymond Vreeland. 2010. “Democracy and

Dictatorship Revisited.” Public Choice 143(1-2):67–101.

Crystal, Jill. 1995. Oil and Politics in the Gulf: Rulers and Merchants in Kuwait and Qatar.

Cambridge: Cambridge Univ. Press. First published in 1990.

Cuaresma, Jesus Crespo, Harald Oberhofer and Paul Raschky. 2011. “Oil and the Duration of

Dictatorships.” Public Choice 148(3-4):505–530.

De Boef, Suzanna and Luke Keele. 2008. “Taking Time Seriously.” American Journal of Political

Science 52(1):184–200.

Dunning, Thad. 2008. Crude Democracy: Natural Resource Wealth and Political Regimes. New

York: Cambridge Univ. Press.

Dunning, Thad. 2010. “Endogenous Oil Rents.” Comparative Political Studies 43(3):379–410.

Geddes, Barbara. 2003. Paradigms and Sand Castles. Ann Arbor: Univ. of Michigan Press.

Geddes, Barbara, Joseph Wright and Erica Frantz. 2013. “New Data Set: Autocratic Breakdown

and Regime Transitions.” Unpublished manuscipt.

Gleditsch, Kristian and Jinhee Choung. 2004. “Autocratic Transitions and Democratization.” Paper

presented at the Annual International Studies Association Convention (Montreal).

Gleditsch, Kristian and Michael D. Ward. 1997. “Double Take: A Re-Examination of Democracy

and Autocracy in Modern Polities.” Journal of Conflict Resolution 41(3):361–383.

Gleditsch, Nils Tetter, P. Wallensteen, M. Eriksson, M. Sollenberg, and H. Strand. 2002. “Armed

Conflict 1946-2001: A New Dataset.” Journal of Peace Research 39(5):615–637.

33

Haber, Stephen and Victor Menaldo. 2011. “Do Natural Resources Fuel Authoritarianism? A

Reappraisal of the Resource Curse.” American Political Science Rev. 105(1):1–26.

Herb, Michael. 2005. “No Taxation without Representation? Rents, Development, and Democracy.”

Comparative Politics 37(3):297–317.

Jensen, Nathan and Leonard Wantchekon. 2004. “Resource Wealth and Political Regimes in Africa.”

Comparative Political Studies 37(7):816–841.

Karl, Terry Lynn. 1997. The Paradox of Plenty. London: Univ. of California Press.

Katz, Ethan. 2001. “Bias in Conditional and Unconditional Fixed Effects Logit Estimation.”

Political Analysis 9(4):379–384.

Luciani, Giacomo. 1990. Allocation vs. Production States: A Theoretical Framework. In The Arab

State, ed. Giacomo Luciani. Berkeley: Univ. of California Press.

Maddison, Angus. 2010. “World Population, GDP and Per Capita GDP, 1-2008 AD.” http:

//www.ggdc.net/maddison.

Mahdavy, Hussein. 1970. The Patterns and Problems of Economic Development in Rentier States:

The Case of Iran. In Studies in the Economic History of the Middle East, ed. M.A. Cook. London:

Oxford Univ. Press.

McFaul, Michael. 2002. “The Fourth Wave of Democracy and Dictatorship: Noncooperative Tran-

sitions in the Postcommunist World.” World Politics 54(2):212–244.

Miller, Michael K. 2012. “Fixed Effects and Instability Bias.” Mimeo, Australian National Univ.

34

http://www.ggdc.net/maddison

http://www.ggdc.net/maddison

Morrison, Kevin. 2009. “Oil, Non-Tax Revenue, and the Redistributional Foundations of Regime

Stability.” International Organization 63(1):107–138.

Mundlak, Yair. 1978. “On the Pooling of Time Series and Cross Section Data.” Econometrica

46(1):69–85.

Pemstein, Daniel, Stephen Meserve and James Mellon. 2010. “Democratic Compromise: A Latent

Variable Analysis of Ten Measures of Regime Type.” Political Analysis 4(18):426–449.

Polity IV. 2010. “Political Regime Characteristics and Transitions, 1800-2010.” http://www.

systemicpeace.org/polity/polity4.htm.

Ramsay, Kris. 2011. “Revisiting the Resource Curse: Natural Disasters, the Price of Oil, and

Democracy.” International Organization 65(3):507–529.

Ross, Michael L. 2001. “Does Oil Hinder Democracy?” World Politics 53(3):321–65.

Ross, Michael L. 2006. “A Closer Look at Oil, Diamonds, and Civil War.” Annual Rev. of Political

Science (9):265–300.

Ross, Michael L. 2008. “Oil, Islam, and Women.” American Political Science Rev. 102(1):107–123.

Ross, Michael L. 2009. “Oil and Democracy Revisited.” Mimeo, UCLA.

Ross, Michael L. 2012. The Oil Curse: How Petroleum Wealth Shapes the Development of Nations.

Princeton, NJ: Princeton Univ. Press.

Sachs, Jeffrey D. and Andrew M. Warner. 1995. Natural Resource Abundance and Economic Growth.

Cambridge, MA: National Bureau of Economic Research. Working Paper 5398.

35

http://www.systemicpeace.org/polity/polity4.htm

http://www.systemicpeace.org/polity/polity4.htm

Smith, Benjamin. 2004. “Oil Wealth and Regime Survival in the Developing World: 1960-1999.”

American Journal of Political Science 48(2):232–246.

Svolik, Milan. 2009. “Power Sharing and Leadership Dynamics in Authoritarian Regimes.” Amer-

ican Journal of Political Science 53(2):477–494.

Treier, Shawn and Simon Jackman. 2008. “Democracy as a Latent Variable.” American Journal of

Political Science 1(52):201–217.

Tsui, Kevin. 2011. “More Oil, Less Democracy: Evidence from Worldwide Crude Oil Discoveries.”

The Economic Journal 121(551):89–115.

Ulfelder, Jay. 2007. “Natural-Resource Wealth and the Survival of Autocracy.” Comparative Polit-

ical Studies 40(8):995–1018.

Vandewalle, Dirk. 1998. Libya since Independence: Oil and State-Building. Ithaca: Cornell Univ.

Press.

Wintrobe, Ronald. 1998. The Political Economy of Dictatorship. Cambridge and New York:

Cambridge Univ. Press.

Zorn, Christopher. 2001. “Estimating Between- and Within-Cluster Covariate Effects, with an

Application to Models of International Disputes.” International Interactions 27(4):433–445.

36

Online Supplementary Material

Tables & Figures page

Table S-1: Summary statistics 38Table S-2: Regime collapse events 39Table S-3: Full model results, Table 1 (a) 40Table S-4: Full model results, Table 1 (b) 41Table S-5: Full model results, Table 1 (c) 42Table A-1: Robustness to excluding geographic regions 44Table A-2: Ross 2008 Oil & Gas rents 45Table A-3: Haber & Menaldo 2011 Fuel Income 46Table A-4: Regression of country means 47Table A-5: Linear probability model (country, year FE) 48Table A-6: Oil income and democratic transition w/ ACLP/CGV data 49Table A-7: Modeling calendar time and oil price 50Table A-8: Time period results, pre-1980 & post-1979 51Figure A-1: Non-proportional hazard for Oil Deviations, Autocratic transitions 52Figure A-2: Longer lagged moving averages for deviations in oil income 53Figure A-3: Separation plots 54Table B-1: Replication and extension of Haber & Menaldo (2011) conditional logit 57Table B-2: Potential bias from sample restriction (HM data) 58Table B-3: Potential bias in the conditional logit (GWF data) 59Table C-1: Comparing Polity Durable Failure and GWF Autocratic Regime Collapse 62Table C-2: Autocratic Regime Collapses but No Durable Failure 63Table C-3: Durable Failures but No Autocratic Regime Collapse 64Table D-1: First-stage equations 67Table D-2: Second-stage IV results 68Figure D-1: Oil reserves (5-year lag) and oil income (1-year lag) in Angola 69Figure D-2: Oil reserves (5-year lag) and oil income (1-year lag) in China 70Figure D-3: Oil reserves (5-year lag) and oil income (1-year lag) in Egypt 71Figure D-4: Oil reserves (5-year lag) and oil income (1-year lag) in Malaysia 72Figure D-5: Oil reserves (5-year lag) and oil income (1-year lag) in Mexico 73Figure D-6: First-stage partial correlation, Oil reserves and Oil income. 74Figure E-1: Military spending in autocratic regimes 75Figure E-2: Oil income and military spending in autocratic regimes 76Table E-1: Summary statistics, military spending 77

37

Data Appendix: Summary statistics and sample regime collapses

Table S-1: Summary statistics

Variable N Mean Std Dev Min Max

Autocratic Transition 4138 0.024 0.155 0 1Democratic Transition 4138 0.022 0.148 0 1Regime duration 4138 19.2 17.3 1 105Calendar time 4138 33.3 15.2 1 61Civil War 4138 0.151 0.358 0 1Neighbor Democracy 4138 0.333 0.600 0 2

¯Log(GDPpc) 4138 7.50 0.797 6.16 9.72Log(GDPpc)Dev 4138 -0.0096 0.360 -1.64 1.53

¯Oilpc 4138 1.97 2.54 0 9.67OilpcDev 4138 0.0259 1.13 -6.81 5.46

¯Dict 4138 0 .0246 0 .0336 0 0.194¯Dem 4138 0.0231 0.0369 0 1

38

Tab

leS

-2:

Reg

ime

coll

apse

s

Countr

yY

ear

A-t

o-D

A-t

o-A

Countr

yY

ear

A-t

o-D

A-t

o-A

Countr

yY

ear

A-t

o-D

A-t

o-A

Countr

yY

ear

A-t

o-D

A-t

o-A

Afg

hanis

tan

1973

01

Colo

mbia

1958

10

Iraq

1968

01

Para

guay

1993

10

Afg

hanis

tan

1978

01

Congo-B

rz1963

01

Iraq

1979

01

Peru

1956

10

Afg

hanis

tan

1992

00

Congo-B

rz1968

01

Ivory

Coast

1999

01

Peru

1963

10

Alb

ania

1991

10

Congo-B

rz1991

10

Ivory

Coast

2000

01

Peru

1980

10

Alg

eri

a1992

01

Congo/Z

air

e1997

01

Kenya

2002

10

Peru

2000

10

Arg

enti

na

1955

01

Cost

aR

ica

1949

10

Kore

aSouth

1960

10

Philip

pin

es

1986

10

Arg

enti

na

1958

01

Cuba

1959

01

Kore

aSouth

1987

10

Pola

nd

1989

10

Arg

enti

na

1966

01

Czechosl

ovakia

1989

10

Kyrg

yzst

an

2005

01

Port

ugal

1974

10

Arg

enti

na

1973

10

Dom

inic

an

Rep

1962

10

Laos

1960

01

Rom

ania

1989

10

Arg

enti

na

1983

10

Dom

inic

an

Rep

1978

10

Laos

1962

00

Rw

anda

1973

01

Arm

enia

1998

01

Ecuador

1947

10

Leso

tho

1986

01

Rw

anda

1994

01

Bangla

desh

1975

01

Ecuador

1966

10

Leso

tho

1993

10

Senegal

2000

10

Bangla

desh

1982

01

Ecuador

1972

01

Lib

eri

a1980

01

Sie

rra

Leone

1968

01

Bangla

desh

1990

10

Ecuador

1979

10

Lib

eri

a1990

00

Sie

rra

Leone

1992

01

Bela

rus

1994

01

Egypt

1952

01

Lib

eri

a2003

10

Sie

rra

Leone

1996

10

Benin

1963

01

El

Salv

ador

1948

01

Lib

ya

1969

01

Sie

rra

Leone

1998

10

Benin

1965

01

El

Salv

ador

1982

01

Madagasc

ar

1972

01

Som

alia

1991

00

Benin

1967

01

El

Salv

ador

1994

10

Madagasc

ar

1975

01

South

Afr

ica

1994

10

Benin

1969

01

Eth

iopia

1974

01

Madagasc

ar

1993

10

Sovie

tU

nio

n1991

10

Benin

1970

01

Eth

iopia

1991

01

Mala

wi

1994

10

Spain

1976

10

Benin

1990

10

Gam

bia

1994

01

Mali

1968

01

Sri

Lanka

1994

10

Bolivia

1951

01

Georg

ia2003

10

Mali

1991

10

Sudan

1964

10

Bolivia

1952

01

Ghana

1966

01

Mauri

tania

1978

01

Sudan

1985

01

Bolivia

1964

01

Ghana

1969

10

Mauri

tania

2005

01

Sudan

1986

10

Bolivia

1969

01

Ghana

1979

10

Mauri

tania

2007

10

Syri

a1951

01

Bolivia

1971

01

Ghana

2000

10

Mexic

o2000

10

Syri

a1954

10

Bolivia

1979

01

Gre

ece

1974

10

Mongolia

1993

10

Syri

a1958

01

Bolivia

1982

10

Guate

mala

1958

01

Myanm

ar

1960

10

Syri

a1963

01

Bra

zil

1985

10

Guate

mala

1963

01

Myanm

ar

1988

01

Taiw

an

2000

10

Bulg

ari

a1990

10

Guate

mala

1966

01

Nepal

1951

01

Thailand

1957

01

Burk

ina

Faso

1966

01

Guate

mala

1970

01

Nepal

1991

10

Thailand

1973

10

Burk

ina

Faso

1980

01

Guate

mala

1985

01

Nepal

2006

10

Thailand

1988

10

Burk

ina

Faso

1982

01

Guate

mala

1995

10

Nic

ara

gua

1979

01

Thailand

1992

10

Burk

ina

Faso

1987

01

Guin

ea

1984

01

Nic

ara

gua

1990

10

Thailand

2007

10

Buru

ndi

1966

01

Guin

ea

Bis

sau

1980

01

Nig

er

1974

01

Togo

1963

10

Buru

ndi

1987

01

Guin

ea

Bis

sau

1999

10

Nig

er

1991

10

Turk

ey

1950

10

Buru

ndi

1993

10

Hait

i1956

10

Nig

er

1999

10

Turk

ey

1960

01

Buru

ndi

2003

10

Hait

i1986

01

Nig

eri

a1979

10

Turk

ey

1961

10

Cam

bodia

1970

01

Hait

i1988

01

Nig

eri

a1993

01

Turk

ey

1983

10

Cam

bodia

1975

01

Hait

i1990

10

Nig

eri

a1999

10

Uganda

1971

01

Cam

bodia

1979

01

Hait

i1994

10

Pakis

tan

1958

01

Uganda

1979

01

Cam

ero

on

1983

01

Hait

i2004

10

Pakis

tan

1971

10

Uganda

1985

00

Cen

Afr

ican

Rep

1965

01

Hondura

s1956

10

Pakis

tan

1977

01

Uru

guay

1984

10

Cen

Afr

ican

Rep

1979

01

Hondura

s1971

10

Pakis

tan

1988

10

Venezuela

1958

10

Cen

Afr

ican

Rep

1981

01

Hondura

s1981

10

Panam

a1951

10

Yem

en

1962

01

Cen

Afr

ican

Rep

1993

10

Hungary

1990

10

Panam

a1955

10

Yem

en

1967

01

Chad

1975

01

Indonesi

a1966

01

Panam

a1982

01

Yem

en

1974

01

Chad

1979

00

Indonesi

a1999

10

Panam

a1989

10

Yem

en

1978

01

Chad

1990

01

Iran

1979

01

Para

guay

1948

01

Yugosl

avia

1990

01

Chile

1989

10

Iraq

1958

01

Para

guay

1954

01

Zam

bia

1991

10

Colo

mbia

1953

01

Iraq

1963

01

Cod

ing

rule

san

ddes

crip

tion

of

regim

eco

llap

seev

ents

availab

leatAuthor’s

Website.A-to-D

den

ote

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uto

cracy

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emocr

acy

tran

siti

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s.A-to-A

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ote

sA

uto

cracy

toA

uto

cracy

transi

tion

s.N

ine

regim

eco

llap

ses

do

not

resu

ltin

tran

siti

on

sto

dem

ocr

acy

or

new

au

tocr

acy

bu

tra

ther

end

infa

iled

state

sor

are

occ

up

ied

by

fore

ign

pow

ers.

39

Table S-3: Oil income and all regime failures

Full results from Table 1 (a)


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.121** -0.265**(0.04) (0.10)

Oili -0.092* -0.111** -0.053+ -0.071*(0.04) (0.04) (0.03) (0.03)

OilDev -0.196 -0.166 -0.300* -0.290*(0.12) (0.13) (0.14) (0.13)

¯Faili 13.212** 14.901**(1.15) (1.37)

¯GDPpci 0.028 0.170 0.015 0.036 -0.064(0.13) (0.11) (0.12) (0.08) (0.09)

GDPpcDev 1.070** 0.895** 1.122** 0.499 0.585(0.29) (0.31) (0.30) (0.40) (0.39)

GDPpct−1 0.242(0.37)

Civil wart−1 0.511** 0.778** 0.287 0.502** 0.410* 0.543**(0.18) (0.25) (0.18) (0.19) (0.18) (0.19)

Neighbor democracyt−1 0.376** 0.323** 0.364** 0.374** 0.361** 0.367**(0.10) (0.12) (0.10) (0.10) (0.11) (0.11)

Duration time -0.030 0.004 -0.019 -0.029 0.043 0.049+(0.02) (0.03) (0.02) (0.02) (0.03) (0.03)

Duration time2 -0.000 0.001 -0.000 -0.000 -0.001 -0.001+(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Duration time3 0.000 0.000 0.000 0.000 0.000 0.000+(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)


+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Conditional logit in column 2. Ordinary logit with errors clusteredon country in all other columns. Calendar time polynomials (3) included in all models but notreported. Years: 1947-2007.

40

Table S-4: Oil income and democratic transitoins

Full results from Table 1 (b)


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.153** -0.002(0.05) (0.17)

Oili -0.113+ -0.224** -0.089+ -0.169**(0.07) (0.07) (0.05) (0.06)

OilDev 0.032 0.107 -0.055 -0.038(0.20) (0.16) (0.21) (0.19)

¯Demi 17.634** 24.107**(2.16) (3.55)

¯GDPpci 0.318+ 0.351** 0.396* 0.191+ 0.159(0.17) (0.12) (0.18) (0.11) (0.14)

GDPpcDev 1.578** 0.939* 1.336** 0.583 0.651(0.39) (0.44) (0.40) (0.48) (0.49)

GDPpct−1 -0.075(0.61)

Civil wart−1 0.185 0.199 -0.186 0.236 0.046 0.341(0.26) (0.41) (0.23) (0.26) (0.23) (0.25)

Neighbor democracyt−1 0.360* 0.367* 0.426** 0.365* 0.456** 0.447**(0.15) (0.17) (0.15) (0.15) (0.16) (0.16)

Duration time -0.060* -0.054 -0.036 -0.067* 0.014 0.014(0.03) (0.05) (0.04) (0.03) (0.04) (0.04)

Duration time2 0.001 0.004* 0.000 0.001 -0.000 -0.000(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Duration time3 0.000 -0.000* 0.000 -0.000 0.000 0.000(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)



41

Table S-5: Oil income and autocratic transitoins

Full results from Table 1 (c)


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.055 -0.325*(0.05) (0.13)

Oili -0.097+ -0.014 -0.075+ -0.023(0.05) (0.05) (0.04) (0.05)

OilDev -0.263* -0.280* -0.324* -0.347*(0.12) (0.14) (0.13) (0.14)

¯Dicti 14.344** 19.877**(1.74) (2.71)

¯GDPpci -0.262 0.349* -0.335+ 0.172 -0.318+(0.19) (0.16) (0.19) (0.15) (0.16)

GDPpcDev 0.480 0.408 0.818+ 0.154 0.384(0.41) (0.55) (0.49) (0.62) (0.55)

GDPpct−1 0.249(0.57)

Civil wart−1 0.692** 0.943** 0.431+ 0.643* 0.501* 0.701**(0.26) (0.33) (0.24) (0.27) (0.24) (0.25)

Neighbor democracyt−1 0.433** 0.333+ 0.355* 0.425** 0.300+ 0.313+(0.16) (0.17) (0.16) (0.16) (0.17) (0.17)

Duration time -0.003 0.061 0.013 0.003 0.065 0.083*(0.03) (0.05) (0.04) (0.03) (0.04) (0.04)

Duration time2 -0.001 0.000 -0.001 -0.001 -0.002 -0.003*(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Duration time3 0.000 0.000 0.000 0.000+ 0.000 0.000*(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)



42

Appendix A: Robustness tests

The top panel of Table A-1 shows the variance of the mean levels and deviations of oil wealth, bygeographic region. Oil income has the most variation in the Middle East and North Africa – bothacross and within countries. This is also the region where dictatorships tend to be the most stableduring the sample period, suggesting that statistical approaches (such as a conditional logit) thatdrop countries that do not experience transitions exclude many cases from the region with the mostvariation in oil income.

43

Table A-1: Robustness to excluding geographic regions

(a) Sample variance, by region

Region

MENA LA Asia SSA EU

Variance OilDev 3.72 0.71 0.81 0.82 0.93Variance ¯Oili 8.13 3.91 2.97 3.48 3.25

(b) Democratic transitions

Excluded region

All regions included MENA LA Asia SSA EU

Oil -0.169** -0.141* -0.204* -0.187** -0.154* -0.161*(0.06) (0.07) (0.09) (0.06) (0.06) (0.07)

OilDev -0.038 0.127 -0.044 -0.151 -0.076 -0.074(0.19) (0.19) (0.26) (0.19) (0.20) (0.20)

Wald test (χ2) 0.3 2.9 0.7 1.7 0.9

(c) Autocratic transitions

Excluded region

All regions included MENA LA Asia SSA EU

Oil 0.039 -0.091 0.001 -0.030 -0.016 -0.036(0.07) (0.08) (0.05) (0.06) (0.05) (0.05)

OilDev -0.347* -0.294+ -0.303+ -0.402* -0.344* -0.343*(0.15) (0.17) (0.16) (0.16) (0.15) (0.14)

Wald test (χ2) 0.6 2.1 0.9 1.2 9.1*

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Logit with errors clustered on country. Control variables, timedependence polynomials (3), and calendar time polynomials (3) included in all models but notreported. Years: 1947-2007. Wald tests are for the interaction between the region dummy andoil variables in the full sample.

44

Table A-2: Oil income and autocratic survival

(Ross 2008 Oil & Gas rents)

(a) Democratic transitions


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.183** -0.012(0.06) (0.21)

Oili -0.130* -0.114+ -0.227** -0.206**(0.06) (0.06) (0.07) (0.07)

OilDev -0.044 0.015 0.040 0.056(0.15) (0.17) (0.14) (0.16)

¯Demi 18.889** 28.766**(2.15) (2.97)

Log likelihood -351.1 -169.1 -279.6 -263.8 -349.9 -298.8Observations 3593 1654 1654 1654 3593 3593

(b) Autocratic transitions


(1) (2) (3) (4) (5) (6)

Oilt−1 -0.088 -0.447**(0.06) (0.15)

Oili -0.111+ -0.084 -0.039 -0.046(0.06) (0.05) (0.06) (0.06)

OilDev -0.330** -0.414** -0.371** -0.451**(0.12) (0.13) (0.13) (0.14)

¯Dicti 15.676** 21.787**(1.93) (2.79)



45

Table A-3: Fuel income and autocratic survival

(Haber & Menaldo 2011 Fuel Income)



(1) (2) (3) (4) (5) (6)

Fuelt−1 -0.144** -0.105(0.06) (0.18)

¯Fueli -0.116+ -0.089+ -0.202** -0.163**(0.06) (0.05) (0.07) (0.06)

FuelDev 0.009 -0.071 0.075 -0.048(0.18) (0.20) (0.14) (0.17)

¯Demi 17.684** 24.211**(2.06) (3.34)




(1) (2) (3) (4) (5) (6)

Fuelt−1 -0.161* -0.282*(0.07) (0.13)

¯Fueli -0.097+ -0.069+ -0.128* -0.097(0.05) (0.04) (0.06) (0.06)

FuelDev -0.246+ -0.300* -0.314* -0.348*(0.13) (0.14) (0.15) (0.16)

¯Dicti 14.220** 19.592**(1.77) (2.61)



46

Table A-4: Regression of country means

Oil variable H&M H&M Ross RossTransition Dem Autocratic Dem Autocratic

Oili -0.331+ 0.077 -0.310+ -0.003(0.18) (0.15) (0.18) (0.15)

R2 0.246 0.257 0.233 0.256Observations 113 114 110 110

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variable is¯FailTypei. OLS; standard errors in parentheses. One ob-

servation per country. All models control for mean levelof Log(GDPpct−1), CivilWart−1, NeighborDemocracy,T imeDuration and T imeDuration2. Model in first columnexcludes Costa Rica as an outlier; including it more thandoubles the size of the coefficient. Coefficient estimate infirst column indicates that a 2 standard deviation increasein mean oil level is associated with a 1.7% decrease in meandemocracy. Coefficient in third column indicates a similar1.5% decrease in mean democracy.

47

Table A-5: Linear probability model

(with country and year FE)

Oil variable H&M H&M Ross RossTransition Dem Autocratic Dem Autocratic

Oilt−1 -0.132 -0.723** 0.027 -0.859**(0.26) (0.27) (0.28) (0.29)

R2 0.039 0.035 0.041 0.033Observations 4138 4138 3593 3593

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variableis Regime Transition (Democratic, Autocratic). OLSwith country and year fixed effects; standard errors inparentheses. All models control for: Log(GDPpct−1),CivilWart−1, NeighborDemocracy, T imeDuration. Co-efficient estimate in second column indicates that a 2 stan-dard deviation increase in oil is associated with a 4.0%decrease in the linear probability of autocratic transition.Coefficient in fourth column indicates a similar 4.8% de-crease in linear probability of autocratic transition.

48

Table A-6: Oil income and democratic transition

(with ACLP/CGV data)

Calendar time Trend Trend Year FE Year FE

Oili -0.188** -0.112 -0.196** -0.124+(0.06) (0.07) (0.07) (0.07)

OilDev 0.095 -0.022 0.144 0.043(0.14) (0.19) (0.14) (0.19)

¯Demi 32.485** 33.914**(4.77) (5.02)

Log likelihood -401.1 -332.5 -362.0 -294.3Observations 4368 4368 3273 3273

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variable is Demo-cratic Transition. Ordinary logit with standard errors clus-tered on country. All models control for: Log(GDPpct−1),CivilWart−1, NeighborDemocracy, T imeDuration, andT imeDuration2. Estimates for Oili are much larger and sta-tistically significant at the 0.05 level when we include meanand deviation of log(Population) (not reported).

49

Table A-7: Modeling time and oil price

Year FE Y Y N NYear polynomials N N Y YOil price control N N Y YTransition Democratic Autocratic Democratic Autocratic

Oili -0.189** -0.023 -0.181** -0.023(0.07) (0.05) (0.06) (0.05)

OilDev 0.066 -0.368* 0.006 -0.347*(0.23) (0.15) (0.19) (0.15)

¯Demi 28.780** 24.159**(3.78) (3.41)

¯Dicti 20.977** 19.872**(2.96) (2.69)


+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variable is Regime Transition(Democratic, Autocratic). Ordinary logit with clustered standard errorsin parentheses. All models control for: Log(GDPpct−1), CivilWart−1,NeighborDemocracy, T imeDuration.

50

Table A-8: Time period results

(pre-1980 & post-1979)

Transition Democratic Autocratic

Oil interactions with pre-1980 dummy N Y N Y

Oili -0.189** -0.241** -0.023 -0.073(0.07) (0.09) (0.05) (0.09)

OilDev 0.066 0.164 -0.368* -0.465*(0.23) (0.23) (0.15) (0.23)

Oili × pre80 0.125 0.084(0.13) (0.13)

OilDev × pre80 -0.263 0.150(0.35) (0.23)

Coefficients for the pre-1980 period

βOil + βOil×pre80 -0.116 0.012

(0.11) (0.07)βOilDev

+ βOilDev×pre80 -0.099 -0.315*(0.32) (0.17)


+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variable is Regime Transition (Demo-cratic, Autocratic). Ordinary logit with clustered standard errors in parenthe-ses. All models control for: Log(GDPpct−1), CivilWart−1, NeighborDemocracy,RegimeDuration, and year fixed effects.

51

-.8

-.6

-.4

-.2

0

.2

Oil

de

via

tio

n c

oe

ffic

ien

t

0 5 10 15 20

Duration time

90% CI Oil deviation

Figure A-1: Non-proportional hazard for Oil Deviations, Autocratic transitions model.This graph shows how the coefficient for OilDev varies by duration time in the model reported inTable 1, Panel C, column 6 (with year fixed effects). The Wald-test for the (3) interactions betweenduration time polynomials (3) and OilDev, however, yields a test statistic with p = 0.28, suggestingthat these coefficients are not jointly statistically different from zero. A test of non-proportionalhazards in a Cox model (stratified by country) also indicates that the proportional hazard assump-tion is not violated. Nonetheless, there appears to be a substantive pattern suggesting that thewithin-country effect is concentrated in the first decade the autocratic regime holds power. SimilarWald-tests and tests for non-proportional hazards in Cox models for models reported in column (6)of Panels A and B in Table 1 yield test statistics with p > 0.65, suggesting that the proportionalhazards assumption is not violated.

52


.066.021 .022 .04 .054 .065 .065 .06

-.4

-.2

0

.2

.4

Oil

de

via

tio

n c

oe

ffic

ien

t

0 2 4 6 8

Deviations calculated using MA over d prior years


-.368-.339

-.303-.274

-.257 -.267 -.275 -.277

-.6

-.4

-.2

0

Oil

de

via

tio

n c

oe

ffic

ien

t

0 2 4 6 8

Deviations calculated using MA over d prior years


Figure A-2: Coefficients for oil deviations, calculated using lagged moving averagesfor oil income. This graph reports the results for the OilDev coefficient for the main modelspecification (column 6 in Table 1) when the model includes year fixed effects. The value of 1 on thehorizontal axis of these figures corresponds with the coefficient estimate for OilDev ≡ Oilt−d,i−Oiliwhere d = 1. We then calculate the deviations using the lagged moving average of oil income overthe previous d periods instead of just the one-year lag. The coefficients and confidence intervals forOilDev are shown as d increases from 1 to 8.

53


0

.2

.4

.6

.8

1

Pro

ba

bili

ty o

f tr

an

sitio

n

0 1000 2000 3000 4000

Instability rank

Observed democratic transition Predicted probability of transition

0

.1

.2

.3

.4

.5

.6

.7

Pro

ba

bili

ty o

f tr

an

sitio

n

0 1000 2000 3000 4000

Instability rank

Observed autocratic transition Predicted probability of transition

Figure A-3: Separation plots. The vertical axes and the dashed lines show the predicted riskof transition (models reported in column 6 of Table 1, panels B and C). The horizontal axesorder the observations from the lowest to the highest predicted risk for 4138 observations. Thevertical blue shading marks observations where the dependent variable is equal to one, or observedtransitions. These plots show that the democratic transition model appears to perform better thanthe autocratic transition model.

54

Appendix B: Extension of conditional logit in Haber and Menaldo (2011)

Table B-1 explores why the null result for time-varying oil income and democracy in the conditionallogit model (column 2, Panel B, Table 1 in the main text) differs from the positive result reportedin the Appendix to Haber and Menaldo (2011). Our baseline specification is different from the HMmodel in a couple of ways: we examine a shorter time period (post-1946); we control for regime du-ration; we substitute calendar year polynomials for year fixed effects; we log the oil income variable;and we examine a sample of autocracies (only) to assess the likelihood of democratic transition.This latter difference means that we are using only one-half of a typical Markov switching model,which in this literature simultaneously estimates the risk of transition to and from democracy.

The first column replicates the conditional logit result from the Appendix to Haber and Menaldo(2011). This specification is a simple logit that includes year fixed effects for every year from 1970onwards as well as country fixed effects (thus technically an unconditional logit).50 The secondcolumn includes interactions between the year fixed effects and the lagged dependent variable,allowing the time effects to vary by whether the incumbent regime is a dictatorship or a democracy.The positive result for oil remains. The third column interacts the country fixed effects with laggedregime, allowing the country effects to vary by type of transition. The oil coefficient is now muchsmaller and no longer significant at the 0.10 level. The fourth column includes interactions betweenlagged regime and both year and country effects. The fifth column replicates this result by usingonly one-half of a full Markov switching model in that it only examines autocratic observations thatare at risk of democratizing. The main result for oil income is again smaller and not statisticallydifferent from zero. This suggests that one reason our result is different is that our estimate foroil income is conditioned on a country effect that aggregates information only over autocraticobservations and not over both autocratic and democratic observations.

The sixth column substitutes calendar year polynomials for year fixed effects; the result foroil income is similar to that in column 1. The seventh column uses the natural log of oil incomeinstead of the raw per capita value. The oil income coefficient is no longer significant (because oilincome has been transformed, the size of the coefficient in column 5 is not comparable to that inother columns). In the eighth column, we control for regime duration time with three polynomials(and interact them with lagged regime). The coefficient for oil income is positive, suggesting thatmodeling regime duration does not substantively alter the estimate of the oil coefficient. In theninth column, we restrict the sample to the post-WWII period, and this yields similar results tocolumn 1.

Finally, in the last column we implement all of these changes to the specification at once: welog oil income, use only the autocratic half of the Markov switching model, substitute calendar yearpolynomials for year fixed effects, restrict the sample to post-1946, and control for regime duration.This specification is most similar to the empirical approach we use throughout the paper. The oilincome coefficient is now negative but not statistically different from zero.

The results in this table suggest two changes we make to the model specification that are mostlikely to account for why the null finding in our model of the relationship between oil incomeand democratic transition (column 2, Panel B, Table 1 in the main text) differs from the positivefinding in Haber and Menaldo’s. First, we use the natural log of oil income which, as column7 suggests, diminishes the positive influence of oil income on the risk of democratic transition.

50At T>20, the coefficient estimates for the conditional logit and the unconditional logit withunit FE’s converge (see Katz 2001). The coefficient for the conditional logit is: 1.333 (0.65).

55

Second, we employ one-half of a full Markov transition model and thus analyze only autocraticobservations. This means we estimate cross-country unit effects only for autocratic time periods,which is equivalent to interacting the country fixed effects with the lagged dependent variable(measuring prior state) in a full Markov transition model.

Sample restriction in conditional logit: Oil and regime transition example

The conditional logit model makes two changes to an ordinary logit: it restricts the sample tocountries that transition to or from democracy during the sample period, excluding countries thatnever change states; and it accounts for unit specific effects. Table B-2 explores the relative influenceof these changes by first showing how a simple logit performs using the full (column 1) and restrictedsamples (column 2). The third column introduces unit effects by employing a conditional logit.This is the same model reported in the Appendix to Haber and Menaldo (2011) and shown incolumn 1 of Table B-2. The difference between the odds ratios in the first two columns is the resultof excluding the cases that never change. The difference between the estimates in the second andthird columns results from accounting for unit effects.

First, note that the number of countries drops from 165 to 80 when the sample is restrictedto include only those that change states. This change to the model moves the odds ratio from anegative and significant estimate to a positive (though statistically insignificant) estimate. Addingunit effects in the third column makes the odds ratio more positive and statistically significant.

We are reluctant to interpret these results substantively without estimating both the cross-country and within-country variation, but provide them as a caution to users of conditional logitmodels. For models of oil and democratic transitions, the potential bias from restricting the sample(reflected in the difference in odds ratios in columns 1 and 2 of Table B-2) shows up in the resultsreported in Table 1 of the main text as bias in the estimates of the cross-country correlations (Oili).Comparing the βOili ’s in models in columns 5 and 6 in Panel B in Table 1 (main text) shows thatrestricting the sample biases the estimate in a positive direction (towards zero in this case). Thisshould not be surprising because many of the countries that do not democratize during the sampleperiod have high oil incomes.

Table B-3 reports the results for a similar exercise using the democratic and autocratic transitiondependent variables employed in Tables 1 and 2 of the main text. For both types of transition,restricting the sample in the ordinary logit moves the estimate for Oilt−1 closer to the estimate fromthe conditional logit. Again, we caution against interpreting these estimates substantively becausethe ordinary logit models with a lagged covariate do not separately estimate the cross-country andwithin-country variation.

56

Tab

leB

-1:

Oil

inco

me

and

dem

ocr

atic

tran

siti

ons

(Rep

lica

tion

and

exte

nsi

onof

Hab

eran

dM

enal

do’

sco

nd

itio

nal

logi

ts)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Log

Oil

NN

NN

NN

YN

NY

Mark

ov

Fu

llF

ull

Fu

llF

ull

Half

Fu

llF

ull

Fu

llF

ull

Half

Yea

rsall

all

all

all

all

all

all

all

post

-1946

post

-1946

Reg

ime

du

rati

on

poly

nom

ials

(3)

NN

NN

NN

NY

NY

Cale

nd

ar

tim

ep

oly

nom

ials

(3)

NN

NN

NY

NN

NY

Yea

reff

ects

YY

YY

YN

YY

YN

Yea

reff

ects×

Reg

ime t

−1

NY

NY

NN

NN

Cou

ntr

yeff

ects×

Reg

ime t

−1

NN

YY

NN

NN

Oil

inco

me

per

cap

ita

1.3

16*

1.4

17*

0.2

42

0.4

21

0.4

21

0.9

97**

0.0

59

1.0

03

1.3

45*

-0.0

21

(0.6

6)

(0.7

2)

(0.6

6)

(0.8

8)

(0.8

8)

(0.3

5)

(0.1

2)

(0.6

2)

(0.6

0)

(0.2

5)

Ob

serv

ati

on

s5932

5278

4980

4530

3060

5932

5932

5932

3211

1858

Cou

ntr

ies

insa

mp

le80

80

79

79

73

80

80

80

69

64

+p<

0.1

0;∗

p<

0.0

5;∗∗

p<

0.0

1.

Dep

end

ent

vari

ab

leis

AC

LP

(Boix

an

dR

osa

top

rior

to1946)

tran

siti

on

tod

emocr

acy

,as

com

bin

edin

Hab

eran

dM

enald

o(2

011).

Fix

edeff

ects

logit

wit

hro

bu

stst

an

dard

erro

rscl

ust

ered

on

cou

ntr

y.A

llyea

rs≡

1817-2

002.

Cou

ntr

yfi

xed

effec

tsin

clu

ded

inall

mod

els.

Nu

mb

erof

ob

serv

ati

on

sd

rop

sin

colu

mn

s2-4

bec

au

seso

me

yea

rs/co

untr

ies

(inte

ract

edw

ith

lagged

DV

)d

on

ot

chan

ge

state

s.W

hen

oil

isn

ot

logged

the

per

cap

ita

tota

lis

div

ided

by

1000,

as

inH

ab

eran

dM

enald

o(2

011).

57

Table B-2: Potential bias from sample restriction

Logit Simple Simple ConditionalSample Full Restricted Restricted

Oil incomet−1 -1.02 0.25 1.33(0.026) (0.635) (0.039)

Countries 165 80 80Observations 9305 5932 5932

Replication and extension of conditional logit models inthe Appendix to Haber and Menaldo (2011). Reportedodds ratio for transition from Autocracy to Democ-racy. P-values in parentheses. Model specification isthe same as in column 1 of Table B-1.

58

Table B-3: Potential bias in the conditional logit



Oil incomet−1 -0.169 -0.095 -0.002(0.00) (0.12) (0.99)




Oil incomet−1 -0.056 -0.131 -0.325(0.33) (0.01) (0.01)


Extension of analysis reported in Table 1. Odds ra-tios reported; p-values in parentheses. Model onlyexamines countries coded as autocracy on January 1of the observation year. Control variables include:Log(GDPpc)t−1, CivilWart−1, Neighbor Democracy,Calendar year trend (3), and duration polynomials (3).

59

Appendix C: Oil Wealth and Polity Durable variable

Haber and Menaldo (2011) examine how oil wealth influences democracy using two measures ofthe latter concept: the combined Polity scale (treated as a quasi-continuous variable); and a binaryindicator of transition from non-democracy to democracy from Cheibub, Gandhi and Vreeland(2010). They show that the negative statistical correlation between oil and these measures largelydisappears once they account for unit fixed effects. They find no evidence that time-varying oilwealth is negatively correlated with the level of democracy, changes in the level of democracy, or therisk of democratic transition. In this Appendix, we examine another widely used measure of ‘regimeinstability’, the Polity Durable variable, which is a binary indicator of whether the combined Polityscore has changed three or more points (in either direction) over the past three years.

We begin by taking all the Polity Durable failures where we have non-missing data on oil wealthand the combined Polity scale on December 31 of the prior calendar year is less than six, indicatingthat Polity codes the observation year as not-Democracy. To test the relationship between oilwealth and the risk of a Polity Durable failure, we use a logit model with a similar specification tothat used throughout.51 The first column of Table C-1 reports this result and shows that laggedoil wealth is negatively correlated with the risk of Durable failure – the now standard findingreflected in the bulk of the research on the oil curse. The next column separates the cross-sectionaland time-varying oil wealth information using two separate variables (see main text). This resultindicates that the mean value of oil wealth is negatively correlated with Durable failure but thattime-varying oil wealth, while negative, does not have a strong statistical association with thismeasure of political change.

Next we separate the ‘democratic’ Polity Durable failures from the ‘non-democratic’ failures.The former category includes only those Durable failures where the combined Polity score is 6or more on December 31 of the observation year, indicating that the Durable failure as also a‘democratic transition’ – as defined by Polity. The models in columns 3 and 4 report the resultsfor tests of the democratic Polity Durable failures. There is a strong negative correlation betweenoil wealth and the risk of these failures, but it appears to only be a cross-sectional correlation,consistent with our findings in the text and the main findings in Haber and Menaldo (2011).

In the final two columns of the top panel of C-1, we examine non-democratic Durable failures– all those not marked as ‘democratic’. These include events such as the legalization of oppositionparties in the former Zaire (1992) and the Iranian Revolution (1979) as well as shifts in the IranianPresidency between the moderate and conservative factions of the theocratic regime in 1997 and2005. As these examples illustrates, these failures include events where: (1) the incumbent regimeloses power and (2) events when the incumbent regime remains in power. The results indicate anegative correlation between oil wealth and the risk of non-democratic Durable failures. However,the result in column 6 suggests that this correlation is again mostly cross-sectional.

The bottom panel of C-1 reports the results from identical tests using the GWF measureof autocratic regime collapse (all failures, autocracy-to-democracy transitions, and autocracy-to-autocracy transitions). The results for All Failures and Democratic Transitions in columns 7-10are nearly identical to the results from similar tests using the Polity Durable variable. We do notfind this surprising because – particularly in the case of democratic transitions – both data sourcesare coding similar events. However, the results using the GWF data differ from those using the

51Control variables include: a calendar time trend; duration polynomials; Log GDP per capita;Civil war; and Neighbor democratization, and a constant.

60

Polity Durable data for non-democratic transitions. Tests using the GWF data indicate a strongnegative correlation between time-varying oil wealth and autocracy-to-autocracy transitions whilethe Polity Durable tests show that the correlation between oil wealth and non-democratic Durablefailures is largely cross-sectional. In short, we get similar answers using the two data sets whenasking questions about democratic transitions but very different answers when asking about othertypes of political change in autocracies.

To help readers better understand why the results differ for non-democratic Polity Durablefailures and GWF autocracy-to-autocracy transitions, Table C-2 lists 57 autocracy-to-autocracyregime collapses that are not captured by the Durable failure variable. This is a conservative listof regime collapses because we only include cases where no Durable failure is observed in years t-1,t, or t+1. Thus it is impossible to capture these autocratic regime collapse events with the Politydata.

Finally, Table C-3 lists the 79 observations of Durable failure when there is no autocratic regimecollapse, as measured by GWF. In all of these cases, Polity Durable codes a ‘regime failure’ eventhough the incumbent autocratic regime – as we define it – remains in power. In 59 of these 79cases, the incumbent leader remains in power. Again this is a conservative list because we onlyinclude those Durable failures where no regime collapse occurred in years t-1, t, or t+1.

61

Table C-1: Comparing Polity Durable Failure and GWF Autocratic Regime Collapse

(a) Polity Durable Failure

All Failures Dem Failures Non-Dem Failures

(1) (2) (3) (4) (5) (6)

Oilt−1 -0.320** -0.539* -0.263*(0.11) (0.22) (0.11)

Oili -0.280** -0.528* -0.223*(0.10) (0.22) (0.11)

OilDev -0.135 -0.056 -0.132(0.10) (0.22) (0.10)

(b) GWF Autocratic Regime Failure

All Failures Dem Failures Non-Dem Failures

(7) (8) (9) (10) (11) (12)

Oilt−1 -0.351** -0.474** -0.156(0.11) (0.15) (0.16)

Oili -0.283** -0.571** -0.035(0.10) (0.18) (0.14)

OilDev -0.187 0.121 -0.317*(0.14) (0.18) (0.16)

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Dependent variable in top panel is PolityDurable Failure; Democratic Failures are those where the combined Polity scorecrosses the +6 threshold for ‘democratic’ transition; Non-Democratic Failuresare all other Polity Durable Failures. GWF autocratic regime failures aredescribed in the text and listed earlier in the Appendix. Reported coefficientestimates are multiplied by the in-sample standard deviation of the respectivevariable. Clustered standard errors reported in parentheses. 4179 observationsin 118 countries in the top panel; 4138 observations in 114 countries in thebottom panel.

62

Table C-2: Autocratic Regime Collapse but No Durable Failure

Country Year Country Year

Afghanistan 1973 Iraq 1958Argentina 1958 Iraq 1963Bangladesh 1975 Iraq 1968Benin 1965 Iraq 1979Benin 1967 Laos 1960Bolivia 1964 Laos 1962Burkina Faso 1966 Lesotho 1986Burkina Faso 1982 Liberia 1980Burkina Faso 1987 Libya 1969Burundi 1966 Madagascar 1975Burundi 1987 Mali 1968Cameroon 1983 Mauritania 1978Cen African Rep 1965 Myanmar 1988Cen African Rep 1979 Nepal 1951Cen African Rep 1981 Niger 1974Chad 1975 Nigeria 1993Colombia 1953 Pakistan 1977Congo-Brz 1968 Panama 1982Congo/Zaire 1997 Rwanda 1973Ecuador 1972 Sierra Leone 1968El Salvador 1982 Sierra Leone 1992Gambia 1994 Syria 1951Ghana 1966 Syria 1958Guatemala 1958 Thailand 1947Guatemala 1963 Uganda 1971Guatemala 1970 Yemen 1974Guinea 1984 Yemen 1978Guinea Bissau 1980 Yugoslavia 1990Haiti 1988

Autocratic regime collapse observations when:(1) there is no Durable failure in years t-1, t, ort+1; (2) the autocratic regime collapse did notend in a transition to democracy; and (3) thereis non-missing data on the oil wealth variable. 57observations of regime collapse that end in eithertransition to a subsequent dictatorship or an oc-cupied regime (Laos 1962).

63

Table C-3: Durable Failures but No Autocratic Regime Collapse

Leader Leader LeaderCountry Year Exit Country Year Exit Country Year Exit

Algeria 1989 0 Indonesia 1957 0 Paraguay 1989 1Algeria 1995 0 Iran 1953 1 Peru 1950 1Algeria 2004 0 Iran 1997 1 Peru 1978 0Angola 1991 0 Iran 2004 0 Philippines 1981 0Bangladesh 1978 0 Ivory Coast 2002 0 Senegal 1962 0Brazil 1974 1 Jordan 1951 1 Senegal 1978 0Burkina Faso 1969 0 Jordan 1957 0 Sierra Leone 1971 0Burkina Faso 1977 0 Jordan 1989 0 Sudan 2002 0Burkina Faso 2001 0 Kenya 1969 0 Swaziland 1973 0Cambodia 1997 1 Kenya 1997 0 Taiwan 1987 0Cameroon 1992 0 Korea South 1963 0 Tajikistan 1997 0Central African Republic 1991 0 Korea South 1972 0 Tanzania 1995 1Congo Kinshasa 1992 0 Korea South 1981 0 Thailand 1952 0Czechoslovakia 1968 1 Kuwait 1990 1 Thailand 1955 0Egypt 2005 0 Madagascar 1991 0 Thailand 1968 0El Salvador 1964 0 Mauritania 1962 0 Thailand 1971 0El Salvador 1977 1 Mexico 1977 0 Togo 1991 0El Salvador 1979 1 Mexico 1988 1 Tunisia 1987 1Gabon 1990 0 Mexico 1994 1 Uganda 1993 0Gambia 1996 0 Mongolia 1990 1 Uganda 2005 0Ghana 1991 0 Morocco 1961 1 Yugoslavia 1951 0Ghana 1996 0 Morocco 1965 0 Zambia 1972 0Guatemala 1974 1 Nepal 1957 0 Zambia 2001 0Guinea 1995 0 Nepal 1981 0 Zimbabwe 1983 0Guinea-Bissau 1994 0 Nicaragua 1984 0 Zimbabwe 1987 0Hungary 1956 1 Pakistan 1985 0 Zimbabwe 1999 0Hungary 1988 1

Durable failure observations when: (1) there is no regime collapse in years t-1, t, or t+1; (2) the Durablefailure did not mark an increase in the Polity score such that it passed the Democratization threshold of +6;and (3) there is non-missing data on the oil wealth variable. In all 79 cases, the incumbent autocratic regimeremains in power and in 59 cases the incumbent leader remains in power.

64

Appendix D: Endogenous oil income

In this Appendix we report the results of the two-stage models that use logged Oil reserves percapita (5-year lag) as an excluded instrument for logged Oil income per capita (1-year lag). FiguresD-1 to D-5 depict the de-meaned level of logged oil income per capita (1-year lag) and the de-meansvalue of logged oil reserves per capita (5-year lag) over time for dictatorships in five countries indifferent regions of the world: Angola, China, Egypt, Malaysia, and Mexico. These graphs illustratethat while oil reserves are highly correlated with income, many of the year-to-year fluctuations inoil income that may reflect political expectations do not correspond to known reserves.

For example, in Angola reserves were stagnant after 2000 but oil income increased steadily afterthe death of the long-time rebel leader, Jonas Savimbi, in 2002 (and the increase in internationaloil prices the following year).52 Expectations about increased political stability after Savimbi’sdeath may have contributed to both the steady increase in oil income and regime longevity. Inthis case, while the oil reserves are clearly correlated with oil income, the former does not pick upthe potentially endogenous changes in oil income associated with the expectations about politicalstability stemming from this decisive political event. In China, we see a similar pattern post-1999: while oil income increased, reserves were stagnant. Indeed for much of the economic reformperiod (early 1980s onwards), reserves are relatively stagnant but income bounces up and down.In Egypt, both in the early 1980s (after Sadat’s assassination) and then again after 2000 (oncethe younger cohort of the NDP, led by Gamal Mubarak, took over many leadership positions inthe party), lagged reserves and oil income run in the opposite direction. In Malaysia, oil incomedips during the 1998 Asian financial crisis but then rebounds quickly and increases throughout the2000s. Lagged reserves, however, are relatively stagnant during this period of political turmoil forthe ruling BN coalition. Finally in Mexico the large decline in oil income during the period ofeconomic stagnation in the 1980s (particularly around the crucial 1988 election, prior to which thePRI split) coincides with an increase in lagged resources. Further the potentially decisive decline inoil income over the 15-year period from the mid-1980s to 2000 coincides with a period of stagnantlagged reserves. In the latter four countries, lagged oil reserves appear to reflect the large increasesin oil income associated with the 1970s oil boom. These examples are only illustrative, but webelieve they nonetheless demonstrate, across a range of cases, that oil income changes linked todecisive political events are not generally reflected in the lagged measure of oil reserves.

Table D-1 reports results from the first stage equations. In each model the excluded instrument,ReservesDev, is strongly correlated with OilDev. F-tests indicate that this excluded instrument isstatistically different from zero in the first stage. One standard cut-point for these F-tests is 10;the F-statistics from these models exceed this threshold (> 220 in each case).

Table D-2 reports the second stage results. For each IV result, we first report a naive result forthe same model specification on the same sample. The first four columns report results from probitmodels; the latter four from linear probability models (LPMs). Comparing results from columns(1) and (2) for the transitions to democracy equation, we see that the IV estimate is much largerthan the naive result, both of which are positive. This suggests the naive result under-estimates

52Both rising world oil prices and Savimbi’s death contributed to the rapid increase in Angolanoil income after 2002. Using the Angolan time series for oil income (log) and the world oil price ina linear model, we find that a post-2002 dummy variable (a proxy for Savimbi’s death) accountsfor roughly a 32% increase in oil income from 2003-2006, while the increase in the oil price ($32)accounts for a 47% increase in Angolan oil income after 2002.

65

the positive correlation between increases in oil income and the likelihood of democratic transition.Comparing results from columns (3) and (4) for transitions to new autocracy, we find that thecoefficient estimates of interest are similar: 0.153 and 0.130, respectively. This suggests that thenaive estimates may over-estimate the negative correlation between oil and risk of transition tonew autocracy, but only slightly. The results in columns (7) and (8) largely mirror these. Thoughthe IV estimate in column (8) is not statistically significant at the 0.10 level and is slightly smaller(-0.716) than the naive estimate (-0.921), it is roughly the same size as the estimate reported incolumn (2) of Table A-5 (-0.723). For readers concerned with statistical significance, we note thatthe p-value for the estimate of oil income in (4) is 0.111 and in (8) it is 0.137.

Figure D-6 shows the partial correlation between oil reserves and oil income in the first stage.Two countries – Libya (620) and Chad (483) – stand out as potential outliers in the first stageequation. Dropping one or both of these countries from the analysis strengthens the main findingreported in column (4) of Table D-2: the estimate of interest is greater than 0.16 and statisticallysignificant at conventional levels (results reported in the replication files).

Results reported in the replication files also indicate that when we estimate the model in column(8) but treat both the mean level of oil income and the deviation of oil income as endogenous – byincluding a second excluded instrument, ¯Resourcesi – the estimate of interest is almost identicalto that reported in column (8). Finally, when we estimate (8) but split the sample by time period(pre-1980 and post-1979) we find similar results: the estimate of interest is -0.78 in the early periodand -0.90 and statistically significant at conventional levels in the latter.

66

Table D-1: First-stage equations

Model (1) (2)

ReservesDev 0.691** 0.690**(0.05) (0.05)

GDPpcDev 0.788** 0.783**(0.15) (0.15)

¯GDPpci -0.010 0.003(0.03) (0.03)

Civil war -0.009 -0.013(0.05) (0.05)

Nbr democracy 0.001 -0.002(0.02) (0.02)

Oili 0.027** 0.025**(0.01) (0.01)

¯Demi 0.525(0.54)

¯Dicti 1.286+(0.74)

(Intercept) 0.005 -0.118(0.28) (0.29)

F-statistic 231 222

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. De-pendent variable is OilDev . Excludedinstrument is: ReservesDev . Stan-dard errors in parentheses, clustered onregime. All models control for durationtime (cubic polynomial) and a calendartime trend (cubic polynomial); theseare not reported. 3704 observationsfrom 250 distinct autocratic regimes in114 countries from 1947-2007.

67

Table D-2: Second-stage IV results

Probit (1-4) Linear probability (5-8)

Transition Democracy Autocracy Democracy Autocracy

Naive IV Naive IV Naive IV Naive IV

Model (1) (2) (3) (4) (5) (6) (7) (8)

OilDev 0.037 0.138 -0.153* -0.130 -0.087 0.015 -0.921** -0.716(0.08) (0.10) (0.06) (0.08) (0.30) (0.34) (0.34) (0.48)

Oili -0.075* -0.081* -0.004 -0.004 -0.093 -0.095 0.031 0.027(0.03) (0.03) (0.03) (0.03) (0.07) (0.07) (0.10) (0.10)

GDPpcDev 0.213 0.100 0.230 0.197 0.785 0.645 1.264 0.983(0.21) (0.24) (0.23) (0.23) (1.01) (1.05) (0.92) (0.98)

¯GDPpci 0.057 0.061 -0.189* -0.190* 0.158 0.157 -0.240 -0.247(0.08) (0.07) (0.09) (0.09) (0.28) (0.28) (0.39) (0.39)

Civil war 0.168 0.176 0.325** 0.325** 0.802 0.813 1.560 1.583+(0.13) (0.13) (0.13) (0.13) (0.73) (0.73) (0.95) (0.95)

Nbr democracy 0.124+ 0.125+ 0.166* 0.166* 0.675 0.674 0.903+ 0.903+(0.07) (0.07) (0.08) (0.08) (0.44) (0.44) (0.47) (0.47)

¯Demi 12.378** 12.240** 104.707** 104.570**(1.38) (1.41) (9.78) (9.72)

¯Dicti 10.429** 10.365** 104.399** 103.993**(1.69) (1.70) (19.03) (18.99)

(Intercept) -2.942** -2.941** -0.912 -0.904 -1.540 -1.518 1.775 1.854(0.71) (0.71) (0.76) (0.76) (2.57) (2.58) (4.20) (4.20)

+ p<0.10;∗ p<0.05; ∗∗ p<0.01. Standard errors in parentheses, clustered on regime. Dependent variable is RegimeTransition (Democratic, Autocratic). Binary dependent variable multiplied by 100 in LPMs for ease of interpretation.All models control for duration time (cubic polynomial) and a calendar time trend (cubic polynomial); these are notreported. First stage result reported in Table D-1. 3704 observations from 250 distinct autocratic regimes in 114countries from 1947-2007.

68

4.5

5

5.5

6

6.5

7

Oil

rese

rve

s &

in

co

me

(lo

g)

1975 1980 1985 1990 1995 2000 2005 2010

Year

Reserves (5-yr lag)Income (1-yr lag)

Figure D-1: Oil reserves (5-year lag) and oil income (1-year lag) in Angola. Bothmeasures are denominated by population size and then logged, then de-meaned.

69

0

1

2

3

4

Oil

rese

rve

s &

in

co

me

(lo

g)

1950 1960 1970 1980 1990 2000 2010

Year


Figure D-2: Oil reserves (5-year lag) and oil income (1-year lag) in China. Both measuresare denominated by population size and then logged, then de-meaned.

70

2

3

4

5

6

Oil

rese

rve

s &

in

co

me

(lo

g)

1950 1960 1970 1980 1990 2000 2010

Year


Figure D-3: Oil reserves (5-year lag) and oil income (1-year lag) in Egypt. Both measuresare denominated by population size and then logged, then de-meaned.

71

0

2

4

6

8

Oil

rese

rve

s &

in

co

me

(lo

g)

1960 1970 1980 1990 2000 2010

Year


Figure D-4: Oil reserves (5-year lag) and oil income (1-year lag) in Malaysia. Bothmeasures are denominated by population size and then logged, then de-meaned.

72

3

4

5

6

7

Oil

rese

rve

s &

in

co

me

(lo

g)

1950 1960 1970 1980 1990 2000

Year


Figure D-5: Oil reserves (5-year lag) and oil income (1-year lag) in Mexico. Bothmeasures are denominated by population size and then logged, then de-meaned.

73

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-6

-4

-2

0

2

4

e(

de

v_

hm

oil

| X

)

-6 -4 -2 0 2 4

e( dev_l5reserves | X )

coef = .68995707, (robust) se = .04631742, t = 14.9

Figure D-6: First-stage partial correlation, Oil reserves and Oil income. Bivariate plotfrom model (2) in Table D-1. Observations labeled by country-code. Libya (620) and Chad (483).

74

Appendix E: Military spending

Table E-1 reports the summary statistics for Table 2 in the main text. Figure E-1 shows the distri-bution of military spending in the main sample used in Table 2 of the main text (3288 observations).Military spending data is the logged value of constant dollars. Instead of using population size inthe denominator of the dependent variable, we control for population size in all regressions. Unitfixed effects condition estimates on country size.

In results reported in the replication files, we test the robustness of the model in Table 2,column (2). First, we trim the data to exclude all observations with extreme values for differencesin oil income (absolute value >2). Second, we exclude all observations with dfbeta values greaterthan the 2/sqrt(N) cut-point. Third, we run the model with robust regression which down-weightspotential outliers. Fourth, we control for military personnel size, with data from the Correlates ofWar project. These changes all yield similar substantive results, with all estimated coefficients forthe differenced variables statistically different from zero at conventional levels. Finally, we comparethe coefficients for the LRM using the COW data and military spending data from SIPRI. TheSIPRI sample, however, contains less than 1000 observations, which is less than a third of the sizeof the COW sample. We find substantially stronger estimates for the LRM using the SIPRI data.

0

.1

.2

.3

De

nsity

5 10 15 20

Military spending (log)

Figure E-1: Military spending in autocratic regimes. Logged constant dollar values.

Finally, the graphs in Figure E-2 show the cross-sectional relationship between oil income andmilitary spending in dictatorships. To generate these graphs, we calculated the mean level ofoil income (log units) and the mean level of military spending (log units, constant dollars) foreach autocratic regime in the sample. Some countries, such as Argentina and Iran, have morethan one regime in the sample. Others, such as China and Mexico, have only one autocraticregime during the post-1946 period. The left panel shows the bivariate relationship between thetwo mean level variables, while the right panel shows the partial regression plot of the bivariaterelationship conditional on the (regime-mean) level of GDP per capita (log), population size (log),and international war. In both graphs, there is a strong cross-sectional correlation between oilincome and military spending.

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Unconditional correlation Conditional correlation

5

10

15

20

Mili

tary

sp

en

din

g (

log

)

0 2 4 6 8 10

Oil income (log)

Regime mean Linear fit

-2

-1

0

1

2

3

e(M

ilita

ry s

pe

nd

ing

| X

)

-4 -2 0 2 4 6

e(Oil income | X)

Regime mean Linear fit

Figure E-2: Oil income and military spending in autocratic regimes. Data points reflectthe mean levels of oil income (log) and military spending (log) by autocratic regime. Analysis of230 distinct autocratic regimes from 1947-2007.

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Table E-1: Summary statistics

Variable N Mean Std Dev Min Max

Military spending 3288 12.72 2.09 7.15 19.85Oil income 3288 2.19 2.93 0 11.11Population size 3288 9.18 1.37 5.88 14.09International war 3288 0.06 0.23 0 1Civil war 3288 0.17 .38 0 1Calendar time 3288 37.2 12.9 15 61

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