: Evidence from Oil-Rich Countries

JOE

ZAE

nd

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undratrlyhavfou

wealth may inhibit economic growth (for an excellent reviewof literature see Frankel, 2010). In particular, the curse applies

& Rointals suc2006)curse. Dqualityof rent-sourceSince

relatively constant over time, these studies typically amount

samples.

deed, with a strong government, resource wealth is likely tobe growth enhancing, even when institutions are relatively

the Middle East and North Africa.The literature on the resource curse focuses on rent-seeking

2 ggestst fromivities.cationsourcewhereth.er bal-

ance of dierent political factions in society, using Iran as a

extensive rent dissipation as this intensies the competition

the TU Dresden and ZEWMannheim and the 18th Annual Conference of

the European Association of Environmental and Resource Economists for

helpful comments. Mohammad Reza Farzanegan gratefully acknowledges

World Development Vol. 40, No. 7, pp. 13081316, 2012 2012 Elsevier Ltd. All rights reserved.

0305-750X/$ - see front matter

6/j.worlddev.2012.03.003weak. However, there is another side to this coin, with a moreThe optimistic message from our study is that oil rich, lessdeveloped countries, which typically also have less developedinstitutions, are not destined to be development disasters. In-

*We thank Michael Alexeev, Jessica Dewald, Anne-Kathrin Koch, Ma-

rcel Thum, the referee as well as participants in the Brown Bag seminars at1992 to 2005, we identify political power balance as animportant determinant of the ecient use of resource rents.This result holds when we control for the eects of otherdeterminants of income, time varying common shocks, andcountry xed eects. It is also robust to various alternativemeasures of resource abundance and inclusion of the qualityof democratic institutions, as well as to the instrumental var-iable method of estimation (system and dierenced GMM(Generalized Method of Moments)) and across dierent

for power. In contrast, when resource rents are low, a politicalpower balance may be benecial, since it stimulates investmentby the dierent factions of the elite. Bjorvatn, Farzanegan, andSchneider (2012) lend empirical support to these mechanisms,analyzing factionalism, oil rents, and economic growth in Iran.The present paper builds on these two latter contributions,broadening the perspective from a case study of Iran to astudy involving 30 oil rich countries.try eects. Using panel data for 30 oil-rich countries fromto a comparison of resource wealth and income across coun-tries. In contrast, the present paper focuses on within coun-

case in point. Their theoretical model shows that in a situationof high resource rents, political power balance may lead topessimitions, ae, 2007). Other studies have established that fundamen-h as institutional quality (Mehlum, Moene, & Torvik,are important conditioning factors for the resourceigging deeper, Hodler (2006) argues that institutionalis shaped by ethnic fractionalization through a processseeking, thereby pointing to ethnicity as a fundamentalof the curse. 1

both ethnic composition and institutional quality are

nomic performance. For instance, Torvik (2002) suthat natural resource rents divert entrepreneurial talenproductive activities to low-productive rent-seeking actHe concludes that the fall of income due to this re-alloof entrepreneurs may outweigh the benets of natural rerents. Hodler, 2006 also builds a model of rent-seeking,ethnic groups compete for a share of the resource wealBjorvatn and Selvik (2008) explicitly focus on the powto point source natural resources like oil (Boschini, Pettersson, as a key mechanism linking high resource wealth to low eco-Resource Curse and Power Balance

KJETIL BNHH Norwegian School of

MOHAMMAD REPhilipps-University Marburg (CNMS), Z

a

FRIEDRICHJohannes Kepler Uni

Summary. We examine the role of political fractionalization incountries, we nd that the income eect of resource rents is modesource wealth can generate growth even in an environment of pooernment may have damaging eects on the economy. These resultscountries in the Middle East, which are currently undergoing pro 2012 Elsevier Ltd. All rights reserved.

Key words oil rents, balance of power, resource curse

1. INTRODUCTION

The literature on the resource curse shows that resource

www.elsevier.com/locate/worlddevhttp://dx.doi.org/10.101stic message: Even in an environment of sound institu-dding oil revenues to a fractionalized government can

1308RVATNconomics, Bergen, Norway

FARZANEGANW Mannheim & TU Dresden, Germany

HNEIDER *

sity of Linz, Austria

erstanding the resource curse. Using panel data for 30 oil-riched by the political power balance. With a strong government, re-developed institutions, while adding oil revenues to a weak gov-e important implications for the economic prospects of the oil-richnd political changes.

be like adding fuel to the re, and cause slower economicgrowth. Our analysis provides insights which are timely, giventhe recent political turmoil in many resource-rich countries inthe Georg Forster Postdoctoral grant of Alexander von Humboldt Fou-

ndation. Final revision accepted: March 7, 2012.

the share of oil revenues in the government budget. The data7

The results of pooled, xed, and random eects of panel

E:are taken from Bornhorst, Gupta, and Thornton (2009) .The main proxy for the degree of political power balance is

fractionalization of governing parties, or political fractionaliza-tion for short, and is taken from Beck, Clarke, Gro, Keefer,and Walsh (2001). The power balance index, which goes fromThe remainder of the paper is structured as follows. Section 2presents our empirical strategy and the data. Results are pre-sented and discussed in Section 3. Section 4 concludes the pa-per.

2. EMPIRICAL RESEARCH DESIGN

(a) Data, specication, and empirical strategy

To estimate whether the relationship between oil rents andGDP (Gross Domestic Product) per capita varies systemati-cally with the balance of political power, we use the followingmodel:

incomeit cons b1 oilit b2 powerit b3 oilit powerit b4 Zit li dt eit 1

with country i (1, .., 30), and time t (19922005). 3 Income isthe log of real GDP per capita, oil is the log of oil revenues(as a share of total government revenues), power is a measureof political fractionalization, oilpower is the interaction of oilrevenues and power fractionalization, and Z stands for thecontrol variables. All explanatory variables are lagged oneyear to avoid possible endogeneity problems (see Mehranand Peristiani (2009) for a similar approach). 4

At the margin, the total eect of increasing oil revenues canbe calculated by examining the partial derivatives of real in-come per capita with respect to the oil rent variable:

@incomeit@oilit b1 b3 powerit 2

Based on the theoretical predictions by Bjorvatn and Selvik(2008) and Bjorvatn et al. (2012), we expect the sign of b1 tobe positive and the sign of b3 to be negative. This means thatincreasing oil rents in a situation of weak governments lead toa destructive competition, with detrimental eects on income.Of course, there are other factors such as climate, culture,

geography, and other unobserved time-invariant factors whichare country specic and may correlate with income as well. Ifsuch country specic or time specic factors are correlatedwith oil rents or balance of power, then both pooled cross sec-tion and random eects estimations may lead to biased andinconsistent results. 5 We allow for country (li) and time (dt)specic eects, controlling for the unobservable time-invariantcountry characteristics and shocks which are common to allcountries. Furthermore, we can address the spurious businesscycle eects by including time xed eects (Keller, 2004).

(b) Dependent and independent variables

Following Alexeev and Conrad (2009), Hall and Jones(1999), Easterly and Levine (2003), and Rodrik, Subramanian,and Trebbi (2004) we use the level of real GDP per capita (inlog) as our dependent variable, taken from the World Bank(2008). 6 Our main independent variables are oil revenues andbalance of power. The most relevant proxy for the oil rent is

RESOURCE CURSE AND POWER BALANC0 to 1, is dened as the probability that two members of par-liamentpicked at randomfrom governing parties belongregressions are presented in Table 1. All specications in Ta-ble 1 show a negative and highly signicant eect of the inter-action term. This suggests that the political power balanceplays an important role in determining the income eect of re-source rents. In particular, resource rents are less likely to havea positive eect on income when governments are highly frac-tionalized, and therefore weak.Note also that the direct eect of oil revenues on real GDP

per capita is positive and statistically signicant at the 1% levelin all specications. For example, in the specication withcountry and period xed eects (model 1.3); a 1% increase inthe size of oil revenues leads to a 1.35% increase in levels ofreal GDP per capita. This direct positive eect of oil rentson economic development is in line with the ndings of Alex-eev and Conrad (2009).We also observe that power balance as such does not nega-

tively aect income, as indicated by the fact that the estimatedcoecient of govfrac is positive and statistically signicant. Thisis in line with themodel presented in Bjorvatn and Selvik (2008),which shows that a balanced power structure (which, ceteris par-ibus, implies a higher level of fractionalization) stimulates invest-ment. In a resource- poor country, this eect may dominate thenegative eect of rent-seeking, leading to higher income.In all specications, we have controlled for other important

determinants of income such as trade openness, governmentconsumption, nancial development, ination, investment,and age structure of population. We also control for the com-mon time trend in random eect regressions. 8 Including timetrend controls for other factors such as technological progresswhich may aect the economic development of countries into dierent parties. A high fractionalization index indicatesthat the government consists of a large number of small par-ties, which we shall think of as a weak government, while alow fractionalization index indicates that the government con-sists of a small number of strong parties, possibly only oneparty, and signals a strong government.Our focus on the role of party systems in economic perfor-

mance is supported by Riker (1964), who emphasizes thatstrong political parties are a major cause of high public goodsprovision and economic growth. A large number of indepen-dent candidates and candidates from recently formed new par-ties in presidential or parliament elections indicate a higherdegree of fractionalization of the political system and is notconducive to growth. This is conrmed empirically by Enikol-opov and Zhuravskaya (2007) who show that political frac-tionalization is bad for public goods provision. Similarly,Poteete (2009) argues that one of the main causes of the suc-cessful development path in the resource-rich country of Bots-wana was the strong and stable political coalition during therst decades of independence.The control variables include investment as a ratio of real

GDP, ination rate (as a measure of macroeconomic instabil-ity), real government consumption as a ratio of real GDP (aproxy for size of government distortions in the economy),trade openness, nancial development, and age dependency(to control for the structure of population). Appendix A pre-sents the countries in the sample. The data description andsources are presented in Appendix B. Appendix C reportsthe descriptive statistics of the major variables.

3. EMPIRICAL RESULTS

EVIDENCE FROM OIL-RICH COUNTRIES 1309our sample. The eects of our control variables on incomeare as expected in theory.

Table 1. Oil, fractionalization of government parties and income (panel regressions)

Variables Dependent variable: log (rgdp p.c.), 19932005

Pooled OLS, 1 lagof IVs (1.1)

c. fe, 1 lagof IVs (1.2)

c & t fe, 1 lagof IVs (1.3)

c & t fe, 1 lagof IVs (droppingGovex) (1.4)

c & t fe, 1 lagof IVs (excludingNorway) (1.5)

c.re,1 lagof IVs (1.6)

c re, 1 lagof IVs excludingNorway (1.7)

c & t fe, using 2 lagsof IVs (1.8)

c & t fe, using 5 lagsof IVs (1.9)

Oilrev 3.01***

(5.25)1.22***

(5.54)

1.35***

(5.64)1.96***

(5.97)1.35***

(5.46)1.29***

(6.15)1.34***

(6.28)1.05***

(4.61)0.64***

(2.80)

Govfrac 2.13**

(2.28)0.38***

(2.53)0.33**

(2.13)0.38**

(2.15)0.37*

(1.80)0.42**

(2.44)0.44**

(2.22)0.13(0.82)

0.17(1.28)

Oilrev*Govfrac 6.23***(3.03)

2.08***(3.53)

1.86***(3.14)

2.27***(2.99)

1.94***(2.91)

2.11***(3.71)

2.14***(3.52)

1.31**(2.25)

1.06**(2.28)

Trade 0.33**

(2.47)0.06(0.78)

0.08(1.13)

0.12(1.27)

0.08(1.13)

0.08(1.06)

0.08(1.04)

0.21***

(3.04)0.26***

(3.52)

Govex 0.33(1.56)

0.45***(6.91)

0.45***(7.35)

0.45***(7.37)

0.44***(6.75)

0.43***(6.66)

0.42***(7.35)

0.20**(2.40)

Credit 0.34***

(3.16)

0.01(0.38)

0.000(0.02)

0.02(0.24)

0.001(0.03)

0.03(0.86)

0.03(0.77)

0.03(0.91)

0.00(0.00)

Ination 0.006*

(1.86)0.003***(3.67)

0.003***(4.20)

3.95E05*(1.79)

0.003***(4.23)

0.003***(3.74)

0.003***(3.74)

0.003***(4.04)

0.001***(2.68)

Invest 0.06(0.48)

0.07(1.25)

0.05(0.88)

0.006(0.10)

0.06(0.88)

0.06(1.28)

0.07(1.26)

0.04(0.57)

0.11**

(2.19)

Age dep 2.26***(6.40)

0.11(0.61)

0.06(0.33)

0.38(1.51)

0.05(0.22)

0.07(0.39)

0.13(0.63)

0.01(0.06)

0.53**(1.96)

Country xed eects No Yes Yes Yes Yes Yes (RE) Yes (RE) Yes YesTime trend Yes Yes No No No Yes Yes No NoTime xed eects No No Yes Yes Yes No No Yes YesObs. (countries) 248 (25) 248 (25) 248 (25) 257 (26) 236 (24) 248 (25) 236 (24) 226 (25) 154 (24)R2 0.60 0.99 0.99 0.98 0.99 0.69 0.68 0.99 0.99

Note: t-statistics are in parentheses, basing on heteroskedasticity-robust standard errors (White diagonal s.e. and covariance; df correction); constant is not shown. c & t fe and re refer to country andtime xed and random eects. All variables except for ination are in log. Results are robust using higher lags of explanatory variables.* Signicant at 10% level.** Signicant at 5% level.*** Signicant at 1% level.

1310WORLD

DEVELOPMENT

Table 2. Oil, fractionalization of government parties, income, and political institutions (panel regressions)

Variables Dependent variable: log (rgdp p.c), 19932005

c & t fe, 1 lagof IVs (2.1)

c & t fe, 1 lagof IVs, droppingGovex (2.2)

c & t,1 lagof IVs ex.Norway (2.3)

c & t fe, 1 lagof IVs, using

oil rents per capita(2.4)

c & t fe, 1 lagof IVs, using oil rentsper capita), excluding

Norway (2.5)

One step SYS.GMM, using Oilrev (2.6)

One step Di.GMM, using Oilrev (2.7)

Oilrev 1.39***

(4.13)2.07***

(7.51)1.42***

(4.21)0.13***

(3.42)0.13***

(3.30)0.46**

(2.26)0.45**

(2.19)Govfrac 0.28*

(1.80)0.31(1.62)

0.29(1.32)

0.60**

(2.07)1.07**

(2.46)0.33(1.54)

0.30(1.25)

Oilrev*Govfraco 1.75***(2.99)

2.11***(3.51)

1.77***(2.56)

0.10**(2.23)

0.19**(2.47)

1.25**(2.33)

1.24**(2.00)

Trade 0.07(0.96)

0.11(1.58)

0.07(0.95)

0.06(0.72)

0.06(0.75)

0.002(0.02)

0.000(0.00)

Govex 0.45***(7.32)

0.45***(7.35)

0.44***(8.24)

0.44***(8.09)

0.26*(1.74)

0.25*(1.81)

Credit 0.00(0.22)

0.01(0.34)

0.00(0.14)

0.03(1.04)

0.31(0.88)

0.01(0.22)

0.00(0.16)

Ination 0.003***(4.15)

4.17E05(0.97)

0.003***(4.17)

0.000**(2.44)

0.000**(2.29)

0.001*(1.79)

0.001*(1.86)

Invest 0.06(0.95)

0.00(0.00)

0.06(0.96)

0.05(0.88)

0.05(0.90)

0.13***

(2.89)0.12**

(2.47)Age dep 0.12

(0.64)0.45*

(1.96)0.08(0.36)

0.11(0.57)

0.07(0.37)

0.48(0.87)

0.34(0.60)

Polity 0.21(0.92)

0.32(1.18)

0.23(0.89)

1.15***(2.89)

1.36***(3.02)

0.12(0.62)

0.08(0.49)

Polity*Oilrev 0.15(0.28)

0.33(0.48)

0.24(0.37)

0.24***

(3.38)0.27***

(3.42)Country xed eects Yes Yes Yes Yes Yes Yes YesTime xed eects Yes Yes Yes Yes Yes Time trend Time trendLagged dependent variable 0.95***

(17.39)0.90***

(12.27)Obs. (countries) 248 (25) 257 (26) 236 (24) 258 (25) 246 (24) 253 (25) 226 (25)R2 0.99 0.98 0.99 0.99 0.99 Sargan (p-value) 54.3 (0.49) 57.3 (0.38)AR(1)-p-value 0.04 0.02AR(2)-p-value 0.30 0.27

Note: t-statistics are in parentheses, basing on heteroskedasticity-robust standard errors (White diagonal s.e. and covariance; df correction); constant is not shown. c & t fe and re refer to country andtime xed and random eects. All variables except for ination are in log. The values reported for the Sargan test are the p-values for the null hypothesis of instrument validity. The values reported forAR (1) and AR (2) are the p-values for rst and second order auto correlated disturbances in the GMM models.* Signicant at 10% level.** Signicant at 5% level.*** Signicant at 1% level.

RESOURCECURSEAND

POWERBALANCE:EVID

ENCEFROM

OIL-RICH

COUNTRIES

1311

VEOn average, 50% of total government revenues in the coun-tries of our sample from 1995 to 2002 depend on oil revenues.The size of government spending in oil based economies is rela-tively high and is usually nanced through oil rents. The bivar-iate correlation between oil revenues and government spendingin our sample is not too high (0.42). Even extreme collinearity(as long as it is not perfect) does not violate OLS (OrdinaryLeast Squares) assumptions. OLS estimates are still unbiasedand BLUE (Best, Linear, Unbiased Estimator). 9 However, inorder to examine the sensitivity and robustness of our mainresults, we drop the government expenditure variables fromgeneral specication. Our main results as presented in Model1.4 are quite robust without government expenditure as well.In our sample, Norway has an established democracy, rank-

ing as one of the least corrupt countries in the past years. As arobustness test, we have removed Norway from panel estima-tions and results are presented in Models 1.5 and 1.7 in Ta-ble 1. The main results remain robust without Norway. Therandom eects estimation presented in Models 1.61.7 donot dier from xed eects estimations qualitatively. Our mainvariables of interest such as oil revenues and the interactionterm remain statistically signicant with the expected sign inthe random eects models. 10

In Models 1.8 and 1.9, we also re-estimate the same speci-cation using 2 and 5 year lags of explanatory variables insteadof 1 year lags. Using higher order lags of independent vari-ables reduces the risk of the endogeneity problem within ourestimations. We notice that our main results are robust inmodels with a higher number of right hand side variables lags.The structure of government and institution in countries of

our sample varies to dierent degrees. For example, Iran is ahighly factionalized semi-democratic country while Saudi Ara-bia is an absolute monarchy, Kuwait is a constitutional monar-chy, and Norway is an established democracy. It is thereforeimportant to control for the quality of political institutions inour analysis. In Table 2, we have controlled for the role of polit-ical institutions using the Polity2 index from the Polity IV data-set (Marshall & Jaggers, 2009). Polity2 scores are between 10and +10. A +10 refers to a strongly democratic state and10 to strongly autocratic.We have rescaled thePolity2 indexfrom 0 to 1. The higher the rescaled Polity index, the higher thequality of democratic institutions. We aim to examine whetherincluding this variable aects the intermediary role of fractional-ization in government parties in the oil rents-income nexus. Inother words, we want to make sure that the eect of fractional-ization of government parties does not reect the quality of polit-ical institutions.We notice that even by controlling for thePolityindex and its interaction with oil revenues, our main variables ofinterest remain robust with expected sign and are highly signi-cant (see Model 2.1). As in Table 1, we drop the governmentexpenditure variable in Model 2.2 for the robustness analysis.We notice that this does not aect our main nding. ExcludingNorway also does not change our main results (Model 2.3).Furthermore, we present the results using an alternative

proxy for the oil resources. We use per capita oil rents insteadof the share of oil revenues in total government revenues.Alexeev and Conrad (2009) argue that the best measures ofthe role of natural resources in the long run growth are per ca-pita measures. Using oil wealth as a measure of GDP or ex-port in growth regressions may bias estimates in favor of theresource curse hypothesis. There can be factors unrelated tonatural resources which aect the structure of the economyespecially in the export sector. Thus, a resource rich countrywhich suers from these factors may have a high oil/GDP

1312 WORLD DEor oil/export ratio, presenting a spurious evidence for the re-source curse hypothesis in regressions.Oil rents (in US dollars) are calculated as the productionvolume times the dierence between the international marketunit price and the average production cost. We have dividedthe total oil rents by the population of each country to calcu-late the per capita oil rents. Models 2.4 and 2.5 show the re-sults, using per capita oil rents instead of oil revenues intotal revenues. The direct eect of per capita oil rents on in-come is positive and signicant at the 1% level. However,the magnitude of the eect is now smaller. A 1% increase inthe size of per capita oil rents raises per capita income by0.13%. The moderating eect of fractionalization of govern-ment parties remains statistically signicant at the 5% levelin models with per capita oil rents. Excluding Norway, as inModel 2.4, does not change our results. As we can see fromModels 2.4 and 2.5, the interaction of the Polity index andoil rents is positive and statistically signicant. Higher qualityof political institutions can amplify the positive income eectsof oil rents. This is in line with ndings of previous literaturesuch as Mehlum et al. (2006). The direct eect of Polity on in-come is negative and signicant in Models 2.4 and 2.5. Thismeans that, given a negligible share of oil rents, higher politi-cal openness may reduce the speed of economic growth. Thiscan be due to the increased political instability in poor re-source economies by opening the political system. Previousstudies have found an inverted U-shaped relationship betweendemocracy and conict (Hegre, Ellingsen, Gates, & Gleditsch,2001; Muller & Weede, 1990). Full autocracy and full democ-racy should go along with a low risk of conict (and higher in-come) since highly autocratic regimes can repress dissent andthus avoid civil violence, while highly democratic societiescan solve problems peacefully. For middle values of the Polityindex, we can thus make no clear predictions.Furthermore, as per capita income tends to be persistent over

time, we estimate a dynamic specication (dierence GMM)using the Arellano and Bond (1991) estimator, which allowsthe specication of a common lagged eect (see Model 2.7 inTable 2). We use three lags of potentially endogenous variablesas instruments. The Sargan test validates the adequacy of theinstruments, and the failure to reject the null hypothesis ofthe validity of the instruments indicates that the specicationis correct. In addition, the other diagnostics are also satisfac-tory. The absence of rst order serial correlation is rejectedand the absence of second order serial correlation is not re-jected. The dynamic GMM dierences the model to removecountry specic eects or any time-invariant country specicvariable, eliminating any endogeneity because of the correla-tion of these country specic eects and the right hand sideregressors. It also addresses the possible non-stationarity ofexplanatory variables (Baltagi, Demetriades, & Law, 2009).The results in Model 2.7 provide further support for our ba-

sic models regarding a direct positive eect of oil revenues onincome and a moderating eect of fractionalization of govern-ment parties. We also report the results of system GMM intro-duced by Blundell and Bond (1998) in Model 2.6. The SystemGMM estimator combines the rst dierenced equation andlevels equation to estimate the model, employing both laggedlevels and dierences as instruments. The model is estimatedusing a rst dierence transformation to remove the individualcountry eect. In addition, to control for the country speciceects, the system GMM preserves the cross country dimen-sion of the data which is lost when we use rst dierencedGMM (Castello-Climent, 2008). Again, our results provideempirical supports for our main hypotheses. The diagnosticstatistics are also satisfactory. The Sargan test does not reject

LOPMENTthe over-identication restriction, indicating that instrumentsare not correlated with the error term and, thus, are valid.

Note that we refer to the results estimated on the basis oftwo way xed eects in Table 2 (Model 2.1 with robust stan-dard errors).

ionalization. Note: the middle line shows the marginal impact of a 1% increase in

ierent levels of political fractionalization. The upper and lower lines are 90%

ntervals (CIs).

Table 4. Standardized coecients

RESOURCE CURSE AND POWER BALANCE: EVIDENCE FROM OIL-RICH COUNTRIES 1313We reject the absence of rst order serial correlation whileaccepting the absence of second order serial correlation. Fur-thermore, the lagged dependent variable in both Di GMMand SYS GMM is positive and signicant. The relatively highsize of lagged dependent variables indicates its persistency,however, it is statistically dierent from unity in both models.To sum up, our results show that the nal eect of oil rents

on growth is conditional on the level of fractionalizationamong government parties. Using Eqn. (1), we calculate the

Figure 1. Marginal eects of oil revenues on income at dierent levels of fractthe size of oil revenues (% of total revenues) on real GDP per capita at d

condence imarginal impact of oil rents on growth at dierent levels ofthe fractionalization index (mean, minimum and maximum).We useModels 2.1 and 2.4 (country and time xed eects panelregression) to calculate the marginal eects of a 1% increase inoil revenues [in total revenues and oil rents per capita]. The re-sults are presented in Table 3. At the maximum level of frac-tionalization, a 1% increase in the share of oil revenues intotal government revenues and per capita oil rents increases in-come by 0.41% and 0.07%, respectively. In the case of domi-nance of a single political faction (govfrc = 0), the sameincrease in oil revenues (or oil rent) has a more pronounced po-sitive eect on growth (1.39% and 0.13%, respectively). Theoverall impact of oil revenues on growth is positive but higherfractionalization of politics moderates this positive eect. Wehave also illustrated the marginal impacts of oil on income atdierent values of government fractionalization, reporting90% condence intervals around estimated marginal eects.This approach enables us to determine the conditions under

which the oil revenues have a statistically signicant eect onthe GDP per capita. The results are illustrated in Figure 1.

Table 3. Marginal eects of oil revenues on g

Marginal eects Oil revenues (based on model 2.1

Mean of power balance (govfrac) 1.20Maximum of power balance (govfrac) 0.41Minimum of power balance (govfrac) 1.39

Power balance is the logarithm of one plus govfrac(1). Mean, maximum, arevenues is the logarithm of one plus oilrev(1). Oil rents per capita is the logDependent variable: log (rgdp p.c), 19932005

Variables Standardized coecients

***Oilrev_S 0.20 (5.63)Govfrac_S 0.04* (1.83)Oilrev*Govfrac_S 0.12*** (3.03)Trade_S 0.03 (1.03)Govex_S 0.19*** (7.35)Credit_S 0.006 (0.20)Inf_S 0.92*** (4.14)Invest_S 0.02 (0.93)Age_S 0.03 (0.69)Polity_S 0.03 (1.52)Country xed eects YesYear xed eects Yes

Obs. 248R-square 0.99

Note: t-statistics are in parentheses, basing on heteroskedasticity-robuststandard errors (White diagonal s.e. and covariance; df correction).Standardized coecients show increases or decreases in the dependentvariable if the explanatory variable increases by one standard deviation.* Signicant at 10% level.** Signicant at 5% level.*** Signicant at 1% level.

rowth at dierent levels of power balance

estimations) Oil rents per capita (based on Model 2.4 estimations)

0.120.070.13

nd minimum levels of power balance are presented in Appendix C. Oilarithm of oil rents per capita (1).

NOTES

1. Other empirical studies on institutions and the resource curse includeBrunnschweiler and Bulte (2008), Brunnschweiler (2008), Iimi (2007), and

5. Nevertheless, for comparison of our results we present estimations ofpooled, random and xed eects.

11. Using estimations of GMM in Models 2.6 and 2.7 for the calculation

VELOPMENTAlexeev and Conrad (2010).

2. There is also an older literature focusing on market related mecha-nisms for the resource curse, linked to the concept of the Dutch disease. Inthis case, higher oil prices lead to a higher real eective exchange rate andan appreciation of the domestic currency, thus increasing the price of non-oil exports and causing deindustrialization (see Corden & Neary, 1982;Corden, 1984, and van Wijenbergen, 1984).

3. The main criterion for choosing our sample is the availability of datafor our measure of resource wealth, which is the share of oil revenues intotal government revenues. Rent-seeking is likely to be driven by thesemore tangible rents rather than by oil reserves or production, measured inbarrels of oil (see Hodler, 2006, for a similar view).

4. We have also used lagged 2, 3, 4 and 5 years explanatory variables inestimations to control robustness of our main results in Table 1. The resultshold, even using longer lagged explanatory variables, providing moresupport for estimated coecients. In addition, our main independentvariable, oil revenues, is largely exogenous in income models in eachcountry. For example, OPECmembers must follow the determined level ofthe crude oil production quota by this organization (Farzanegan, 2011).Global oil prices are also mostly exogenous for the countries in our sample.For instance, Farzanegan and Markwardt (2009) argue that demand forIranian oil largely depends on global economic growth, energy intensities

within industrial countries, speculator operation in the global oil markets,

expectations of other key oil producers about current and future developments

of the market, international oil companies decisions on liquidation of their

stocks and nally, the policy of key oil consumers on strategic petroleumreserves. Alexeev and Conrad (2010) argue that natural resourceendowments and the output of a country are largely exogenous.of the marginal impact does not change our results in Figure 1.6. If we consider a linear regression of Y on X, then the standardinterpretation of the coecient of X is the change in Y in response to thechange in X. In our case, Y is the GDP level, then the change of Y isgrowth and our coecient would be the eect of the change of X onchanges in the level of the GDP (i.e. growth as a result of the change of X).If, however, our Y is the growth rate, then our coecient is the change ingrowth rate of the GDP in response to a change in X.

7. We thank Fabian Bornhorst for making this data available to us. Asfor a robustness test, we also use the per capita oil rents instead of theshare of oil revenues in total government revenues (See Table 2).

8. We do not include common time trend in panel regressions withcountry and time xed eects because of perfect multicollinearity betweentime trend and time xed eects.

9. For more details see Richard Williamss note at: http://www.nd.edu/rwilliam/stats2/l11.pdf.

10. We have carried out the Hausman test under the null hypothesis sothat the individual eects are uncorrelated with the other regressors in themodel (Hausman, 1978). The p-value = 0 rejects the null hypothesis,indicating that xed eect models are preferred.The marginal eects (the middle solid line) are statisticallysignicant when the 90% condence interval bands (dashedlines) fall above or under the zero line. The leftmost sevenpoints represent the statistically signicant marginal eects ofoil revenues on income. The histogram in the background addsinteresting information by showing us how the cases are dis-tributed. The statistically signicant marginal impacts covermost parts of our observations. At the maximum level of gov-ernment fractionalization, the marginal eect is at its minimumbut insignicant at the 90% level of condence. As we can see,for the majority of countries in our sample, the marginal eectof oil revenues on income is positive and signicant. 11

In order to examine the relative importance of explanatoryvariables such as oil revenues and fractionalization of govern-ment parties in a growth model, we use standardized variablesin regression analysis by subtracting their mean and dividingthem by their standard deviation. Table 4 presents the stan-dardized coecients of the main explanatory variables in acountry and year xed eect regression. Standardized vari-ables have the same unit of measurement, making an interpre-tation of the results easier. The standardized coecientindicates the impact of the explanatory variable in terms ofstandard deviation units. It shows us the number of standarddeviations that the dependent variable (standardized real GDPper capita) increases or decreases with a one standard devia-tion increase in the standardized independent variable.In other words, we can evaluate the relative importance of

our main independent variables in explaining growth. We ndthat ination, oil revenues, government consumption, and

1314 WORLD DEinteraction of fractionalization and oil revenues have the mostimportant eects on growth.

4. CONCLUSION

Our analysis shows that the evolution of political power bal-ances and imbalances over time can have important eects ona countrys ability to benet from its oil revenues. Indeed,when the level of fractionalization is high, indicating a weakgovernment, oil revenues appear to be fully wasted: Above acritical level of fractionalization, there is no signicant, posi-tive eect of oil revenues on income. In contrast, when govern-ments are less fractionalized, for instance, consisting of asingle party, oil revenues have a pronounced positive eecton income.We believe that these results have important implications for

countries in the Middle East and North Africa which are cur-rently undergoing fundamental changes in their political struc-ture. While these changes may hopefully lead to more soundpolitical and economic institutions, they may also open upfor more intensied power struggles. This was certainly thecase in Iran, where the omnipotent Shah in 1979 was replacedby a system which invited factionalist inghting, leading to arather disappointing economic performance. There is a riskthat intensied rent-seeking will lead to lackluster growth alsoin the wake of the current revolutions in the Middle East andNorth Africa, leading to a period of prolonged political insta-bility in the region.

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APPENDIX B

Data description

Variable Description and Source

rgdp p.c. Logarithm of GDP per capita (constant 2000 USD).

Source: World Bank (2008)

Oilrev Logarithm of one plus Oil revenues/Total revenues of

government. Source: Bornhorst et al. (2009)

Govfrac Logarithm of one plus fractionalization of

government parties. The probability that two members

of parliament picked at random from among the

government parties will be of dierent parties. Missing

if there is no parliament, if there are any government

parties where seats are unknown, or if there are no

parties in the legislature. Scale from 0 to 1.Source:

Beck et al. (2001)

Trade Logarithm of sum of imports and exports in GDP.

Source: World Bank (2008)

Govex Logarithm of government consumption in GDP.

Source: World Bank (2008)

Credit Logarithm of domestic credit to private sector (% of

GDP). Source: World Bank (2008)

Ination Ination, consumer prices (annual%). Source: World

Bank (2008)

Invest Logarithm of gross xed capital formation (% of

GDP). Source: World Bank (2008)

Age_dep Logarithm of age dependency ratio (% of working-age

population). Source: World Bank (2008)

Polity2 Democracy Index. Scale from 10 (full autocracy) to10 (full democracy). Rescaled from 0 to 1. Source:

Marshall and Jaggers (2009)

APPENDIX C

Summary statistics

Variable Obs. Mean Standard

deviation

Minimum Maximum

Log (rgdp p.c.) 396 7.70 1.43 5.09 10.61

Log (govfrac(1)+1) 298 0.11 0.18 0 0.56Log (oilrev(1)+1) 359 0.39 0.17 0 0.64Log (govfrac(1)+1) Log (oilrev(1)+1)

276 0.03 0.07 0 0.32

1316 WORLD DEVELOPMENT

Resource Curse and Power Balance: Evidence from Oil-Rich Countries1 Introduction2 Empirical research design(a) Data, specification, and empirical strategy(b) Dependent and independent variables

3 Empirical results4 ConclusionReferencesAppendix A Appendix B Appendix C