Download Resource Curse and Power Balance: Evidence from Oil

World Development Vol. 40, No. 7, pp. 1308–1316, 2012
Ó 2012 Elsevier Ltd. All rights reserved.
0305-750X/$ - see front matter
www.elsevier.com/locate/worlddev
http://dx.doi.org/10.1016/j.worlddev.2012.03.003
Resource Curse and Power Balance: Evidence from Oil-Rich Countries
KJETIL BJORVATN
NHH Norwegian School of Economics, Bergen, Norway
MOHAMMAD REZA FARZANEGAN
Philipps-University Marburg (CNMS), ZEW Mannheim & TU Dresden, Germany
and
FRIEDRICH SCHNEIDER *
Johannes Kepler University of Linz, Austria
Summary. — We examine the role of political fractionalization in understanding the ‘‘resource curse”. Using panel data for 30 oil-rich
countries, we find that the income effect of resource rents is moderated by the political power balance. With a strong government, resource wealth can generate growth even in an environment of poorly developed institutions, while adding oil revenues to a weak government may have damaging effects on the economy. These results have important implications for the economic prospects of the oil-rich
countries in the Middle East, which are currently undergoing profound political changes.
Ó 2012 Elsevier Ltd. All rights reserved.
Key words — oil rents, balance of power, resource curse
1. INTRODUCTION
be like adding fuel to the fire, and cause slower economic
growth. Our analysis provides insights which are timely, given
the recent political turmoil in many resource-rich countries in
the Middle East and North Africa.
The literature on the resource curse focuses on rent-seeking
as a key mechanism linking high resource wealth to low economic performance. 2 For instance, Torvik (2002) suggests
that natural resource rents divert entrepreneurial talent from
productive activities to low-productive rent-seeking activities.
He concludes that the fall of income due to this re-allocation
of entrepreneurs may outweigh the benefits of natural resource
rents. Hodler, 2006 also builds a model of rent-seeking, where
ethnic groups compete for a share of the resource wealth.
Bjorvatn and Selvik (2008) explicitly focus on the power balance of different political factions in society, using Iran as a
case in point. Their theoretical model shows that in a situation
of high resource rents, political power balance may lead to
extensive rent dissipation as this intensifies the competition
for power. In contrast, when resource rents are low, a political
power balance may be beneficial, since it stimulates investment
by the different factions of the élite. Bjorvatn, Farzanegan, and
Schneider (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 a
study involving 30 oil rich countries.
The literature on the resource curse shows that resource
wealth may inhibit economic growth (for an excellent review
of literature see Frankel, 2010). In particular, the curse applies
to point source natural resources like oil (Boschini, Pettersson,
& Roine, 2007). Other studies have established that fundamentals such as institutional quality (Mehlum, Moene, & Torvik,
2006) are important conditioning factors for the resource
curse. Digging deeper, Hodler (2006) argues that institutional
quality is shaped by ethnic fractionalization through a process
of rent-seeking, thereby pointing to ethnicity as a fundamental
source of the curse. 1
Since both ethnic composition and institutional quality are
relatively constant over time, these studies typically amount
to a comparison of resource wealth and income across countries. In contrast, the present paper focuses on within country effects. Using panel data for 30 oil-rich countries from
1992 to 2005, we identify political power balance as an
important determinant of the efficient use of resource rents.
This result holds when we control for the effects of other
determinants of income, time varying common shocks, and
country fixed effects. It is also robust to various alternative
measures of resource abundance and inclusion of the quality
of democratic institutions, as well as to the instrumental variable method of estimation (system and differenced GMM
(Generalized Method of Moments)) and across different
samples.
The optimistic message from our study is that oil rich, less
developed countries, which typically also have less developed
institutions, are not destined to be development disasters. Indeed, with a strong government, resource wealth is likely to
be growth enhancing, even when institutions are relatively
weak. However, there is another side to this coin, with a more
pessimistic message: Even in an environment of sound institutions, adding oil revenues to a fractionalized government can
* We thank Michael Alexeev, Jessica Dewald, Anne-Kathrin Koch, Marcel Thum, the referee as well as participants in the Brown Bag seminars at
the TU Dresden and ZEW Mannheim and the 18th Annual Conference of
the European Association of Environmental and Resource Economists for
helpful comments. Mohammad Reza Farzanegan gratefully acknowledges
the Georg Forster Postdoctoral grant of Alexander von Humboldt Foundation. Final revision accepted: March 7, 2012.
1308
RESOURCE CURSE AND POWER BALANCE: EVIDENCE FROM OIL-RICH COUNTRIES
The remainder of the paper is structured as follows. Section 2
presents our empirical strategy and the data. Results are presented and discussed in Section 3. Section 4 concludes the paper.
2. EMPIRICAL RESEARCH DESIGN
(a) Data, specification, and empirical strategy
To estimate whether the relationship between oil rents and
GDP (Gross Domestic Product) per capita varies systematically with the balance of political power, we use the following
model:
incomeit ¼ cons þ b1 oilit þ b2 powerit þ b3 ðoilit
powerit Þ þ b4 Z it þ li þ dt þ eit
ð1Þ
with country i (1, .., 30), and time t (1992–2005). 3 Income is
the log of real GDP per capita, oil is the log of oil revenues
(as a share of total government revenues), power is a measure
of political fractionalization, oilpower is the interaction of oil
revenues and power fractionalization, and Z stands for the
control variables. All explanatory variables are lagged one
year to avoid possible endogeneity problems (see Mehran
and Peristiani (2009) for a similar approach). 4
At the margin, the total effect of increasing oil revenues can
be calculated by examining the partial derivatives of real income per capita with respect to the oil rent variable:
@ðincomeit Þ
¼ b1 þ b3 ðpowerit Þ
@ðoilit Þ
ð2Þ
Based on the theoretical predictions by Bjorvatn and Selvik
(2008) and Bjorvatn et al. (2012), we expect the sign of b1 to
be positive and the sign of b3 to be negative. This means that
increasing oil rents in a situation of weak governments lead to
a destructive competition, with detrimental effects on income.
Of course, there are other factors such as climate, culture,
geography, and other unobserved time-invariant factors which
are country specific and may correlate with income as well. If
such country specific or time specific factors are correlated
with oil rents or balance of power, then both pooled cross section and random effects estimations may lead to biased and
inconsistent results. 5 We allow for country (li) and time (dt)
specific effects, controlling for the unobservable time-invariant
country characteristics and shocks which are common to all
countries. Furthermore, we can address the spurious business
cycle effects by including time fixed effects (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 (in
log) as our dependent variable, taken from the World Bank
(2008). 6 Our main independent variables are oil revenues and
balance of power. The most relevant proxy for the oil rent is
the share of oil revenues in the government budget. The data
are taken from Bornhorst, Gupta, and Thornton (2009) 7.
The main proxy for the degree of political power balance is
fractionalization of governing parties, or political fractionalization for short, and is taken from Beck, Clarke, Groff, Keefer,
and Walsh (2001). The power balance index, which goes from
0 to 1, is defined as the probability that two members of parliament—picked at random—from governing parties belong
1309
to different parties. A high fractionalization index indicates
that the government consists of a large number of small parties, which we shall think of as a weak government, while a
low fractionalization index indicates that the government consists of a small number of strong parties, possibly only one
party, and signals a strong government.
Our focus on the role of party systems in economic performance is supported by Riker (1964), who emphasizes that
strong political parties are a major cause of high public goods
provision and economic growth. A large number of independent candidates and candidates from recently formed new parties in presidential or parliament elections indicate a higher
degree of fractionalization of the political system and is not
conducive to growth. This is confirmed empirically by Enikolopov and Zhuravskaya (2007) who show that political fractionalization is bad for public goods provision. Similarly,
Poteete (2009) argues that one of the main causes of the successful development path in the resource-rich country of Botswana was the strong and stable political coalition during the
first decades of independence.
The control variables include investment as a ratio of real
GDP, inflation rate (as a measure of macroeconomic instability), real government consumption as a ratio of real GDP (a
proxy for size of government distortions in the economy),
trade openness, financial development, and age dependency
(to control for the structure of population). Appendix A presents the countries in the sample. The data description and
sources are presented in Appendix B. Appendix C reports
the descriptive statistics of the major variables.
3. EMPIRICAL RESULTS
The results of pooled, fixed, and random effects of panel
regressions are presented in Table 1. All specifications in Table 1 show a negative and highly significant effect of the interaction term. This suggests that the political power balance
plays an important role in determining the income effect of resource rents. In particular, resource rents are less likely to have
a positive effect on income when governments are highly fractionalized, and therefore weak.
Note also that the direct effect of oil revenues on real GDP
per capita is positive and statistically significant at the 1% level
in all specifications. For example, in the specification with
country and period fixed effects (model 1.3); a 1% increase in
the size of oil revenues leads to a 1.35% increase in levels of
real GDP per capita. This direct positive effect of oil rents
on economic development is in line with the findings of Alexeev and Conrad (2009).
We also observe that power balance as such does not negatively affect income, as indicated by the fact that the estimated
coefficient of govfrac is positive and statistically significant. This
is in line with the model presented in Bjorvatn and Selvik (2008),
which shows that a balanced power structure (which, ceteris paribus, implies a higher level of fractionalization) stimulates investment. In a resource- poor country, this effect may dominate the
negative effect of rent-seeking, leading to higher income.
In all specifications, we have controlled for other important
determinants of income such as trade openness, government
consumption, financial development, inflation, investment,
and age structure of population. We also control for the common time trend in random effect regressions. 8 Including time
trend controls for other factors such as technological progress
which may affect the economic development of countries in
our sample. The effects of our control variables on income
are as expected in theory.
1310
Table 1. Oil, fractionalization of government parties and income (panel regressions)
Variables
Oilrev
Govfrac
Oilrev*Govfrac
Govex
Credit
Inflation
Invest
Age dep
Country fixed effects
Time trend
Time fixed effects
Obs. (countries)
R2
Pooled OLS, 1 lag
of IVs (1.1)
c. fe, 1 lag
of IVs (1.2)
c & t fe, 1 lag
of IVs (1.3)
c & t fe, 1 lag
of IVs (dropping
Govex) (1.4)
c & t fe, 1 lag
of IVs (excluding
Norway) (1.5)
c.re,1 lag
of IVs (1.6)
c re, 1 lag
of IVs excluding
Norway (1.7)
c & t fe, using 2 lags
of IVs (1.8)
c & t fe, using 5 lags
of IVs (1.9)
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)
2.13**
(2.28)
6.23***
(3.03)
0.33**
(2.47)
0.33
(1.56)
0.34***
(3.16)
–0.006*
(1.86)
0.06
(0.48)
2.26***
(6.40)
No
Yes
No
248 (25)
0.60
0.38***
(2.53)
2.08***
(3.53)
0.06
(0.78)
0.45***
(6.91)
0.01
(0.38)
0.003***
0.33**
(2.13)
1.86***
(3.14)
0.08
(1.13)
0.45***
(7.35)
0.000
(0.02)
0.003***
(4.20)
0.05
(0.88)
0.06
(0.33)
Yes
No
Yes
248 (25)
0.99
0.38**
(2.15)
2.27***
(2.99)
0.12
(1.27)
0.37*
(1.80)
1.94***
(2.91)
0.08
(1.13)
0.45***
(7.37)
0.001
(0.03)
0.003***
(4.23)
0.06
(0.88)
0.05
(0.22)
Yes
No
Yes
236 (24)
0.99
0.42**
(2.44)
2.11***
(3.71)
0.08
(1.06)
0.44***
(6.75)
0.03
(0.86)
0.003***
(3.74)
0.06
(1.28)
0.07
(0.39)
Yes (RE)
Yes
No
248 (25)
0.69
0.44**
(2.22)
2.14***
(3.52)
0.08
(1.04)
0.43***
(6.66)
0.03
(0.77)
0.003***
(3.74)
0.07
(1.26)
0.13
(0.63)
Yes (RE)
Yes
No
236 (24)
0.68
0.13
(0.82)
1.31**
(2.25)
0.21***
(3.04)
0.42***
(7.35)
0.03
(0.91)
0.003***
(4.04)
0.04
(0.57)
0.01
(0.06)
Yes
No
Yes
226 (25)
0.99
0.17
(1.28)
1.06**
(2.28)
0.26***
(3.52)
0.20**
(2.40)
0.00
(0.00)
0.001***
(2.68)
0.11**
(2.19)
0.53**
(1.96)
Yes
No
Yes
154 (24)
0.99
(3.67)
0.07
(1.25)
0.11
(0.61)
Yes
Yes
No
248 (25)
0.99
0.02
(0.24)
3.95E05*
(1.79)
0.006
(0.10)
0.38
(1.51)
Yes
No
Yes
257 (26)
0.98
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 and
time fixed and random effects. All variables except for inflation are in log. Results are robust using higher lags of explanatory variables.
*
Significant at 10% level.
**
Significant at 5% level.
***
Significant at 1% level.
WORLD DEVELOPMENT
Trade
Dependent variable: log (rgdp p.c.), 1993–2005
Table 2. Oil, fractionalization of government parties, income, and political institutions (panel regressions)
Variables
Govfrac
Oilrev*Govfraco
Trade
Govex
Credit
Inflation
Invest
Age dep
Polity
Polity*Oilrev
Country fixed effects
Time fixed effects
Lagged dependent variable
Obs. (countries)
R2
Sargan (p-value)
AR(1)-p-value
AR(2)-p-value
(4.13)
0.28*
(1.80)
1.75***
(2.99)
0.07
(0.96)
0.45***
(7.32)
0.00
(0.22)
0.003***
(4.15)
0.06
(0.95)
0.12
(0.64)
0.21
(0.92)
0.15
(0.28)
Yes
Yes
248 (25)
0.99
–
–
–
(7.51)
0.31
(1.62)
2.11***
(3.51)
0.11
(1.58)
0.01
(0.34)
4.17E05
(0.97)
0.00
(0.00)
0.45*
(1.96)
0.32
(1.18)
0.33
(0.48)
Yes
Yes
257 (26)
0.98
(4.21)
0.29
(1.32)
1.77***
(2.56)
0.07
(0.95)
0.45***
(7.35)
0.00
(0.14)
0.003***
(4.17)
0.06
(0.96)
0.08
(0.36)
0.23
(0.89)
0.24
(0.37)
Yes
Yes
(3.42)
0.60**
(2.07)
0.10**
(2.23)
0.06
(0.72)
0.44***
(8.24)
0.03
(1.04)
0.000**
(2.44)
0.05
(0.88)
0.11
(0.57)
1.15***
(2.89)
0.24***
(3.38)
Yes
Yes
(3.30)
1.07**
(2.46)
0.19**
(2.47)
0.06
(0.75)
0.44***
(8.09)
0.31
(0.88)
0.000**
(2.29)
0.05
(0.90)
0.07
(0.37)
1.36***
(3.02)
0.27***
(3.42)
Yes
Yes
236 (24)
0.99
–
–
–
258 (25)
0.99
–
–
–
246 (24)
0.99
–
–
–
(2.26)
0.33
(1.54)
1.25**
(2.33)
0.002
(0.02)
0.26*
(1.74)
0.01
(0.22)
0.001*
(1.79)
0.13***
(2.89)
0.48
(0.87)
0.12
(0.62)
(2.19)
0.30
(1.25)
1.24**
(2.00)
0.000
(0.00)
0.25*
(1.81)
0.00
(0.16)
0.001*
(1.86)
0.12**
(2.47)
0.34
(0.60)
0.08
(0.49)
Yes
Time trend
0.95***
(17.39)
253 (25)
–
54.3 (0.49)
0.04
0.30
Yes
Time trend
0.90***
(12.27)
226 (25)
–
57.3 (0.38)
0.02
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 and
time fixed and random effects. All variables except for inflation are in log. The values reported for the Sargan test are the p-values for the null hypothesis of instrument validity. The values reported for
AR (1) and AR (2) are the p-values for first and second order auto correlated disturbances in the GMM models.
*
Significant at 10% level.
**
Significant at 5% level.
***
Significant at 1% level.
RESOURCE CURSE AND POWER BALANCE: EVIDENCE FROM OIL-RICH COUNTRIES
Oilrev
Dependent variable: log (rgdp p.c), 1993–2005
c & t fe, 1 lag
c & t fe, 1 lag
c & t,1 lag
c & t fe, 1 lag
c & t fe, 1 lag
One step SYS.
One step Diff.
of IVs (2.1) of IVs, dropping of IVs ex.Norway (2.3)
of IVs, using
of IVs, using oil rents GMM, using Oilrev (2.6) GMM, using Oilrev (2.7)
Govex (2.2)
oil rents per capita(2.4) per capita), excluding
Norway (2.5)
2.07***
1.42***
0.13***
0.13***
0.46**
0.45**
1.39***
1311
1312
WORLD DEVELOPMENT
On average, 50% of total government revenues in the countries of our sample from 1995 to 2002 depend on oil revenues.
The size of government spending in oil based economies is relatively high and is usually financed through oil rents. The bivariate correlation between oil revenues and government spending
in our sample is not too high (0.42). Even extreme collinearity
(as long as it is not perfect) does not violate OLS (Ordinary
Least Squares) assumptions. OLS estimates are still unbiased
and BLUE (Best, Linear, Unbiased Estimator). 9 However, in
order to examine the sensitivity and robustness of our main
results, we drop the government expenditure variables from
general specification. Our main results as presented in Model
1.4 are quite robust without government expenditure as well.
In our sample, Norway has an established democracy, ranking as one of the least corrupt countries in the past years. As a
robustness test, we have removed Norway from panel estimations and results are presented in Models 1.5 and 1.7 in Table 1. The main results remain robust without Norway. The
random effects estimation presented in Models 1.6–1.7 do
not differ from fixed effects estimations qualitatively. Our main
variables of interest such as oil revenues and the interaction
term remain statistically significant with the expected sign in
the random effects models. 10
In Models 1.8 and 1.9, we also re-estimate the same specification using 2 and 5 year lags of explanatory variables instead
of 1 year lags. Using higher order lags of independent variables reduces the risk of the endogeneity problem within our
estimations. We notice that our main results are robust in
models with a higher number of right hand side variables’ lags.
The structure of government and institution in countries of
our sample varies to different degrees. For example, Iran is a
highly factionalized semi-democratic country while Saudi Arabia is an absolute monarchy, Kuwait is a constitutional monarchy, and Norway is an established democracy. It is therefore
important to control for the quality of political institutions in
our analysis. In Table 2, we have controlled for the role of political institutions using the Polity2 index from the Polity IV dataset (Marshall & Jaggers, 2009). Polity2 scores are between 10
and +10. A +10 refers to a ‘‘strongly democratic” state and
10 to ‘‘strongly autocratic”. We have rescaled the Polity2 index
from 0 to 1. The higher the rescaled Polity index, the higher the
quality of democratic institutions. We aim to examine whether
including this variable affects the intermediary role of fractionalization in government parties in the oil rents-income nexus. In
other words, we want to make sure that the effect of fractionalization of government parties does not reflect the quality of political institutions. We notice that even by controlling for the Polity
index and its interaction with oil revenues, our main variables of
interest remain robust with expected sign and are highly significant (see Model 2.1). As in Table 1, we drop the government
expenditure variable in Model 2.2 for the robustness analysis.
We notice that this does not affect our main finding. Excluding
Norway 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 instead
of the share of oil revenues in total government revenues.
Alexeev and Conrad (2009) argue that the best measures of
the role of natural resources in the long run growth are per capita measures. Using oil wealth as a measure of GDP or export in growth regressions may bias estimates in favor of the
resource curse hypothesis. There can be factors unrelated to
natural resources which affect the structure of the economy
especially in the export sector. Thus, a resource rich country
which suffers from these factors may have a high oil/GDP
or oil/export ratio, presenting a spurious evidence for the resource curse hypothesis in regressions.
Oil rents (in US dollars) are calculated as the production
volume times the difference between the international market
unit price and the average production cost. We have divided
the total oil rents by the population of each country to calculate the per capita oil rents. Models 2.4 and 2.5 show the results, using per capita oil rents instead of oil revenues in
total revenues. The direct effect of per capita oil rents on income is positive and significant at the 1% level. However,
the magnitude of the effect is now smaller. A 1% increase in
the size of per capita oil rents raises per capita income by
0.13%. The moderating effect of fractionalization of government parties remains statistically significant at the 5% level
in models with per capita oil rents. Excluding Norway, as in
Model 2.4, does not change our results. As we can see from
Models 2.4 and 2.5, the interaction of the Polity index and
oil rents is positive and statistically significant. Higher quality
of political institutions can amplify the positive income effects
of oil rents. This is in line with findings of previous literature
such as Mehlum et al. (2006). The direct effect of Polity on income is negative and significant in Models 2.4 and 2.5. This
means that, given a negligible share of oil rents, higher political openness may reduce the speed of economic growth. This
can be due to the increased political instability in poor resource economies by opening the political system. Previous
studies have found an inverted U-shaped relationship between
democracy and conflict (Hegre, Ellingsen, Gates, & Gleditsch,
2001; Muller & Weede, 1990). Full autocracy and full democracy should go along with a low risk of conflict (and higher income) since highly autocratic regimes can repress dissent and
thus avoid civil violence, while highly democratic societies
can solve problems peacefully. For middle values of the Polity
index, we can thus make no clear predictions.
Furthermore, as per capita income tends to be persistent over
time, we estimate a dynamic specification (difference GMM)
using the Arellano and Bond (1991) estimator, which allows
the specification of a common lagged effect (see Model 2.7 in
Table 2). We use three lags of potentially endogenous variables
as instruments. The Sargan test validates the adequacy of the
instruments, and the failure to reject the null hypothesis of
the validity of the instruments indicates that the specification
is correct. In addition, the other diagnostics are also satisfactory. The absence of first order serial correlation is rejected
and the absence of second order serial correlation is not rejected. The dynamic GMM differences the model to remove
country specific effects or any time-invariant country specific
variable, eliminating any endogeneity because of the correlation of these country specific effects and the right hand side
regressors. It also addresses the possible non-stationarity of
explanatory variables (Baltagi, Demetriades, & Law, 2009).
The results in Model 2.7 provide further support for our basic models regarding a direct positive effect of oil revenues on
income and a moderating effect of fractionalization of government parties. We also report the results of system GMM introduced by Blundell and Bond (1998) in Model 2.6. The System
GMM estimator combines the first differenced equation and
levels equation to estimate the model, employing both lagged
levels and differences as instruments. The model is estimated
using a first difference transformation to remove the individual
country effect. In addition, to control for the country specific
effects, the system GMM preserves the cross country dimension of the data which is lost when we use first differenced
GMM (Castelló-Climent, 2008). Again, our results provide
empirical supports for our main hypotheses. The diagnostic
statistics are also satisfactory. The Sargan test does not reject
the over-identification restriction, indicating that instruments
are not correlated with the error term and, thus, are valid.
RESOURCE CURSE AND POWER BALANCE: EVIDENCE FROM OIL-RICH COUNTRIES
1313
Figure 1. Marginal effects of oil revenues on income at different levels of fractionalization. Note: the middle line shows the marginal impact of a 1% increase in
the size of oil revenues (% of total revenues) on real GDP per capita at different levels of political fractionalization. The upper and lower lines are 90%
confidence intervals (CIs).
We reject the absence of first order serial correlation while
accepting the absence of second order serial correlation. Furthermore, the lagged dependent variable in both Diff GMM
and SYS GMM is positive and significant. The relatively high
size of lagged dependent variables indicates its persistency,
however, it is statistically different from unity in both models.
To sum up, our results show that the final effect of oil rents
on growth is conditional on the level of fractionalization
among government parties. Using Eqn. (1), we calculate the
marginal impact of oil rents on growth at different levels of
the fractionalization index (mean, minimum and maximum).
We use Models 2.1 and 2.4 (country and time fixed effects panel
regression) to calculate the marginal effects of a 1% increase in
oil revenues [in total revenues and oil rents per capita]. The results are presented in Table 3. At the maximum level of fractionalization, a 1% increase in the share of oil revenues in
total government revenues and per capita oil rents increases income by 0.41% and 0.07%, respectively. In the case of dominance of a single political faction (govfrc = 0), the same
increase in oil revenues (or oil rent) has a more pronounced positive effect on growth (1.39% and 0.13%, respectively). The
overall impact of oil revenues on growth is positive but higher
fractionalization of politics moderates this positive effect. We
have also illustrated the marginal impacts of oil on income at
different values of government fractionalization, reporting
90% confidence intervals around estimated marginal effects.
This approach enables us to determine the conditions under
which the oil revenues have a statistically significant effect on
the GDP per capita. The results are illustrated in Figure 1.
Note that we refer to the results estimated on the basis of
two way fixed effects in Table 2 (Model 2.1 with robust standard errors).
Table 4. Standardized coefficients
Dependent variable: log (rgdp p.c), 1993–2005
Variables
Standardized coefficients
Oilrev_S
Govfrac_S
Oilrev*Govfrac_S
Trade_S
Govex_S
Credit_S
Inf_S
Invest_S
Age_S
Polity_S
Country fixed effects
Year fixed effects
0.20*** (5.63)
0.04* (1.83)
0.12*** (3.03)
0.03 (1.03)
0.19*** (7.35)
0.006 (0.20)
0.92*** (4.14)
0.02 (0.93)
0.03 (0.69)
0.03 (1.52)
Yes
Yes
Obs.
R-square
248
0.99
Note: t-statistics are in parentheses, basing on heteroskedasticity-robust
standard errors (White diagonal s.e. and covariance; df correction).
Standardized coefficients show increases or decreases in the dependent
variable if the explanatory variable increases by one standard deviation.
*
Significant at 10% level.
**
Significant at 5% level.
***
Significant at 1% level.
Table 3. Marginal effects of oil revenues on growth at different levels of power balance
Marginal effects
Mean of power balance (govfrac)
Maximum of power balance (govfrac)
Minimum of power balance (govfrac)
Oil revenues (based on model 2.1 estimations)
Oil rents per capita (based on Model 2.4 estimations)
1.20
0.41
1.39
0.12
0.07
0.13
Power balance is the logarithm of one plus govfrac(1). Mean, maximum, and minimum levels of power balance are presented in Appendix C. Oil
revenues is the logarithm of one plus oilrev(1). Oil rents per capita is the logarithm of oil rents per capita (1).
1314
WORLD DEVELOPMENT
The marginal effects (the middle solid line) are statistically
significant when the 90% confidence interval bands (dashed
lines) fall above or under the zero line. The leftmost seven
points represent the statistically significant marginal effects of
oil revenues on income. The histogram in the background adds
interesting information by showing us how the cases are distributed. The statistically significant marginal impacts cover
most parts of our observations. At the maximum level of government fractionalization, the marginal effect is at its minimum
but insignificant at the 90% level of confidence. As we can see,
for the majority of countries in our sample, the marginal effect
of oil revenues on income is positive and significant. 11
In order to examine the relative importance of explanatory
variables such as oil revenues and fractionalization of government parties in a growth model, we use standardized variables
in regression analysis by subtracting their mean and dividing
them by their standard deviation. Table 4 presents the standardized coefficients of the main explanatory variables in a
country and year fixed effect regression. Standardized variables have the same unit of measurement, making an interpretation of the results easier. The standardized coefficient
indicates the impact of the explanatory variable in terms of
standard deviation units. It shows us the number of standard
deviations that the dependent variable (standardized real GDP
per capita) increases or decreases with a one standard deviation increase in the standardized independent variable.
In other words, we can evaluate the relative importance of
our main independent variables in explaining growth. We find
that inflation, oil revenues, government consumption, and
interaction of fractionalization and oil revenues have the most
important effects on growth.
4. CONCLUSION
Our analysis shows that the evolution of political power balances and imbalances over time can have important effects on
a country’s ability to benefit from its oil revenues. Indeed,
when the level of fractionalization is high, indicating a weak
government, oil revenues appear to be fully wasted: Above a
critical level of fractionalization, there is no significant, positive effect of oil revenues on income. In contrast, when governments are less fractionalized, for instance, consisting of a
single party, oil revenues have a pronounced positive effect
on income.
We believe that these results have important implications for
countries in the Middle East and North Africa which are currently undergoing fundamental changes in their political structure. While these changes may hopefully lead to more sound
political and economic institutions, they may also open up
for more intensified power struggles. This was certainly the
case in Iran, where the omnipotent Shah in 1979 was replaced
by a system which invited factionalist infighting, leading to a
rather disappointing economic performance. There is a risk
that intensified rent-seeking will lead to lackluster growth also
in the wake of the current revolutions in the Middle East and
North Africa, leading to a period of prolonged political instability in the region.
NOTES
1. Other empirical studies on institutions and the resource curse include
Brunnschweiler and Bulte (2008), Brunnschweiler (2008), Iimi (2007), and
Alexeev and Conrad (2010).
2. There is also an older literature focusing on market related mechanisms for the resource curse, linked to the concept of the Dutch disease. In
this case, higher oil prices lead to a higher real effective exchange rate and
an appreciation of the domestic currency, thus increasing the price of nonoil 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 data
for our measure of resource wealth, which is the share of oil revenues in
total government revenues. Rent-seeking is likely to be driven by these
more tangible rents rather than by oil reserves or production, measured in
barrels of oil (see Hodler, 2006, for a similar view).
4. We have also used lagged 2, 3, 4 and 5 years explanatory variables in
estimations to control robustness of our main results in Table 1. The results
hold, even using longer lagged explanatory variables, providing more
support for estimated coefficients. In addition, our main independent
variable, oil revenues, is largely exogenous in income models in each
country. For example, OPEC members must follow the determined level of
the 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 for
Iranian 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 finally, the policy of key oil consumers on strategic petroleum
reserves.” Alexeev and Conrad (2010) argue that natural resource
endowments and the output of a country are largely exogenous.
5. Nevertheless, for comparison of our results we present estimations of
pooled, random and fixed effects.
6. If we consider a linear regression of Y on X, then the standard
interpretation of the coefficient of X is the change in Y in response to the
change in X. In our case, Y is the GDP level, then the change of Y is
growth and our coefficient would be the effect of the change of X on
changes 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 coefficient is the change in
growth rate of the GDP in response to a change in X.
7. We thank Fabian Bornhorst for making this data available to us. As
for a robustness test, we also use the per capita oil rents instead of the
share of oil revenues in total government revenues (See Table 2).
8. We do not include common time trend in panel regressions with
country and time fixed effects because of perfect multicollinearity between
time trend and time fixed effects.
9. For more details see Richard Williams’s note at: http://www.nd.edu/
rwilliam/stats2/l11.pdf.
10. We have carried out the Hausman test under the null hypothesis so
that the individual effects are uncorrelated with the other regressors in the
model (Hausman, 1978). The p-value = 0 rejects the null hypothesis,
indicating that fixed effect models are preferred.
11. Using estimations of GMM in Models 2.6 and 2.7 for the calculation
of the marginal impact does not change our results in Figure 1.
RESOURCE CURSE AND POWER BALANCE: EVIDENCE FROM OIL-RICH COUNTRIES
1315
REFERENCES
Alexeev, M., & Conrad, R. (2009). The elusive curse of oil. The Review of
Economics and Statistics, 91(3), 586–598.
Alexeev, M., & Conrad, R. (2010). The natural resource curse and
economic transition. Economic Systems, 35(4), 445–461.
Arellano, M., & Bond, S. (1991). Some tests of specification for panel
data: Monte Carlo evidence and an application to employment
equations. Review of Economic Studies, 58(2), 277–297.
Baltagi, B. H., Demetriades, P. O., & Law, S. H. (2009). Financial
development and openness: Evidence from panel data. Journal of
Development Economics, 89(2), 285–296.
Beck, T., Clarke, G., Groff, A., Keefer, P., & Walsh, P. (2001). New tools
in comparative political economy: The database of political institutions. World Bank Economic Review, 15(1), 165–176.
Bjorvatn, K., Farzanegan, M.R., & Schneider, F. (2012). Resource curse
and power balance: Evidence from Iran. Mimeo. Available at: http://
works.bepress.com/mr_farzanegan/11/.
Bjorvatn, K., & Selvik, K. (2008). Destructive competition: Factionalism
and rent-seeking in Iran. World Development, 36(11), 2314–2324.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions
in dynamic panel data models. Journal of Econometrics, 87(1),
115–143.
Bornhorst, F., Gupta, S., & Thornton, J. (2009). Natural resource
endowments and the domestic revenue effort. European Journal of
Political Economy, 25(4), 439–446.
Boschini, A. D., Pettersson, J., & Roine, J. (2007). Resource curse or not:
A question of appropriability. Scandinavian Journal of Economics,
109(3), 593–617.
Brunnschweiler, C. N. (2008). Cursing the blessing? Natural resource
abundance, institutions and economic growth. World Development,
36(3), 399–419.
Brunnschweiler, C. N., & Bulte, E. H. (2008). The resource curse revisited
and revised: A tale of paradoxes and red herrings. Journal of
Environmental Economics and Management, 55(3), 248–264.
Castelló-Climent, A. (2008). On the distribution of education and
democracy. Journal of Development Economics, 87(2), 179–190.
Corden, M. W. (1984). Booming sector and Dutch disease economics:
Survey and consolidation. Oxford Economic Papers, 36(3), 359–380.
Corden, M. W., & Neary, J. P. (1982). Booming sector and deindustrialisation in a small open economy. The Economic Journal,
92(368), 825–848.
Easterly, W., & Levine, R. (2003). Tropics, germs, and crops: How
endowments influence economic development. Journal of Monetary
Economics, 50(1), 3–39.
Enikolopov, R., & Zhuravskaya, E. (2007). Decentralization and political
institutions. Journal of Public Economics, 91(11–12), 2261–2290.
Farzanegan, M. R. (2011). Oil revenue shocks and government spending
behavior in Iran. Energy Economics, 33(6), 1055–1069.
Farzanegan, M. R., & Markwardt, G. (2009). The effects of oil price
shocks on the Iranian economy. Energy Economics, 31(1), 134–151.
Frankel, J.F. (2010). The natural resource curse: A survey. NBER working
paper, no. 15836.
Hall, R., & Jones, C. (1999). Why do some countries produce so much
more output per worker than others?. Journal of Economics, 114(1),
83–116.
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica,
46(6), 1251–1271.
Hegre, H., Ellingsen, T., Gates, S., & Gleditsch, N. P. (2001). Toward a
democratic civil peace? democracy, political change, and civil war,
1816–1992. American Political Science Review, 95(1), 33–48.
Hodler, R. (2006). The curse of natural resources in fractionalized
countries. European Economic Review, 50(6), 1367–1386.
Iimi, A. (2007). Escaping from the resource curse: Evidence from
Botswana and the rest of the world. IMF Staff Papers, 54(4), 663–699.
Keller, W. (2004). International technology diffusion. Journal of Economic
Literature, 42(3), 752–782.
Marshall, M., & Jaggers, K. (2009). Polity IV project: political regime
characteristics and transitions, 1800–2002. University of Maryland.
Mehlum, H., Moene, K., & Torvik, R. (2006). Institutions and resource
curse. Economic Journal, 116(508), 1–20.
Mehran, H., & Peristiani, S. (2009). Financial visibility and the decision to
go private. The Review of Financial Studies, 23(2), 519–547.
Muller, E. N., & Weede, E. (1990). Cross-national variation in political
violence: A rational action approach. The Journal of Conflict Resolution, 34(4), 624–651.
Poteete, A. R. (2009). Is development path dependent or political? A
reinterpretation of mineral- dependent development in Botswana.
Journal of Development Studies, 45(4), 544–571.
Riker, W. (1964). Federalism: Origins, operation, significance. Boston,
MA: Little, Brown and Co..
Rodrik, D., Subramanian, A., & Trebbi, F. (2004). Institutions rule: The
primacy of institutions over geography and integration in economic
development. Journal of Economic Growth, 9(2), 131–165.
Torvik, R. (2002). Natural resources, rent-seeking and welfare. Journal of
Development Economics, 67(2), 455–470.
van Wijenbergen, S. (1984). Inflation, employment, and the Dutch disease
in oil exporting countries: A short-run disequilibrium analysis.
Quarterly Journal of Economics, 99(2), 233–250.
World Bank (2008). World development indicators. Washington, D.C.:
World Bank.
APPENDIX A
Countries included in the sample
OPEC
Non-OPEC
Algeria
Angola
Ecuador
Iran
Kuwait
Libya
Nigeria
Qatar
Saudi Arabia
United Arab Emirates
Venezuela
Azerbaijan
Bahrain
Brunei
Cameroon
Chad
Congo
Equatorial Guinea
Gabon
Indonesia
Kazakhstan
Mexico
Norway
Oman
Russia
Sudan
Syria
Trinidad and Tobago
Vietnam
Yemen
1316
WORLD DEVELOPMENT
APPENDIX B
Invest
Age_dep
Data description
Variable
Description and Source
rgdp p.c.
Logarithm of GDP per capita (constant 2000 USD).
Source: World Bank (2008)
Logarithm of one plus Oil revenues/Total revenues of
government. Source: Bornhorst et al. (2009)
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 different 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)
Logarithm of sum of imports and exports in GDP.
Source: World Bank (2008)
Logarithm of government consumption in GDP.
Source: World Bank (2008)
Logarithm of domestic credit to private sector (% of
GDP). Source: World Bank (2008)
Inflation, consumer prices (annual%). Source: World
Bank (2008)
Oilrev
Govfrac
Trade
Govex
Credit
Inflation
Polity2
Logarithm of gross fixed capital formation (% of
GDP). Source: World Bank (2008)
Logarithm of age dependency ratio (% of working-age
population). Source: World Bank (2008)
Democracy Index. Scale from 10 (full autocracy) to
10 (full democracy). Rescaled from 0 to 1. Source:
Marshall and Jaggers (2009)
APPENDIX C
Summary statistics
Variable
Obs. Mean Standard Minimum Maximum
deviation
Log (rgdp p.c.)
Log (govfrac(1)+1)
Log (oilrev(1)+1)
Log (govfrac(1)+1)
Log (oilrev(1)+1)
396
298
359
276
7.70
0.11
0.39
0.03
1.43
0.18
0.17
0.07
5.09
0
0
0
10.61
0.56
0.64
0.32