Download Resource Curse and Power Balance: Evidence from Oil

Resource Curse and Power Balance: Evidence from Oil-Rich Countries
Dr. Mohammad Reza Farzanegan (Corresponding author)
Dresden University of Technology & ZEW Mannheim
Address:
Dresden University of Technology, Faculty of Business and Economics, Department of Public
Economics, D-01062 Dresden, Germany
Tel.: +49-351- 463-32895
Fax: +49-351- 463-37052
E-Mail: [email protected]
Email: [email protected]
Web: http://www.tu-dresden.de/wwvwlfw/team_mitarbeiter_mohammad_e.htm
Prof. Kjetil Bjorvatn
Address:
Norwegian School of Economics and Business Administration
NHH, Department of Economics, Helleveien 30, NO-5045 Bergen, Norway
Tel: +47 55 959 585
Fax: +47 55 959 543
Email: [email protected] Web: http://www.nhh.no/Default.aspx?ID=698
Prof. Friedrich Schneider
Address:
Department of Economics, Institute of Economic Policy, Johannes Kepler University of Linz, A
4040 Linz Auhof, Austria
Tel: +43-732-2468-8210,
Fax: +43-732-2468 -8209,
Email: [email protected] Web: http://www.econ.jku.at/schneider/
1
Resource Curse and Power Balance: Evidence from Oil-Rich Countries
Mohammad Reza Farzanegan, Kjetil Bjorvatn and Friedrich Schneider
Abstract
In this paper, we examine the role of political fractionalization for the “resource curse”
hypothesis in oil rich economies. Using panel data for 30 oil-rich countries from 1992-2005, we
find a positive direct relationship between oil rents and income. However, this positive effect is
moderated by factional politics. This suggests that the resource curse does not emerge from oil
revenues per se but from the rent seeking of political factions. Our basic results hold 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 resource abundance and
inclusion of the quality of democratic institutions, as well as to the instrumental variable method
of estimation (system and differenced GMM) and across different samples.
JEL: O13, O43, Q32, Q43
Keywords: oil rents, balance of power, natural resource curse, panel data
2
1. Introduction
Our goal is to show that contrary to common wisdom, oil rents do not reduce the income of oil
based economies. Instead, the often observed negative effect of oil revenues can emerge from
rent seeking of political factions. The political struggles among factions may dampen the positive
effect of oil revenues on income. Considering the recent political turmoil in a group of resource
rich countries in the Middle East and North Africa such as Libya, Iran, Syria and Oman, this
analysis provides timely insights on the possible role of political fractionalization and lack of
political authority in oil-growth nexus. The contribution of this paper is to take into account the
role of distribution of political power within government administration to explain the natural
resource curse hypothesis for a panel of 30 oil-rich economies from 1992-2005. Our estimates
confirm that the relationship between oil rents and income depends on the level of power balance.
We find that the overall effect of oil rents on growth is positive but is reduced significantly by a
higher degree of fractionalization of the government. Our basic results hold 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 resource abundance and inclusion of the
quality of democratic institutions, as well as to the instrumental variable method of estimation
(system and differenced GMM) and across different samples.
Bjorvatn and Selvik (2008) argue theoretically how the lack of dominant political coalition may
lead to higher rent-seeking and misallocation of resources and less growth. Riker’s (1964) theory
emphasizes the important role of party systems in economic performance. Riker indicates that strong
political parties and lack of factional politics is a major cause of higher growth and provision of
public goods. The 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. Enikolopov and Zhuravskaya (2007) show that the less fractionalized countries are
3
superior in provision of public goods compared to countries with a high degree of factional politics.
Bjorvatn and Selvik argue that the negative effect of factional politics is because of higher rentseeking activities and corruption which lead to lower growth effects of natural resources. In other
words, fractionalization of a political system that expands rent-seeking in oil-rich economies
amplifies the destructive effects of rent-seeking on growth.
We explore the interconnections between oil revenues, growth and political power in subsequent
sections. The remainder of the paper is structured as follows: in section 2 we discuss the
theoretical framework and empirical literature. Section 3 presents our main hypotheses, empirical
strategy and the data. Results are presented and discussed in section 4. Section 5 concludes.
2. Theoretical framework and empirical literature
In the previous literature, oil is seen as a curse because of factor X. Some link it to the Dutch
disease. Higher oil prices lead to a higher real effective exchange rate and an appreciation of the
domestic currency. This increases the price of non-oil exports and leads to de-industrialization of
the economy (see Corden and Neary, 1982; Corden, 1984; van Wijenbergen, 1984 for theoretical
explanation of this phenomenon). The other group of researchers highlights the rent-seeking
problem in oil economies. For example, Torvik (2002) theoretically investigated the effect of
natural resources on entrepreneurs’ activities. He suggests that increasing natural resource rents
motivates the citizens’ activity in rent-seeking, diverting them from the productive part of the
economy. He concludes that fall of income due to this re-allocation of entrepreneurs outweighs
the benefits of natural resource rents. Neglect of human capital, reduction of saving and
investment and inequality are discussed, among others, by Gylfason (2001). Due to ample oil
revenues, oil economies may invest less in their human capital. Besides these explanatory factors,
we can observe an increasing attention to the role of political institutions in the literature (see for
4
example Robinson et al., 2006; Boschini et al., 2007; Mehlum et al., 2006; Brunnschweiler and
Bulte, 2008; Brunnschweiler, 2008 and Iimi, 2007).
Our study aims to highlight another factor, the fractionalization of political power. Our paper is
closely related to the studies of Bjorvatn and Selvik (2008), Montalvo and Reynal-Querol (2005)
and Hodler (2006).
We can explain the negative intermediary growth effect of political fractionalization in oil based
economies as follows: Assume that the oil rent in the economy is R and the number of symmetric
political groups is n. Higher n means higher political fractionalization.
Each group maximizes expected profits:
i 
ri
r
R  ri
(1)
j
where ri is group i’s rent seeking effort, and
r
j
is the sum of all n groups’ rent-seeking efforts.
Maximizing (1) with respect to ri, and using the fact that in equilibrium, all n groups are identical,
we find the equilibrium rent seeking effort of each group as:
r* 
n 1
R
n2
(2)
For instance, in the case of two groups, the rent dissipation (2r/R) is 50 percent. Aggregate
income net of rent seeking is given by I  R  nr , which using (2) gives:
I* 
R
n
(3)
Clearly, the more fractionalized is the country (larger n), the lower is the positive income effect
of higher rents. The reason is simply that with more rent seeking groups, the share of the rents
being crowded out by rent-seeking increases.
5
The theoretical model of Bjorvatn and Selvik (2008) analyzes the struggle for power and
resources within a factionalized political system, characterized by lack of transparency and weak
property rights protection. They developed a theoretical model of rent-seeking following Tornell
and Lane (1999), Baland and Francois (2000), and Torvik (2002). In their model, rent-seekers are
investors associated with competing political factions. The direct effect of oil revenues on
investment and thus growth in their model is positive. This is because of the flow of oil revenues
to investors through procurement contracts, subsidized loans and subsidized energy products
among other protections. However, the “imbalances in the distribution of political power between
groups lead to a distortion in the allocation of capital between investors, depressing the average
returns to investment” (Bjorvatn and Selvik, 2008). The key political variable is the relative
strength of the interest groups, where “power” is measured in terms of the relative rent-seeking
efficiency of a given faction. Given the high level of oil rents, the higher symmetry or balance of
power among different political factions may increase investment efficiency but at the same time
intensifies rent-seeking inefficiencies. The dominance of one political faction reduces rentseeking inefficiencies in their model. Thus, higher political heterogeneity can explain the
negative indirect effect of oil rents on growth.
Montalvo and Reynal-Querol (2005) show that polarized societies with large rivalling groups
have a greater potential for rent-seeking, corruption, and conflict. Hodler (2006) examined the
role of ethnical fractionalization in natural resource-growth nexus. He uses the ethnical
fractionalization index as a proxy for potential destructive competition between rivalling groups.
He concludes that natural resources are a blessing in homogenous countries and a curse in
heterogeneous ones. Poteete (2009) argues that one of the main causes of the successful
development path in the resource-rich country of Botswana was the lack of factionalism and
stable coalition during the first decades of independence. Farzanegan et al. (2011) show
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theoretically and empirically that factionalism does matter for the growth effects of oil rents for
the case study of Iran.
3. Empirical research design
3.1. Data, specification, and empirical strategy
Our discussions on the theoretical framework and in particular the model of Bjorvatn and Selvik
(2008) yields two testable implications for the relationship between income and oil rents:
H1: An increase in the oil revenues raises the level of income.1
H2: The effect of the oil revenues on income depends on the fractionalization of political power.
Thus, the final effect of oil revenues on income varies with the (im-) balance of political power.
We test these hypotheses using panel data for 30 oil-rich countries from 1992-2005.
To estimate whether the relationship between oil rents and GDP per capita varies systematically
with the balance of political power, we use the following model:
incomeit  cons  1.oilit   2 . powerit  3.(oilit . powerit )   4 .Zit  i   t   it
(4)
with country i, and time t. 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,
oil.power 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
1
This is also shown by Alexeev and Conrad (2009). Alexeev and Conrad (2009) estimate cross-country regressions,
using the level of real GDP per capita instead of the growth rate of GDP per capita. They show that oil revenues have
been on balance positive for growth. Alexeev and Conrad discuss the main reasons behind their “elusive curse of oil”
thesis: “unless the period over which the growth rates are measured is sufficiently long, the direct use of GDP
growth rates runs a risk of reflecting slow growth of oil producers that have partly depleted their resources than the
true impact of oil on overall growth”. Using natural resource wealth data of the World Bank (1997, 2005),
Brunnschweiler (2008) carried out a cross-country analysis, showing a positive direct association between resource
wealth and economic growth.
7
problems (see Mehran and Peristian, 2009 for a similar approach).2 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.3 We allow for country
(µi) and time (t) 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).
3.2. Dependent and independent variables
Following Alexeev and Conrad (2009), Hall and Jones (1999), Easterly and Levine (2003), and
Rodrik et al. (2004) we use the level of real GDP per capita (in log) as our dependent variable,
taken from the World Bank (2008).4 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. What matters for economic growth (and rent seeking) is not the number of
oil barrels produced but the value of these barrels. The data is taken from Bornhorst et al.
(2009)5. The main proxy for the degree of political power balance is fractionalization of
governing parties. This is defined as the probability that two members of parliament – picked at
random – from governing parties belong to different parties. High fractionalization of the
2
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.
3
Nevertheless, for comparison of our results we present estimations of pooled, random and fixed effects.
4
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.
5
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
government parties means that there are many small relatively weak parties, while low
fractionalization indicates that the government consists of a small number of strong parties. The
source of data for the degree of power balance indicators comes from Beck et al. (2001).6 This
index varies from 0 to 1 where 0 indicates that there is a single dominant political party. 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. Data description and sources are presented in Appendix B. Appendix C reports the
descriptive statistics of the major variables.
3.3. Hypothesis testing
Eq. (4) postulates that income is determined by the variables of interest - oil revenues and power
balance - alongside a set of conditioning variables such as: trade openness, government size,
financial development, inflation, investment, age dependency ratio, and political institutions.
The interaction term between oil revenues and fractionalization of political power is expected to
shed light on the theoretical predictions which presented in section 2 and discussed in more
details by Bjorvatn and Selvik regarding the negative effect of destructive competition within
factional politics. 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 oil rent variable:
 (incomeit )
 β1  β3 .( powerit )
 (oilit )
6
(5)
The fractionalization of government parties’ variables is used by other scholars as well. For example, Enikolopov
and Zhuravskaya (2007) use this concept to measure effectiveness of decentralization in provision of public goods by
the government.
9
Based on the theoretical predictions of Bjorvatn and Selvik (2008), the sign of β1 should be
positive and the sign of β3 should be negative. This means that increasing political factionalism
and oil rents lead to “destructive competition” and intensive rent-seeking. The final effect of oil
rents on income is conditional on the level of factionalism and on the distribution of political
power.
4. Empirical results
The results of pooled, fixed and random effects of panel regressions are presented in Table 1. The
results are in line with our theoretical framework and the Bjorvatn and Selvik theoretical
arguments. 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).
The interaction term between the degree of fractionalization of government parties and oil
revenues is a key factor in the Bjorvatn and Selvik (2008) model. All specifications in Table 1
show a negative and highly significant effect of the interaction term. The positive effect of oil
revenues on income is reduced in highly factionalized governments. Factional politics means
higher political competition which may increase transparency and reduce corruption. Again in
line with Bjorvatn and Selvik’s theory, our results show that given a low level of oil revenues
(assuming oil revenues variable equals to zero), higher fractionalization of politics is positive for
the economic development, i.e. the estimated coefficient of govfrac is positive and statistically
significant. However, in those countries with considerable amount of oil revenues, such a
fractionalization fuels rent-seeking among different factions. In all specifications, we have
controlled for other important determinants of income such as trade openness, government
10
consumption, financial development, inflation, investment, and age structure of population. We
also control for the common time trend in random effect regressions.7 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.
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 1.4 and 1.6 in Table 1. The main results remain
robust without Norway. The random effects estimation presented in models 1.5-1.6 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.8 In models 1.7 and 1.8, we also re-estimate the same specification using 2 and 5
years 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 higher number of right hand side variables’ lags.
In Table 2, we have also controlled for the role of political institution using Polity2 index from
the Polity IV dataset (Marshall and Jaggers, 2009). Polity2 scores are between -10 and +10. A
+10 refers to “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 oil rents-income nexus. In other
7
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.
8
We have carried out the Hausman test under the null hypothesis 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
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 the Polity index and
its interaction with oil revenues, our main variables of interest remain robust with expected sign
and are highly significant. Furthermore, we present the results using an alternative proxy for the
oil resources. We use per capita oil rents instead of share of oil revenues in total revenues of the
government. Alexeev and Conrad (2009) argue that the best measures of the role of natural
resources in 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 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 dollar) are calculated as:
Rent= (production volume)(international market unit price-average production cost)
(6)
We have divided the total oil rents by population of each country to calculate the per capita oil
rents. Models 2.3 and 2.4 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% levels in models with per capita oil
rents. Excluding Norway, as in model 2.4, does not change our results.
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 2.6 in Table 2). We use 3 lags of potentially
12
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-stationary of explanatory variables (Baltagi et al., 2009).
The results in model 2.6 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 2.5. 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 (CastellóCliment, 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.
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 variable indicates its
persistency, however, is statistically different from unity in both models.
13
To sum up, our results show that the final effect of oil rents on growth is conditional and depends
on the level of fractionalization among government parties. Using Eq. 5 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 2.3 (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 revenues of government 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.
14
Table 1.Oil, fractionalization of government parties and income (panel regressions)
Variables
c&t fe, using 2
lags of IVs (1.7)
c&t fe, using 5
lags of IVs
(1.8)
1.35***
(5.64)
Dependent variable: log (rgdp p.c.), 1993-05
c&t fe, 1 lag
c.re,1 lag of IVs c re,. 1 lag of
of IVs
(1.5)
IVs excluding
(excluding
Norway (1.6)
Norway)
(1.4)
1.35***
1.29***
1.34***
(5.46)
(6.15)
(6.28)
1.05***
(4.61)
0.64***
(2.80)
0.38***
(2.53)
0.33**
(2.13)
0.37*
(1.80)
0.42**
(2.44)
0.44**
(2.22)
0.13
(0.82)
0.17
(1.28)
-6.23***
(-3.03)
-2.08***
(-3.53)
-1.86***
(-3.14)
-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)
0.08
(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)
0.001
(0.03)
0.03
(0.86)
0.03
(0.77)
-0.03
(-0.91)
-0.00
(-0.00)
Inflation
-0.006*
(-1.86)
-0.003***
(-3.67)
-0.003***
(-4.20)
-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)
0.06
(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)
0.05
(0.22)
-0.07
(-0.39)
-0.13
(-0.63)
0.01
(0.06)
-0.53**
(-1.96)
Country fixed effects
No
Yes
Yes
Yes
Yes(RE)
Yes (RE)
Yes
Yes
Time trend
Yes
Yes
No
No
Yes
Yes
No
No
Time fixed effects
No
No
Yes
Yes
No
No
Yes
Yes
Obs. (countries)
248 (25)
248 (25)
248 (25)
236 (24)
248(25)
236 (24)
226 (25)
154 (24)
R2
0.60
0.99
0.99
0.99
0.69
0.68
0.99
0.99
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)
Oilrev
3.01***
(5.25)
1.22***
(5.54)
Govfrac
2.13**
(2.28)
Oilrev*govfrac
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. *** significant at 1% level; ** significant at 5% level; * significant at 10% level. All variables except for
inflation are in log. Results are robust using higher lags of explanatory variables.
15
Table 2. Oil, fractionalization of government parties, income and political institutions (panel regressions)
Variables
Dependent variable: log (rgdp p.c), 1993-2005
c&t fe, 1 lag of
IVs (2.1)
c&t,1 lag of IVs
ex.Norway (2.2)
c&t fe, 1 lag of IVs,
using oil rents per
capita(2.3)
c&t fe, 1 lag of IVs , using
oil rents per capita),
excluding Norway (2.4)
One step SYS.GMM,
using Oilrev
(2.5)
One step Diff.GMM,
using Oilrev
(2.6)
1.42***
(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
0.13***
(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
0.13***
(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
0.46**
(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)
0.45**
(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)
Country fixed effects
1.39***
(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
Yes
Time fixed effects
Yes
Yes
Yes
Yes
248 (25)
0.99
-------
236 (24)
0.99
-------
258 (25)
0.99
-------
246 (24)
0.99
-------
Time trend
0.95***
(17.39)
253 (25)
--54.3 (0.49)
0.04
0.30
Time trend
0.90***
(12.27)
226 (25)
--57.3 (0.38)
0.02
0.27
Oilrev
Govfrac
Oilrev*govfrac
Trade
Govex
Credit
Inflation
Invest
Age dep
polity
Polity*oilrev
Lagged dependent variable
Obs. (countries)
R2
Sargan (p-value)
AR(1)-p-value
AR(2)-p-value
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.*** significant at 1% level; ** significant at 5% level; * significant at 10% level. 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.
16
Table 3. Marginal effects of oil revenues on growth at different levels of power balance
Marginal effects
Oil revenues (based on
Oil rents per capita
model 2.1 estimations)
(based on model 2.3
estimations)
Mean of power balance (govfrac)
Maximum of power balance (govfrac)
Minimum of power balance (govfrac)
1.20
0.12
0.41
0.07
1.39
0.13
Note: 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 rent
per capita (-1).
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).
The marginal effects (the middle solid line) can be seen 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.
Thus, for the majority of countries in our sample the marginal effect of oil revenues on income is
positive and significant. At the minimum level of fractionalization this positive effect is in its
maximum9.
9
Using estimations of difference GMM in model 2.5 for calculation of marginal impact does not change our results in
Figure 1. For more details on graphing interaction effects see Braumoeller (2004).
17
Fig. 1. Marginal effects of oil revenues on income at different levels of fractionalization
For illustration, using Eq.5 we show the calculated marginal effect of increasing a 1% in the share of
oil revenues in government budget on growth for three groups of countries in our sample, i.e., countries
with the minimum, mean, and maximum degrees of political fractionalization. The results are presented
in Table 4.
Table 4: Marginal effect of a 1% increase in the share of oil revenues on growth for different countries
(2005)
Countries with minimum
fractionalization:
Countries with average
fractionalization:
Countries with maximum
fractionalization:
(Kazakhstan, Nigeria, Oman,
Venezuela
Russia, Saudi Arabia, Sudan, Syria,
Vietnam and Yemen)
Indonesia
1.40%
0.66%
1.21%
18
In order to examine the relative importance of explanatory variables such as oil revenues and
fractionalization of government parties in growth model, we use standardized variables in regression
analysis by subtracting their mean and dividing them by their standard deviation. Table 5 presents the
standardized coefficients of main explanatory variables in a country and year fixed effect regression.
Standardized variables have the same unit of measurement, making interpretation of 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.
Table 5. Standardized coefficients
Dependent variable: log (rgdp p.c), 1993-2005
variables
Standardized coefficients
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 fixed effects
Yes
Year fixed effects
Yes
Obs.
248
R-square
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 dependent variable if explanatory
variable increases by one standard deviation.
19
In other words, we can judge about the relative importance of our main independent variables in
explaining growth. We consider that inflation, oil revenues, government consumption, and interaction
of fractionalization and oil revenues have the most important effects on growth.
5. Conclusion
The balance and distribution of political power plays an important role in changing the blessing of oil
revenues into a curse. The paper focuses on the role of political fractionalization in an oil-development
nexus. We re-examine the hypothesis of the natural resource curse, using panel data for 30 oil-rich
countries from 1992-2005. Our theoretical framework and the work of Bjorvatn and Selvik (2008)
shed lights on another possible channel through which the blessing of natural resources may become a
curse for development, i.e., the relative influence of political factions in power. Our paper shows that
oil rents are not per se harmful for the economy. What is detrimental is the “destructive competition”
within the political structure. Controlling for indirect effects of oil rents on the economic development
through changes of balance of power between factions (measured as the fractionalization degree of
government parties), we conclude that higher levels of balance of power (higher fractionalization) in
the political system and bolder struggles among different political groups in a factionalized political
system reduce the positive effects of oil wealth on growth. At the maximum level of fractionalization, a
1% increase in the share of oil revenues in total revenues of government raises income by 0.41. In the
case of dominance of a single political faction the same increase in oil revenues has a more pronounced
positive effect on growth (1.39%). In other words, an increase in oil rents may result in lower income.
This is more likely to happen when political influence within government administration is relatively
equally distributed between the factions. These results have relevant policy making implications for oilrich countries of the Middle East and North Africa which are undergoing changes in their political
structure. Such unexpected and significant changes in the distribution of power in the MENA countries
may intensify political fractionalization as for the case of Iran after the revolution of 1979.
20
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23
Appendix A. Countries included in the sample
OPEC
Algeria
Angola
Ecuador
Iran
Kuwait
Libya
Nigeria
Qatar
Saudi Arabia
United Arab Emirates
Venezuela
Non-OPEC
Azerbaijan
Bahrain
Brunei
Cameroon
Chad
Congo
Equatorial Guinea
Gabon
Indonesia
Kazakhstan
Mexico
Norway
Oman
Russia
Sudan
Syria
Trinidad and Tobago
Vietnam
Yemen
24
Appendix B. Data description
Variable
rgdp p.c.
Description and Source
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 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).
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).
Inflation
Inflation, consumer prices (annual %).
Source: World Bank (2008).
Invest
Logarithm of gross fixed 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) to 10 (full democracy). Rescaled
from 0 to 1. Source: Marshall and Jaggers
(2009)
A7-point (0-6) index with higher values
indicating less corruption. Rescaled in a way
that higher values means higher corruption.
Source: ICRG, The PRS Group
corruption
25
Appendix C. Summary statistics
Variable
Obs.
Mean
Log (rgdp p.c.)
396
7.70
Log (govfrac(-1)+1)
298
Log (oilrev(-1)+1)
Log(govfrac(-1)+1)*Log(oilrev(-1)+1)
Standard
Minimum
Maximum
1.43
5.09
10.61
0.11
0.18
0
0.56
359
0.39
0.17
0
0.64
276
0.03
0.07
0
0.32
26
deviation