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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 6 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. 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World Bank, Washington, D.C. 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