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Licensed Unlicensed Requires Authentication Published by De Gruyter August 30, 2013

Resource Curse and Power Balance: Evidence from Iran

Kjetil Bjorvatn, Mohammad Reza Farzanegan and Friedrich Schneider

Abstract

Empirical research shows that natural resources have a detrimental effect on economic growth, a phenomenon known as the “resource curse”. Competition between influence groups for access to the resource rents, that is, rent-seeking, is often blamed for this curse. In this article, we dig deeper into the link between political competition and the resource curse by studying the case of Iran from 1960 to 2007. We present a theoretical model demonstrating how the effect of rents on the economy depends on the balance of political power. The model shows that an increase in rents may lead to a sharp reduction in income when the distribution of power between influence groups is relatively balanced. The empirical evidence confirms the predictions of the model.

JEL Classification: Q32; D72

Appendix A

Table 4

Data description and sources.

VariableDescriptionSource
gReal per capita GDP growth rateCBI (2009)
oil revenuesChanges in the share of oil revenues in total revenues of government (oil dependency)CBI (2009)
oilex_pcChanges in oil export revenues per capita (US dollars)Oil export revenues from OPEC (2012) and Population from World Bank (2013)
power distributionvan_index: Index of distribution of power calculated as the products of Van_comp and Van_part divided by 100.Vanhanen (2000)
power balanceVan_comp: Index of balance of power among political factions, groups, and partiesVanhanen (2000)
inv_gdpChanges in the share of real investment in real GDPCBI (2009)
govex_gdpChanges in the share of real government expenditures in real GDPCBI (2009)
infCPI inflationCBI (2009)
oil_gGrowth rate of global average oil pricesIFS (2009)
OECD_gdppcgReal growth rate of OECD per capita GDPWorld Bank (2013)
military_gdpChanges in the military spending (as a % of GDP)World Bank (2013)
Figure 4 Trend of oil revenues share in total revenues in Iran (%).Source: CBI (2009).

Figure 4

Trend of oil revenues share in total revenues in Iran (%).Source: CBI (2009).

Appendix B

Unit-root tests without controlling structural break (ADF and PP).

VariableIncluded in test equationADFPP
LevelFirst Diff.LevelFirst Diff.
oil revenuesIntercept–2.24–6.42*–2.50–6.42*
Intercept and trend–2.33–6.42*–2.55–6.42*
None–0.62–6.48*–0.62–6.48*
oilex_pcIntercept–0.49–5.46*–0.75–5.46*
Intercept and trend–1.08–5.49*–1.45–5.49*
None0.72–5.35*0.51–5.35*
inv_gdpIntercept–2.05–5.72*–2.23–5.75*
Intercept and trend–2.53–5.69*–2.27–5.72*
None–0.05–5.76*0.07–5.79*
govex_gdpIntercept–1.28–4.97*–1.44–4.91*
Intercept and trend–0.96–5.18*–1.08–5.18*
None–0.14–5.02*–0.25–4.96*
infIntercept–2.61***–2.46–9.76*
Intercept and trend–3.22***–3.23***
None–1.41–7.27*–1.14–9.18*
pcgdp_gIntercept–3.59*–3.59*
Intercept and trend–3.61**–3.61**
None–3.38*–3.38*
gt–1Intercept–3.59*–3.59*
Intercept and trend–3.61**–3.61**
None–3.38*–3.38*
oil_gIntercept–6.28*–6.28*
Intercept and trend–6.21*–6.21*
None–5.84*–5.84*
OECD_gdppcgIntercept–3.96*–3.86*
Intercept and trend–4.90*–4.58*
None–1.93***–1.73***
militaryIntercept–1.97–6.23*–2.02–6.31*
Intercept and trend–1.96–6.18*–1.96–6.22*
None–0.67–6.29*–0.59–6.38*

Notes: Null hypothesis: respected variable has a unit-root. [*][**] and [***]

ZA unit-root tests with structural break in intercept and trend.

Variablet-StatisticsBreak year
oil revenues–2.841974
oilex_pc–2.151984
inv_gdp–3.621985
govex_gdp–3.97**1976
inf–5.141993
pcgdp_g–5.771975
pcgdp_g(–1)–5.771975
oil_g–7.361979
OECD_gdppcg–6.201972
military–3.701974

Notes: Critical values: 1%: –5.57 and 5%: –5.08. If the test statistic (e.g. –7.36 for oil_g variable) is smaller than the critical values (e.g. for breaks in trend and intercept –5.5 and –5.0), then we have an I(0) series with a trend and a break. If the test reports otherwise, we have an I(1) series. [*][**]

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  1. 1

    See Frankel (2010) for an overview of this literature.

  2. 2

    In a companion article, we use panel data for 30 oil-rich countries to investigate the interaction between the distribution of power, resource rents, and economic outcomes, see Bjorvatn, Farzanegan, and Schneider (2012). This case study of Iran allows us to dig deeper into the mechanisms of rent-seeking and the resource curse in a particular institutional setting. Other studies such as Karshenas and Hakimian (2005; 2007) have examined the interaction of oil, democratization, and the process of economic diversification in Iran.

  3. 3

    Within the modernization paradigm, factionalism precedes the formation of modern parties which develop as channels of mass participation and thus act as important agencies of eventual democratization” (Lewis, 1995, 105). Indeed, factionalism may refer to a primitive and immature form of democracy which is a property of political systems in transition to democracy.

  4. 4

    See also Amuzegar (2009) and Bjorvatn and Selvik (2008) for an analysis of factionalism in Iran.

  5. 5

    Noland (2008) investigates the paradox of high stability of undemocratic regimes in Arab region.

  6. 6

    Andersen and Aslaksen (2008) highlight the role of constitutional arrangements when explaining the resource curse. Shahnawaz and Nugent (2004) use a dynamic game-theoretic model to explain different political outcomes in resource-rich countries. Another group of researchers refers to the role of ethnical fractionalization as a possible explanation for the resource curse hypothesis (Hodler, 2006 and Montalvo, Reynal-Querol, 2005). Mavrotas, Murshed, and Torres (2011) show how an increase in resource rents can have large, negative impacts on growth when there are increasing returns to scale in rent-seeking.

  7. 7

    Oil dependency captures materialized rents in the government budget. Political factions are more interested in these more tangible rents rather than oil reserves or production. In other words, what matters for economic growth and rent-seekers is ultimately the value of produced oil barrels and not necessarily the number of barrels (see Hodler, 2006 for similar view). However, we have also carried out the estimations using the per capita daily oil production. The results (not shown) remain robust by using the oil abundance indicator as well.

  8. 8

    In 2SLS and GMM estimations, we treat all explanatory variables as endogenous except for the OECD growth rate and different dummy variables.

  9. 9

    See Karim Sadjadpour’s note on the role of supreme leader in Iran: http://iranprimer.usip.org/resource/supreme-leader

  10. 10

    Furthermore, there can be another concern about the effect of the “oil rent” specific variables on power distribution indicators which in turn influence economic growth. In this case, we still have a correct model, but it may not be an efficient specification. We show that this issue is a not a source of concern for our case. The correlation of the “oil rent” dependency variable with the power distribution variables is not statistically different from zero.

  11. 11
  12. 12
  13. 13

    Moreover, there were two periods with a high power index: the four years after the Revolution and the period 1996–1999. In these periods, the vote shares of the largest group were 53 and 44%. We also controlled for these two periods of time in some of our regressions to see whether they drive our main findings. However, we do not find the evidence for sensitivity of our main results to developments under these two distinct periods.

  14. 14

    Using 2SLS or GMM estimations does not change the marginal impacts qualitatively.

Published Online: 2013-08-30

©2013 by Walter de Gruyter Berlin / Boston

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