The stylized facts of the resource curse

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The stylized facts of the resource curse

Although some countries have well harnessed their resource wealth to expand their economies faster, most of them have experienced bad macroeconomic performance. A useful starting point for clarification of this subject is to discuss a well-known example of countries where have failed to benefit from their wealth and another where has benefited. The most dramatic example of the first group of countries is perhaps Nigeria (Sala-i-Martin and Subramanian, 2013). Although oil revenues per capita in Nigeria had increased 10 times over the period 1965-2000, income per capita had stagnated (around 1100 $ in PPP terms) since 1960. It made the population share of people who survived by 1$ per day shoot up 2.7 times during 1970-2000. Evidence shows that the top 2% had the same share of income as the bottom 17% in 1970, while they had the same share as the bottom 55% in 2000 (Sala-i-Martin and Subramanian, 2013). Recent evidence demonstrates that the real GDP per capita growth rate in Nigeria has dropped at an average of 4% between 1965 and 2000 (source PWT90). Further, two-thirds of capacity utilization in manufacturing which is often owned by the government and had a principal role in creating new job opportunities goes to waste. These suggest clearly that the middle and low-income classes of Nigerian have not benefited from revenues resulting from oil export (Van der Ploeg, 2011b).
Others discuss examples of countries having positive experiences. 40% of Botswana’s GDP results from diamonds, but Botswana has succeeded to protect its economy from the resource curse (Van der Ploeg, 2011b). The second rank country in public expenditure on education (% of GNP) made Botswana enjoy the world’s highest growth rate (Sarraf and Jiwanji, 2001) and income per capita increase about 20 times (from 467$ to 9228$ in PPP terms) on average of 8.5% during the period of 1965-2000 (source PWT90). Botswana’s well economic performance seems to explain why its real GDP per capita which was one-tenth that of Nigeria in 1965 overtook during this period so that it became about ten times that of Nigeria in 2000 (source PWT90). The Botswana experience is noteworthy since it started its post-colonial experience with minimal investment (Van der Ploeg, 2011b). Nevertheless, evidence shows that the real capital stock per capita had accumulated on average 6% annually during the period (source PWT90).

The explanations of the natural resource curse

The stylized-facts, discussed in section 1, seem to clarify why the question that the natural resource is a curse or blessing for economic performance has still remained controversial. Here we represent the main hypothesis and evidence about the effect of resource windfall income on economic performance. We first put forward the Dutch disease hypothesis and then the effect of volatility in commodity price on economic performance to illustrate the economic reasons behind the resource curse. Then, we address the political-economics issues to see to what extent the quality of the institution, corruption, and political structure influence the nexus between resource dependence and economic performance.

Economics issues

The first enduring explanations for the resource curse refers to the Dutch disease hypothesis. The term of the Dutch disease was originally named in 1977 by T he Economist to describe the unfavorable repercussions of natural gas discoveries in the late 1950s on the Dutch manufacturing sector. The idea behind the hypothesis is that The windfall income generated by the sale of the natural resource wealth, through an appreciation in the real exchange rate, decreases the competitiveness of the traded sector (Corden and Neary, 1982). We can demonstrated this with a small open economy model in a Salter-Swan framework, as in M. Nkusu, 2004. Let us assume the following statements hold. 1) There are two sectors: the traded (e.g. manufacturing and agricultural sectors) and non-traded (e.g. non-traded services and construction) sectors, 2) There are no asset and capital accumulation and the labor force (i.e. Human capital) is only production factor, 3) The labor force is normalized to unity to expel the population growth, 4) The labor force is fully employed by sectors and they can move freely across sectors, 5) There are decreasing returns to labor in each sector, 6) Agents have an identical taste and there is perfectly elastic demand for traded goods, 7) The windfall income (resource boom) is a constant exogenous gift over time.
The literature on the dutch disease highlights two different effects. The first one is the spending effect. Resource boom brings about an expansion in the total income of the economy and so increases the demand for both traded and non-traded goods. Since The excess supply for traded goods can be provided by the world market, the price of traded goods is exogenously constant and so the demand is perfectly elastic. However, the domestic supply can not confront the expanded demand for non-traded goods and this drives the relative price of non-traded to traded goods, defined as the real exchange rate, to appreciate. The second one called the resource movement effect suggests that relative price appreciation increases the real wage of the labor force working in the non-trade sector, with respect to those working in the traded sector. It makes a signal for the labor force to shift away from the traded sector and into the non-traded sector. Consequently, the traded sector shrinks and the non-traded sector expands.
For longer run effects one must include capital in the framework. In the Heckscher-Ohlin model with two goods, two sectors, two factors (i.e. capital and labor) and constant returns to scale in the production functions, the real exchange rate appreciation resulting from a resource windfall income increases the demand of factor used intensively in the non-traded sector relative to another factor. This, in turn, increases the relative factor price (Stolper and Samuelson, 1941). In any case, the reallocation of factors, shifting away from the traded sector and into the non-traded sector, causes the non-traded sector to expand and the traded sector to contract.
Later on, researchers challenged this strand of the literature and put forward that the traded sector is the engine of growth. Further, evidence supports that the traded sector benefits more from learning by doing in the long-term (Ulku, 2004). Hence, the non-resource traded sector hit by worsening competitiveness is more likely to not fully recovered once the resource income runs out (Van der Ploeg, 2011b). As an initial attempt, Van Wijnbergen, 1984 studied a two-period and two-sector model in which future productivity in the traded sector depends increasingly on the current production of the traded sector. Later on, by an assumption that only labor employment in the traded sector contributes in generation of learning, Krugman, 1987 proposed a model of increasing returns to scale in the traded sector. While J. D. Sachs and Warner, 1995 and Gylfason, Herbertsson, and Zoega, 1999 made an assumption that learning generated by labor employment in the traded sector spills over perfectly to the non-traded sector. These later models clarify that the learning process induces the endogenous growth in both sectors: a natural resource boom reduces labor employment share in the traded sector, hampers learning by doing (LBD) and thus decelerates economic growth. In recent literature, Torvik, 2001 presents a general model in which both sectors can contribute to the learning process and there are imperfect learning spillovers between sectors. He demonstrates that within such a model a resource boom tends to depreciate the steady-state real exchange rate, while the steady-state economic growth is independent of a resource boom and the sectoral productivity growth depends on which one of the direct or the spillover effect is stronger. Bjørnland and Thorsrud, 2016 put forward Torvik, 2001 model so that the productivity spillovers between the booming resource sector and other domestic sectors. They show that a booming resource sector leads to the real exchange rate depreciation over the transition path and increasing the rate of growth in the economy and in both sectors, contrary to standard Dutch disease models.
The influential works by J. D. Sachs and Warner, 1995; J. D. Sachs and Warner, 2001 and Rodriguez and J. D. Sachs, 1999 are representatives of a stream of empirical literature showing that natural resource dependence decelerates economic growth. In particular, in a cross-section of countries during 1970–90 J. D. Sachs and Warner, 2001 show that a 10% increase in the ratio of natural resource exports (% of GDP) is associated with as much as 0.4–0.7 % lower annual GDP per capita growth. In recent studies, researchers have applied panel data rather than cross-section approach to avoid the problem of omitted variables bias.
Regarding the empirical studies in supporting the Dutch disease hypothesis, the early evidence to show the contraction of the manufacturing sector because of the exchange rate appreciation has been somewhat mixed (Sala-i-Martin and Subramanian, 2013). However, more recent empirical work seems to support the hypothesis. More evidence for 135 countries for the period 1975-2007 shows that a resource windfall income induces savings of about 30 %, shrinks non-resource exports by 35-70 %, and expands non-resource imports by 0-35 % (Harding and Venables, 2010). Using annual data for 81 manufacturing sectors in 90 countries over the period 1977-2004, Ismail, 2010 put forward that a 10 % increase in oil price slows down, on average, the manufacturing growth rate by 3.4 %. Further, it shows that this negative effect is stronger in countries that have a more open capital market to foreign investment and also sectors that are less capital intensive. Consistent with the former work, a recent study for 41 resource exporter countries over the period 1970-2006 shows that price movement is negatively correlated with the manufacturing value-added (Harding and Venables, 2016). Further, among a few limited studies that have systematically studied the Dutch disease hypothesis, evidence for 6 selected South-East Asian economies over the period 1981-2007 confirms that a foreign aid inflows leads to an appreciation in the real exchange rate, a contraction in the manufacturing sector and an expansion in the service sector (Javaid and Riazuddin, 2009).
Other possible explanation that has absorbed more attention in recent years is the destructive effect of volatility in commodity price on economic performance. During the 1970s when commodity prices were high, resource-rich countries used their resource wealth as collateral for debt but during the 1980s when commodity prices fell significantly, many of those countries faced debt crises (Van der Ploeg and Poelhekke, 2009). They further point out that boom-bust cycles induced by volatile commodity prices, debt overhang, and credit constraints are the main driving forces of the resource rent-growth nexus. Using cross-country evidence for 62 countries, they found that the adverse effect of resources on growth is mainly driven by the volatility of commodity prices, especially for point-based resources (oil, diamonds), such that the indirect negative effect of resources on growth resulting from volatility erodes any direct positive effect of resources on growth. Furthermore, a strand of the literature indicates that volatility in resource price through instability in government revenue may lead to boom and bust in public spending and thus undermines economic performance (Hausmann and Rigobon, 2003; El-Anshasy, Mohaddes, and Nugent, 2017). In the same vein, Aghion et al., 2009 suggests that the adverse effect of volatility on economic growth is more intense in countries with less developed financial system.

Political-economics issues

The second enduring explanations for the resource curse follows political economic per-spectives. Mehlum, Moene, and Torvik, 2006a provide a rent-seeking framework and argue that a producer-friendly institution makes the profits of entrepreneurs be larger than that of rent-seekers and so gives entrepreneurs the incentive to engage in productive activities. Hence, natural resources tend to push aggregate income up in countries with producer-friendly institutions. In contrast, a grabber-friendly institution makes activities such as rent-seeking increase and so reduces the tendency of entrepreneurs to engage in productive activities. Hence, natural resources tend to push aggregate income down in countries with grabber-friendly institutions . Institution quality may explain the performance of resource-rich countries. Examples of countries with strong institutions in place at the time of their resource discovery are Norway, Canada, Australia, New Zealand, Iceland, and US. While countries with weak institutions in place at the time of their resource discovery are Angola, Nigeria, Sudan, Sierra Leone, Liberia, Congo, and Venezuela (Acemoglu, Johnson, and Robinson, 2002). In a simple rent-seeking model, Torvik, 2002 shows that the greater amount of natural resources, the more the number of entrepreneurs in rent-seeking and the lower the number of entrepreneurs in productive firms. This thus leads to falling the income such that the increase in income from the natural resource can not offset it. The pioneering study on the empirical cross-country evidence shows that institution quality does not have any effect on the growth rate (J. D. Sachs and Warner, 1995). However, Mehlum, Moene, and Torvik, 2006b demonstrates that a natural resource rent decelerates the growth of countries with bad institutions and accelerates the growth of countries with good institutions. Related cross-country evidence strongly suggests that natural resources hit the rate of growth in an economy by weakening the institutions inside the economy (Sala-i-Martin and Subramanian, 2013). Further, Isham et al., 2002 categorize two types of natural resources: diffuse resources (plantations such as coffee, cocoa, rice) and point-source resources (minerals such as oil, gas, coal). The former category is exploited by more people, while the latter type is held by a few owners. They conclude that countries with heavy dependence on point-source resources (oil) have worse institutions which, in turn, leads to having almost 33 % lower GDP per capita relative to countries with better institutions, 25 years after the oil shock of the early 1970s. In the same vein, Mavrotas, Syed Mansoob Murshed, and Torres, 2011 investigate this nexus for 56 developing countries over the period 1970–2000 and demonstrate that point-source resources influence negatively institutions and thus hampers growth.
There is, further, evidence that resource dependence is associated with a worse corruption level which, in turn, is associated with lower growth (Mauro, 1995). Leite and Weidmann, 1999 were among the first to illustrate the interrelationships between natural resources, corruption, and economic growth. They developed an open economy growth model in which corruption acts like a bribe paid by the firm to the government employee to get the administrative approval of investment projects. The model supported by cross-country evidence for 72 countries elicited that corruption decelerates growth and this negative effect is more pronounced in less developed economies. Among others, Khan, 1994, Shaxson, 2007, and Vicente, 2010 in a case study on Nigeria, The Gulf of Guinea, and Sao Tome and Principe, respectively, confirm that oil revenues are associated with higher levels of corruption. More evidence on the relationship between corruption and resource dependence is presented by Bhattacharyya and Hodler, 2010. In panel evidence for 99 countries over the period 1980-2004, they suggest that natural resources only arise corruption in countries where have had a non-democratic regime for more than 60% of the years since 1956.
The literature also contains studies seeking to reveal the relationship between resource rents and political regimes. Evidence shows that there is a positive relationship between authoritarian regimes and economic dependence on resource rent (Ross, 2004a; Ross, 2004b). Except for resource-rich countries in the Middle East, most of the non-democratic countries went gradually toward a democratic system since the 1970s (Huntington, 1993). This pattern observes also in Russia and African countries. One possible explanation is that the resource rent allows dictators to buy off political rivals and therefore makes the path toward democracy slow down (Acemoglu, Verdier, and Robinson, 2004). This negative relationship between democracy and dependency of economies on natural resources does not necessarily mean that resource rents hinder economic growth (Frankel, 2010). Collier and Hoeffler, 2005 show that weak democracy in developing countries can lead to poor checks and balances and therefore can hamper the economy’s performance. In the same vein, using a cross-country sample of 90 countries, Andersen and Aslaksen, 2008 find that the resource curse occurs in presidential, not parliamentary democracies. They show that presidential systems are less accountable and less representative and hence it is likely to provide more scope for rent-seeking activities, while parliamentary systems have shown to be more efficient in using natural resource revenues to promote growth.

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Purpose of the study

Although the literature has deepened our understanding of the determinants in the economic performance of natural resource-dependence countries, many unanswered questions have still remained. Hence, this study seeks response to three specific following questions: a) Does the Dutch disease hypothesis meet the empirical evidence and vice versa?
b) Does the income inequality-growth nexus modify the standard view on the Dutch disease hypothesis?
c) Is the natural resource curse more intensive in countries with more absorption capacity constraints?
The next subsections present in more detail the contribution, the methodology, and the results of each chapter.

The Dutch Disease Revisited: Theory and Evidence

This chapter aims to revisit the Dutch disease hypothesis in terms of theory and empirical evidence. Literature reveals, on the one hand, there are several limitations in the earlier theories presented to describe the Dutch disease hypothesis. Contrary to the empirical evidence, models, driven by Learning By Doing (LBD), predict that a resource boom tends to depreciate the steady-state real exchange rate and has no effect on the rate of economic growth in the long-term. On the other hand, less attention has been paid to systematically analyze the symptoms of Dutch disease. These symptoms are 1) an appreciation in the real exchange rate due to a resource boom, and 2) asymmetric response of the rate of growth in the sectors to the real exchange rate appreciation. Hence, the first contribution of this study is to revise the influential model such that its predictions meet the empirical evidence. In addition, the second contribution is to find a direct nexus between booming resource rent and the real exchange rate and then to provide a clear assessment of the response of the relative output level, sectoral growth, and economic growth to the real exchange rate appreciation.
To address the objectives, I first develop a two-sector model driven by LBD approach.


The productivity is generated in both manufacturing and service sectors, while there are an imperfect spillover from the manufacturing to the service sector. Different from the model of Torvik, 2001, I assume that there is a technology spillover from the resource sector to the manufacturing sector. The model describes the standard Dutch disease mechanism, the same as Torvik, 2001. But it shows that a resource boom conditionally appreciates the real exchange rate and decelerates the rate of growth in the economy and in both sectors in the long-term, contrary to Torvik, 2001.
I then collect an unbalanced panel data set of 132 countries from the period 1970 to 2014. Using the Generalized Method of Moments (GM M) technique, I adopt a dynamic estimation procedure to avoid the autocorrelation problem and the problem of reverse causality between variables of interest (endogeneity problem). The symptoms of the Dutch disease is investigated in four stages: 1) the response of the real exchange rate to the resource-dependence proxy and the impact of the real exchange rate appreciation, respectively, on 2) the relative productivity of the manufacturing (traded) sector to the service (non-traded) sector, 3) sectoral GDP per capita growth rate, 4) economic growth rate.
The empirical results, taken together, do not contradict the presented model of the Dutch disease hypothesis. The main findings can be summarized in three points. First, the empirical strategy suggests a strong and statically significant positive effect on the real exchange rate from a natural resource boom. Second, the real exchange rate appreciation decelerates the rate of growth in both sectors such that the shrinkage is larger in the manufacturing sector than in the service sector. Accordingly, the relative output level of the manufacturing sector to the service sector diminishes and economic growth decelerates. Third, these effects are more intensive in resource-rich countries than resource-poor countries.

Does income inequality feed the Dutch disease?

The second chapter aims to find out how the impact of a resource boom on the link between income inequality and growth changes the standard view of Dutch disease. Literature shows that two questions that have attracted increasing attention from researchers are the impact of windfall income on economic growth and income inequality. Surprisingly these variables of interest have been studied in isolation from each other and a little attention has been paid to study these variables in a unified framework. In addition, they reveals that economic growth and income inequality are endogenous variables and their co-movements affect the underlying economic forces to which they are both responding (Turnovsky, 2011). From this prospective, these variables of interest need to be simultaneously studied since their relationship seems to be associative and not causal (Ehrlich and J. Kim, 2007).
Therefore, the main contribution of this paper is to analyze how income inequality responds to a natural resource boom and how a combination of income inequality and resource rent motivates the intensity of the Dutch disease.
In theory, I develop a two-sector growth model in which each sector employs skilled and unskilled workers. Workers’ groups allocate different consumption expenditure shares on traded and non-traded goods. The gap in expenditure shares captures the feedback of a change in income inequality on economic growth. I have analyzed the model in the short-run under a comparative static and in the long-run under a dynamic approach driven by learning-by-doing (LBD) model.
The model yields a number of theoretical findings. In the short-run study, a permanent rise in the windfall income leads respectively to an appreciation in the real exchange rate, a reallocation in the factor inputs, a shrinkage in the traded sector and a deceleration in economic growth. By the way, a change in inequality depends on the factor intensity and the distribution of the resource rent benefits (subsidies) between workers’ groups. Income inequality falls if the traded sector is relatively intensive in skilled workers and the resource rent benefits are distributed more evenly than the real wage between workers’ groups. In the long-run study, a change in income inequality is only driven by a resource boom. In response to a windfall income boom, income inequality falls (rises) if the relative resource rent benefit of skilled workers is smaller (larger) than the relative real wage. In addition, falling income inequality deepens the Dutch disease if the skilled workers, with respect to unskilled workers, allocate a larger expenditure share for traded goods.
Consistently with the theory, I lead a panel data study to evaluate the theoretical predic-tions. In this respect, I first collect the data for 79 countries over the period 1975-2014 and then apply the first-differenced and the system GMM approaches to estimate dynamic panel data regressions. The impact of a natural resource boom on the real exchange rate and then the response of income inequality to a change in the windfall income are examined. Further, I estimate the effect of interaction between a natural resource boom and income inequality on the intensity of the Dutch disease as well as the sectoral growth rate. These empirical studies represent some clear evidence in supporting the crucial role of income inequality in economic performance of the resource-dependent countries. A natural resource boom reduces income inequality and falling income inequality is associated with a more intensive natural resource curse.

Table of contents :

0 Introduction – version Française 1
0.1 Les faits stylisés de la malédiction des ressources
0.2 Les explications de la malédiction des ressources naturelles
0.2.1 Problèmes économiques
0.2.2 Problèmes politico-économiques
0.3 But de l’étude
0.3.1 Chapitre 2: La maladie hollandaise revisitée: théorie et preuves
0.3.2 Chapitre 3: Les inégalités de revenus alimentent-elles la maladie hollandaise?
0.3.3 Chapitre 4: Capacité d’absorption et malédiction des ressources naturelles
1 Introduction – English version
1.1 The stylized facts of the resource curse
1.2 The explanations of the natural resource curse
1.2.1 Economics issues
1.2.2 Political-economics issues
1.3 Purpose of the study
1.3.1 Chapter 2: The Dutch Disease Revisited: Theory and Evidence
1.3.2 Chapter 3: Does income inequality feed the Dutch disease?
1.3.3 Chapter 4: Absorption capacity and Natural Resource Curse
2 The Dutch Disease Revisited: Theory and Evidence
2.1 Introduction
2.2 A model of the Dutch disease
2.3 Theory meets empirical model
2.3.1 Data and Methodology
2.4 Econometric Results
2.4.1 Resource-dependence and the real exchange rate
2.4.2 Real exchange rate and relative sectoral output
2.4.3 Real exchange rate and sectoral growth
2.4.4 Real exchange rate and economic growth
2.5 Conclusion
2.A Steady state response of a resource boom
2.B Commodity Price index: Data Description, Sources and Methodology
2.C The net foreign assets and resource-dependence
2.D List of Countries
3 Does income inequality feed the Dutch disease? 
3.1 Introduction
3.2 The model
3.2.1 Short run analysis (static model)
3.2.2 Long run analysis (dynamic model)
3.2.3 Dutch disease dynamics
3.2.4 Absolute Productivity Growth
3.3 Empirical approach
3.3.1 Data and Empirical methodology
3.3.2 Impact of the windfall income on the real exchange rate
3.3.3 Income inequality response
3.3.4 The intensity of the Dutch disease
3.3.5 The sectoral growth
3.4 Conclusion
3.A Proof of Proposition 3
3.B Proof of Proposition 4
3.C Proof of Proposition 7
3.D List of countries included in the sample database
4 Absorption capacity and Natural Resource Curse
4.1 Introduction
4.2 Implication of Absorption capacity
4.3 Stylized Facts
4.4 Empirical approach
4.4.1 Methodology and Data
4.4.2 Estimation Results
4.4.3 Robustness Tests
4.5 The Model
4.5.1 Consumer problem
4.5.2 Economic response to the windfall income
4.5.3 Imperfect financial market and changing the demand composition
4.6 Conclusion
4.A Statistical cross-country estimation
4.B List of countries included in the sample database
4.C Windfall income and social value of wealth
4.D Solution of the dynamic system
4.E The real exchange rate and the capacity constraint
5 References


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