Natural resource abundance, institutional quality and manufacturing development: Evidence from resource-rich countries

Resources Policy xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Resources Policy journal homepage: www.elsevier.com/locate/resourpol

Natural resource abundance, institutional quality and manufacturing development: Evidence from resource-rich countries Hossein Amiria, , Farzaneh Samadianb, Masoud Yahooa, Seyed Jafar Jamalib ⁎

a b

Faculty of Economics, Kharazmi University, No. 43, Taleghani Ave, Tehran 15719-14911, Iran Faculty of Economics, Allameh Tabataba’I University, Tehran, Iran

ARTICLE INFO

ABSTRACT

Keywords: Natural resources Institutions Manufacturing Natural resource curse Dutch disease

The past decades provide many examples of the manufacturing sectors of resource-rich countries failing to develop more rapidly than their counterparts in countries lacking such resources. This study evaluates the impacts of natural resource rents and the quality of institutions on performance of tradable and non-tradable sectors in resource-rich countries. It examines data from 2000 to 2016 and the panel data model from 28 countries rich in natural resources and having different levels of institutional quality. The dependent variable is services etc. value added to manufacturing value added ratio. Model estimation results show that in the case of natural resource-dependent economies, this ratio increases unless there is a high level of institutional quality. Various panel regression estimations confirm that, the efficient institutional structure in natural resource-based countries, through alleviating the effects of the natural resource curse phenomenon, improves the manufacturing sector's performance in these economies. The paper concludes with a clear message to policy makers on the contributions of institutions in economic performance. It is apparent that enhancements in institutional quality allow for more effective utilization of a country's rich natural resources in strengthening the manufacturing sector to achieve higher economic growth and to mitigate the effects of the natural resource curse.

1. Introduction Many empirical studies such as the core study by Sachs and Warner (1995) observed that natural resource richness does not necessarily lead to higher economic growth, and that although resource-rich countries (including oil-exporting ones) have the large sources of wealth (as an important factor for capital resources mobilization), they do not enjoy commensurate economic growth. On the other hand, many earlier development economists such as Nurkse (1953) and Rostow (1990) believed that natural resources are a fundamental prerequisite for achieving economic growth. Known as Paradox of Plenty in the economic development literature, it describes the situation of poor performance and slow economic growth. As the natural resource richness and its resulting rent cannot intrinsically exert negative and inhibitory effects on economic growth, economics theoreticians such as Corden and Neary (1982) initially tried to explain the abundance puzzle using merely economic approaches (like Dutch disease). However, the economic success in some of resource-rich countries like Chile, Australia, Malaysia, Canada, and the Netherlands, reveal the

inability of these approaches to address the issue of why some resourcerich countries have succeeded and others have not. Proponents of the economic approach believe that the rents from natural resources are the source of the natural resource curse for resource-rich countries in theoretical and general terms and, given the deviations made in economic variables by these rents, it doesn’t matter how, when, and under what institutional conditions such revenues have entered a country. Other researchers discussed institutions in explaining the reasons for the failure or success of resource-rich countries by emphasizing the role of institutional quality on growth (Brunnschweiler and Bulte, 2008; Bulte et al., 2005; Hartwell, 2016; Isham et al., 2005). Accordingly, the positive influences of resource richness and an efficient institutional framework1 on growth are a resource blessing for economies. On the other hand, the richness of resources in countries having an inefficient and rentier institutional framework,2 create rent-seeking and deviations in economic variables resulting in the natural resource curse. Basing their studies on the Dutch disease argument, Sachs and Warner (1999) and Rajan and Subramanian (2011) emphasized that the natural resource richness (particularly oil) of countries exerts negative impacts on

Corresponding author. E-mail address: [email protected] (H. Amiri). 1 In countries with efficient and strong governments and power distribution. 2 These countries are characterized by inefficient and weak governments and power distribution. ⁎

https://doi.org/10.1016/j.resourpol.2018.11.002 Received 5 December 2017; Received in revised form 1 November 2018; Accepted 1 November 2018 0301-4207/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Amiri, H., Resources Policy, https://doi.org/10.1016/j.resourpol.2018.11.002

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

manufacturing and dampens their prospects for economic growth. Using a political-economic model, Ross (2001) differentiated two types of natural resources as fuel-based (crude oil), and non-fuel-based and explored three aspects of the claim that the availability of oil resources in a country has a negative impact on its democratic processes. The author analyzed whether oil has antidemocratic properties and tried to explain the effects of oil-richness on the democratic characteristics of a state. This study differs from Ross (2001) in a number of ways such as having a broader scope and focusing on two forms of natural resources, as i) fuel-based namely oil, natural gas, coal (hard and soft) and ii) non-fuel-based namely minerals, and forests. Further, this paper adds to the current literature by exploring the impacts of richness in all types of resources through two different channels. First, a country blessed with natural resources such as oil and gas, can either extract and export them in crude form or refine them and export the final products, and by doing this, obtain the needed foreign currency to import the intermediate equipment. Or because of having relative advantage in natural resources, it could also progress in producing manufacturing goods, that use these resources as raw materials or energy providers. The outcome will be the improvement of competitiveness for the country. This study employs the institutional approach in explaining the effects of natural resource rents on tradable (manufacturing) and non-tradable (services) sectors and economic growth. Accordingly, the following research questions are addressed:

exports have a negative influence on resource governance, quality of regulations, judicial security, and control of corruption. Such a negative influence is not significant in OECD countries with their strong executive, technical, and regulatory bodies and structures. Pourjavan et al. (2013) conclude that an increase in a country's foreign-currency incomes from oil exports led to a reduction in accountability, continuous change in rent-seeking policies, corruption, and authoritarianism, and delays in attaining economic development objectives. In the international context, Sala-i-Martin and Subramanian (2008) believe that in Nigeria, a relatively high level of corruption has reduced the economic benefits from exporting the natural resources and acted as a barrier in reaching a high economic growth. Other researchers have also underlined that the natural resources have negative impacts on public health expenditures (Cockx and Francken, 2014), on financial development (Bhattacharyya and Hodler, 2014), on education (Cockx and Francken, 2016), and on democracy (Andersen and Ross, 2014; Jensen and Wantchekon, 2004; Ross, 2001). Further, Collier and Hoeffler (1998) and Fearon (2005) show that the probability of civil war would increase due to natural resources. Bulte et al. (2005) and Mehlum et al. (2006a) argue that in countries with institutions of acceptable quality, the effects of natural resources on growth is positive. Iimi (2007) in case of Botswana, as a developing country which is rich in diamond resources, shows that these resources have improved its institutional framework and promoted its economic growth. Boschini et al. (2007) found that the control of institutional quality and interference between institutional quality and resource richness came from the threshold effect. They argue that there are levels of institutional quality above which resource richness leads to improvements in growth and is therefore a blessing. Using the surface cross-section approach, Beland and Tiagi (2009) shows that how the influence of natural resources on economic development and growth depends on the institutional quality. Keikha et al. (2012) illustrate the negative impact of oil prices on economic growth, in describing the natural resource curse in oil-exporting countries. They show that oil price fluctuations have a weaker impact on the economic growth of countries having good institutions and exert more severe and extensive effects on those with poor institutions. The authors believe that the mismatch between the investment ratio's sign and the economic theories in selected oil-exporting countries can show their qualitative weaknesses in investments, corruption, and rentier activities. Horváth and Zeynalov (2016) show the effect of natural resource exports on economic performance of 15 former Soviet Union countries after independence during the 1996–2010 period. The authors highlight that the countries’ economic performances have changed remarkably after the fall of communism with respect to economic development and institutions. Recently, ElAnshasy et al. (2017) argue that there is a significant negative relationship between oil incomes and economic growth and that institutional structures can, to some extent, reduce the negative effects of fluctuations in oil incomes.

1. How and through which mechanism do natural resource rents and institutional quality affect the performance of tradable and nontradable sectors and thereby economic growth? 2. Do efficient institutions help alleviate the negative impacts such as weakening the manufacturing sector and slowing economic growth which are generated by natural resource richness? Section 2 of this paper reviews the existing literature from the theoretical and empirical perspectives, followed by the data and the structure of the econometric model in Section 3. Section 4 discusses the estimation results by showing the impacts on different variables. Finally, Section 5 concludes the paper with a special reference to policy implications. 2. Literature review This section reviews related local and international studies on the issue in order to highlight the contributions of this paper. Based on a new political economics point of view, Kheirkhahan (2003) examines incomes in Iran and Norway and, using comments by institutional economists such as Douglass North, underlines the major importance of institutions and the role played by interest groups in economic growth. The author addresses the issue of rent-seeking in Iran during the Second Pahlavi regime (1941–79) under the precarious property rights and natural resource richness conditions and, by explaining the institutional legacy of Iran's economy, he shows that it suffered from two major problems, namely the weaknesses of legal institutions and the desire of interest groups to gain power in the institutional environment. Mohammadzadeh et al. (2009) argues that the occurrence of the natural resource curse in oil economies is associated with the weakness of institutions, social infrastructures, and level of human development. The authors conclude that despite their abundance of natural resources, such oil-dependent countries cannot experience high and sustainable economic growth due to poor institutions and social infrastructures and low level of human development. In this regard, Mehrara et al. (2010) shows that the determinant and key variable for translating natural resource rent into a blessing or a curse is the institutional quality prevailing in oil-exporting countries. Pourjavan et al. (2013) investigated the effects of natural resource booms on governance performance index in selected developed countries having oil resources. They demonstrate that natural resource

2.1. Theoretical review The existing literature on the influence of natural resources on economic growth is pioneered by Sachs and Warner (1995). Their empirical analysis shows that over the long run, the economic performance observed in resource-scarce economies is higher than that in resource-rich ones. Many economists have been tried to discuss its origins and to test the robustness of their results. To date, studies on this indicate that the emergence of the Paradox of Plenty and growth can be attributed to several causes. Resource-rich countries such as Chile, Australia, Malaysia, Canada, and Netherlands used these resources to maximize their economic growth thus showing that the natural resource availability does not always have negative effects, but rather it is a country's institutional framework that determines how the resources are employed and the attendant consequences. Poor institutional 2

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

structures characterized by a weak and inefficient government and an inefficient power distribution framework would result in inefficient use of incomes, while the converse will apply in effecting the productive use of oil incomes (Shakeri et al., 2013). Robinson et al. (2006) in a theoretical model, demonstrate that the natural resource booms are related with a larger public sector, since these booms would be desirable for political representatives in order to remain in power. Accordingly, the public sector employs extra and inefficient forces, so that in spite of the additional revenues coming from natural resources, it results in a lower level of economic growth. A review of the literature reveals that the revenues of natural resource exports do not enter economic subsystems directly and immediately but also get in the economic subsystem under an institutional framework. So, institutional factors will have an essential role in the allocation and distribution of oil rents and, as a result, in the influence mechanism of oil incomes on the economic subsystem (Hemati, 2012). According to this, the higher level of stability, maturity and establishment of the institutional framework achieved in a historical process, the less the institutional structure is affected by huge rents of oil resources; and the institutional framework, in a predetermined way, leads the oil rent to the economic subsystem and the organizational levels, and will determine how the rents are allocated and distributed. Under such circumstances, the efforts of economic activists at the organizational levels to utilize the potential opportunities of gaining benefit from the new source of rent cannot significantly change the rules of the game due to stability, maturity, and the establishment of efficient institutional structures. However, if the oil rent enters the economy of a newly-established government with a weak and fragile bureaucratic system, it might cause the state, in establishing the government's framework, to become a rentier state (Mehlum et al., 2006a). The reinforcement and expansion of a rentier government is accomplished over a period of time through its interaction within the institutional context. This gradually changes the institutional framework in a way that is inappropriate or even detrimental for strengthening economic development and growth (Stevens and Dietsche, 2008); as under such a condition, the efforts of economic activists at the organizational level to utilize potential opportunities to gain benefit from the source of the rent, due to inefficient institutional structure, seriously change the rules of the game, in order to serve the interests of political and economic rent-seeker agents and therefore the institutional framework would be directed toward inefficiency and encouraging further rent-seeking activities over the time. There is consensus that the rentseeking begets undesirable outcomes for the economy. It detracts from long-term development objectives and causes further taking possession of the rent. Rent-seeking reduces the steady and constant flow of incomes and thereby affects economic growth. Disrupted resource allocations, reduced productive activities, decreased economic efficiency, rising social inequality, and slow economic growth are the main consequences of rent-seeking behaviours (Yavari and Salmani, 2005). Dargahi (2008) notes that compared to their resource-poor counterparts, resource-rich economies suffer severely from rent-seeking, so that the national policy tends to appropriate the rent coming from natural resources. Considered as an important factor in specification of levels of corruption, natural resource booms create opportunities for rent-seeking, such that focusing on how to extract a share of this unearned income becomes interesting. This question comes to mind of those who are aware of such an income, or see its consequences in the lives of those who have enjoyed it earlier. These beneficiaries are usually either from the political system or close to it. New arrangements are formed in order to spend and grasp this wealth or to control it in the organization of power in society, so that according to Karl (1997), some actors of the political arena can grasp their interests by increasing the general costs for society and creating obstacles in the country's evolution and progress (Shakeri et al., 2013). It can therefore be said that the natural resource booms in an inefficient institutional framework lead to deviations and specific tendencies in the

economy and thereby economic backwardness. One can consider the mismanagement of governments in taking advantage of these resources and the weakness in economic decision-making as these deviations. Also, natural resource booms intensify rent-seeking behaviours in the economy thereby disrupting the allocation of resources, reducing productive activities, lowering economic efficiency, and slowing the economic growth in countries having those resources (Behboudi et al., 2012). Therefore, researchers should explore why such inefficient institutional structures exist in these countries and hamper their economic development and growth. This will help them address the issue of why economic development and fast and sustainable growth do not occur in natural resource-exporting countries. With this theoretical foundation, it can be claimed that the dominance of a rentier atmosphere and rentseeking incentives in these economies are the most significant contributory factors. The second question is with which mechanisms the dominance of rentier atmosphere and rent-seeking incentives (due to interaction between the revenues of natural resource exports and inefficient institutions) weaken the manufacturing sector in resource-rich countries. In the economic literature on this, it is stated that by shaping motivational structures in order to support production and also provide appropriate grounds for productive activities, the institutional framework can act as a driving factor; in contrary, by creating a deviation from production and raising the transaction costs, it acts as a deterrent. Based on this, the institutional framework which helps strengthen rentseeking easily encourages scarce resources of entrepreneurship to exit productive activities and direct them into unproductive ones. This is particularly important and more complicated in petroliferous countries, due to the rent of oil exports. The existence of ambiguous property rights, poor and imperfect law enforcement, creating opportunities for corruption and bribery, preventing flow of information in the market, increasing uncertainty about contracts enforcement, raising exchange costs, and the prevalence of rent-seeking incentives causes rent-seeking activities to gain priority over the more productive ones. Thus, short-term concerns and the temporary interests of agents become the basis for setting up the relationships of the executive construct with economic activities. The consequence of property rights insecurity, in other words, reflects its own effects in the shape of dominant behavioural patterns in three dimensions; i.e. the firms will move toward production activities which need little capital, short-term contracts, and are small in scale (Douglass, 1991). It means that firms become very small in size, and organizational capacities shaped in this framework will be too limited and little. All these hamper the reaping of benefits from the division of labor, specialization, and economies of scale, and eventually increase the production costs. As a natural result of these problems, if there is no solution in the form of a well-thought-out plan, the production capacity of petrolic countries will be severely diminished. Accordingly, it can be concluded that revenues from oil exports divert resource-rich countries from the need to develop their industrial sector and prevent the attainment of their long-term economic growth. The abundance of natural resources raises real exchange rates and reduces industrial goods exports, and results in reallocation of scarce capital and labor inputs from production of manufactured and exportable final goods to the natural resource extraction industries. This leads to an increase in the production costs of other non-resource-based sectors. From the monetary perspective, rising exchange rates caused by the injection of oil export revenues, will increase money supply and liquidity and ultimately will lead to increased demand and higher commodity prices. To meet this excess demand, there will be an increase in imports of basic consumer commodities such as agricultural and manufactured goods. Such an increase will shift factors of production such as labor and capital toward the production of non-tradable and less competitive sectors as well as reduce the competitiveness of domestic producers due to the higher production costs and product prices resulting from high inflation 3

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

rates. Finally, international trade deficits will negatively affect the external sector of the economy. Further, the import of tradable goods (agricultural and manufactured) will reduce their prices relative to those of non-tradable goods (construction and services), and lead to labor force and capital moving toward the production of these lower value-added outputs (Bravo-Ortega and De Gregorio, 2005). This mechanism clarifies that the increased foreign currency incomes from natural resource, in the presence of inefficient institutional framework and dysfunction of manufacturing sectors, rather than being an opportunity for the promotion of industrial production and economic growth, converts the blessings of natural resources to the resource curse phenomenon. It should be noted that efficient institutional frameworks are a necessary but not an adequate condition for economic growth in resourcerich countries. This was seen in the experiences of some developed countries with good institutions, such as the Netherlands,3 which faced a great systemic disequilibrium caused by thriving incomes that were not derived from real economic activities.4 It shows that despite its well-developed institutional structures, the Netherlands was unable for a long time to overcome the adverse negative impacts of resource abundance and could not diversify its economic structures to avoid being overly dependent on revenues from the supply of raw materials (Momeni, 2008). The answers of the research questions through empirical studies are discussed in the next subsection.

Figs. 2 and 3 on the effect of natural resource rents on SVA-to-MVA ratio clearly demonstrates that the positive relationship between natural resources rents and SVA-to-MVA ratio is only confirmed for countries with poor institutional quality. As the literature shows, institutional quality has an effect on the interaction between natural resources and economic growth. However, how institutional quality affects the two factors in the context of resource-rich countries is still not clear and needs to be researched. This study adds to the literature through different research contributions. First, the countries are categorized based on natural resource rents. Then the natural resource rents-oriented countries are differentiated based on an institutional quality index of good and poor. This paper differs from Horváth and Zeynalov (2016) in which authors have chosen a set of 15 former Soviet Union countries with common history and legal system, based on some criteria such as number of years under socialism, proximity to the West, and possibility of accession to the EU (regarding Baltic countries). They also studied, in an institutional framework, the effects of natural resource exports on the manufacturing sector. The focus of this paper is on a group of countries that are rich in natural resources and whose common feature is to achieve natural resources rent. Within this categorization, countries that are highly dependent on incomes derived from natural resources are differentiated from those less dependent on such revenues. Secondly, most of the previous researches have focused on crosssection data. However, Rajan and Subramanian (2011) and Van der Ploeg (2011) emphasize that because cross-section data is subject to the bias from omitted variable, 8 using panel data is of importance. Therefore, this study applies panel data regressions for the selected resourcerich countries. Thirdly, this paper adds to the literature from the modeling perspective when the set of selected countries are differentiated based on institutional qualities and where the corresponding variables are included in the model. Finally, compared to the current literature, an important feature of this paper is its systematic review of the literature. The theoretical framework section, for instance, highlights the issue of why natural resource rents do not necessarily lead to fast and sustainable economic growth by showing that the rentier environment is one of the important factors contributing to that situation. In turn this will lead to an inefficient institutional framework which would prevent the growth of the economy. Further, reviewing the empirical evidence shows the efficient mechanisms and circumstances required for natural resource rents being used to contribute to economic growth.

2.2. Empirical review As noted by the studies discussed earlier, the quality of institutions managing natural resources determines the overall effects of natural resource booms on the economic growth. To gain a comprehensive picture of the existing reality, we reviewed the effect of natural resource rents on the tradable and non-tradable sectors using empirical evidence and the institutional approach. For this purpose, the effect of natural resource rents on “SVA-to-MVA”5 ratio for a set of 28 countries6 with different levels of institutional quality (poor and good) during the 2000–2016 period is shown in Fig. 1. The "SVA-to-MVA" ratio is the service value added (as the non-tradable sector) to the manufacturing value added (as the tradable sector), in the sense that an increase in this indicator would denote an increase in non-tradable goods value added (compared to tradable goods). Accordingly, a decrease in the indictor would imply a relative decrease of non-tradable goods value added compared to tradable ones. As can be observed, the negative relationship between natural resource rents and SVA-to-MVA ratio is confirmed for the sample countries. Nonetheless, it is necessary to mention that this result is confirmed for a variety of countries that first, rely on natural resource rents at different levels and, second, some of which possess a high-quality institutional framework, while others have a low-quality one. Thus, based on Fig. 1, it can be concluded that there is empirical evidence of the Dutch disease and the natural resource curse in general. In order to enhance the accuracy of the results, the selected countries are divided into those with high-quality (I) and low-quality (II) institutions. 7

3. Data and methodology This study investigates the impact of the abundance of natural resources such as oil, natural gas, coal (hard and soft), mineral and forest in a set of countries at different levels of institutional quality and unlike Ross (2001), all-natural resources, both fuel-based (crude oil), and nonfuel-based are considered in this paper. It attempts to answer the question of when do natural resource abundance and institutional quality become a curse or a blessing. It tests the hypothesis of whether a country with natural resources and a well-functioning institutional framework can use its resource rents to boost manufacturing production and lessen (or neutralize) the effects of the resource curse or promote economic growth. As emphasized by Havranek et al. (2016), which is based on a meta-analysis of 43 studies that contains 605 regression specifications analyzing the natural resources curse phenomenon, the

3 A country with over 500 years of colonial experience during which it had always been a first-class global power or, at least, a second-tier world power. 4 Referred to as the "Dutch disease" in the economic literature. 5 SVA and MVA refer to services (etc.) value added and manufacturing value added, respectively (both of them are % of GDP). 6 The case study involves two groups of countries: countries with high-quality institutions, including Romania, Mexico, Sweden, India, Brazil, New Zealand, Netherland, United States of America, England, Canada, Australia, Norway, Malaysia, and Chile; and also, countries with low-quality institutions, including Russia, Venezuela, Kazakhstan, Algeria, Uzbekistan, Qatar, Iran, Azerbaijan, Oman, Saudi Arabia, Turkmenistan, Angola, Kuwait, and Libya. 7 It should be noted that countries with high-quality institutions are assumed to be those whose institutional indexes were positive during the 2000–2016

(footnote continued) period, while countries with low-quality institutions were those with negative institutional indexes during the same period. 8 This is caused by the correlation between the omitted initial level of productivity and initial income. 4

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

Fig. 1. Natural resource rents and SVA-to-MVA ratio for the full sample. Source: World Bank (2017)

Fig. 2. Natural resource rents and SVA-to-MVA ratio for low-quality institutions. Source: World Bank (2017)

Fig. 3. Natural resource rents and SVA-to-MVA ratio for high-quality institutions. Source: World Bank (2017)

institutional quality and the interaction term between it and natural resources abundance are systematically important for measuring the effect of natural resources on economic growth. For this purpose, indices of governance quality are used as a proxy for institutional quality,

and a review of the literature shows that this index ranges between 2.5 and −2.5. In this paper, thresholds of 0–2.5 show the good institutional quality while those between −2.5 and 0 are considered as poor institutional thresholds. 5

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

For this study, countries with the highest share of revenues from natural resources in their economies were first selected and then, categorized based on the defined thresholds of 2.5 and 0 and 0 to −2.5, deemed as having good or poor institutional quality, respectively. The dataset in this paper consists of 28 countries studied over the 2000–2016 period. To address the research questions, it should be noted that the natural resource rents affect the tradable and non-tradable sectors through direct and indirect channels. Where both natural resource rents and institutions are likely to be endogenous, natural resources and institutional quality require instruments based on terms of trade and latitude, respectively or the use of other proxy variables. Indeed, an interaction term needs to be added into the model. Therefore, following Brunnschweiler and Bulte (2008) and Isham et al. (2005)9 the institutional quality is shown in Eq. (1) as:

Iit =

0

+

1 Latitudei

+

2 Volx it

+

it

rents. It should be noted that the term natural resource in the paper refers to the total rents received from all types of natural resources. 12 It shows the natural resource rents (NRRit ) as a function of the terms of trade (ttit ) and natural resource abundance (RRi ) variables as:

NRRit =

(1)

SMVAit =

• • •

1 ttit

+

2 RRi

+

(2)

it

0

+

1 NRRit

+

2 Iit

+

3 NRRit *Iit

+

4 REERit

+

5 LPDit

+ uit (3)

13

where SMVAit is the SVA-to-MVA ratio ; NRRit is total natural resource rents (% of GDP); and Iit represents the six measures for institutional quality. Also, the interaction term of Iit and NRRit is included to examine the hypothesis that the natural resource curse is observed only in countries lacking high-quality institutions. REERit is real effective exchange rate; is labor productivity difference; and uit represents the error term. The data on variables is adopted from the World Bank (2017) database and Direction of Trade Statistics from International Monetary Fund (2018). Natural resources as an input, which gives considerable financial resources to a country, can have notable impacts on the manufacturing. The results of Corden and Neary (1982) and Égert (2009) illustrate that an increase in revenues coming from natural resources in resource-rich countries would result in a shrinking tradable sector (manufacturing), an amplifying non-tradable sector (services), as well as movement of capital and labor from other sectors to non-tradable one. Therefore, natural resources can partially account for the industrial growth performance in resource-rich countries. Institutional quality plays a very influential role in industrial development. Most of the studies in this regard such as Auty (2002), Sachs and Warner (1995), Karl (1997), Ross (1999), and Mehlum et al. (2006b) underline the role played by quality of the institutional framework in directing the effects of natural resources on tradable and non-tradable sectors. The real effective exchange rate is the weighted average of a country's currency in relation to an index or basket of other major currencies, adjusted for the effects of inflation (Hinkle and Nsengiyumva, 1999). The growth of this index would indicate a decrease in competitiveness power of a country vis-à-vis a weighted average of trading partners. It is important to use this index, since the

• Control of Corruption: refers to perceptions of the extent to which

•

+

ttit shows the terms of trade as a ratio of the export price index to the import price index and RRi is added to the model as a dummy variable. Also, it represents the error term. In other words, since the regression sample in natural resource-rich countries, the RRi variable is calculated firstly by taking the total average of natural resource rents (% of GDP) for all countries and then categorizing the countries based on their location in the upper or lower value of the average. An RRi equal to 1 or 0 shows that the country has a natural resource rents (% of GDP) that is above or below the average, respectively. The results of the regression for Eqs. (1) and (2) are shown in Tables 1 and 2 respectively. According to the above results, it is clear that both of natural resource rents and institutions are endogenous. In the next step, following the theoretical and empirical discussions, the specification of the research's econometric model is shown by Eq. (3).

When, Iit shows the institutional quality variable, Latitudei is the absolute value of latitude measured in the range between 0 and 1 (see La Porta et al., 1999 for more details), and Volxit shows the institutional intensity of the country's exports (following the method presented by Huber, 2018). Also, it represents the error term. Following Horváth and Zeynalov (2016), six measures for quality of institutions are used; namely, government effectiveness, political stability and absence of violence/terrorism, voice and accountability, control of corruption, regulatory quality, and rule of law. Data of the institutional quality have been obtained from World Bank (2017)10 The definitions of the measures are as follows: 11

•

0

public power is exercised for private gain, including both petty and grand forms of corruption. Rule of Law: refers to perceptions of the extent to which agents have confidence in, and abide by, the rules that govern society. In particular, it looks at the quality of contract enforcement, property rights, confidence in the police and the courts, as well as the likelihood of being affected by either crime and/or violence. Government Effectiveness: refers to perceptions of the quality of public services, and the quality of the civil service and how independent it is perceived to be from political pressures. It also captures perceptions of the quality of policy formulation and implementation, as well as the credibility of the government's commitment to carrying such policies through. Regulatory Quality: refers to perceptions of the government's ability to formulate and implement sound policies and to develop regulations that permit and promote private sector development. Political Stability and Absence of Violence/Terrorism: this dimension refers to perceptions of the likelihood that a government will be destabilized or overthrown by unconstitutional or violent means and includes both politically-motivated violence and terrorism. Voice and Accountability: refers to perceptions of the extent to which a country's citizens are able to participate in selecting the government, how free they are to express their feelings and attitudes in public, to what extent freedom of association is permitted and finally how free and open the media is.

12 In the World Bank (2017) data, total natural resource rents are the sum of rents from oil, natural gas, coal (hard and soft), minerals, and forests. 13 It should be noted that the indexation of Dutch disease is somewhat difficult, whether in developing countries or in developed economies, as there may be some sub-sectors of the service sector which are tradable, while some of the manufactured goods (due to the establishment of quota system and protectionism policies) become semi-tradable, or it can be said that they are categorized in non-tradable goods group. It is clear that the exact separation of these two sorts of goods is not this paper's subject and needs broader studies. In this study, it is assumed that the manufacturing sector, mostly, is dealing with tradable goods; and non-tradable goods have a greater weight in the service sector.

Accordingly, Eq. (2) shows the determinants of natural resource 9 Some of the variables such as latitude, regional dummy variables, net exports composition, predetermined variables etc. have been used in both two studies, in order to check endogeneity of the institutional quality. 10 World bank, itself, calculates these data based on the Kaufmann et al. (2009) method. Details on the aggregation method and the description of the indicators, can be found in the WGI methodology paper (Kaufmann et al., 2010). 11 The definition of each measurement is adopted from Hough (2013).

6

Resources Policy xxx (xxxx) xxx–xxx

0.09 (0.04) 0.13 (0.206) 31.23** 0.11 (0.025) 0.05*** (0.011) 63.52**

0.06** (0.027) 0.11* (0.052) 65.76***

Table 2 The results of the determinants of natural resources exports.

− 0.09 (0.025) 0.01* (0.004) 62.34** 0.13 (0.078) 0.11*** (0.026) 83.41** − 0.06 (0.026) 0.04** (0.019) 51.21*** 0.11 (0.052) 0.09 (0.056) 53.62*** 0.01 (0.002) 0.05*** (0.013) 31.26*** 0.02 (0.005) 0.01*** (0.002) 24.35***

II

Latitude Volx F statistic

***

Variable /country

Good institutional framework

Poor institutional framework

tt RR F statistic

0.04*** (0.011) 0.12*** (0.038) 118.45***

0.05** (0.023) 0.09** (0.045) 105.36***

Note: standard errors are reported in parenthesis. Note: ***, ** and * denote significance at 1%, 5% and 10%, respectively.

occurrence of Dutch disease is implying that the extreme appreciation of exchange rate because of sudden entrance of natural resources revenues to the economies, would reduce their competitive power and thereby, affect the manufacturing negatively. Export is a decreasing function of the real exchange rate, and import is an increasing function of it; in this case, an increase in exchange rate would reduce the real cost of import and lead to weakening the sector compete with imports. The consequence would have destructive effects on tradable sector (manufacturing) and is neutralizing the benefits generated in the booming sector. Alternatively, given that the resource-rich countries do not trade only with US dollars, the effective real exchange rate has been used in the paper in order to see the consequent effects of exchange rate appreciation, in a more obvious way. In practice, labor productivity differences in the manufacturing sector among resource-rich and poor countries are very large. The literature on economic growth shows that the differences at sectoral labor productivity lead to the differences in total productivity criterion in the economy. The Balassa-Samuelson effect shows that a positive shock to labor productivity in the tradable rather than non-tradable sectors increases the wage rates in the former (Kravis and Lipsey, 1991). Accordingly, a positive labor productivity difference can alleviate the negative impacts of real exchange rate appreciation on the manufacturing. In fact, high-productivity labor in resource-rich countries in comparison to their trading partners can adjust, to some extent, the weakening impacts of Dutch disease on the industrial sector in their economies. According to Habib and Kalamova (2007), labor productivity difference is calculated based on the weighted product of GDPs per capita (based on PPP) belonging to country i compared to its trading partners, in which weights are the sum of the trading partner's imports-exports to the sum of those for country i. “Dutch disease” and natural resource curse are two phenomena explain the negative impact of natural resources on economic performance. “Dutch Disease” states that the growth in manufacturing sector reduces due to the natural resource richness (Corden and Neary, 1982; Égert, 2009). On the other side, institutions explain the natural resource curse. Sachs and Warner (2001) emphasized that natural resources affect economic growth through institutions. Rajan and Subramanian (2011), Sachs and Warner (1999) and Harb (2009) used “the annual average growth of value added of industry i in country j over a ten-year period, obtained by normalizing the growth in nominal value”, “the growth of PPP-adjusted GDP per capita”, and “non-oil GDP” as the dependent variables, respectively, while acknowledging the effects of institutions. Given the dependent variable in the model's regression, the results can be interpreted as the evidence of the Dutch disease. To choose the appropriate model between Pooled OLS, fixed effects (FEM), and random effect (REM) models, the F-Limer, Breusch-Pagan, and Hausman tests are used in the next step. The following section presents the model validation and estimation and discusses the research findings.

Note: I: control of corruption, II: rule of law, III: Regulatory quality, IV: government effectiveness, V: political stability and absence of violent, VI: voice and accountability. Note: standard errors are reported in parenthesis. Note: ***, ** and * denote significance at 1%, 5% and 10%, respectively.

− 0.03 (0.014) 0.11*** (0.031) 21.22* − 0.08 (0.037) 0.12** (0.056) 35.85**

*

II

**

I III I Variable /Model

***

Good institutional framework country

Table 1 The results of the determinants of institutional quality.

**

IV

**

V

VI

***

Poor institutional framework

III

**

0.14 (0.065) 0.08** (0.065) 35.42**

**

V IV

VI

H. Amiri et al.

4. Results and discussion Firstly, in selecting the appropriate approach (panel vs. pooled), the conventional F-Limer and Breusch-Pagan (1980) tests are used; further, the Hausman (1978) test is applied to choose between the fixed and the 7

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

Table 3 The results of choosing the appropriate approach. Test F-Limer Breusch- Pagan

Country/model

I

II ***

Good institutional framework Poor institutional framework Good institutional framework Poor institutional framework

III ***

31.61 57.28*** 332.02*** 755.24***

30.90 52.43*** 470.53*** 724.71***

IV ***

28.86 47.95*** 492.63*** 648.82***

V ***

VI ***

50.33 58.18*** 492.63*** 821.97***

32.80 57.59*** 508.81*** 779.02***

19.73*** 51.30*** 260.33*** 587.33***

Note: I: control of corruption, II: rule of law, III: Regulatory quality, IV: government effectiveness, V: political stability and absence of violent, VI: voice and accountability. Note: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Table 4 The results of Hausman test to choose between the fixed and the random effect models. Test Hausman

Country/model

I

II ***

Good institutional framework Poor institutional framework

39.42 14.08**

III

IV ***

8.42 10.31*

50.85 102.47***

38.98 7.47

***

V

VI

8.82 9.45*

5.92 1.73

Note: I: control of corruption, II: rule of law, III: Regulatory quality, IV: government effectiveness, V: political stability and absence of violent, VI: voice and accountability. Note: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Table 5 The results of the residual tests. Test

Country/model

I

II

III

IV

V

VI

Heteroscedasticity

Good institutional framework Poor institutional framework Good institutional framework Poor institutional framework

0.85 17.12*** 72.91*** 4.80**

0.05 29.57*** 76.38*** 5.19**

0.18 12.56*** 72.49*** 4.66**

1.15 41.61*** 77.74*** 4.41**

0.45 4.97*** 79.51*** 3.25*

9.92*** 20.77*** 77.21*** 5.89**

Serial correlation

Note: I: control of corruption, II: rule of law, III: Regulatory quality, IV: government effectiveness, V: political stability and absence of violent, VI: voice and accountability. Note: ***, ** and * denote significance at 1%, 5% and 10%, respectively.

tests, for all the six models and both types of countries, individual cross effects are significant. Table 4 shows the results of the Hausman test in selecting the fixed and the random effect models. Based on results, in models I, III and IV of countries with good institutional structure, the Hausman test is significant and therefore its null hypothesis stating the model is random would be rejected. Contrary to that, it is not significant in models II, V and VI and therefore random effects would be the estimation approach for these three models. In the context of countries with poor institutional structures, the approach of models I, II and III would be a fixed effect, while it would be random for the IV, V and VI models. The determinants of the manufacturing performance are investigated for each group of countries and then the significance of the interaction term between natural resource rents and institutions is analyzed in order to address the main research question. Table 5 on the presence or absence of heteroscedasticity and serial correlation in residuals shows the validation of the model before estimating it and after an initial estimation. The results in Table 5 show that the residuals of most of the models, according to White and Wooldridge tests, have heteroscedasticity and serial correlation (with the exception for models I, II, III, IV, and V which have not heteroscedasticity). Further, before estimating the model using the centered variance inflation factor (VIFc) test, the multicollinearity issue was checked for the independent variables such as I , NRR , REER and LPD (see

Table 6 The results of multicollinearity. Country

Good institutional framework

Poor institutional framework

Variables

I NRR REER LPD Mean VIFc I NRR REER LPD Mean VIFc

VIFc Test model I

II

III

IV

V

VI

2.13 1.15 1.36 1.92 1.64 4.15 1.16 1.07 4.36 2.68

1.99 1.14 1.27 1.85 1.56 3.74 1.10 1.11 3.86 2.45

2.01 1.14 1.23 1.91 1.57 1.81 1.13 1.04 1.92 1.47

1.97 1.14 1.42 1.72 1.56 3.24 1.44 1.07 3.37 2.28

1.78 1.13 1.27 1.65 1.46 1.72 1.11 1.04 1.70 1.39

1.58 1.19 1.15 1.49 1.35 1.26 1.14 1.07 1.25 1.18

Note: I: control of corruption, II: rule of law, III: Regulatory quality, IV: government effectiveness, V: political stability and absence of violent, VI: voice and accountability.

random effects models for each of the six measures. Based on the results shown in Table 3, the F-Limer and BreuschPagan tests are significant in all six measures and also for both groups of countries with good and poor institutional frameworks. Therefore, individual cross effects are of significance. Thus, according to these two Table 7 Selection of estimation method in different models. Country/model

I

II

III

IV

V

VI

Good institutional framework Poor institutional framework

within estimator vce (robust) estimator

GLS estimator vce (robust) estimator

within estimator vce (robust) estimator

within estimator vce (robust) estimator

GLS estimator vce (robust) estimator

vce (robust) estimator vce (robust) estimator

8

9

NRR*VA

NRR*ST

NRR*EF

NRR*RL

NRR*RQ

VA NRR*CC

ST

RL EF

CC RQ

− 0.02 (0.003) − 1.78*** (0.202) 84.97***

***

− 0.19*** (0.028)

− 0.02 (0.003) − 1.77*** (0.205) 77.49***

***

− 0.19*** (0.038)

− 0.18 (0.189)

− 0.02 (0.003) − 1.85*** (0.206) 79.02***

***

− 0.26*** (0.045)

0.37 (0.220)

0.41 (0.069)

***

Note: standard errors are reported in parenthesis. Note: ***, ** and * denote significance at 1%, 5% and 10%, respectively. a Between governance indicators and natural resource rents.

Model significance test (F statistic)

LPD

REER

Interaction termsa

Governance indicators

0.30 (0.050)

III

0.32 (0.047) 0.29* (0.145)

***

NRR

II

I

Coefficients

***

Countries with good institutional framework

Models

***

− 0.02 (0.004) − 2.11*** (0.221) 62.74***

***

− 0.12** (0.049)

− 0.06 (0.181)

0.21 (0.074)

IV

Table 8 The model estimation results in countries with good and poor institutional framework.

***

− 0.02 (0.003) − 2.23*** (0.207) 67.22***

***

− 0.21*** (0.046)

0.61*** (0.215)

0.17 (0.035)

V ***

− 0.20*** (0.032) − 0.02*** (0.002) − 1.82*** (0.160) 109.00***

0.04 (0.222)

0.23 (0.041)

VI

33.67***

0.001 (0.0002) 0.54 (0.323)

***

− 0.02* (0.010)

0.83* (0.434)

0.03 (0.074)

I

0.01 (0.003) − 0.60*** (0.216) 26.65***

***

0.005*** (0.001)

1.24*** (0.379)

0.01 (0.006)

II ***

0.03 (0.005) − 0.49*** (0.126) 16.37***

***

0.01*** (0.002)

0.68* (0.365)

0.05 (0.007)

III

Countries with poor institutional framework

***

0.02 (0.005) − 0.48** (0.195) 9.06***

***

0.02*** (0.005)

− 1.16* (0.607)

0.05 (0.008)

IV

**

(0.004)

0.008 (0.002) − 0.76*** (0.236) 9.02***

***

0.007*** (0.002)

1.02*** (0.206)

0.01

V

5.99***

− 0.02*** (0.005) − 0.43 (0.251)

- 0.22 (2.44)

1.28** (0.579)

0.01* (0.005)

VI

H. Amiri et al.

Resources Policy xxx (xxxx) xxx–xxx

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

Chatterjee and Hadi, 2012 for more details). It should be noted that with considering the research hypothesis and in answering the research questions, the variables of the model have been left unchanged. The results in Table 6 show that there is not a multicollinearity between the independent variables in the model.14 According to the results shown by Tables 4 and 5 and with their combination, different methods have been used to estimate the different modes of institutional quality and for countries with good or bad institutional structure. Within estimator for fixed effects and GLS estimator for random effects models are used when the residuals have autocorrelation (Baltagi and Wu, 1999). In other words, in the case of countries with good institutional structure, models I, III, and IV have been estimated with the former, while GLS estimator is used for models II and V estimation. When the residuals have the both auto-correlation and heteroscedasticity, we use vce (robust) estimator. It is called Huber/White/ Sandwich VCE estimator, as well.15 It should be noted that this estimator is applied for both fixed and random effects (Arellano, 2003; Wooldridge, 2015). Model VI in countries with good institutional structure and Models I-VI in countries with bad institutional structure have therefore been estimated with vce (robust) estimator. In Table 7, the estimation method of Models I-VI in countries with good and bad institutional structure is shown in a summarized form. Table 8 shows the model estimation results for countries with good and poor institutional quality. There are six columns for different measures of institutional quality. The results show that natural resource rents in both groups with good and poor institutional quality lead to a shrinking tradable sector (manufacturing) and strengthening non-tradable sector (services). The results are in line with the findings of Sachs and Warner (1999) that used cross-country data, and also Rajan and Subramanian (2011) that utilized panel regressions at the industry level. The results suggest that most of institutions have positive effect on the SVA-to-MVA ratio of the economy. Although it should be considered that, in economies with good institutional framework, institutions exert a positive influence on the non-tradable sector supporting the production (hard and soft infrastructures which support the production). While institutions, in a poor institutional framework economy, have positive effect on that non-tradable sector which is destructive for production capacity (brokerage and intermediation services). In addressing the first research question, the estimation results show that the natural resource rents and institutional quality have positive effect on SVA-to-MVA ratio, which is consistent with the conclusions from Beck and Laeven (2006). To answer the second research question, an analysis on the effects of the interaction term between the institutional quality and the natural resource rents is done through examining the role of institutions. Estimation results reveal the positive effect of natural resource rents and institutional quality variables on SVA-to-MVA ratio. Also, for countries with good institutions, the interaction term is negative and statistically significant suggesting that they do not suffer from the natural resource curse. On the other hand, this term's sign for countries with poor institutional structure is positive; meaning that natural resource booms would bring a curse for them. This result is robust for most of the institutional measures and in line with other empirical evidences, thus suggesting that the effect of the natural resource curse disappears in the former countries (Arezki and van der Ploeg, 2010; Brunnschweiler and Bulte, 2008).

The results show that in the good institutional framework economies, real effective exchange rate would exert a negative influence on the SVA-to-MVA ratio. While in economies with poor institutional framework, abrupt entrance of earnings from natural resources entails the ungovernable exchange rate appreciation. This in turn makes the imports cheap and the exports expensive. Increased demand for the tradable goods is met by imports, while there would be an increase in non-tradable goods prices; as a result, the resources tend to the latter. Thus, the model results, too, suggest that the real effective exchange rate positively affects non-tradable products compared to tradable ones, in economies with a poor institutional framework. Labor productivity difference variable is functioning as an adjusting component in the model, which means that a positive productivity difference can alleviate the negative effects of real effective exchange rates appreciation on the manufacturing. Indeed, high-productivity labor in resource-rich countries can somewhat adjust the weakening impact of Dutch disease on the industrial sector in their economies. The results of the model confirm this impact for countries with the both good and poor institutional framework as well. 5. Conclusion and policy implications This paper examined how the natural resource rents and the quality of institutions would affect the performance of tradable and non-tradable sectors in resource-rich countries. In doing so, it applied data of 28 resource-rich countries at different levels of institutional quality over the period 2000–2016 and employed the panel data model. The estimation results indicate the existence of a natural resource curse in countries with poor quality of institutions, while it is shown that this is not the case for countries having good institutional quality. In fact, the economic, social, and political characteristics of the former group show that the manufacturing sector's growth reduces due to the natural resource rents, while the same characteristics in countries with good institutional quality indicate that the natural resource rents would result in the promotion of manufacturing sector. The estimation results support the existence of Dutch disease in the case of countries having poor institutional quality. In other words, the research outcome shows that natural resource rents exert a detrimental effect on the other productive sectors of the economy. Furthermore, estimation results show that natural resources exert a curse only in countries that have poor institutions. Interestingly, this result holds for most of our institutional measures. In conclusion, the findings provide important insights for policymakers seeking to address the positive role played by institutions in promotion of economic growth. The positive and direct effects of institutions’ quality on the performance of the manufacturing sector which accompanied by an indirect support i.e. helping alleviate the impacts of the natural resource curse, have significant implications for the policy-makers from the observed natural-resource rich countries. Therefore, the crucial policy recommendation for these countries is the promotion of institutional quality. As the results in this paper show, improvements in institutional quality can play an extremely effective role in helping countries to reap additional benefits from natural resource booms, to strengthen their manufacturing sector, to achieve higher economic growth, and to mitigate the negative effects of the natural resource curse. Acknowledgments

14

According to the rules for the VIFc test, there exists a multicollinearity problem when the largest VIFc is greater than 10 and/or the mean of all the VIFc is considerably larger than 1. 15 The specification of vce (robust) is equivalent to the specification of vce (cluster panelvar) in which panelvar is a variable recognizing the panels (Arellano, 2003; Stock and Watson, 2008; Wooldridge, 2015).

The authors would like to express their sincere appreciation to the Editor-in-Chief and two anonymous referees for their helpful comments and suggestions, which tremendously improved the quality of the paper. 10

Resources Policy xxx (xxxx) xxx–xxx

H. Amiri et al.

References

Development. Springer International Publishing, Cham, pp. 91–120. https://doi.org/ 10.1007/978-3-319-76093-3_4. Iimi, A., 2007. Escaping from the resource curse: evidence from Botswana and the rest of the world. IMF Staff Pap. 54, 663–699. International Monetary Fund (IMF), 2018. Direction of Trade Statistics, In: IMF (Ed.), International Monetary Fund. Isham, J., Woolcock, M., Pritchett, L., Busby, G., 2005. The varieties of resource experience: natural Resource export structures and the political economy of economic growth. World Bank Econ. Rev. 19, 141–174. Jensen, N., Wantchekon, L., 2004. Resource wealth and political regimes in Africa. Comp. Political Stud. 37, 816–841. https://doi.org/10.1177/0010414004266867. Karl, T.L., 1997. The Paradox of Plenty: Oil Booms and Petro-States. University of California Press, Berkeley. Kaufmann, D., Aart, K., Massimo M., 2009. Governance Matters VII: Aggregate and Individual Governance Indicators, 1996–2007. World Bank Policy Research Working Paper 4280, pp. 1–93. 〈http://dx.doi.org/10.1596/1813-9450-4654〉. Kaufmann, D., Aart, K., Massimo M., 2010. The Worldwide Governance Indicators: A Summary of Methodology, Data and Analytical Issues. World Bank Policy Research Working Paper No. 5430, 〈http://papers.ssrn.com/sol3/papers.cfm?Abstract_id= 1682130〉. Keikha, A., Keikha, A., Mehrara, M., 2012. Institutional quality, economic growth and fluctuations of oil prices in oil dependent countries: a panel cointegration approach. Mod. Econ. 3, 218–222. https://doi.org/10.4236/me.2012.32030. Kheirkhahan, J., 2003. Oil Booms, Rent-Seeking and the Role of Institutions (Iran and Norway Case Study), [In Persian], Unpublished Work, Economics. Allame Tabatabaaei University, Tehran, Iran. Kravis, I.B., Lipsey, R.E., 1991. The International Comparison Program: Current Status and Problems, International Economic Transactions: Issues in Measurement and Empirical Research. University of Chicago Press, pp. 437–468. https://doi.org/10. 3386/w3304. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1999. The quality of government. J. Law Econ. Organ. 15, 222–279. https://doi.org/10.1093/jleo/15.1.222. Mehlum, H., Moene, K., Torvik, R., 2006a. Cursed by resources or institutions? World Econ. 29, 1117–1131. https://doi.org/10.1111/j.1467-9701.2006.00808.x. Mehlum, H., Moene, K., Torvik, R., 2006b. Institutions and the resource curse. Econ. J. 116, 1–20. https://doi.org/10.1111/j.1468-0297.2006.01045.x. Mehrara, M., Abrishami, H., Zamanzadeh Nazar Abadi, H., 2010. Natural curse or nstitutions? Oil-exporting countries case study [In Persian]. J. Econ. Res. Mohammadzadeh, Y., Daneshjafari, D., Majidi, S.Y., 2009. Analysis of resource curse and the factors affecting it: oil- exporting countries case study with emphasis on Iran [In Persian]. Econ. Res. 11, 127–157. Momeni, F., 2008. How to deal with oil revenue surplus in three experiences of oil price shocks [In Persian]. Econ. Soc. 17, 18. Nurkse, R., 1953. Problems of Capital Formation in Developing Countries. Columbia UP, New York (doi:https://doi.org/10.7135/UPO9781843318187.010). Pourjavan, A., Shahabadi, A., Ghorbannezhad, M., Amiri, H., 2013. The effect of the natural resources abundance on the governance performance in the selected oil exporting and developed countries (A panel GMM approach), [In Persian]. J. Econ. Model. Res. 4, 1–32. Rajan, R.G., Subramanian, A., 2011. Aid, Dutch disease, and manufacturing growth. J. Dev. Econ. 94, 106–118. https://doi.org/10.1016/j.jdeveco.2009.12.004. Robinson, J.A., Torvik, R., Verdier, T., 2006. Political foundations of the resource curse. J. Dev. Econ. 79, 447–468. https://doi.org/10.1016/j.jdeveco.2006.01.008. Ross, M.L., 1999. The political economy of the resource curse. World Polit. 51, 297–322. Ross, M.L., 2001. Does oil hinder democracy? World Polit. 53, 325–361. https://doi.org/ 10.1353/wp.2001.0011. Rostow, W.W., 1990. The Stages of Economic Growth: A Non-Communist Manifesto. Cambridge university press. Sachs, J.D., Warner, A.M., 1995. Natural Resource Abundance and Economic Growth. National Bureau of Economic Research (doi:https://doi.org/10.3386/w5398). Sachs, J.D., Warner, A.M., 1999. The big push, natural resource booms and growth. J. Dev. Econ. 59, 43–76. https://doi.org/10.1016/S0304-3878(99)00005-X. Sachs, J.D., Warner, A.M., 2001. The Curse of Natural Resources. Eur. Econ. Rev. 45, 827–838. Sala-i-Martin, X., Subramanian, A., 2008. Addressing the Natural Resource Curse: An Illustration From Nigeria, Economic Policy Options for a Prosperous Nigeria. Palgrave Macmillan, UK, London, pp. 61–92. https://doi.org/10.1057/ 9780230583191_4. Shakeri, A., Mohammadi, T., Nazeman, H., Taherpour, J., 2013. Examining occurrence of Dutch disease in Iran's economy and its impact on economic growth [In Persian]. Econ. Res. 13, 63–86. Stevens, P., Dietsche, E., 2008. Resource curse: an analysis of causes, experiences and possible ways forward. Energy Policy 36, 56–65. https://doi.org/10.1016/j.enpol. 2007.10.003. Stock, J.H., Watson, M.W., 2008. Heteroskeda sticity‐robust standard errors for fixed effects panel data regression. Econometrica 76, 155–174. https://doi.org/10.1111/j. 0012-9682.2008.00821.x. Van der Ploeg, F., 2011. Natural resources: curse or blessing? J. Econ. Lit. 49, 366–420. Wooldridge, J.M., 2015. Introductory econometrics: A modern approach. Nelson Education. World Bank, 2017. World Development Indicators 2017. The World Bank. Yavari, K., Salmani, B., 2005. Economic growth in countries with rich natural resources: oil-exporting countries case, [In Persian]. J. Commer. Res. 37, 1–24.

Andersen, J.J., Ross, M.L., 2014. The big oil change: a closer look at the Haber–Menaldo analysis. Comp. Political Stud. 47, 993–1021. https://doi.org/10.1177/ 0010414013488557. Arellano, M., 2003. Panel Data Econometrics. Oxford University Press. Arezki, R., van der Ploeg, F., 2010. Trade policies, institutions and the natural resource curse. Appl. Econ. Lett. 17, 1443–1451. https://doi.org/10.1080/ 13504850903035881. Auty, R., 2002. Sustaining Development in Mineral Economies: the Resource Curse Thesis. Routledge. Baltagi, B.H., Wu, P.X., 1999. Unequally spaced panel data regressions with AR (1) disturbances. Econom. Theory 15, 814–823. Beck, T., Laeven, L., 2006. Institution building and growth in transition economies. J. Econ. Growth 11, 157–186. https://doi.org/10.1596/1813-9450-3657. Behboudi, D., Asgharpour, H., Mohammadlou, N., 2012. The role of institutional quality on relationship between resource abundance and economic growth in oil economies [In Persian]. J. Econ. Res. Policies 20, 95–116. Beland, L.-P., Tiagi, R., 2009. Economic Freedom and the" Resource Curse": an Empirical Analysis. Fraser Institute. Bhattacharyya, S., Hodler, R., 2014. Do natural resource revenues hinder financial development? The role of political institutions. World Dev. 57, 101–113. https://doi. org/10.1016/j.worlddev.2013.12.003. Boschini, A.D., Pettersson, J., Roine, J., 2007. Resource curse or not: a question of appropriability. Scand. J. Econ. 109, 593–617. https://doi.org/10.1111/j.1467-9442. 2007.00509.x. Bravo-Ortega, C., De Gregorio, J., 2005. The Relative Richness of the Poor? Natural Resources, Human Capital, And Economic Growth. Policy Research Working Papers, 71-103. 〈http://dx.doi.org/10.1596/1813-9450-3484〉. Breusch, T.S., Pagan, A.R., 1980. The lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47, 239–253. https://doi.org/ 10.2307/2297111. Brunnschweiler, C.N., Bulte, E.H., 2008. The resource curse revisited and revised: a tale of paradoxes and red herrings. J. Environ. Econ. Manag. 55, 248–264. https://doi.org/ 10.1016/j.jeem.2007.08.004. Bulte, E.H., Damania, R., Deacon, R.T., 2005. Resource intensity, institutions, and development. World Dev. 33, 1029–1044. https://doi.org/10.1016/j.worlddev.2005. 04.004. Chatterjee, S., Hadi, A.S., 2012. Regression Analysis by Example. Wiley, New York. Cockx, L., Francken, N., 2014. Extending the concept of the resource curse: natural resources and public spending on health. Ecol. Econ. 108, 136–149. https://doi.org/10. 1016/j.ecolecon.2014.10.013. Cockx, L., Francken, N., 2016. Natural resources: a curse on education spending? Energy Policy 92, 394–408. https://doi.org/10.1016/j.enpol.2016.02.027. Collier, P., Hoeffler, A., 1998. On economic causes of civil war. Oxf. Econ. Pap. 50, 563–573. https://doi.org/10.1093/oep/50.4.563. Corden, W.M., Neary, J.P., 1982. Booming sector and de-industrialisation in a small open economy. Econ. J. 92, 825–848. https://doi.org/10.2307/2232670. Dargahi, H., 2008. Oil boom and the economic development challenges: with emphasis on trade structure and reduced competitiveness of Iran's economy [In Persian]. Econ. Res. 43. Douglass, C.N., 1991. Institutions, Institutional Change and Economic Performance. Cambridge University Press. Égert, B., 2009. Dutch disease in former Soviet Union: Witch-hunting? BOFIT Discussion Paper No. 4/2009 380 〈https://ideas.repec.org/p/sec/cnstan/0380.html〉. El-Anshasy, A., Mohaddes, K., Nugent, J.B., 2017. Oil, Volatility and Institutions: CrossCountry Evidence from Major Oil Producers. Globalization and Monetary Policy Institute. 〈http://dx.doi.org/10.24149/gwp310〉. Fearon, J.D., 2005. Primary commodity exports and civil War. J. Confl. Resolut. 49, 483–507. https://doi.org/10.1177/0022002705277544. Habib, M.M., Kalamova, M.M., 2007. Are There Oil Currencies? The Real Exchange Rate of Oil Exporting Countries. European Central Bank Working Paper Series 839. Harb, N., 2009. Oil exports, non‐oil GDP, and investment in the GCC countries. Rev. Dev. Econ. 13, 695–708. https://doi.org/10.1111/j.1467-9361.2009.00524.x. Hartwell, C.A., 2016. The institutional basis of efficiency in resource-rich countries. Econ. Syst. 40, 519–538. https://doi.org/10.1016/j.ecosys.2016.02.004. Hausman, J.A., 1978. Specification tests in econometrics. Économ.: J. Econom. Soc. 1251–1271. https://doi.org/10.2307/1913827. Havranek, T., Horvath, R., Zeynalov, A., 2016. Natural Resources and Economic Growth: A Meta-Analysis. World Dev. 88, 134–151. https://doi.org/10.1016/j.worlddev. 2016.07.016. Hemati, B., 2012. Political Economy of Oil: Markets of Economy and Policy in Iran [In Persian], Economics. Allame Tabatabaaei University, Tehran, Iran. Hinkle, L.E., Nsengiyumva, F., 1999. External real exchange rates: purchasing power parity, the Mundell-Fleming model, and competitiveness in traded goods. Exchange Rate Misalignment: Concepts and Measurement. World Bank. Horváth, R., Zeynalov, A., 2016. Natural Resources, Manufacturing and Institutions in Post-Soviet Countries. Resour. Policy 50, 141–148. https://doi.org/10.1016/j. resourpol.2016.09.007. Hough, D., 2013. Corruption, Anti-Corruption and Governance. Palgrave Macmillan, UK. https://doi.org/10.1057/9781137268716. Huber, S., 2018. Trade Patterns and Endogenous Institutions: Global Evidence, Product Characteristics in International Economics: Role and Impact on Economic

11