D O C U M E N T O
D E T R A B A J O
Instituto de Economía
TESIS de MA
GÍS
TER
I N S T I T U T O D E E C O N O M Í A
w w w . e c o n o m i a . p u c . c l
Economic Development of Mineral Abundant Countries: The Effects of Types of
Natural Resources and Institutions
Tania Elena Rodriguez Auad.
PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE
I N S T I T U T O D E E C O N O M I A MAGISTER EN ECONOMIA
TESIS DE GRADO
MAGISTER EN ECONOMIA
Rodriguez Auad, Tania Elena
1
PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE
I N S T I T U T O D E E C O N O M I A MAGISTER EN ECONOMIA
ECONOMIC DEVELOPMENT OF MINERAL ABUNDANT
COUNTRIES: THE EFFECTS OF TYPES OF NATURAL RESOURCES
AND INSTITUTIONS
Tania Elena Rodriguez Auad
Comisión
Jaime Casassus
Francisco Rosende
Verónica Mies
Luis Felipe Lagos
1
Abstract
This thesis studies the effects of specialization in different types of natural resources and their interaction with institutions on per capita income level for a sample of mineral abundant countries. It is argued that countries rich or dependent on minerals or oil –“point-source” resources- will suffer more from the resource curse. However, the hypothesis that different types of natural resources impact differently on economic development, and in some cases this effect depend on the quality of institutions, irrespective of the resource endowment, is tested on a sample of 24 mineral abundant countries over the period 1965-2009. Using 3-digit SITC Rev.2 export data I identify 27 types of non-processed and non-processed natural resources comprised into four aggregated categories: mineral, fuel, forest and agricultural exports; and regressing a dynamic panel model using system GMM estimations the major findings are presented. First, in the medium-run, mineral countries are not hindered by their exports of mineral resources; and in the long-run, there is no evidence of a resource curse. Second, primary products or non-processed natural resources do not hamper growth, and as long as moderately institutional quality exists, primary specialization may exert a positive influence on income level per capita. Third, specific types of natural resource products, both primary and manufactured resources, are estimated to have a positive, negative or insignificant effect on income level per capita in the long-run, and hence the pattern of trade specialization matters for economic development of mineral abundant countries. Further, there are some resource types whose growth effect is not affected by institutional quality, while other types heavily depend on them to reverse the resource curse. Among the primary products, the “winners” types are vegetables and fruit, and cereals which are a blessing irrespective of the level of institutional quality. The “losers” ones are unprocessed tobacco, dairy products and eggs, crude fertilizer and crude minerals, and natural gas. The latter seems to be more detrimental to growth since goods institutions cannot neutralize its negative long-run effect. Among the manufactured natural resource products, the “winners” types are pulp and waste paper, and to some extent processed meat; and the “losers” types are coal-based products and manufactured tobacco whose marginal effects are conditional on the quality of institutions.
2
Table of Contents
1. Introduction………..………...3
2. Literature Review ………..….6
2.1. Natural resources, institutions and growth ……….6
2.2. Types of natural resources and growth………..……….………...10
3. Empirical Methodology……….14
3.1. Sample and description of data………..………19
3.2. Econometric estimation method…..…….………..…22
4. Estimation Results………..26
5. Conclusions………51
6. References………..54
3
1. Introduction
The central question addressed in this study is how the dependency on different types of natural resources and their interaction with institutional quality can affect economic development of mineral countries. This issue has received little attention in theoretical analysis and empirical studies. The „resource curse‟ literature has been built over the empirical fact that resource-rich countries have grown more slowly than the resource-poor countries, and over the last few decades, a wide range of explanations has been found to understand the negative correlation between natural resources and growth, and thus progress has been made in understanding the transmission channels of natural resources on growth. However, recent studies have cast doubt on the fundamentals and the evidence of the curse, and hence this topic is still controversial. This literature has little to say, however, on the effects of different types of resources on development and how their characteristics are related to certain transmission channels.
Auty (2001) and Isham et al. (2005) differentiate between “point source” and “diffuse” resources where “point source” resources, such as oil, minerals and plantation crops, are extracted mostly by capital-intensive methods implying concentrated ownership, and hence their rents can be easily appropriable becoming a source of rent-seeking and conflict. Rents of “diffuse” resources, such as rice, wheat and livestock, are more widely dispersed among the population. This suggests that countries rich or dependent on minerals or oil will suffer more from the resource curse. However, these theories are not able to answer the following questions. Why countries with similar, or even the same, natural resources have gained from their endowments when others lose? For instance, Sierra Leone, Liberia and the Democratic Republic of Congo have had bad growth experiences from having diamonds, while this have not been the case in countries like Australia, South Africa or Botswana (Boschini et al., 2007). Is trade specialization or dependence on different types of natural resources and their interaction with institutional quality the explanation of the different development experiences of countries with similar endowments of mineral resources and similar initial income level in the 60‟s? Why they have grown unevenly, some faster than others?
Neither has modern growth literature paid much attention to this issue. Neoclassical growth theory focuses on the role of factor accumulation and its assumption of constant returns to scale does not allow for economic structure based on natural resources to affect the growth rate. Endogenous growth models have incorporated increasing returns to scale but the level of aggregation assumed in these models has led them to focus on factors other than the pattern of specialization. Moreover, only until recently, some
4
formal models have been built as extensions of the neoclassical or endogenous growth models to include the role that natural resources play in the production of GDP as essential factor of production. However, the level of aggregation does not leave room for heterogeneous resource-based sectors to be analyzed. Growth empirics have at most looked at how exports of all primary products may explain differences in growth rates.
On the other hand, empirical endogenous growth literature, particularly the work of Acemoglu, Johnson and Robinson (2001, 2005), has shown that institutions are the fundamental factor in explaining economic growth and, therefore, differences in different levels of economic development across countries. Recent research on the resource curse has stressed the crucial role of institutions for countries abundant in natural resources. In particular, empirical results suggest that the curse is conditional on the quality of institutions, where countries with weak institutions suffer a negative impact of resources, whereas countries with strong institutions do not (Mehlum et al., 2006). Once again, these findings assume all natural resources as identical and they do not differentiate between types of resources. We claim that the resource curse is conditional on the type of natural resources a country produces, together with the quality of institutions.
Since countries abundant and/or dependent on mineral resources have been pointed out as the most affected by the negative consequences of extracting or producing these natural resources, those are of special interest and this thesis investigates a sample of that group of countries1. The purpose is to study whether the dependency on different types of natural resources has been detrimental or beneficial to economic development of these economies as well as to know which role institutions have played in determining a resource type‟s influence on development. The hypothesis is that different types of natural resources have different effects on economic development, and in some cases these effects depend on the quality of institutions.
To address these issues, resource dependence measures are used. Specifically, export values of primary products are used -as a proxy for production of natural resources- due to the availability of long-term disaggregated data. Using 3-digit SITC Rev.2 exports data from United Nations Commodity Trade Statistics Database (COMTRADE) and Feenstra et al. (2005) we are able to categorize natural resources by types according to sectors and subsectors criteria. Starting from a measure of all primary exports, four aggregated types are identified: agricultural commodities, forest products, minerals and metals, and fuels; and then, within these sectors, disaggregation into 27 specific types of resources is
1
5
made. Additionally, in order to complement the analysis, resource rents values for minerals and metals, fuels and forest resources is used as an alternative measure of resource dependence. Then, once a dynamic panel model is defined, where the dependent variable is per capita GDP level, several regressions are run for a panel database of 24 mineral-based countries constructed over 9 five-year time periods from 1965 to 2009.
The main variables of interest are the effects of natural resources, institutions and their interaction on income level. Separate regressions using resource exports and resource rents are run using Blundell-Bond system GMM estimator2. The estimation results present the medium and long term effect of total natural resources and institutions on GDP per capita, both dividing the sample by the degree of processing, and splitting the resource variable into the four aggregated types of resources. Then, the long-run effects of disaggregated types of natural resources and institutions are reported, and we are able to distinguish between „winners‟ and „losers‟ primary commodities. This is an innovative and more informative way to investigate the influence of natural resources on economic development; and to my knowledge, it has not been explored before by the literature on the resource curse.
With new econometric technique, this research contributes to the empirical literature on economic development, natural resources and trade specialization shedding light on the role of different types of natural resources in the pattern of specialization for economic development of mineral countries and showing in which cases institutional quality has a crucial role in determining these effects.
This thesis is organized as follows. In section 2 we first review and attempt to synthesize some theories about the relationship between natural resources and growth, and their transmission channels. Then, special attention is paid on the scarce empirical evidence on growth and types of natural resources. Section 3 specifies the empirical model and the econometric techniques used for the estimation of growth regressions, as well as the sample and data description. Then, we define the types of natural resources according to the classification criteria mentioned. In section 4 the estimation results are presented for total resource measures and types of natural resources; and we analyze the impact on GDP per capita and its conditions by resource type. Finally section 5 concludes and provides suggestions for further research.
2
This econometric technique addresses endogeneity problems augmenting the consistency of the estimates. Some authors recommend using Blundell-Bond system GMM estimator because it also includes the equation in levels to rescue some of the cross-sectional variance that is lost in the difference Arellano-Bond GMM estimator (Bond, 2002; Lederman and Maloney, 2007).
6
2. Literature Review
2.1 Natural resources, institutions and growth
Natural resources have been assigned a minimal -if not no role in most standard growth models. Indeed, a formal acceptable theory is absent to describe the conditions under which resource abundance and resource dependence are associated with faster or slower economic growth. However, some economists have recently begun to examine the growth effects of natural resources -either non-renewable or renewable- in neoclassical or endogenous growth models.
In the last decades, the so called „resource curse hypothesis‟ has emerged and it suggests a negative correlation between natural resources and economic growth. This hypothesis argues that resource abundant countries grow slower than those countries with lower endowments of natural resources. This implies that the relationship between abundance of natural capital and growth passes through the production of natural resources; and consequently the resource curse would imply that the production or exploitation of natural resources leads to slower growth, and hence to a lower income level.
Before going further, is important to point out that resource abundance is not the same as resource dependence. As Gyfalson (2008) mentions, by abundance is meant the amount or endowment of natural resources that a country has at its disposal, i.e. the stock of available resources. By dependence is meant the extent to which an economy relies on natural resources in producing output or that an important share of national income is derived from natural resources. This is related with a measure of flow of the value generated by utilized natural resources. Some countries with abundant natural resources are not dependent on them, whereas other countries with relatively few resources strongly depend on them because those are very important to the national economy (e.g. they account for most of their export earnings). In consequence, the „resource curse hypothesis‟ is associated more with the concept of resource dependence than with resource abundance per se, given the fact that natural resources not exploited cannot affect economic performance.
Since resource abundance and resource dependence are distinct, we are interested to know the effect of a change in abundance and dependence of natural resources on economic development and growth. Gylfason and Zoega (2006, 2002) incorporate natural resources in the production function of a neoclassical growth model, on one hand, and in a endogenous growth model a la Romer which allow for learning-by-investing and knowledge spillovers, on the other hand. The implications of both models
7
are: i) the greater is the dependence on natural resources in producing output, the greater the decline on growth rate, ii) a natural resource boom or –increased abundance – would increase the rate of growth of output per capita, and iii) the abundance of natural resources has a positive effect on the level of output and consumption per capita in long-run equilibrium.
The intuition is that a rise in the stock of natural resources leads to an increase in the quantity used in production. This raises the marginal product of capital and also the demand for capital and its price. A higher real interest rate increases saving and investment and so learning and knowledge spillovers, and hence the rate of economic growth increases. This is a scale effect. Another way is to view the positive external effect of a larger size of natural stock on the flow of produced goods, as an increase of total factor productivity or a lower implicit cost of finding the harvested natural resource (Aznar-Marquez and Ruiz-Tamarit, 2005).
Aznar-Marquez and Ruiz-Tamarit (2005) use an endogenous growth model a la Lucas and show -contrary to Gylfason and Zoega (2006, 2002) that the long-run rate of growth depends positively on resource dependence. The condition for a sustainable growth is an efficient management of the natural resources use, under a system of property rights well defined. Furthermore, these authors find that the initial endowment of natural resources not the initial endowment of physical capital, determines the long-run levels of per capita output. The higher the initial natural capital, the higher the level of output per capita in the long-run. Although, during the transition, the growth rates across countries may differ due to differences in the levels of both natural and physical capital stocks.
Another framework is a one-sector endogenous growth model developed by Chambers and Jang-Ting Guo (2009) who find that: i) growth rate rises with the productive utilization of natural resources, and ii) long-run growth rate increases with the regeneration rate of natural resources. Aznar and Ruiz (2005) also predict the second implication.
In contrast to the scarce theoretical literature, a large amount of work has been developed searching explanations for the „resource curse hypothesis‟. In the following, we examine the link between natural resources, institutions and economic development, as this is the purpose of this dissertation. In Appendix 1, we present a brief description of the internal and external transmission channels -or indirect effects- of the resource curse, which explain why resource-rich countries sometimes fail to manage their natural resources wealth conveniently.
8
Natural resources and Institutions
Is widely recognized that institutional quality3 play a key role for economic development. In particular, political institutions (for the need of adequate policies) and the type of property rights system (which entail security for investors), determines how well natural resource rents will be used. Before going further, it is important to distinguish the cases where the institutional context is taken as given from the cases where institutional quality is endogenous. As Wick and Bulte (2009) mention the first analyzes the development effects of resources, conditional on the quality of institutions, whereas the second considers the impact of natural resources on the institutional framework as a transmission channel.
Several studies show that the diverging growth experiences of resource-rich countries are due to differences in the quality of institutions (Melhum et al., 2006; Torvik, 2002 among others). The point is that the resource curse is seen as a problem of resource rents which create perverse incentives in turn leading to dysfunctional behaviour of agents in bad institutional settings, with negative impacts on the economy. In this way, we can identify „rentier state‟ models and „rent-seeking‟ models4.
The „rentier state’ models focus on the decisions of politicians governing resource rich economies. The state receives a large share of resource rents and it has to decide the allocation or distribution of these rents. According to Caselli and Cunningham (2007) cited by Kolstad and Wiig (2009), an increase in resource rents has two types of effects: i) it increases the value of staying in power and ii) it raises the likelihood that others will dispute the government for power, i.e. raises the political competition for resource rents.
To stay in power, governments spend more resources in activities that raise its political support, for instance, offering public sector jobs or investing in politically unproductive projects, which lead to inefficient allocation of resources and welfare costs. Another action is reducing the level of taxation, given the fact that the state lives off rents, which results in a lack of accountability by citizens and a high degree of autonomy or unrestricted authority of the government on the decision-making powers. This tends to increase corruption and plunder. Moreover, the state has a weaker incentive to provide the public goods needed by people according to the general interest. On the other hand, the intensification
3
We follow the definition of institutions from Kolstad and Wiig (2009) “as an equilibrium outcome of repeated interactions of agents”. It includes property rights arrangements, legal system, political institutions, informal norms, cultural values and so on.
4
Next paragraphs summarize some of the central ideas of political economy models mentioned in the paper by Kolstad and Wiig (2009) which, in my opinion, sum up the extent literature on the most important political and institutional forces behind the resource curse.
9
of political competition may cause government to spend more resources on fighting or dissuading potential challengers, which can be done by repression or buying off potential opponents. This may even result in social conflicts or civil war5. In some cases, these two effects might result in beneficial activities on the economy, for instance, improving public policies or profitability in the private sector to gain political support from citizens or to keep opponents outside the political competition. Therefore, the effect of natural resources and institutions on economic development is ambiguous. Generally, however, well-functioning institutions of public voice and accountability will make productive means to stay in power more attractive than distortive policies, and in turn may spur government effectiveness.
On the other hand, rent-seeking models focus on the decisions and actions of private agents, who choose between using their effort on rent extracting activities, and using them on productive activities. The more representative rent-seeking theoretical model in the context of natural resources is the one developed by Melhum, Moene and Torvik (2006) who distinguish between “producer friendly institutions” and “grabber friendly institutions”. The former implies an adequate property rights protection system which reduces risk for investors and attract entrepreneurs into production. The equilibrium allocation of entrepreneurs between production and grabbing is determined by the relative profitability of the two activities, which is influenced by the type of prevailing institutions. More natural resources rents increase income when institutions are producer friendly because entrepreneurs enter a productive sector where there are positive externalities, while more resources reduce income, when institutions are grabber friendly because entrepreneurial efforts flow into unproductive activities and generate full dissipation of the income created by rents.
In sum, initial institutions matter and may be decisive for how increasing resource rents influence economic development. Melhum et al. (2006) present empirical evidence for testing this theory in cross-country growth regressions including an interaction term between resource dependence and institutional quality. They find a negative impact of resources on GDP growth, but a significantly positive and larger interaction effect. Their conclusion is that natural resources do hinder economic growth in countries with bad institutions, but do not in countries with good institutions, or in other words, resource curse only occurs in countries with low institutional quality. These findings are consistent with prior theoretical models by Torvik (2002) and Tornell and Lane (1999) where natural resources, in economies with a weak legal-political institutional infrastructure, increase the perverse incentives for entrepreneurs or political powerful groups to become involved in non-productive
5
According to Frankel (2010), some authors find that economic dependence on oil and mineral wealth is correlated with civil war.
10
activities to capture the rents from the resources, which are easily appropriable by elites, through bribes and distortions in public policies. From the important insights provided by this literature, we can learn that natural resources have different growth effects conditional on the quality of institutions, albeit these papers do not distinguish between the effects of different types of resources.
Hence, in this thesis we assume institutions as a conditional factor of the marginal effect of natural resources on economic development rather than as a transmission channel, because we believe that the conditional effect of natural resources is larger and more important than their indirect effect by way of affecting institutions. Indeed, some authors provide empirical evidence of the small either insignificant or significant effect of resource intensity on institutional quality (see Appendix 1 for evidence of institutions as a transmission channel of the resource curse).
Further, even if resource endowments have had an historical initial role in shaping institutions as argued by Engerman and Sokoloff (2002) and Acemoglu et al. (2001), we believe that the prevailing political institutions are more likely to influence the export structures than the other way around. In some cases, the institutional framework and consequently the social and political structure may have helped to perpetuate a certain type of specialization over time. For instance, in Latin America countries that were mineral “extraction colonies” today are still dominated by mineral products in their production and trade structure. Moreover, institutions can evolve gradually during a long period of time, and this institutional change may explain part of the variations in the development paths of countries with similar initial conditions. Venezuela and Norway, for example, both oil exporter countries had a similar income level in the 1960s, but evolution of institutional quality between these two countries has been very different in the last decades, getting worse for Venezuela which reached almost the same income level in the 2005-2009 period as in 1965-1970, whilst Norway achieved an income three times higher in the same span. In addition, as Boschini et al. (2007) point out, in some cases natural resources have been discovered -or have become important in trade composition- after institutions had already been established. For instance, according to Cotet and Tsui (2009), the global oil discovery peaked in the early 1960s, while most of oil-rich Middle East countries had a peak discovery period before 1950.
2.2 Types of natural resources and growth
Until now, the theoretical and empirical literature on growth and natural resources has treated all natural resources in aggregate form. However, it is implausible that all types of resources have the same effect on growth. Even if all natural resources negatively impact on economic growth, as Lay and Omar
11
(2004) state “The oil curse may well function different from the banana curse.” The characteristics of different types of natural resources and how these characteristics are related to the institutional quality or the transmission channels might explain why some countries have been hit by the resource curse and others have not. Some authors point out that minerals and oil have a stronger negative growth impact when institutions are bad. Therefore, as Torvik (2009) also mention, it needs to be investigated whether different types of resources have different effects on growth and which resources have the strongest growth effect.
This issue has received little attention in theoretical analysis and empirical studies. The impact of natural resource use in multi-sector models is fairly new, and to my knowledge, the most representative authors are Peretto (2008, 2010), Pittel and Bretschger (2008) and Lopez and Stocking (2009).
Peretto (2008, 2010) use a Schumpeterian growth model for a closed economy and for open economies, respectively. He derives conditions under which the „resource curse‟ occurs or not. A resource-rich country trades resource-based intermediates for final manufacturing goods produced by a resource-poor economy. When the resource endowment increases, the sign of the growth effect depends on the elasticity of substitution between labor and the raw resource in the production of resource-based goods.6 If they are substitutes, the resource boom generates higher resource income, lower employment in the resource-intensive sector, higher knowledge creation and faster growth in the resource-rich economy. If factors are complements, the expected effect goes in the opposite direction. The resource-poor economy adjusts to the shock by increasing (if substitution) or reducing (if complementarity) the relative wage, and experiences a positive or negative growth effect due to trade.
Pittel and Bretschger (2008) study sectoral heterogeneity with respect to the intensity of natural resource use and its impact on growth, stressing the role of sectoral research activities and directed technological change. They develop a growth model with non-renewable resources and find that long term development depends on resource prices, sectoral composition and technological change. Technical change is found to be biased towards the resource-intensive sector because of resource scarcity.
6
Substitution implies elastic demand for the natural resource, which means that a resource boom requires a mild reduction in the resource price, so that the quantity effect dominates over the price effect, and resource income rises. Complementarity, instead, implies inelastic demand, which lead to a severe fall of the price with the result that resource income declines (Peretto, 2008 and 2010).
12
Lopez and Stocking (2009) develop an endogenous growth model with renewable natural resources integrating resource dynamics and structural change to explain patterns of economic growth of a small open economy. They find that changes in resource wealth and economic growth are closely linked in the intermediate run. They describe conditions under which this relationship is positive or negative. Also, they find that in the long run the rate of economic growth is not affected by natural resources, but long run resource wealth does affect the likelihood of being able to sustain economic growth or fall into a long run stagnation trap.
Characteristics of types of natural resources
The specific-characteristics of natural resource types may strengthen or weaken the different transmission channels of the curse, and thereby resource types might have a different impact on development and growth. Auty (2001) points out two characteristics: (1) production technology of the resource; and (2) the degree of rent dispersion. Lay and Omar (2004) add three more specific aspects: (3) the potential of forward and backward linkages; (4) the feasibility of rent appropriation through state institutions; and (5) the long-term trends in commodity prices and price volatility.
Auty (2001) differentiates between “point source” and “diffuse” resources where “point source” resources, such as oil, minerals (such as copper and diamonds) and plantation crops (such as sugar and bananas), are extracted mostly by capital-intensive methods implying concentrated ownership, and hence their rents can be easily appropriable becoming a source of rent-seeking and conflict. Rents of “diffuse” resources, such as rice, wheat and livestock, are more widely dispersed among the population. Moreover, point resources are more immobile than diffuse resources, which make them more likely to be taxed and regulated, and susceptible to rent extraction.
Regarding rent appropriation, Boschini, Petterson and Roine (2007) develop the concept appropriability of a resource which is “how easy it is to realize large economic gains within a relatively short period and having control over the resources”. This concept has two dimensions. First, due to specific characteristics, certain types of resources are more likely to cause appropriative behavior; this is named technicalappropriability of a resource. Resources which are “very valuable, can be stored, are easily transported and are easily sold” are more attractive to be appropriated. This suggests that diamonds or precious metals are more vulnerable to this problem. Second, institutional quality determines institutional appropriability of a resource. Having good institutions may counteract the potential problem of having certain types of resources. The importance of good institutions increases
13
with the technical appropriability of resources. These authors argue that in countries where resources are highly appropriable –in both dimensions- resource abundance hampers economic growth.
Lay and Omar (2004) argues that the potential of forward and backward linkages and integrating into global value chains of many resource sectors, in particular agricultural sectors, has improved with increasing factor mobility.
Different types of resources show different long-run price trends and different degrees of short-term price volatility. Long-run commodity price trends reflect permanent demand (changes in tastes and technological advance) and supply shocks (new extraction technologies). Price volatility is driven by transitory shocks (weather shocks or political events). Frankel (2010) points out that world market prices for oil and natural gas are more volatile than those for mineral and agricultural commodities. Cooper and coffee are among the most volatile products as well. The reason for the high volatility is low price elasticity of demand and supply, i.e. a small fluctuation in demand or in supply need a large change in price to restore the equilibrium.
Evidence
Boschini, Petterson and Roine (2007) study how different types of natural resources affect growth. They use four measures of resources: value of primary exports; value of exports of ores and metals plus fuels; value of mineral production; and value of production of gold, diamonds and silver, all as a fraction of GDP for 80 countries from 1975 to 1998. They prove that what matters for growth is the combination of institutional quality and the appropriability of the type of natural resources a country possess, rather than the resource endowment per se. Their results indicate that gold, diamonds and silver have the strongest negative effect on growth.
Based on export structure data for 66 countries from 1975 ton 1997, Isham et al. (2005) classify countries into point-source, coffee and cocoa, diffuse, and manufacturing exporters. They show that the dependence on point source resources and coffee and cocoa, in contrast to diffuse resources, negatively impact on institutional quality, which in turn has an effect on economic development.
To my knowledge, Lay and Omar (2004) is so far the most detailed study on how different types of resources affect growth. These authors use export data for 119 countries from 1980 to 2000 and differentiate types of resources by aggregation levels within three subgroups: i) agricultural products, ii)
14
wood and paper, and iii) minerals, metals and fuels. Then, those subgroups are disaggregated into non-processed and non-processed resources. Using OLS cross-section method, they find that the growth impact of natural resources depends on the resource type. Their findings contradict the prediction that abundance in point-source resources hinders growth, whereas diffuse resources favour it, because in their disaggregated analysis some resource types have positive, negative or insignificant effect on growth. In addition, these authors show that country characteristics are crucial in determining the impact of different types of resources on growth. Good institutions are critical for the impact of point-source repoint-sources and partially explain the growth effect of vegetables and fruits, fuels and non-ferrous metals. Terms of trade and growth volatility partly account for oilseeds and fuels. Secondary education is relevant in the case of aggregated primary exports and oilseeds, while openness is crucial for minerals and fuels.
Stijns (2001) distinguishes between different types of natural resources using the reserves of land, oil, gas, coal, and minerals as proxies for resource abundance for 29 countries from 1970 to 1989. He finds land to be the only reserve to have a significant negative effect on growth. He also examines the channels of influence by resource type. He finds that land abundance is correlated with poor quality of institutions and bad economic policies. By contrast, reserves of oil, gas and minerals are positively correlated with education, quality of institutions, investment and saving rates and market-oriented economic policies. Also, he identifies Dutch disease effects for oil and gas, but not for minerals.
3. Empirical Methodology
As outlined above, the relationship between natural resources and economic development is expected to differ depending on the type of resources a country specializes and the quality of its institutions. This dissertation tests the hypothesis that different types of natural resources have different effects on economic development, and in some cases this differential effect depends on the quality of institutions. More specifically, institutional quality is most crucial for some countries specialized in certain types of natural resources than others, and we expect to be able to identify which natural resources have the strongest direct development effect and which have direct effects conditional on the quality of institutions.
The basic econometric specification for testing the proposed effects of resources and institutions is a dynamic model which uses elements from neoclassical and endogenous growth model and becomes:
15
, 0 , 1 1 , 2 , 1 3 , 4 , 1 5 , 6 , 1 7 , 8 , 1 9 , 10 , 1 11 , , ln ln sec sec ( * ) ( * ) i t i t i t i t i t i t i t i t i t i t i t i t i t i i tGDPpc GDPpc inv inv inst inst yr yr NRX NRX NRX inst NRX inst gtot
where the dependent variable is the natural log of PPP adjusted per capita GDP in 1990 international dollars of country i over 5-year period (between t-1 and t). As Alexeev and Conrad (2009), we follow the approach of Hall and Jones (1999), Easterly and Levine (2003), and Rodrik, Subramanian and Trebbi (2004) and measure long-term growth via GDP per capita levels rather than by calculating growth rates over a given period of time. 7
Regarding the conditioning variables, the investment ratio to GDP is included as a proxy for growth of capital stock, which is averaged over 5-year period. To account for differences in human capital, a variable of education measured at the beginning of each period is introduced, which equals the average years of secondary schooling for the entire population aged 25 and above. The average annual growth of terms of trade over 5-year period controls for macroeconomic vulnerability. Regional dummies for Latin America, Asia and Africa are also included. Moreover,
i is a country-specific effect and
itis and i.i.d. stochastic shock with zero mean and standard deviation
2.To capture institutional quality, the executive constraints variable is used, which comes from the Polity IV database, and it is a measure of the extent of institutionalized constraints on the decision-making powers of chief executives. This index is a proxy for the degree of accountability and in turn protection of citizens and investors against government expropriation. It has a value that ranges from 1 to 7, with higher values representing less executive-branch discretion. This index is averaged over 5-year period and it is rescaled into an index between zero and one.
NRX is a measure of natural resources (for which two measures discussed below are used) and
*
NRX inst
is the interaction term between natural resources and institutional quality, included to investigate whether resources work differently in countries with good and bad institutions. Both variables are averaged over 5-year period. All the explanatory variables, with exception of growth of terms of trade, are lagged one 5-year period in order to account for medium-run effects of these7
Hall and Jones (1999) argue that the focus on levels is to explain the differences in log-run economic performance, whereas the focus in growth rates is to explain the transitory differences in growth rates across countries, since under the neoclassical growth model, long-run growth rates are the same across countries. Moreover, Alexeev and Conrad (2009) point out that using growth rates for a short period of time may reflect a relatively slow growth of natural resource producers that have partly depleted their resources rather than the true impact of natural resources on growth.
16
variables on economic performance, being aware that a time period longer than 5 years is necessary for the effects of an improvement in the quality of institutions or education to be registered fully.
Measuring natural resources
In order to measure natural resources it is important to refer to the concepts of resource abundance and resource dependence. Having higher relative endowment or abundance of specific natural resources does not necessarily imply that an economy‟s exports will be more intensive in those resources. For instance, resource-rich countries like Canada, United States and Australia do not depend heavily on exports of primary products. Conversely, countries like Zambia and Niger that do not have relatively abundant natural resources depend heavily on primary exports. Moreover, some resource-rich countries may fail to exploit its resource base or, by contrast, use their natural resources on a higher activity in the value chain for producing manufactured products.
Resource abundance can be measured as natural resources reserves (or stock of available resources), and resource dependence as the production of those reserves, either for sale in domestic markets or in world markets. The implications of these variables on economic development are different. Thus, as Stijns (2001) points out, it is worthwhile study separately the effects of natural resource wealth, production and trade on economic growth. Since data on the stock of natural resources is not available for different types of resources, for many countries and for a long time span, the scope of our empirical analysis is restricted to the concept of resource dependence or intensity. In particular, natural resources exports and natural resource rents are used as measures of resource dependence.
Natural resource rents measure the value (at world market price) of natural resource output net of extraction or production costs. Data of resource rents for 10 minerals and metals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin and zinc); fuels (oil, natural gas and coal); and forest resources is used. In the regressions natural resource rents as shares of GDP is employed, in order to capture the contribution of resource rents to the aggregate income. This measure allows us to test indirectly the hypothesis of rent dispersion and rent appropriation of different types of resources. Since they use very different technologies, the rents generated in each sector vary largely in magnitude, and then some resources might produce perverse incentives for rent appropriation, and hence hinder economic development.
17
Natural resource exports or export values of primary products are used as a proxy for production of natural resources, since data of actual production is scarce and not easy to collect. Following trade statisticians, primary products refer to categories 0, 1, 2, 3, 4 plus division 68 of the Standard International Trade Classification (SITC). Additionally, group 667 consisting of precious metals and diamonds is included. This is a measure of natural resource intensity of exports, and consequently, a measure of trade specialization in the primary sector. This indicator allows us to distinguish between the different effects of country‟s dependence on different types of natural resource with respect to income generation. Primary exports as a share of GDP (Sachs and Warner‟s measure) is used in the econometric regressions. This is the most popular and extensive indicator of natural resources applied in the resource curse literature.Measuring natural resources as shares of GDP has been criticized by Brunnschweiler and Bulte (2008) and Aleexev and Conrad (2009) due to the introduction of an endogeneity bias which biases the estimates downward. Since one is interested in the effect of natural resources on GDP, a country which has a low GDP would have a higher resource: GDP ratio, then, this might result in an artificial negative effect of resources on GDP. Furthermore, using export-based measures introduce an additional bias, since trade structure is endogenous to the economy‟s performance; and since more developed countries consume a large part of their natural resources domestically and export a smaller share of their resources. Alternatively, Aleexev and Conrad (2009) have suggested using per capita measures, instead of shares of GDP. Per capita measures may be a better approximation of the resource dependence, however, normalizing by current population can be also problematic since we are interested in explaining GDP per capita, and then it might be also endogenous8.
Being aware of the limitations of these measures, the preferred indicator is resource exports as shares of GDP, because of: i) the availability of disaggregated data of exports to study different types of natural resources, ii) the need of correcting for size differences across countries, and iii) the need to make the results comparable to previous works that commonly use primary exports shares. Additionally, resource exports per capita are employed to compare the results.
8
Other trade-based proxy, although less used, is natural resources net exports per worker (Leamer proxy). Net exports of resources are defined as exports minus imports of primary products. Lederman and Maloney (2008) argue this indicator is a preferable measure of resource dependence, because this is positively correlated with resources/labor ratio. The problem in calculating this proxy, however, is that labor force data is only available since 1980.
18
Types of natural resources
Because of long-term exports disaggregated data is available for a large number of countries; the analysis of different types on natural resources is based on exports data. Using 3-digit SITC Rev.2 exports of primary products it is possible to categorize them into detailed subgroups according to different criteria.Starting from a measure of all natural resource exports, the most aggregate level, four subgroups are identified: agricultural products, forest products, mineral and metals, and fuel products. Then, within these four aggregated sectors, disaggregation into 27 specific resource types is made, checking over the economic and world trade relevance of them. A detailed description of all variables can be found in Appendix 2.
The resource curse literature differentiates between “point source” and “diffuse” resources according to the concentration of the productive activity and the feasibility of rent appropriation. On the other hand, Boschini et al (2007) develop the concept of technical appropriability of a resource, related to certain characteristics of natural resources which make their rents easy to appropriate or which foster a rent-seeking behavior. A special characteristic of the most appropriable resources is that the extraction of these resources is unusually valuable compared to other economic activities and it is possible to have control over them, e.g. their extraction and sale can be monopolized. The empirical strategy focus on testing these arguments, running regressions for minerals, fuels, forest and agricultural products, where the most appropriable resources are expected to have the higher negative effect on economic development.
But the empirical strategy goes beyond, and interaction terms between institutions and types of natural resources are tested under the hypothesis that growth effects of types of natural resources depend on the combination of institutional quality and resource‟s characteristics. In this context I follow the concept of institutionalappropriability mentioned by Boschini et al (2007). So that, given a good institutional framework, “a type of resource might boost a country‟s economic development, whereas the same resource produced in a country with poor institutions might hamper development”. In other words, one may infer that institutional quality matters if appropriable resources are produced in an economy.
Additionally, as mentioned in the literature review, technological characteristics of natural resources may explain the different impact of different types of resources on growth. To briefly test for these additional explanations, natural resources are classified by skill intensity and degree of processing. First, four types of natural resources are identified by skill intensity: i) low skill (LS), ii) medium-low
19
skill (MLS), iii) medium skill (MS), and iv) medium-high skill (MHS) based on the sectoral taxonomy proposed by Peneder (2007). This classification estimate skill requirements as measured by levels of education of the workforce by sector. Second, natural resources are divided into non-processed and processed resource products using the classification proposed by Lall (2000) and Wood and Mayer (1998).9
3.1 Sample and description of the data
A panel database of 24 mineral abundant and/or dependent countries is constructed over 9 five-year time periods, from 1965 to 2009, for a total of 216 observations. See Appendix 3 for the list of countries. Mineral countries were selected according to the following criteria: i) Ores and metals exports as a share of GDP > 5% in 1965-1969 and > 2% in 1995, ii) Ores and metals exports as a share of total merchandise exports >17% in 1965-1969 and 1995-2000, iii) mineral rents as a share of GDP > 2% in 1970-1975, iv) Subsoil wealth per capita (present value of stocks of oil, gas, coal and 10 metals and minerals -bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin and zinc) in 1994 (>400$us per capita) and 2000 (>900$us per capita). This data comes from the World Bank; and v) current mineral reserves in 1995-2000 using data from U.S Geological Survey. Countries were selected according to the greatest number of criteria they met. Additionally, in order to know the determinants of long-run differences in economic performance, mineral countries that have had similar income levels in 1965-1970 and have shown a different path of growth in the last decades were finally selected for this research.
The panel dataset is constructed using data from different databases. Appendix 2 summarizes variables definition and data sources. Information on GDP per capita and population are taken from Madisson (2010) that provide wider coverage than the World Bank‟s World Development Indicators (WDI) and Penn World Tables often used in the literature. GDP per capita based on purchasing power parity (PPP) is expressed in constant 1990 international dollars prior to taking the natural log. Investment rate and external terms of trade are obtained from World Bank‟s WDI database. Investment‟s share of GDP and growth of terms of trade are expressed as percentages. The education series comes from Barro and Lee
9
Non-processed primary products are goods either raw or after a little on-site processing and they leave the farm or the mine in the state they are extracted or harvested. Processed primary products definition is based on transport costs, those are goods made by processes located close to the source of the raw material, because the natural resource is more fragile or perishable than the output, or the process is weight-reducing or volume-reducing. Additionally, the cost structure of the productive process contains a high share of payments for raw materials (Wood and Mayer, 1998).
20
(2000), and measures the average years of secondary schooling for the entire population aged 25 and above.
The preferred institutional variable is the executive constraints index which comes from the Polity IV Project, produced by the Center for Systematic Peace and George Mason University and conducted by Marshall and Jaggers (2009). This variable is expressed in natural logs and measures the extent of discretion on the decision-making powers of government authorities. This variable is expressed in natural logs and measures the extent of discretion on the decision-making powers of government authorities. The executive constraints index is selected because this is a proxy for the protection of property rights and for the government‟s effectiveness in the use of resource rents, thus it allows us to uncover the country‟s institutional appropriability. Furthermore, this indicator is available for a long time span and for a large number of countries.
The disaggregated data on primary exports for the period 1965-2009 comes from United Nations Commodity Trade Statistics Database (COMTRADE) and from the work of Feenstra et al. (2005), which converted SITC Rev. 1 codes to SITC Rev. 2. Then, our long-term resource exports data is expressed at 3-digit SITC Rev. 2 in nominal US dollars, which is used to categorize different types of natural resources. Natural resource exports as shares of GDP are expressed as percentages, whereas primary exports per capita are expressed in natural logs after converting into constant dollars using an export deflator10. Resource rents are taken from the World Bank‟s Adjusted Net Savings database. These variables are also employed as shares of GDP and in per capita terms. The GDP deflator is used to deflate rents prior to taking the natural log of resource rents per capita.
Finally, an interaction term between institutions and natural resources is included, in order to analyze whether the marginal effect of different types of natural resources on economic development depends on the quality of institutions. All variables with exception of years of secondary schooling are averaging in five year periods for the 9 panels of the database.
Descriptive statistics of the above variables are shown in Table 1, whilst the corresponding description for different types of natural resources is shown in Appendix 4. The mean share of primary exports in GDP is 16%, while the mean share of resource rents is 7% for the entire sample.
10
Export deflator is calculated as the ratio of total exports in current US dollars and total exports in constant US dollars.
21
Table 1: Characteristics of Sample Data
Table 2 reports the correlation matrix between these main variables and correlations between aggregated types of natural resources can be found in Appendix 5. It is noteworthy that the association between natural resource exports share and GDP per capita level is negative and very low, in contrast to the positive and quite high correlation between resource exports per capita and income level. A similar pattern is observed in the case of resource rents measures, even though in both cases the correlation is positive. This supports the potential tendency of measures as shares of GDP to bias results by underestimating the resource endowments of more developed economies.
On the other hand, primary exports share is fairly correlated with resource rents share, but it is weakly correlated with resource rents per capita and almost uncorrelated with resource exports per capita. Therefore, resources exports per capita are also used to test our hypothesis, with results expected to be different given the very low association with resource exports share11. Furthermore, since resource rents per capita is reasonably correlated with rents share, it will be sufficient to look at the development effects of resource rents per capita.
11
According to Tsui and Cotet (2009) per capita measures tend to overstate the impact of natural resources on economic development.
Variables Unit Abbreviation Obs Mean Std. Dev. Min Max
GDP per capita, PPP ln(.) lnGDPpc 216 8.12 1.10 6.16 10.24 Investment % of GDP inv 212 21.90 6.04 7.86 40.88 Years of secondary
schooling years yr_sec 199 1.41 1.24 0.01 5.05 Growth of terms of trade % gtot 136 -0.15 5.22 -27.05 13.83 Institutions: Executive
constraints index # inst_execonst 216 0.68 0.32 0.14 1 Natural resources
exports/GDP % of GDP NRX_gdp 216 16.10 10.76 0.64 58.97 Interaction term # NRX_gdp*inst 216 10.03 7.40 0.32 38.97 Natural resources
rents/GDP % of GDP rents_gdp 178 7.04 8.12 0 51.22 Interaction term # rents_gdp*inst 178 4.85 6.20 0.00 37.05 Natural resources exports,
per capita ln(.) NRX_pc 194 5.46 1.60 0.32 9.26 Interaction term # NRX_pc*inst 194 3.89 2.56 0.34 9.26 Natural resources rents, per
capita ln(.) rents_pc 177 4.79 1.89 -1.67 8.68 Interaction term # rents_pc*inst 177 3.11 2.41 -1.11 9.02
22
Table 2: Correlation matrix
Finally, Table 2 reports institutions to be low and negatively correlated with resource exports share, whilst the association with per capita measures is positive, being resource exports per capita the variable that has the highest correlation with institutional quality. This supports our assumption of a potential small effect of natural resources in determining institutions and indirectly income level; whereas the correlations between the interaction terms and GDP per capita are higher.
3.2 Econometric estimation method
Most empirical work on the relation between natural resources and growth has used a cross-sectional analysis. However, this methodology has some well-known drawbacks. On the one hand, the regressions can yield biased and inconsistent coefficient estimates due to omitted variable and endogeneity problems. On the other hand, cross-country regressions do not capture the dynamic behavior of the data and they are sensitive to the inclusion of variables. Endogeneity problems associated to reverse causality are usually tackled using initial or lagged regressors, or alternatively, using instrumental variables. The omission of relevant variables, such as specific country characteristics, not captured by other regressors, can be addressed using regional or other dummy variables, or using a panel data of countries.
Panel data offers a potential solution to these problems allowing for increased estimation efficiency and the control of unobserved individual heterogeneity. One of its main advantages is the possibility to study the dynamics of adjustment which are of interest in a wide range of economic applications, including empirical models of economic growth. Bond (2002) points out that allowing for dynamics in the econometric process may be important not only for recovering consistent estimates of coefficients
lnGDPpc inv yr_sec gtot inst_
execonst NRX_gdp NRX_gdp *inst rents_gdp rents_gdp *inst NRX_pc NRX_pc *inst rents_pc inv 0.30 1.00 yr_sec 0.83 0.06 1.00 gtot 0.22 0.08 0.17 1.00 inst_execonst 0.70 0.18 0.60 0.21 1.00 NRX_gdp -0.15 0.06 -0.12 0.12 -0.27 1.00 NRX_gdp*inst 0.39 0.20 0.29 0.26 0.46 0.65 1.00 rents_gdp 0.09 0.09 -0.08 0.23 0.01 0.55 0.58 1.00 rents_gdp*inst 0.25 0.09 0.07 0.27 0.29 0.43 0.70 0.93 1.00 NRX_pc 0.91 0.25 0.82 0.25 0.56 0.16 0.58 0.24 0.36 1.00 NRX_pc*inst 0.90 0.20 0.81 0.25 0.88 -0.08 0.57 0.10 0.33 0.87 1.00 rents_pc 0.57 0.36 0.35 0.22 0.35 0.35 0.60 0.63 0.66 0.65 0.53 1.00 rents_pc*inst 0.80 0.26 0.65 0.28 0.75 0.08 0.65 0.37 0.57 0.83 0.89 0.82
23
on lagged dependent variables, but also those of other parameters. For these reasons, in this dissertation a dynamic panel data model is used.
Using standard fixed or random-effect methods to estimate a dynamic panel model generates biased and inconsistent estimates because of: i) the presence of a negative correlation between the error term and the lagged dependent variable in the differentiating transformed equation (Bond, 2002; Lederman and Maloney, 2007); and ii) all realizations of the disturbances series are introduced into the transformed error term, then omitted variable bias arises if some explanatory variable is not strictly exogenous and hence give coefficient estimates biased downwards (Bond, 2002). An alternative is using instrumental variables estimators; however, 2SLS is not asymptotically efficient in this context. Normally, these problems are solved by using Generalized Method of Moments (GMM) estimators along with appropriate instruments.
Indeed, GMM estimators are useful when the model of interest is a linear one with dynamic dependent variable and contains endogenous or predetermined explanatory variables, as it is our case. Strict exogeneity cannot be assumed, i.e.
E x
[
it
is]
0
for all t and s, due to current or past shocks have some feedback on the current values of variables. Thus, ifE x
[
it
is]
0
fors
t
butE x
[
it
is]
0
for allst the variable is said to be predetermined, whereas it is endogenous if
E x
[
it
is]
0
for st but[
it is]
0
E x
for alls
t
. The difference is that the latter allow for correlation betweenx
it and
it in time t, while the former do not. Furthermore, GMM estimators offer solving the estimation problems of using a short panel -where a large number of individuals (N) are observed for a small number of time periods (T), and dealing with a potential for fixed effects and a lack of good external instruments. Additionally, GMM provides a suitable framework for obtaining asymptotically efficient estimators.Within the family of GMM estimators, Arellano and Bond‟s (1991) two-step estimator is one of the most known methods. The first step of Arellano-Bond‟s method is to take first differences of the variables to eliminate any unobserved country-specific effect
i.
, , 1 1 , 2 , ,
i t i t i t i t i i t
y
y
x
w
i1, 2,...,Nt 2,3,...,T
24
where xi t, is a vector of strictly exogenous variables; wi t, is a vector of endogenous and predetermined covariates;
i are vectors of parameters to be estimated;
i is a fixed-specific effect, and
i t, is and i.i.d. idiosyncratic shock with zero mean and standard deviation
2.The next step is to build an appropriate set of internal instruments. The strictly exogenous variables can serve as their own instruments. Under the assumption that
is not autocorrelated, we can employ lagged levels of the dependent variable, the predetermined and the endogenous variables, dated t-2 and earlier, as instruments of the differenced regressors using the following moment conditions:[
it s,
it]
0
E y
for each s2,t1,...T[
it s,
it]
0
E w
for each
s2,t0,...T
However, as explained by Bond (2002), when regressors are highly persistent across time, lagged levels tend to be weak instruments for differenced variables; and hence this methodology can be deficient in growth regressions. Based on the work of Arellano and Bover (1995), Blundell and Bond (1998) developed a system GMM estimator that uses additional moment conditions in which lagged differences are used as instruments for the level equation in addition to the ones of lagged levels as instruments for the differenced equation. The assumption behind these new instruments is that variables are uncorrelated with the unobserved individual effect
i, and then the additional moment conditions are:[
it s,
i]
0
E
y
for each s1,t0,...T[
it s,
i]
0
E
w
for each
s1,t0,...T
Thus, Blundell-Bond system GMM method estimates simultaneously equations in levels and in differences. Rescuing information from level equation, this estimator reduces the downward bias found in the Arellano-Bond estimator, providing more precise coefficient estimates. Moreover, the system GMM estimator has shown to have substantial efficiency gains over the first-difference GMM estimator (Baltagi, 2005). Therefore, the estimation procedure of choice for this dissertation is Blundell-Bond system GMM estimator.
