Natural Resources: Curse or Cure?

Natural Resources:

Curse or Cure?

An analysis of the impact of natural resource abundanceon the economic growth of developed countries

LINDA BODEs1059459

[email protected]

November 24, 2006Supervisor: dr. G. Péli

Rijksuniversiteit GroningenFaculty of Economics

International Economics and Business- Master Thesis -

Natural Resources: curse or cure?

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CONTENTS

ABSTRACT 3

I INTRODUCTION 4

II LITERATURE REVIEW 7

III DATA AND MEASUREMENT 18

IV RESEARCH METHOD AND RESULTS 22

V DISCUSSION AND CONCLUSION 31

REFERENCES 35

APPENDIXES 37


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ABSTRACT

Some countries have managed to turn their resource wealth into economic wealth; other

countries have failed to do so and still struggle with their policy. The question is if the

presence of natural resources causes lower economic growth. My research is directed

towards developed countries. A two-sample t-test showed that there is in fact a significant

difference between the economic growth of resource-poor and resource-rich developed

countries, for benefit of the former. Additional tests involving regression analysis support

this finding. However, significance of the results decreases when taking period

heteroskedasticity and general correlation of observations into account.


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Men of a fat and fertile soil, are most commonly effeminate and

cowards; whereas contrariwise a barren country make men temperate

by necessity, and by consequence careful, vigilant, and industrious.

Jean Bodin (1576)1

I INTRODUCTION

In the 1990s, economic researchers discovered a striking relationship between the degree

of endowment of a country in natural resources, and its economic growth and

development. They found that this relationship was inverse, meaning that an abundance

of natural resources tends to slow down economic growth. This is against intuitive

thoughts of natural resources as sources of economic wealth. Economists call this

phenomenon the “resource curse” or the “paradox of plenty” (Auty 1993).

Jeffrey Sachs and Andrew Warner were among the first to do in-depth research on

the topic. They wrote an influential article about the resource curse, “Natural resource

abundance and economic growth”, published in 1995. They came up with statistical

evidence to prove the theory. They showed that developing countries had a lower

economic growth due to their possession of natural resources. The term resource curse is

used to describe how countries rich in natural resources are not able to use that wealth to

boost their economies and how, counter-intuitively, these countries have lower economic

growth than countries without an abundance of natural resources.

The cause of the curse is not natural resources, but government mismanagement

when resources are present. Jean Bodin very early recognized the jeopardies of

possession of natural resources (Bodin 1576). Interpreting his words, natural resources

encourage imprudence, where as resource scarcity leads to more well-considered policies.

Of course, the presence of many underdeveloped countries with no natural resources

presumes that scarcity of natural resources does not automatically lead to better

governance.

1 Jean Bodin, The Six Books of the Commonwealth (1576, reprint 1994).


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Nowadays, the thought of spoiling natural resources is directly linked to developing

countries and most research is done with the focus on these countries. However,

developing countries are not the only ones to be rich in natural resources. Many

developed countries also have high endowments. So, somehow they seem to have found a

way to overcome the curse. Nevertheless, the question remains if these countries still

experience lower economic progression than their resource-poor counterparts.

To give a better impression and background information, the next section will

very shortly summarize the economic development path of developed countries together.

Historical context of development

The First Industrial Revolution started around 1780 in the United Kingdom, with the

introduction of the steam-engine. The steam-engine was made of iron and ran on coal. So

along with the success of the engine, the coal and iron industries flourished as well. From

the end of the 19th century, science would start to deeply penetrate industry. While the

First Industrial Revolution passed off without scientific influence, the Second Industrial

Revolution was the result of innovations in physics and chemical science.

In 1951, European Coal and Steel Community, was founded by France, West-

Germany, Italy, Belgium, Netherlands and Luxembourg to pool the steel and coal

resources. It was the start of a successful cooperation between West-European nations.

Oil and electricity became competitors of coal, but the prosperous coal industry did not

stop until in the 1960s (Caljé 1998).

America's rise to economic supremacy in the late 1800's occurred just as it was

becoming the leading producer of almost every major natural resource of the industrial

age, including iron ore, lead, coal, copper, zinc, timber, zinc and nickel. Such leadership

did not hold America back, nor did it hold back other nations like Australia and Canada.

Britain, where the industrial revolution started, was notably rich in coal reserves, not to

mention wool for its critical textile industry. Rather, as Wright and David (1997)

convincingly summarize in a paper, the nation invested heavily in mineral exploration,

new techniques and mining education. Other industries received spill-over benefits from

new technologies, the low costs of natural resources and a technically trained labour

force.


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Historically, increases in inputs and technological progress were important

sources of economic growth in the industrialized nations. In the future some factors of

production, such as labour, will not increase as rapidly as they have in the past. The effect

of this decline on growth depends on the interplay among the law of diminishing

marginal productivity, substitution possibilities, and technological progress (Tietenberg

2000).

A preliminary conclusion about the economic growth of developed countries, as

compared to developing countries, is that the former used their natural resources as inputs

for industrial evolution. Countries sold their natural resources, but also used them for

their own industries and technological development. In addition, countries started to

cooperate to benefit from economies of scale. This is contrasting with developing

countries, whose resources are almost exclusively exported, instead of being used in the

manufacturing sector, and who have to deal with internal struggle. With the notable

exception of a relatively few oil-rich nations, most developing countries import a great

deal of energy. Because this demand is relatively price inelastic (Taheri and Stevenson

2002), their expenditures on imports have risen tremendously without similar

compensating increases in receipts from the sale of exports.

The situation is reversed in many of the oil-exporting countries, which are

obtaining high prices for their oil. Their favourable terms of trade, however, have not

always kept them away from development difficulties. Nigeria is a classic example. The

oil exports affected the local wage structure and exchange rates in such ways, that they

ended up severely harming agricultural production. Resources flowed out of agricultural

production and into oil production. Even the income distribution was adversely affected,

becoming much more unequally distributed (Tietenberg 2000).

Countries with natural resources can be divided into point-sourced and diffuse

economies. Point-sourced resources have a tendency to lead to concentrated production

and revenue patterns, like oil and minerals, while for example agriculture is more

diffused throughout the economy (Murshed 2004). Due to time and data limitations, I will

focus on three types of point-sourced resources: oil, coal and gas. Agriculture and

minerals are excluded in my research.


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A preliminary conclusion about what is most important for resource-based

development is the nature of the learning process through which the economic potential

of natural resources is achieved, and not the inherent character of the resources (Stijns

2005). This will be elaborated upon in the next section. The remainder of this thesis is

structured as follows: after giving a more theoretical overview of the resource curse and

an evaluation of existing literature about research done on the topic, economic growth of

resource-rich developed countries will be compared with resource-poor developed

countries to see if the resource curse still holds for developed countries.

II LITERATURE REVIEW

The historical record suggests, oddly enough, that countries with abundant natural

resources tend to suffer a disadvantage in economic development. After reviewing

existing literature about the topic and the formulation of hypotheses, this part will be

concluded with a short analysis of the role of institutions, which are generally considered

as a ‘cure’ for the resource curse.

II.A General literature

Many researchers have noticed the development failure of resource-led growth in the

1970s and 1980s. Examples are Alan Gelb (1988) and Richard Auty (1990). But, as

mentioned above, Jeffrey Sachs and Andrew Warner were the first to address the specific

problem of a resource curse in their paper ‘Natural resource abundance and economic

growth’ (1995). According to them, economies with a high ratio of natural resource

exports to GDP in 1971 (the base year) tended to have low growth rates during the

subsequent period 1971-1989. This negative relationship holds true even after controlling

for variables found to be important for economic growth, such as initial per capita

income, trade policy, government efficiency, and investment rates. Sachs and Warner

explored possible pathways for this negative relationship by studying the cross-country

effects of resource endowments on, for example, trade policy and bureaucratic efficiency.

They focused on primary export intensity, which is the ratio of primary exports to


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national income. The consequence of their paper was far reaching. During the years,

many researchers retested and elaborated on their findings, but there was hardly any

criticism expressed.

In a next paper, Sachs and Warner (2001) extended their previous research and

state that also geographical or climate variables cannot explain the curse, and that there is

no bias resulting from some other unobserved growth-slowing factor. The list of variables

includes the percent of land area within 100 kilometres of the sea, kilometres to the

closest major port, the fraction of land area in the geographic tropics and a malaria index

from 1966. However, these four geography and climate variables are taken from Gallup

et al. (1999) and their conclusion is simply copied by Sachs and Warner, instead of being

tested again with natural-resource variables. Their other conclusion is that resource-

abundant countries tend to be high-price economies and, maybe as a result, these

countries do not focus on export-led growth in manufactures. It has to be noted that

maybe, these countries have made some other growth-facilitating investments not directly

linked to export, but to domestic progress.

Stijns (2005) does not oppose the findings of Sachs and Warner; however, he does

have some comments. First, a country may be resource-rich, but it can use high amounts

in its manufacturing sector, instead of exporting pure natural resources. Then, the amount

of export of primary goods will not be representative, since the natural resources being

used in manufacturing are not counted. In addition, measures of ‘resource dependence’

(such as the share of resources in exports) can be seen as proxies for development failure,

because as a country becomes more developed, the relative size of primary exports will

decline in favour of manufactured exports. And final, the role played by resource

abundance for economic growth depends significantly on the growth model that is

implemented. In order to deal with this defect, he focuses on resource production and

reserves data. He made an analysis according to the method of Sachs and Warner, but

using reserves as a measure of resource abundance. He concludes that natural resource

abundance has not been a significant structural determinant of economic growth in the

1970s and 1980s, because of the coexistence of ‘positive’ and ‘negative’ channels of

effect running from natural resources to factors that affect economic growth, like

education, investment, and economic policy.


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I want to add that countries can also consume their own resources (domestic

consumption is not taken into account by Sachs and Warner), without the need to develop

and use their resources more efficient, because there is plenty. An example of this is the

former Soviet Union.

To my opinion, an important shortcoming of Sachs and Warner is the absence of a

control group consisting of countries which have no natural resource abundance. This

would alter or strengthen their results more and give optional pathways of how economic

development is possible without complete reliance on these resources. Instead of only

ascertain the existence of the resource curse, Sachs and Warner could also give potential

solutions, which would increase the academic and practical value of their findings. This

still remains an option for future research.

Neumayer (2004) tests if the resource curse still holds true for growth in real net

domestic product (NDP) instead of GDP, and whether the negative effect of natural

resource-intensity on growth is over- or underestimated by erroneously examining growth

in GDP. This is important, because GDP is a particularly wrong measure of income for

resource-intensive economies, because GDP contains an element of capital depreciation

that should not be counted as income. This is corrected by using NDP. He finds that

natural resource-abundant countries do suffer from a resource curse, but this is weaker in

terms of growth of genuine income than growth of GDP. However, he does not question

the existence of the curse itself; it is taken for granted.

Dunning (2005) developed a game-theoretic model to explain the behaviour of elites.

Distinctive features of global resource markets and national political economies may

make diversification more or less attractive to political elites. The author argues that in

three cases which illustrate the equilibrium paths of the game-theoretic model developed

here (post independent Botswana, Zaire under the regime of Mobutu, and Indonesia

during Suharto) three variables influenced the elites’ incentives for diversification and

thereby shaped outcomes along the dimensions of political stability and economic

performance: the world market structure for the resource, the degree of societal

opposition to elites, and the prior development of the non-resource private sector.

Resource dependence is the outcome of strategic decisions by incumbent elites to limit


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the extent to which political opponents can challenge their power.The countries’ varied

paths from resource wealth to political and economic outcomes suggest the need for

‘conditional theories’ of the resource curse. With this article, Dunning gives potential

different pathways that reinforce conditional theories in a mathematical way. The

following reviews are supported by his findings.

Papyrakis and Gerlagh (2004) try to explain empirically the direct and indirect effect of

natural resource abundance on economic growth, because they want to investigate the

causes for the underperformance of most countries rich in natural resources. They

discover that natural resources have a positive influence on growth when considered in

isolation, but when other variables such as corruption, investments, openness, terms of

trade, and schooling are included and a country is badly performing on these variables,

the presence of natural resources is even deteriorating the negative effect of these

variables. An empirical analysis has been performed to show that natural resources

increase growth, when abstracting from possible negative indirect effects. The analysis

also made clear that, when accounting for the transmission channels, the overall effect of

natural resource abundance on economic growth is strongly negative.

But they fail to explain why the influence of natural resources is increasing these

negative effects and they do not prove that it is indeed caused by natural resources, and

that it is not a coincidence. A country that suffers from corruption, low investments,

protectionist measures, deteriorating terms of trade, and low educational standards will

probably not experience economic growth at all, whether it has natural resources or not.

Murshed et al. (2004) discovered that it may not be natural resource endowment

per se, but its type that matters. Some types of natural resources, such as oil and minerals

have a tendency to lead to production and revenue patterns that are concentrated, while

revenue flows from other types of resources such as agriculture are more diffused

throughout the economy. They use a panel data estimation to prove a point-source type

natural resource, like oil and minerals, hampers democratic and institutional

development, which in turn slow down economic growth.


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There is more than only a one-sided view on the relationship between natural resources

and low economic growth; some papers have been published about possible explanations

for the contrasting development paths of developed and developing countries with

resource abundance.

For instance, Hodler (2004) develops a model to explain why natural resources

can have such different effects for different countries. He looks at the impact of natural

resources on the cohesion among the people, in terms of aggressive behaviour between

rivalling groups. Fighting reduces productive activities and weakens property rights,

making productive activities even less attractive. This negative effect exceeds the natural

resources’ direct positive income effect if the number of rivalling groups is large enough.

The model thus predicts that natural resources lower incomes in fractionalized countries

like Nigeria and Angola, but increase income in a homogenous country like Botswana.

However, the focus has always been on developing countries and no sufficient attention

is paid to developed countries with natural resource abundance. I hope I can make a start

in filling this gap.

II.B Four negative effects

There are several explanations for the existence of the resource curse, which can be

summarized into four negative effects. This part will be concluded with a short analysis

of the role of institutions, which are generally considered as a ‘cure’ for the resource

curse.

1. Dutch Disease

‘Dutch Disease’ is a famous example of the phenomenon described by the Rybczynski

theorem2. The term refers to the late 1960s, when huge reserves of gas were found in the

Netherlands. Dutch Disease is an economic phenomenon in which the revenues from

natural resource exports de-industrialise a nation’s economy by causing an increase of the

real exchange rate and thus making the manufacturing sector less competitive in the

2 In a two-good world with constant product prices, the growth of one factor of production results in a decrease in the output of the good that does not use this factor intensively.


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world market. Traditional agricultural or manufactured exports are crowded out, as

experienced by the Netherlands and the United Kingdom.

The decrease in the manufacturing sector and dependence on natural resource

revenue is bad, because it leaves the economy extremely vulnerable to price changes of

the natural resource. Also, since productivity generally increases faster in the

manufacturing sector, the economy will lose out on some of those productivity gains

(Van de Ploeg 2006). Domestic price changes may encourage output and investment in

non-traded goods and services, construction and retailing for example. There is a switch

from products and internationally traded goods to services and non-traded goods, because

of the worse international trade position (Murshed 2004).

So, as natural resources tend to increase the money demand, the exchange rate

goes up and unemployment will increase because of a worse trade position due to more

expensive exports (Van Wijnbergen 1984). When looking at Finland, this is supported

because this country has high economic growth but also a high unemployment rate

(OECD).

Hypothesis 1a: the exchange rate is inversely related to economic growth.

Hypothesis 1b: the unemployment rate is inversely related to economic growth.

2. Extreme rent-seeking behaviour

The second aspect is extreme rent-seeking behaviour. Rent seeking is the process by

which an individual, organization, or firm seeks to gain through manipulation of the

economic environment, rather than through trade and the production of added wealth.

Rent seeking generally implies the extraction of uncompensated value from others

without taking actions which improve productivity, such as by gaining control of land

and other pre-existing natural resources, or by imposing regulations or other government

decisions that may affect consumers or businesses.

In many market-oriented economies, government restrictions upon economic

activity are pervasive facts of life. These restrictions give rise to rents of a variety of

forms, and people often compete for the rents. Sometimes, such competition is perfectly


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legal. In other instances, rent seeking takes more extreme forms, such as bribery,

corruption, smuggling, and black markets (Krueger 1974).

In cases of extreme rent-seeking behaviour concerning natural resources,

individual agents (politicians, entrepreneurs and elites) expose strategic behaviour to

obtain resource rents, thereby disturbing the allocation of resources and reducing

economic efficiency as well as social equity (Stiglitz 2004). Elites in control of resources

resist industrialization, which would weaken their power base. The result is delayed

modernization and lower levels of development (Bulte 2005).

Besides dampening the industrialization from inside the country, foreign

investment is also expected to decrease or grow less, because political instability

increases the risk and costs involving investment. Foreign companies are restrained to

invest, when the return on their investments is precarious.

Thirty years ago, Indonesia and Nigeria – both depending on oil – had comparable

incomes per capita. Nowadays, the income of Indonesia is four times as high as Nigeria’s.

Nigeria's income even has decreased (Stiglitz 2004). Capturable resource rents can lead

to rent seeking behaviour: revenues and royalties from oil or mineral resources are much

more readily appropriable as compared to the income flows from agricultural

commodities, because oil and mineral sources are more concentrated than agriculture.

Increases in the availability of resource rents following a boom in their world

market prices can increase the greed for resource rents amongst certain individuals or

groups within society. Consequently, these economies become weak and inefficient and

eventually experience a growth collapse. Furthermore, the presence of competition over

capturable or lootable natural resource rents can lead to civil war, like in Sierra Leone

and Angola. In other cases oil, or the possible obstruction of oil-pipelines, fuel civil wars

as in Sudan (Murshed 2004).

A more technical explanation is given by Torvik (2002). In a model with rent

seeking, a greater amount of natural resources increases the number of entrepreneurs

engaged in rent seeking and reduces the number of entrepreneurs running productive

firms. With a demand externality, it is shown that the drop in income as a result of this is

higher than the increase in income from the natural resource.


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Summarizing: more natural resources lead to lower welfare. An increase in the

level of corruption will lower the transparency, which implies less openness,

communication and accountability, and decrease foreign inflow investment because of

higher risks and costs (O’Higgins 2006).

Hypothesis 2a: transparency is positively correlated with economic growth.

Hypothesis 2b: foreign inflow investment is positively correlated with economic growth.

3. Overconfidence

The third negative effect is overconfidence; in this case the feeling of governments of

more certainty than circumstances warrant. Governments feel overconfident about their

future earnings, so they increase their spending and commit themselves to social

arrangements which they cannot afford in the future. The real exchange rate increases

through capital inflows from resource exports. This is tempting governments to

accumulate debt because the interest payments are cheap, even though they are receiving

natural resource revenues as well. But, if prices begin to fall, the real exchange rate falls

as well and the debt payments increase (Stiglitz 2004). The generous social security

system of the Netherlands, which has its base in the 1970’s, is currently the cause of

many economy measures and resulting cuts in the welfare system (Van de Ploeg 2006).

Economic diversification may be neglected by authorities or delayed in the light of

the temporary high profitability of the limited natural resources. The attempts at

diversification that do occur are often grand public works projects which may be

misguided or mismanaged. Some have suggested that a more effective mechanism than

state monopoly would be to simply distribute revenues from state-controlled natural

resources evenly among the population, as is done in the oil-rich Alaska (Stiglitz 2004).

Concluding: governments will borrow more with future revenues of natural resources

in mind (Sachs and Warner 2001).

Hypothesis 3: debt is negatively correlated with economic growth.

4. Neglect of investment in human capital


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Another possible effect of the resource curse is the crowding out of human capital.

Natural resources are a form of capital, which, if depleted, must be either replenished or

substituted if countries are to expand their asset base and sustain their consumption levels

(Tietenberg 2000). It needs to be emphasized that it is not the existence of natural wealth

per se that is the problem, but rather the failure of governments to avoid dangers

accompanying natural resource abundance. Countries that rely on natural resource

exports may tend to neglect education because they see no immediate need for it

(Gylfason 2000).

Resource-poor economies like Taiwan or South Korea spent enormous efforts on

education, and this contributed in part to their economic success (see East Asian Tigers).

These countries felt the need of investing in human capital in order to obtain economic

growth (Gylfason 2000).

So public investment is also especially important for resource-rich countries. An

early example of how to execute this is the 1879 United States Geological Survey, a

detailed mapping of reserves and potential reserves, which was critical to development.

Many state colleges offered mining degrees by the 1890's, including the University of

California at Berkeley (Madrick 2004).

Public investment in human capital is expected to be lower for developed

countries with an abundance of natural resources. It is indicated by three variables: public

expenditure on education, labour productivity and research & development. The first is

chosen as a general measure of investment in basic human capital. R&D indicates a high-

level effort to develop human capital. Labour productivity can be seen as the output of

development of human capital, since higher qualified skills are needed to increase this.

Hypothesis 4a: public expenditure on education is positively related to economic growth.

Hypothesis 4b: labour productivity is positively correlated with economic growth.

Hypothesis 4c: investment in R&D is positively correlated with economic growth.


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II.C Role of institutions

Is there an inevitability that condemns point-sourced economies to poor economic

performance? According to Sachs and Warner (1995), this is the case. However, rich

mineral resource endowments did not prevent economic growth in Australia, Canada and

the USA a century ago. Comparing how countries with diverse political systems use their

natural resources suggests that systems of governance have important effects on good

resource use.

The empirical evidence points to the importance of institutions that are the crucial link

between endowments of natural resources and economic outcomes. Good institutions

refer to aspects like voice and accountability, political stability, government

effectiveness, regulatory quality, rule of law and control of corruption. Institutions

determine the extent to which political incentives map into policy outcomes (Robinson et

al. 2006). Three of the four negative effects of natural resource abundance can be averted

by good institutions: overconfidence, investment in human capital and, above all, rent-

seeking behaviour. Only Dutch Disease cannot be directly solved by institutions, since

this is outside government control.

Institutional reform could, therefore, be the key to altering economic outcomes as

expected by Sachs and Warner. This is confirmed by Bulte et al. (2005). They explore

the impact of natural resources, possibly channeled through institutional quality, on

several human development indicators. They conclude, after doing panel data analyses,

that resource-intensive countries tend to suffer lower levels of human development, and

that institutional reform may be a necessary condition for countries to develop.

The findings of Mehlum et al. (2006) contradict the claims of Sachs and Warner

that institutions do not play a role. Their main hypothesis – that institutions are decisive

for the resource curse – is confirmed statistically. Countries rich in natural resources

constitute both growth ‘losers’ and growth ‘winners’. The combination of institutions that

encourage rent seeking at the expense of production and resource abundance leads to low

growth. Institutions that make production and rent seeking complementary activities,

however, help countries to take full advantage of their natural resources. Sachs and

Warner stated that institutional quality is empirically unimportant. However, a lack of

evidence for institutional decay caused by resource abundance is not sufficient to dismiss


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the role of institutions. Institutions may be decisive for how natural resources affect

economic growth even if resource abundance has no effect on institutions. Panel data is

used to prove this statement.

Robinson (2006) argues that the political incentives that resource endowments

generate are the key to understanding whether or not they are a curse. Countries with

institutions that promote accountability and state competence will tend to benefit from

resource booms since these institutions ameliorate the perverse political incentives that

such booms create. Countries without such institutions, however, may suffer from a

resource curse.

Deacon (2004) gives a thorough analysis of a failing political system on the

potential rents of natural resources. When a country’s political system is unstable or not

representative, the individual’s claim to a resource stock’s future return can be rendered

insecure. This reduces the payoff to natural resource conservation, leading to more rapid

depletion of resource stocks. When insecurity is a general feature of an economy,

however, it can have the secondary effect of raising the cost of resource extraction,

rendering some stocks uneconomic and slowing rates of depletion. In addition, when a

country’s natural resources are capable of generating significant rents, but institutions of

democratic governance and the rule of law are not well-established, corruption by

government officials responsible for resource management can encourage rent-seeking,

dissipating the benefits those resources would otherwise confer.

Developed countries were able to establish a stable political system, with Norway

as often-used example. “Oil wealth in many other countries has been used to finance

colossal fortunes for the few, or bread and circuses for the many,” the Organization for

Economic Cooperation and Development wrote in a recent report.3 Its system “'sets a

powerful example of enlightened policies to other resource-rich countries,” the OECD

said, even if many economists agree there are some elements that would be hard to copy,

like Norway’s historic lack of serious corruption and tradition of consensus-based

politics. To prevent their economy from the negative effects of the resource curse, the

Norwegians used oil income for national debt payments. By 1995, Norway’s financial

balances were stable and have been kept that way (Ekman 2005).

3 OECD, Economic Survey - Norway (2005).


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So, developed countries seem to have dealt successfully with these negative aspects of

natural resource abundance, due to better institutional development. Still, the question

remains whether the economic growth of resource-rich developed countries is lower than

that of their non-abundant counterparts. Based upon the idea that the four negative effects

will play a part in the economic growth, together with the idea that all developed

countries have good institutions, so that is no aspect of differentiation, I expect developed

countries with natural resources to experience lower economic growth than developed

countries without natural resources.

Hypothesis 5: the economic growth of resource-rich developed countries is lower than

the economic growth of resource-poor developed countries.

In addition, to what extent do the four negative aspects have an impact on the economic

growth of these countries? These two questions are addressed in the next sections.

III DATA AND MEASUREMENT

The five hypotheses will be operationalized in this section. Several steps have to be taken

in order to get useful datasets. To construct the data lists, I had to decide upon which

countries and which variables to use, and how to define natural resource abundance. The

time period chosen is from 1990 until 2003, because of the availability of data. The first

choice to be made is to decide which countries to include in the sample. Three questions

have to be answered: which countries are developed, what definition of resource

abundance should be used, and which data about resource abundance are useful and

representative?

Sample selection and classification

The country sample was straight forwardly composed. I combined the member countries

of the OECD4 with the countries belonging to the advanced economies, as grouped by the

4 http://www.oecd.org/document/58/0,2340,en_2649_201185_1889402_1_1_1_1,00.html


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IMF5. There was already a huge overlap between the two lists and together they form a

sample of 33 countries (table 1 appendix).

The second question is what resources to pick. I made a list of the countries

within the sample and checked them with lists with data about energy reserves per

country, composed by British Petroleum6. Mineral abundance is left out, since statistical

information about this resource is biased. Stijns (2005) presents convincing evidence that

mineral reserves have unclear effects on economic growth, when split out into the four

negative effects, whereas results are more unambiguous regarding coal, gas and oil. So

these depletable, non-recyclable fuels oil, gas and coal remain. It has to be mentioned that

none of the resource-poor countries is rich in minerals, so the sample would not alter if

minerals were included. The division between resource-rich and resource-poor countries

can be found in table 1 of the appendix. A country is classified as resource rich, when it

has one or more of the selected resources, coal, gas and oil. A country is classified as

resource poor, when it has none of the resources.

The third step is to label the developed countries as resource-abundant and as non-

resource-abundant. So I had to deal with the definition of resource abundance. Different

opinions exist about when a country can be considered as being resource-abundant. Sachs

and Warner (2005) focused on primary export intensity, which is the ratio of primary

exports to national income. Stijns (2005) instead looks at production and reserves data,

which is also recommended by Deacon (2005). However, these studies dealt with the

differences between countries concerning the degree of resource possession, instead of

giving a threshold value dividing countries into resource rich and resource poor. As a

guide line, I took charts of British Petroleum7 ranking countries which have most of the

world reserves in oil, gas or coal. A country named in that list is labelled ‘resource rich’;

countries not mentioned on any of the charts are labelled ‘resource poor’.

Stijns (2005) found a high degree of correlation between production and reserves

data for oil, coal, gas and minerals. Correlations all range between 71 and 97 per cent,

except for mineral production and reserves. I follow his suggestion partly and use

production data as measure for resource abundance, because more specific data can be

5 http://www.imf.org/external/pubs/ft/weo/2005/02/data/dbcselm.cfm?G=1106 British Petroleum: Statistical Review of World Energy (June 2005) 7 http://www.xist.org/charts/


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found on production. By dividing the sample into a resource-abundant and a non-

resource-abundant subsample, I also have a control group to compare the results with.

Most studies do not take this into account, but a control group will increase the validity

and robustness of the results.

Dependent variable

In this paper, it is investigated if the possession of natural resources has an impact on the

economic development of a developed country, so the dependent variable is economic

growth. This is measured as the annual change in percentages.

Independent variables

The next problem to deal with is what variables to choose to determine economic growth.

The problem with most studies is that they focus primarily on developing countries,

thereby looking at their distinctive features. Sachs and Warner (1995) choose as

independent variables share of primary exports in GDP (as measure of resource

abundance), openness of the economy, access to the sea, investment, bureaucracy, and

income inequity. They added a dummy variable to control for regional differences.

Because I want to look specifically at developed countries, some variables have to be

replaced and other variables have to be included as well. All the specifics of the variables

can be found in table 2 of the appendix.

According to Rick van de Ploeg (2006), most important are transparency in

accounting, distance to politics, and above all, strong institutions and a law system

promoting entrepreneurship instead of corruption. Joseph Stiglitz (2004) adds the

importance of stability funds, which save part of the money earned with high prices, to

diminish the impact of fluctuating prices. A good example of this is Norway, which has a

Petroleum Fund for the oil income of the government. It is a fiscal regulation that

stipulates how much of this income should be spent and gives a good measure of public


21

education and debate, where politicians and economists non-stop argue that Norway

should avoid spending too much (Ekman 2005).8

Having this in mind, I want to see to what extent the four negative aspects of resource

abundance can be assigned to resource-rich developed countries. In order to find this out,

I have to translate the arguments as posed by Van de Ploeg and Stiglitz (among many

others) into operational variables. My solution is the following, based on the

argumentation in part II: I adopt unemployment rate (U) and exchange rate (XR) as

variables for negative effect 1, higher exchange rates because of Dutch Disease, causing

higher unemployment rates. There is a chance of lower growth affecting the

unemployment rate; this will be dealt with in the next section.

Transparency (TR) and investment (INV) are the variables for negative effect 2,

extreme rent-seeking behaviour. These variables also comprise the important factors by

Rick van de Ploeg (2006), as mentioned in the former paragraph.

Debt (DE) is picked as variable for negative effect 3, overconfidence, because

governments will borrow more with future revenues of natural resources in mind (Sachs

and Warner 2001). Debt is used as a proxy for overconfidence; there are some difficulties

with this choice because it does not cover social arrangements, but due to limited data

availability, I see it as the best option.

Labour productivity (LP), public expense on education (ED; as % of GNI,

because data in relation to GDP was not available) and expenses on R&D (RD) are

variables for the last negative effect, neglect of investment in human capital.

Control variable

Another problem with most studies is that variables found to be significantly explaining

why developing countries failed to develop, are not tested on developed countries to

confirm that they really are determinants of economic growth. By testing to what degree

these variables do account for economic growth in case of developed countries, by adding

a dummy variable, I can judge upon their validity. The dummy variable indicates whether

8 It is a fiscal regulation that stipulates how much of this income should be spent and gives a good measure of public education and debate, where politicians and economists non-stop argue that Norway should avoid spending too much


22

a country is resource abundant or not, so one can see if the test results are influenced by

this factor.

IV RESEARCH METHOD AND RESULTS

In this section, the five hypotheses will be tested and results will be discussed. The

reflection on the research done will especially focus on heteroskedasticity,

autocorrelation and causality, since these are roadblocks for the test results.

IV.A Economic growth differences

Step 1: Is the economic growth of resource-abundant developed countries significantly

lower than the growth of their non-abundant counterparts?

GDP is a generally accepted measure of economic activity. Growth of real GDP, so GDP

corrected for differences in price changes, is widely used to evaluate the performance of

states in managing their economies.9 The country sample contains data of the countries’

GDP annual percentage change, in constant prices for the period 1991-2003. The data

were published by the IMF10.

The average growth of the whole sample is 3,05 per cent. The average growth of

developed countries rich in natural resources is 2,69 per cent. The average growth of non-

resource abundant countries is 3,66 per cent. This seems to support the resource curse,

but one cannot say that natural resources are the cause of lower economic growth.

Another point of attention is that in there is no correction of initial GDP. This means that

growth for countries with a relative low GDP will result in a higher percentage GDP

growth than the case where GDP is relative high with equal economic growth. However,

since all countries in the sample are developed and the time frame is recent, I expect the

differences in economic growth no to be very large.

9 http://caliban.sourceoecd.org/vl=3393905/cl=24/nw=1/rpsv/factbook/02-02-01.htm10 IMF: World Economic Outlook Data Base


23

A two-sample t-test is useful to see if the average growth rates of the two groups

are equal or not. Two hypotheses can be derived:

Hypothesis0: B=0 the economic growth of both groups are equal

Hypothesis1: B=0 the economic growth of both groups is not equal

The number of degrees of freedom is the number of both groups minus two, in

this case 31. The t-value is 2,1111, the critical t-value (a = 0,05) is 2,045. Because the

observed t-value is exceeding the critical t-value, H0 must be rejected in favour of the

alternative. The general conclusion from this is that the average economic growth of

resource-rich developed countries is significantly lower than the average economic

growth of resource-poor developed countries.

IV.B Testing the four hypotheses

Step 2: to what extent have the four negative aspects an impact on the economic growth

of the resource-rich developed countries?

The fundaments of test 2 are based upon the work of Sachs and Warner. Their basic idea

is that economic growth in economy I between time t=0 and t=T (in my case, 1991 and

2003) should be a function of initial income Yi0 and a vector of other structural

characteristics of the economy Zi. This is denominated as dependent variable G7089,

because they chose a time span from 1970 to 1989. Sachs and Warner constructed the

variables share of primary exports in GDP in 1971 (SXP), openness (SOPEN, dummy

variable), investment to GDP ratio (INV), quality of bureaucracy (BUR), and initial

income in 1970 (LGDP70).

(1) G7089 = α0 + α1*SXP + α2*SOPEN + α3*INV7089 + α4*BUR + α5*LGDP70 + ε

I want to use the same type of equation form as Sachs and Warner, but since their

variables are more applicable to distinct between developed and developing countries, I

use variables which make it easier to differentiate between developed countries, based on

the discussion in section III. For instance, the variable SXP is not precise, because most

11 t =X1-X2 / √ (S1

2/N1 + S22/N2).


24

developed countries have a high-developed manufacturing industry, using their natural

resources in this sector. (Stijns 2005). Natural resources are indirectly exported in this

way, but this is not accounted for in SXP, since only raw-natural resource exports are

counted, so instead I look at the reserves data.

The dummy SOPEN is controversial, because countries get a 1 when their

economies are open for the entire period 1965-1989, and a 0 when they are not. This

invokes a bias for the 0, because all countries becoming open during the time period are

grouped under 0. Instead, I made a dummy variable NRD, grouping countries into

possession and non-possession of natural resources.

BUR is a measure for quality of bureaucracy. I think it is more suitable to use the

transparency index of Transparency International, because this index includes

bureaucrational level.

The investment variable INV is also used by me, but initial income LGDP not.

The main critic I have regarding this variable is the equal start of all countries, while

natural resources or additional reserves can have been found earlier or later on. The

linked profits, therefore, start accumulating on different moments. For example, gas was

found in the Netherlands in the 1960s. Gains were made shortly after that, so the initial

income year had to be chosen earlier. Maybe most profits were already made, or still had

to be made because of technological progress in later periods.

I want to see to what extent the negative aspects of resource abundance have an impact

on economic growth. The following equation expresses the link between economic

growth and all negative aspects (also summarized in table 2):

(2) GDP9103 = ß1 + ß2*U + ß3*XR + ß4*TR + ß5*INV + ß6*DE + ß7*ED + ß8*LP +

ß8*RD + ß8*NRD + ε

Table 4 shows the Panel Least Squares regression with fixed effects. The variable

transparency (TR) has already been excluded, due to its limited time span (1995-2003)

and its high p-value, indicating low significance of the variable. In addition, there is a

high possibility for collinearity with investment (INV), since transparency reduces rent-


25

seeking behaviour, thereby generating an environment with lower risk coupled with

lower costs, which makes it more attractive for foreign investors to make investments.

The argument can also be reversed; higher corruption positively influencing foreign

investment, because rent-seeking institutions, groups or persons can give lucrative

contracts to foreign investors and keep the benefits for themselves or share them with the

foreign investors. So there is also a chance of TR influencing INV, which is excluded by

terminating TR.

Fixed effects for cross section was not possible, because it would result in a near

singular matrix. Fixed period effects were accounted for in the regression. This approach

is relevant when one expects that the averages of the dependent variable will be different

for each cross-section unit, or each time period, but the variance of the errors will not.

In table 5, I excluded education (ED), because of its high p-value. Data from the

OECD also show that public expenditure on education has not enormously changed per

country over the last two decades.12 So its merit for the model can be doubted. The yield

of education can also be acknowledged when looking at labour productivity, which I

consider the output of educational expenses. This exclusion increases the significance of

the other variables, except for the already non-significant ones. It also increases the

explanatory power of the equation as a whole, because less regressors give more strength

to the equation.

According to table 5, the next equation is adequate:

(3) GDP9103 = 0,693 - 0,041*U + 0,024*XR + 0,001*INV – 0,009 *DE + 0,683*LP –

0,505*RD – 2,228*NRD

The exchange rate (XR), debt (DE), labour productivity (LP), research & development

(RD) and the natural-resource dummy (NRD) are accepted at the 5% level. Investment

(INV) has no significant influence, but its coefficient is also very low. Attention needs to

be paid to the dummy variable NRD, which indicates that the possession of natural

resources has a negative impact on the growth of GDP, a very important proposition of

this thesis. This partly confirms the theory of Sachs and Warner, with support for

12 http://lysander.sourceoecd.org/vl=3475532/cl=26/nw=1/rpsv/factbook/08-02-02.htm


26

hypotheses 3 and 4. Hypotheses 1 and 2 are rejected, because XR (as represented by H1

about Dutch Disease) has a positive parameter, and extreme rent-seeking behaviour (H2)

is rejected, because investment (INV) is not significant. The parameter of research and

development (RD) (H4) also has the opposite sign than expected. It could be the case that

the initial expenditure in R&D leads directly to lower economic growth, since resources

put into R&D cannot be used elsewhere. Before output is produced out of R&D efforts,

many years can pass. So there is a time delay between input and output. Labour

productivity (LP) seems to be a very important indicator of economic growth (GDP9103).

This would suggest that active government policy to improve labour productivity will

result in higher economic development.

Assumptions of the simple linear regression model

The simple linear regression model is based upon six assumptions (Carter Hill 2001) .

SR1: Linear relationship between xt and yt. The value of y, for each value of x, is:

Y = b1 + b2x + e

SR2: The error term e is zero on average. This is assumed, because the mean

value is denoted in terms of independent variables: E(y) = b1 + b2x

SR3: The variance of the error term var(et) is constant over time. This implies that

at each level of an independent variable, we are equally uncertain about how far values of

the dependent variable may fall from their mean value. If this assumption is violated, data

are said to be ‘heteroskedastic’. This will be further explained below.

SR4: The errors are uncorrelated. The data collected must be statistically

independent.

SR5: x must take at least two different values; regression analysis is used to

measure the effects of changes of the independent variable on the dependent variable. To

obtain this, the independent variable most at least take two different values within the

sample of data.

SR6: The errors are normally distributed. This is assumed, because in nature

many physical phenomenons are described by a bell-shaped curve.


27

Correlation

The correlation matrix (table 10) shows that NRD, the dummy for resource abundance, is

negatively correlated with debt and R&D, and positively correlated with labour

productivity and unemployment. The relationship between natural resource abundance

and research & development, and natural resource abundance and labour productivity are

as expected, nevertheless these are statistical, not causal, relationships.

Covariance is another measure for correlation, to show the range of two variables.

According to tables 11 and 12, covariance values are very low, indicating a low

correlation between the variables. Table 12 gives values corrected for period

heteroskedasticity and general correlation of observations within a given cross-section. Its

numbers are in general more extreme; correlations are stronger under Period SUR

(Seemingly Unrelated Regression), a function within Eviews which is an example of the

Parks estimator.13. Both tables confirm the evidence of table 10, about the relatively low

correlations between the variables, leaving some exceptions there.

Autocorrelation and heteroskedasticity

Two important problems can arise when analyzing a regression: autocorrelation and

heteroskedasticity (Carter Hill 2001). The former is linked to time-series analyses, while

the latter is suffered by cross-sectional analyses. Since panel data has both cross-sectional

and time dimensions, the problems could be very serious. According to Carter Hill et al.

(2001) error terms of panel data cannot be correlated, because the randomness of the

sample implies that the error terms for the different observations are uncorrelated.

However, in time-series data observations follow a natural ordering through time,

generating a possibility that the successive errors will be correlated with each other. This

means a violation of the assumption of a simple linear regression model that covariances

are zero. The Durbin-Watson test statistic is a good indication of this autocorrelation. A

value around 2 indicates no autocorrelation; below 2 positive autocorrelation. As can be

13

The Parks estimator (1967) was invented as an functional estimator for equation systems with serially and contemporaneously correlated distortions. Such models include the SUR model and various restricted forms of it such as pooled time series cross-section models. In this context, the Parks estimator was shown to be consistent and more efficient than other unbiased estimators, such as the ordinary least squares (OLS), which corrects for neither. Since time series cross section data in social science research often fits this framework, the Parks estimator has been widely used (Messemer and Parks 2004).


28

seen in tables 4 and 5, the Durbin-Watson statistic is 1,11, indicating the existence of

positive autocorrelation. This means that the error term of the equation is affected by

shocks earlier in time and not only by current shocks.

Heteroskedasticity, or the existence of different variances in the data (violation of

assumption SR3), does not affect the values of the estimated coefficients, but it has an

impact on the standard errors of the coefficients and consequently on the reported t-

statistics. It is dealt with in table 6. It shows the regression with Period SUR. It corrects

for both period heteroskedasticity and general correlation of observations within a given

cross-section.

According to table 6, the following equation is adequate:

(4) GDP9103 = 0,593 - 0,046*U + 0,024*XR + 0,026*INV – 0,002 *DE + 0,642*LP –

0,680*RD – 1,322*NRD

Only the exchange rate (XR), investment (INV), labour productivity (LP) and research &

development (RD) are not rejected at the 5% level.

Compared to the results of the regression in table 5, overall significance of the

variables has decreased, but the explaining power has increased, and the positive

autocorrelation has disappeared. It seems that heteroskedasticity and general correlation

within cross-sections play a large role in determining the results. This can be explained

by looking at the type of data. Economic data are inherently vulnerable to correlation,

which can also be seen when looking at R-squared. It has increased compared to table 5.

R-squared is still relatively high; although other possible influential factors are still

missing.

Normality

Graph 7 and 8 show the normality tests. The Jarque-Bera test for normality is based on

two measures: skewness and kurtosis (Carter Hill 2001). Skewness refers to how

symmetric the residuals are around zero. Its value is closer to zero, perfect symmetry, in

graph 8. Kurtosis refers to the peakedness of the distribution; normal distribution is

indicated by ‘3’. Again, graph 8 comes closest to normality. For both graphs, the Jarque-


29

Bera test gives insufficient evidence to conclude that the normal distribution assumption

is unreasonable. When the residuals are normally distributed, the Jarque-Bera statistic has

a chi-squared distribution with 2 degrees of freedom. Thus, the hypothesis of normally

distributed errors is rejected when a calculated value exceeds a critical value. In this case,

the 5% critical value from a chi-squared distribution with 2 degrees of freedom is 5,99.

Because Jarque-Bera 285,7719 (table 7) and Jarque-Bera 21,09130 (table 8) both exceed

the critical value 5,99, the hypothesis of normally distributed errors cannot be accepted.

This conclusion means that SR6 is violated.

The equation quite fits the actual observations (table 9), because a balance can be

seen of positive and negative residuals throughout the sample, except for two negative

peaks.

Causality

Another possible roadblock for my research is the matter of causality, in other words, the

question that resource-rich developed countries have lower economic growth does indeed

depend on their possession of natural resources.

Granger Causality is a method to see if one time series is useful in predicting

another. Normally, regressions show just correlations. However, Clive Granger (1969)

stated that there is an interpretation of a set of tests as indicating something about

causality. It is important to know that the statement ‘x Granger causes y’ does not

indicate that y is the effect or the result of x. Granger causality measures precedence and

information content, but does not by itself indicate causality in the more common use of

the term.

Table 13 shows the results of Pairwise Granger Causality Tests. The significant

results of the significant variables Exchange Rate (XR), Investment (INV), Labour

Productivity (LP) and R&D (RD) will be highlighted. It can be concluded that XR

Granger causes GDP and vice versa, meaning that the exchange rate and GDP influence

each other. The same goes for GDP and RD. These two results indicate that there is some

sort of interaction between the variables is occurring, so GDP is not only dependent, but

also affecting the independent variables.


30

INV Granger causes RD, meaning that inflow foreign direct investment is

affecting investment in R&D. This seems logical, because more foreign expenditure

points to a higher interest in a country to develop economic activities within this country.

R&D is necessary to maintain or improve this progression.

A danger with using labour productivity is the risk that it is also caused by the

dependent variable, economic growth, since higher economic growth can also boost

labour productivity. A reason for this can be that an increase in economic growth will

provide funds to invest in public health and education. This, in turn, will increase labour

productivity (Mahmud and Rashid 2006). However, by looking at table 13, it can be seen

that this is not the case. So, maybe it only works the other way around: economic growth

can make people ‘lazy’, because there is no urge to improve.

In conclusion, the negative effects extreme rent-seeking behaviour (H2) and investment

in human capital (H4) are confirmed. However, the dummy variable NRD (H5) has a

higher p-value, which means that the significance of the claim that natural resource

abundance is diminishing. The negative sign remains, indicating a negative relationship

between GDP change and the possession of natural resources. Hyothesis 3 about

overconfidence resulting in higher debt is not confirmed. The exchange rate XR (H1) and

research & development RD (H4) still have the opposite parameters.

A possible explanation for the exchange rate (XR) is that a higher exchange rate

improves macro-economic stability, easing inflationary pressures and a lower interest rate

set by the Central Bank (East Med 2005). Another possible explanation is given by

Torvik (2001). He developed a model of learning by doing and the Dutch disease that

extends the earlier literature in two ways. First, he assumed that both the traded and the

non-traded sector can contribute to learning. Second, he assumed that there are learning

spillovers between the sectors. He shows that within such a model a foreign exchange gift

results in real exchange rate depreciation in the long run, due to a shift in the steady-state

relative productivity between the traded and the non-traded sector. In contrast to standard

models of the Dutch disease, production and productivity in both sectors may go up or

down.


31

A possible explanation why investment in research and development (RD) can

influence GDP negatively is because it can take many years of input and costs before

progress is made and the fruits can be reaped.

V DISCUSSION AND CONCLUSION

Some countries have managed to turn their resource wealth into economic wealth; other

countries have failed to do so and still struggle with their policy. The question is if the

presence of natural resources causes lower economic growth. My research is directed

towards developed countries. A two-sample t-test showed that there is in fact a significant

difference between the economic growth of resource-poor and resource-rich developed

countries, for benefit of the former. Additional tests involving regression analysis support

this finding. However, significance of the results decreases when taking period

heteroskedasticity and general correlation of observations into account.

The resource curse has four main negative effects, which I transformed into four

hypotheses. The fifth hypothesis combined economic growth with natural resources.

Hypothesis 1a: the exchange rate is inversely related to economic growth.

Hypothesis 1b: the unemployment rate is inversely related to economic growth.

Both hypotheses are rejected. The first hypothesis is not accepted, because the correlation

between the exchange rate and economic growth is positive instead of negative.

Indirectly, Dutch Disease is not supported, possibly because an appreciation of the

exchange rate does not necessarily mean a lower growth of GDP. I expected a negative

relationship, and still believe that the chosen variable is correct, because its relation with

economic growth is significant. The problem is that the dampening effect is hard to

measure. What would the economic development have been without the exchange rate

appreciation? That is difficult to predict.

In addition, the correlation between economic growth and unemployment is

negative, as expected. However, unemployment had no significant impact on economic

growth in the tests.


32

Hypothesis 2a: transparency is positively correlated with economic growth.

Hypothesis 2b: foreign inflow investment is positively correlated with economic growth.

The first hypothesis is rejected, because transparency was to complex to measure and

made the equation instable. So this variable was left out during further tests.

The second hypothesis is confirmed by the tests; it can be seen when looking at

the variable INV corresponding with direct foreign inflow investment. The direction of

the sign is negative and significance is high. Both variables indirectly represent extreme

rent-seeking behaviour, the second negative aspect of natural resource abundance

according to the theory. However, developed countries are characterized by well-

developed instititutional structures, so the occurrence of extreme rent-seeking behaviour

is less likely in these countries than in developing countries.

Hypothesis 3: debt is negatively correlated with economic growth.

This hypothesis is rejected when looking at the debt of a country, since the result was not

significant. This is not surprising, since in developed countries one can assume that debt

problems do not play a significant role. In addition, debt may have been not the perfect

variable to cover the term overconfidence, as was mentioned in part II. Furthermore, the

presence of high-developed institutions is characteristic for developed nations, as was

explained in section II. This leads to stable political systems, which are a required

condition for sustainable economic growth with the exploitation of natural resources.

Hypothesis 4a: public expenditure on education is positively related to economic growth.

Hypothesis 4b: labour productivity is positively correlated with economic growth.

Hypothesis 4c: investment in R&D is positively correlated with economic growth.

Hypothesis 4a is rejected, because the relation between public expenditure on education

and economic growth was highly insignificant. For this reason, and the fact that H4b and

H4c are also good measures of human capital, the variable was left out during further

tests.

Hypothesis 4b is accepted. Labour productivity is strongly correlated with

economic growth and its parameter is also relatively high compared to the other


33

parameters. This is one of the most important results of this paper. The correlation is

unilateral, so the dependent variable economic growth is not affecting labour

productivity.

Hypothesis 4c is rejected; research and development is significant, but negatively

correlated with economic growth. It seems that the economic results of R&D are more

long-term orientated; costs on the short run might results in profit on the long run, but the

time period in this paper is too short to account for that.

So indirect evidence was found for the link between natural resource abundance

and neglect of investment in human capital .Economic policies that result in the

productive reinvestment of a substantial portion of resource rents are strongly

recommended to avoid this trap.

H5: the economic growth of resource-rich developed countries is lower than the

economic growth of resource-poor developed countries.

This hypothesis is justified, because of the negative sign of the dummy variable for

natural resource abundance, NRD. However, significance was strongly decreasing when

accounting for heteroskedasticity and autocorrelation. This was not only the case for the

dummy, but also for the other variables. Shocks earlier in time have consequences for the

current situation. It seems very hard to prove the existence of the resource curse, because

it is hard to isolate and analyse the independent variables outside their context.

In conclusion, some of the negative effects are confirmed, other rejected. Developed

countries do not face all negative aspects of natural resource abundance. Good

institutions and modest debt policy can almost be taken for granted in these countries. To

really benefit from natural wealth, the focus has to be on improving labour productivity.

The test results very strongly show the importance of labour productivity in boosting

economic growth, more than the other variables. The incentive for resource-rich

developed countries to really put effort in obtaining higher labour productivity might be

lower than for resource-poor developed countries, since the former have more natural

guarantees of economic growth, whereas the latter have to invest in human capital to

progress economic development.


34

It seems that natural wealth is not per se deteriorating economic development in

developed countries. If exploited wisely, resource abundance can be turned into a growth

industry that provides a solid and even long-term foundation for economic growth. This

is hardly a curse.

Some shortcomings in exploring the resource curse remain, like the ignorance of the

degree of natural resource possession. I simply divided countries in resource-poor and

resource-rich groups, but more research needs to be done on the impact of more or less

abundance on economic growth. Currently, most industrialized countries depend on oil

and natural gas for most of their energy needs. In the United States, for example, these

two resources together supply 67% of all energy consumed. Both are depletable, non-

recyclable sources of energy. Crude oil proven reserves peaked during the 1970s and

natural gas peaked in the 1980s in the United States and Europe, and since that time, the

amount extracted has exceeded additions to reserves (Tietenberg 2000).

In addition, it is hard to involve the speed of resource depletion, possible increase

of proven reserves, or the moment of resource discovery, which variates between

countries. Maybe some methodological solution can be found for this; increasing the

relatively low R-squared result obtained now.

Since prior research has mainly focused on developing countries, this may be a

start of exploring the resource curse in developed countries, in order to adjust or

strengthen the conclusions drawn in this paper.


35

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http://www.bp.comhttp://www.imf.orghttp://www.oecd.orghttp://www.transparency.orgWorld Development Indicators 2005Eviews 5.1


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APPENDIXES

Table 1: classification

Resource rich

Country Natural resource

Resource poor

Australia Canada Czech Republic Denmark France Germany Greece Italy Netherlands New Zealand Norway Poland Spain Turkey United Kingdom United States

123123123113123313231311123123

AustriaBelgiumCyprusFinlandHongkong HungaryIcelandIrelandIsraelJapanKoreaLuxembourgPortugalSingaporeSlovak RepublicSwedenSwitzerland

1 = coal reserves2 = oil reserves3 = gas reserves


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Table 2: Variables

Code Variable Unit Negative effect / hypothesis

Source Predicted sign

GDP9103 GDP growth Annual change in %

- IMF dependent variable

U Unemployment % of labour force

1 IMF / WDI -

XR Exchange rate change

Annual change in %

1 OECD -

TR Transparency Index, value between 0-10

2 Transparency International

+

INV Investment Inflow FDI as % of GDP

2 WDI +

DE Debt % of GDP 3 OECD -ED Education Public

expenditure on education as % of GNI

4 WDI +

LP Labour productivity

GDP per hour worked, annual change in %

4 OECD +

RD R&D Gross domestic expenditure on R&D as % of GDP

4 OECD +

NRD Possession of natural resources

Dummy1= abundance NR0= otherwise

- BP -


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Table 3: Statistics

GDP U XR INV DE LP RDMean 2.778805 7.922814 100.8458 5.135342 65.20259 2.366760 1.818099Median 2.800000 7.200000 101.7714 1.866020 62.23801 2.303438 1.870000Maximum 11.70000 24.20000 137.3661 349.9594 154.0327 8.341024 4.290000Minimum -6.900000 1.300000 55.36202 -0.334853 5.191555 -4.122943 0.360000Std. Dev. 2.424346 4.356115 10.64047 22.79311 30.04402 1.852164 0.764000Skewness 0.135127 1.233935 -0.716606 13.53796 0.579586 0.027515 0.236790Kurtosis 5.581490 4.683304 5.796312 201.7232 3.390090 4.225904 2.670172

Jarque-Bera 73.82767 97.79102 108.1967 440788.1 16.39201 16.50181 3.649840Probability 0.000000 0.000000 0.000000 0.000000 0.000276 0.000261 0.161231

Sum 730.8257 2083.700 26522.45 1350.595 17148.28 622.4579 478.1600Sum Sq. Dev. 1539.892 4971.643 29663.55 136115.8 236492.6 898.7942 152.9284

Observations 263 263 263 263 263 263 263

Table 4: LS regression with fixed effects

Dependent Variable: GDPMethod: Panel Least SquaresDate: 07/27/06 Time: 15:05Sample: 1991 2003Cross-sections included: 27Total panel (unbalanced) observations: 261

Variable Coefficient Std. Error t-Statistic Prob.

U -0.042882 0.030831 -1.390887 0.1655 XR 0.025700 0.012013 2.139375 0.0334**INV 0.001565 0.004958 0.315758 0.7525DE -0.008409 0.004279 -1.965046 0.0506**ED 0.021638 0.088363 0.244883 0.8068LP 0.677566 0.064856 10.44717 0.0000*RD -0.519331 0.182234 -2.849806 0.0048*NRD -2.192956 0.530000 -4.137651 0.0000*C 0.444829 1.504691 0.295628 0.7678

Effects Specification

Period fixed (dummy variables)

R-squared 0.507470 Mean dependent var 2.772129Adjusted R-squared 0.466426 S.D. dependent var 2.429560S.E. of regression 1.774700 Akaike info criterion 4.062178


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Sum squared resid 755.8947 Schwarz criterion 4.348979Log likelihood -509.1143 F-statistic 12.36399Durbin-Watson stat 1.114327 Prob(F-statistic) 0.000000

One star indicates a variable statistically different from 0 at a 1% level of significance, 2 stars at 5%, 3 stars at 10%.

Table 5: LS regression with fixed effects, ED excluded

Dependent Variable: GDPMethod: Panel Least SquaresDate: 07/27/06 Time: 15:07Sample: 1991 2003Cross-sections included: 27Total panel (unbalanced) observations: 263


U -0.041353 0.030231 -1.367916 0.1726XR 0.024194 0.011940 2.026251 0.0438**INV 0.001412 0.004934 0.286134 0.7750DE -0.008900 0.004184 -2.126997 0.0344**LP 0.683014 0.064374 10.61016 0.0000*RD -0.504837 0.178592 -2.826768 0.0051*NRD -2.228301 0.523079 -4.259972 0.0000*C 0.693403 1.457770 0.475660 0.6347

Effects Specification

Period fixed (dummy variables)

R-squared 0.503607 Mean dependent var 2.778805Adjusted R-squared 0.464795 S.D. dependent var 2.424346S.E. of regression 1.773596 Akaike info criterion 4.056894Sum squared resid 764.3911 Schwarz criterion 4.328541Log likelihood -513.4815 F-statistic 12.97537Durbin-Watson stat 1.119041 Prob(F-statistic) 0.000000


Table 6: EGLS regression, ED excluded

Dependent Variable: GDPMethod: Panel EGLS (Period SUR)Date: 07/27/06 Time: 16:29Sample: 1991 2003Cross-sections included: 27Total panel (unbalanced) observations: 263


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Linear estimation after one-step weighting matrix


U -0.046261 0.037638 -1.229101 0.2202XR 0.024065 0.012034 1.999695 0.0466**INV 0.026051 0.011201 2.325740 0.0208**DE -0.002120 0.004769 -0.444457 0.6571LP 0.642287 0.058224 11.03138 0.0000*RD -0.679512 0.209208 -3.248018 0.0013*NRD -1.322047 1.319908 -1.001621 0.3175C 0.593386 1.451552 0.408794 0.6830

Weighted Statistics

R-squared 0.610046 Mean dependent var 0.943520Adjusted R-squared 0.599341 S.D. dependent var 1.279584S.E. of regression 0.809947 Sum squared resid 167.2834Durbin-Watson stat 1.953644

Unweighted Statistics

R-squared 0.287324 Mean dependent var 2.778805Sum squared resid 1097.445 Durbin-Watson stat 0.939275


Table 7: Histogram Residuals

0

4

8

12

16

20

24

28

32

36

-10.0 -7.5 -5.0 -2.5 0.0 2.5

Series: Standardized ResidualsSample 1991 2003Observations 263

Mean 9.52e-17Median 0.029583Maximum 4.148906Minimum -10.22455Std. Dev. 1.708076Skewness -0.857043Kurtosis 7.810401

Jarque-Bera 285.7719Probability 0.000000


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Table 8: Histogram Residuals, Period SUR

0

10

20

30

40

50

60

70

80

-3 -2 -1 0 1 2

Series: Standardized ResidualsSample 1991 2003Observations 263

Mean -0.020391Median 0.000000Maximum 2.497075Minimum -2.776394Std. Dev. 0.798792Skewness -0.196757Kurtosis 4.330349

Jarque-Bera 21.09130Probability 0.000026

Table 9: Fitted, actual and residual values graph

-12

-8

-4

0

4

8

-8

-4

0

4

8

12

25 50 75 125 175 227 261 300 350

Residual Actual Fitted

Upper line: actual and fitted graphLower line: residual graph


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Table 10: Correlation matrix

Table 11: Covariance matrix

U XR INV DE LP RD NRDU 1.000000 0.156987 -0.066661 0.213775 0.081872 -0.403993 0.206723XR 0.156987 1.000000 0.047556 -0.090936 0.062387 -0.407736 0.097915INV -0.066661 0.047556 1.000000 -0.079860 0.011744 -0.014228 0.004094DE 0.213775 -0.090936 -0.079860 1.000000 -0.293878 -0.016432 -0.213476LP 0.081872 0.062387 0.011744 -0.293878 1.000000 -0.078195 0.252185RD -0.403993 -0.407736 -0.014228 -0.016432 -0.078195 1.000000 -0.402626NRD 0.206723 0.097915 0.004094 -0.213476 0.252185 -0.402626 1.000000

Table 12: Covariance matrix onder Period SUR

U XR INV DE LP RD NRD CU 0.001417 -4.36E-05 9.43E-05 -5.47E-05 -7.24E-05 0.002678 -0.001165 -0.008065XR -4.36E-05 0.000145 7.23E-06 3.40E-06 -6.65E-05 0.000708 -2.16E-05 -0.015755INV 9.43E-05 7.23E-06 0.000125 -6.73E-07 -3.70E-05 0.000303 -0.000430 -0.002397DE -5.47E-05 3.40E-06 -6.73E-07 2.27E-05 5.82E-05 -0.000101 0.000594 -0.001327LP -7.24E-05 -6.65E-05 -3.70E-05 5.82E-05 0.003390 -0.001360 -0.004294 -0.000699RD 0.002678 0.000708 0.000303 -0.000101 -0.001360 0.043768 0.026832 -0.172122NRD -0.001165 -2.16E-05 -0.000430 0.000594 -0.004294 0.026832 1.742157 -0.092676C -0.008065 -0.015755 -0.002397 -0.001327 -0.000699 -0.172122 -0.092676 2.107004

U XR INV DE LP RD NRD CU 0.000914 -3.38E-05 4.87E-06 -3.78E-05 -0.000135 0.001418 -0.004112 -0.003373XR -3.38E-05 0.000143 -7.94E-08 7.84E-06 -3.33E-05 0.000869 0.001258 -0.016208INV 4.87E-06 -7.94E-08 2.43E-05 1.25E-06 -1.98E-06 3.70E-05 9.13E-05 -0.000306DE -3.78E-05 7.84E-06 1.25E-06 1.75E-05 6.65E-05 4.33E-05 0.000596 -0.001917LP -0.000135 -3.33E-05 -1.98E-06 6.65E-05 0.004144 -0.000402 -0.006342 -0.008542RD 0.001418 0.000869 3.70E-05 4.33E-05 -0.000402 0.031895 0.027247 -0.160772NRD -0.004112 0.001258 9.13E-05 0.000596 -0.006342 0.027247 0.273611 -0.186907C -0.003373 -0.016208 -0.000306 -0.001917 -0.008542 -0.160772 -0.186907 2.125094


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Table 13: Granger Causality Test

Pairwise Granger Causality TestsDate: 07/27/06 Time: 15:43Sample: 1991 2003Lags: 2

Null Hypothesis: Obs F-Statistic Probability

U does not Granger Cause GDP 360 3.83357 0.02253** GDP does not Granger Cause U 3.94273 0.02025**

XR does not Granger Cause GDP 311 14.7201 7.9E-07* GDP does not Granger Cause XR 2.79905 0.06243***

INV does not Granger Cause GDP 343 1.09869 0.33449 GDP does not Granger Cause INV 0.40025 0.67047

DE does not Granger Cause GDP 263 0.72862 0.48357 GDP does not Granger Cause DE 9.92113 7.1E-05*

LP does not Granger Cause GDP 284 0.52668 0.59115 GDP does not Granger Cause LP 0.41045 0.66375

RD does not Granger Cause GDP 229 2.15245 0.11860*** GDP does not Granger Cause RD 7.93959 0.00047*

XR does not Granger Cause U 310 0.11588 0.89062 U does not Granger Cause XR 0.02861 0.97179

INV does not Granger Cause U 344 2.82336 0.06080*** U does not Granger Cause INV 0.07628 0.92657

DE does not Granger Cause U 263 0.88851 0.41252 U does not Granger Cause DE 5.21612 0.00602*

LP does not Granger Cause U 284 0.47903 0.61989 U does not Granger Cause LP 2.69214 0.06950***

RD does not Granger Cause U 231 1.16888 0.31259 U does not Granger Cause RD 4.43436 0.01291*

INV does not Granger Cause XR 300 0.17321 0.84105 XR does not Granger Cause INV 0.15226 0.85883

DE does not Granger Cause XR 263 4.12091 0.01731** XR does not Granger Cause DE 1.66901 0.19046


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LP does not Granger Cause XR 284 0.00967 0.99038 XR does not Granger Cause LP 1.96910 0.14152

RD does not Granger Cause XR 223 0.90873 0.40456 XR does not Granger Cause RD 0.61050 0.54400

DE does not Granger Cause INV 252 2.52775 0.08190*** INV does not Granger Cause DE 0.49925 0.60759

LP does not Granger Cause INV 273 0.17631 0.83845 INV does not Granger Cause LP 0.77546 0.46152

RD does not Granger Cause INV 231 0.02276 0.97750 INV does not Granger Cause RD 3.09979 0.04698**

LP does not Granger Cause DE 255 0.72514 0.48527 DE does not Granger Cause LP 3.12512 0.04565**

RD does not Granger Cause DE 188 1.30902 0.27260 DE does not Granger Cause RD 0.27540 0.75958

RD does not Granger Cause LP 197 0.47312 0.62378 LP does not Granger Cause RD 1.04429 0.35393

One star indicates a variable statistically different from 0 at a 1% level of significance, 2 stars at 5%, 3 stars at 10%.Bold variable: significant variable according to table 6, with significant outcome in table 13.

Natural Resources: Curse or Cure?

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