Natural Resources and Economic Development in...

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Natural Resources and Economic Development in Transition Economies

Louis-Marie Philippot *

Very Preliminary Version - Please do not Quote

November 2010

Abstract

In this paper, we investigate the impact of natural resources on economic growth among transition

countries. Since the seminal work of Sachs and Warner (1995), it is widely admitted that natural

resource abundance is a “curse” for economic performances. Transition countries provide an

interesting case of study. Despite their initial conditions were rather the same around 1990, their

growth rates dramatically diverged during the next decade. Some have recovered quickly whereas

other still have a lower GDP than before the beginning of the transition.

We want to know if differences in growth patterns can be (at least partly) explained by the resource

curse since some transition countries have oil and ores whereas other are resource-poor. We provide

an empirical analysis over the 1990-2003 period using panel estimations. To measure natural

resource abundance, we use data on rents compiled by the World Bank. Our main results do not

seem to corroborate the existence of a “resource curse” among transition countries. In fact, most of

our measures of resource abundance have a positive effect on economic growth. These results hold

even for point-resources which are generally said to be the most detrimental to growth. On the

contrary, agriculture seems to have a negative effect on growth. These results are robust to the

inclusion of additional control variables widely used in the literature.

Keywords : Natural Resources, Economic Development, Transition Economies

JEL Classification : Q0, O13, P20

*PRES de Clermont Université (CERDI-CNRS, Université d’Auvergne)

65, boulevard François Mitterrand, 63000 Clermont-Ferrand

[email protected]

mailto:[email protected]

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1. Introduction

The “curse of natural resources” is a well know phenomenon since the seminal work of Sachs and

Warner (1995) in which they provide evidence that resource-rich countries have lower economic

growth rates than their resource-poor counterparts. Case studies and historical examples tends also

to confirm the negative impact of natural resource abundance on economic performances.

An important number of both theoretical and empirical studies have tried to understand why natural

resources are a “curse” rather than a “blessing” for economic development. Gylfason (2001) consider

that natural capital crowds out other forms of capital (human, institutional, physical, foreign). Recent

contributions underline that the type of natural resources also matters. “Point resources” (oil, ores

and crop plantations) are said to have more detrimental effects than mores “diffuse” resources.

Transition countries of Central and Eastern Europe (CEE) and the Former Soviet Union (FSU) provide

an interesting case of study. In fact, their initial conditions were rather the same around 1990 : they

lived under communist regimes in centrally planned economies. Situation changed when transition

began. During the 1990’s, transition countries diverged dramatically as far as growth rates are

concerned. Some recovered quickly while other still have today a lower GDP than before transition.

The hypothesis we defend in this paper is that, at least a part of this divergence in growth patterns

can be explained by natural resource endowment since some transition countries are resource-rich

whereas other are resource-poor. Previous studies on this subject have led to mixed results. In 2004,

Kronenberg find evidence of a curse of natural resources in transition countries. The main channel of

transmission seems to be corruption. Some authors also consider that rents from natural resources

can help governments to delay painful but necessary economic reforms (Auty, 2001; Gylfason, 2001).

On the contrary, Brunnschweiler (2010) found that oil production/reserves have a positive robust

effect on economic growth over the 1990-2006 period. Ahrend (2002) provides evidence that Russian

regions well-endowed in oil grew faster than resource-poor regions at least during the first few years

of the transition. Alexeev and Conrad (2010) also find little evidence of a resource curse among the

transition economies. Resource-rich transition economies do not seem to perform worse.

In this paper, we provide empirical analysis over the 1990-2003 period using random effects panel

estimates. To measure natural resource abundance, we use data on rents from natural resources

coming from the World Bank and our own calculations. Our main results show that, on average,

natural resources have increased growth since the start of the transition. In particular, this result

holds for “point resources” and especially for oil which are generally said to have the most negative

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effect on economic and institutional development (Isham et al, 2003 ; Subramnian and Sala-I-Martin,

2003). On the contrary, “diffuse resources” seem to have negative effects on economic growth which

is the opposite result to what we generally find in the literature on the curse of natural resources. In

a nutshell, we do not find any evidence of a curse of natural resources among transition countries.

The rest of the paper is organized as follows : Section 2 presents a literature review of the curse of

natural resources. Section 3 deals with natural resources in transition countries while Section 4 is

dedicated to the determinants of economic growth in transition countries. In Section 5, we present

data and estimation strategy. Main results are commented in Section 6 and conclusion follows.

2. The Curse of Natural Resources

In a famous paper written in 1995, Sachs and Warner identify a negative impact of natural resource

abundance on economic growth over the 1960-1990 period. Their result is robust even when they try

to introduce several control variables like trade openness, investment or institutional quality.

This phenomenon is generally called “the curse of natural resources” and since this seminal work, an

important number of theoretical and empirical studies have tried to understand why and how

natural resources can become a “curse” for a country rather than a “blessing” like it should be if we

follow the beliefs of the classic economic theory (if production factors are characterized by

diminishing returns, having more natural resources can be a way to enjoy higher growth rates).

Several empirical studies find similar results than Sachs and Warner. For example, Sala-I-Martin

(1997) finds that the share of primary products on exports belongs to the top twenty of variables that

are able to explain GDP growth. Gylfason (2001a) shows that growth rate is reduced by one point per

year when the share of natural capital on national wealth increases by 10 points. This is far from

being insignificant since the world average growth rate since 1965 is about 1.5% per year!!!

Case studies (Gelb, 1988 ; Auty, 1993) and historical analysis also provide evidence of a “resource

curse”. For example, none of the Asian Tigers can be considered as resource-rich whereas they

exhibit the highest growth rates during the second part of the 20th century. In the same vein, Japan

registered better economic performances than the Russian Empire during the 19th century even if the

latter benefit from abundant natural resources (land, forests, mining products, hydrocarbons, ….).

a. Relations between Natural Resources and Economic Performances

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The main challenge in this strand of literature is to understand how natural resources abundance can

affect negatively growth. Gylfason (2001b) identifies four main channels of transmission that can be

described as crowding out effects. Natural resources tend to crowd out other forms of capital.

- Dutch Disease and foreign capital: The Dutch Disease theory was developed in the 1970’s to

explain economic difficulties the Netherlands had to face after the discovery of natural gas in the

North Sea. The main idea of the Corden and Neary model (1982) is that natural resource abundance

(discovery and/or a price increase) causes an overvaluation of national currency. Non mineral exports

suffer from a decrease of their competitiveness. It is particularly true for the industrial sector which

generates learning by doing processes and positive externalities (Matsuyama, 1992). Fluctuations in

exports earnings trigger exchange rate volatility which creates uncertainty that can be harmful to

exports of goods and services and other forms of trade including foreign direct investment.

- Saving, Investment and physical capital : Natural resources can have a negative effect on

economic growth if they reduce incentives to save and to invest. Natural capital generates a false

sense of security, it is a form of wealth alternative to the wealth resulting from the accumulation of

physical capital in the industrial sector. Gylfason and Zoega (2001) show that when the share of the

primary sector in GDP increases, the demand for capital is reduced and this leads to lower interest

rates and less rapid economic growth. Natural resources can also impede investment if they retard

financial development. The level of investment is important but we have also to take into account

the quality of projects which is generally low in resource-rich countries (the “white elephants”).

- Education and human capital : For Gylfason, Herbertsson and Zoega (1999) and Gylfason

(2001a), economic agents tend to underestimate the long term benefits of education when they

benefit from natural resources revenues. In resource-rich countries, public spending on education

and school enrollment rates are lower than in resource-poor countries. On the contrary, for Stijns

(2006), natural resources can promote education. His main point is that Gylfason’s results are not

very robust because of his measure of natural resource abundance (the share of natural capital in

national wealth). In a previous work, we showed that natural rents from point resources are

negatively related to public spending on education and school enrollment rates (Philippot, 2010).

- Social Capital and Institutional quality : This channel is probably the most studied in the

literature. For several authors, the curse of natural resources is a pure institutional phenomenon. As

an example, Sala-I-Martin and Subramanian (2003) identify a negative effect of natural resources on

economic growth but this relationship does not hold anymore when they control for institutional

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quality. For Moene, Mehlum and Torvik (2006), natural resources are a “blessing” if institutions are

“good” that is to say in favor of productive activities. On the contrary, natural resources become a

curse if institutions tend to favor rent-seeking activities. Several mechanisms link natural resource

abundance and institutional quality among them we find colonial heritage, rent-seeking models,

development of corruption, political instability, civil wars, bad quality of economic policies, ….

b. Measuring Natural Resource Abundance

Measuring natural resource abundance is probably the most important challenges the literature on

the “curse of natural resources” has to face with. In fact, it seems that results can be very different

according to the index used. For example, authors who use natural resources as a share of exports

and/or as a share of GDP generally identify a negative impact of natural resources on economic

growth. But, do these indexes are good proxies for resource abundance ? No, say some authors like

Brunnschweiler (2008), because they reflect more resource dependence than resource abundance.

In their seminal paper, Sachs and Warner (1995) use the share of primary exports on GDP or on total

exports. Since these data are easy to find and reliable, they have been widely used in the literature.

Other studies (Gylfason, 2001a) rely on natural capital as a share of national wealth (data coming

from the World Bank). The workforce on the primary sector is used by Gylfason (1999) and arable

land per capita by Auty (1995) and Birdsall (2001). Hodler (2005), Stijns (2006) and Brunnschweiler

(2008) use data on production and on reserves. Finally, in more recent papers, authors use rents

coming from natural resources by using the database provided by the World Bank for hydrocarbons

and ten mining products. Undoubtedly, the use of natural resource rents is an improvement since

these data take into account the world price of the resource and local cost of extraction/production.

c. Does the Type of Natural Resources really Matter ?

In 2003, Isham and al propose a classification of natural resources based on their geographical

concentration and their degree of appropriability. Their main results indicate that “point resources”

characterized by a high level of geographical concentration and a high degree of appropriability have

a more detrimental effect on economic growth than more “diffuse” resources. “Point resources” are

mainly oil, mining products and crops plantations whereas wheat, rice, maize, … belong to “diffuse”

resources. Several studies have stressed the particularly negative effects of oil on growth (Karl,

1997), on institutions (Sala-I-Martin, 2003), on political stability and on the prevalence of civil wars

(see for example Ross (1999, 2001, 2004), Rosser (2006), Dunning (2005), Collier & Hoeffler (2004)).

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3. The Curse of Natural Resources in Transition Countries

a. Why Studying the Curse of Natural Resources in the Transition Countries ?

As Kronenberg (2004) and Brunnschweiler (2009), we consider that Transition countries of Central

and Eastern Europe and of Central Asia provide a very interesting case of study. In fact, they

exhibited rather similar economic and political conditions when they began their transition around

1990. During the next decade, they registered divergent economic performances and differences in

natural resources endowment can be explained, at least partly, differences in growth rates.

They were all ruled by communist regimes since the end of World War II and were centrally planned

economies. All the decisions concerning the level of production, wages and evolutions of prices were

taken at the central level by a small group of leaders. The degree of trade openness was very low and

commercial partners mainly belonged to the CMEA (Council for Mutual Economic Assistance) or were

“brothers countries” like Cuba or other communist countries. Exports were not promoted, their main

objective was to earn enough foreign currency to pay for the essential imports.

These economies were also characterized by the public property of the production goods including

land, machinery and natural resources deposits. Private economic initiatives were very limited.

Economic priority was the heavy industry that has frequently led to an over industrialization. Black

market activities were very developed since the “official” system was unable to satisfy the needs of

the population. Corruption and bribery were hugely used among bureaucrats and civil servants.

From a political point of view, transition economies lived under the rule of the Communist Party

which was the single one. Elections were generally not competitive since opposition movements

were forbidden. Authorities also controlled closely journalists and media (newspapers, radios, …).

At the end of the 1980’s, situation dramatically changes. Communist regimes are thrown over and

were replaced by new governments more or less democratic. In some countries like Poland or the

Baltic states, the political elite is largely changed whereas in others like in Ukraine, Belarus or in

Central Asia, former members of the communist power stay into power. All these governments have

to face the same problem : the transition from centrally planned economies to market economies.

During the 1990’s, transition economies dramatically diverge from an economic point of view. Some

exhibit positive growth rates a few time after the initial shock (Central Europe) whereas others have

important and persistent difficulties since they hadn’t yet recovered their 1989’s level of GDP. It is

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possible that at least a part of these difficulties can be explained by natural resources since transition

economies differ strongly from this point of view : some are resource-rich and others resource-poor.

b. Natural Resources in Transition Economies

Thanks to the wealth estimates of The World Bank, we are able to identify “where is the wealth of

nations”. Is we look at data for 1994, we see that “natural capital” (comprising agricultural land,

forests, minerals and fossil fuels) represents only 20% of the national wealth in transition economies.

(15 points less than in low-income countries). But, we can notice a huge difference from one country

to another. For example, natural capital represents 70% and 63% of national wealth in oil-rich Russia

and Turkmenistan whereas its share is less than 15% in Slovak Republic and Baltic countries. Among

natural capital, agricultural land represents 50%, forests, 12% and minerals and fossil fuels, 38%.

Some countries especially those that are located on the Caspian Sea Basin are well-endowed in oil

and natural gas since this region has the second world reserves of fossil fuels after the Persian Gulf.

As an example, one third of the world proved reserves of natural gas belongs to the Russian company

Gazprom. New recent discoveries contribute to the rapid increase of oil supply coming from this

region. The Russian Federation, Azerbaijan, Kazakhstan, Turkmenistan, Uzbekistan and Romania are

the main oil producers and exporters. Russia is even the second world oil exporter after Saudi Arabia

and about 25% of its GDP come from hydrocarbons, mining products and other commodities.

Transition economies also produce mining products. Russian subsoil can be seen as a “safe” since we

find in it the majority of ores and metals (its huge area can partly explain this phenomenon). Russia is

the first world producer of diamonds and nickel, the third for brown coal, the fourth for phosphates,

the fifth for iron ore, the sixth for uranium, copper, gold and coal, … Bulgaria, Kazakhstan, Poland,

Mongolia, Ukraine and Uzbekistan are also important producers of mining products.

Transition economies rely also on agriculture and forestry. Most of them export wood products and

once again, Russia belongs to the top five of the main exporters. Wheat production is also very

important especially in Russia (4th world producer), in Ukraine (11th world producer), in Romania (19th

world producer) and in Uzbekistan (20th world producer). Production and exports of barley and other

cereals are also important. This is generally not a new specialization since USSR promoted cereals

production and Ukraine was already the wheat loft of the Russian empire during the 18th century.

Cotton production is important in Central Asia especially in Uzbekistan (5th world producer) and in

Turkmenistan (11th world producer). This crop was promoted during the communist period according

to the central plan. In Uzbekistan, government buys cotton to producers with a decrease of 30%

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compared to the world price. This margin is used to finance investment and to pay wages. According

to the IMF, this form of embezzlement represents about 16% of the GDP at the end of the 1990’s.

c. Evidence and Explanations of the Resource Curse in the Transition Economies

The first paper dealing directly to the curse of natural resources in the transition economies is

Kronenberg (2004). For him, differences in growth rates among transition economies can be largely

explained by natural resources. Kronenberg uses a stepwise regression and finds evidence of a curse

of natural resources since the share of primary goods in total exports can explain 2/3 of the variation

in per capita growth among transition countries. This result is robust to the inclusion of several

variables (past growth, initial GDP, trade liberalization, capital formation, school enrollment …) and

to several and successive refinements of the baseline model.

His results indicate that corruption seems to be the main channel of transmission between natural

resources and growth in the transition countries. This result is consistent with the classification of

most corrupted countries provided each year by the NGO Transparency International. For example,

in 2010, five transition countries (Russia, Turkmenistan, Uzbekistan, Tajikistan, Kirghizstan) belong to

the “top twenty” of the most corrupted countries in the world. Recent studies provided by NGOs

consider that “unofficial” activities could represent 50% of the GDP in a country like Russia.

It would be unfair to say that natural resources are the only explanation to the high level of

corruption in transition countries. Communist heritage surely plays a role since bribery was very

developed before 1989 (some authors even believe that the Soviet Union has survived until 1990

mainly because of corruption). Privatization programs can also have fostered corruption.

Natural resources generate rents and each time there are rents to be captured, corruption and rent-

seeking activities appear and develop. For example, extractive companies and state authorities share

close ties because they negotiate mining concessions and contracts and corruption can be a way to

find an agreement quickly. Even if some authors think that corruption can increase efficiency, it is

generally well admitted that corruption create distortions in an economy. According to Bardhan

(1997), corruption reduces incentives to invest and thus, in turn, depress the economic growth rate.

Kronenberg (2004) tests for others channels of transmission. He finds mixed evidence for the Dutch

Disease hypothesis since there is a positive relationship between natural resource abundance and

national price level but the last one does not seem to depress economic growth. The eviction of

human capital (neglect of education) by natural resources does not seem to take place in transition

countries since, before 1989, they invested a lot in education for ideological reasons (creating a

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classless society). Schooling was free and available even in the countryside (contrary to what we can

find in developing countries). School enrollment rates were near to 100% at the end of the 1980’s.

Gylfason (2000) also provide evidence that dependence on natural resources and agriculture fosters

rent-seeking activities and corruption. For him, natural resource abundance is associated with the

emergence of powerful interest groups. Their rent-seeking can have many objectives: have an access

to natural resource deposits, obtain protection from foreign competition, benefit from subsidies …

He uses cross-sectional data for the 1990’s. His results show that an increase in the labor share of

agriculture by 14 points is associated with an increase in the corruption level by 1 point (CPI from

Transparency International). Gylfason also studies the channel of education and finds that results are

not very convincing since estimates are sensitive to the inclusion of outliers.

As pointed by Auty (2001) natural resources generates a false sense of security. Thanks to the rents

they generate, painful (but necessary) reforms can be delayed. In the same vein, interests groups

have more chances to oppose successfully a reform in resource-rich countries since we have already

said that rent-seeking activities are more developed when natural resources are abundant.

De Melo et al (1997) are the first to include natural resources when they try to find the determinants

of growth, inflation and liberalization in transition economies. Using principal components analysis,

they identify the degree of economic distortion and the extent of over-industrialization as the key

parameters for transition success. They create four “clusters” of transition economies according to

their degree of success in reforms. The two less successful groups (Central Asia, Russia, Caucasus) are

more resource-abundant than those that succeeded (0.55 cropland per hectare versus 0.30). So,

natural resources can explain, at least partly, why reforms were postponed and/or bad designed.

Auty (2001) proposes to include endowments of natural resources in the baseline model used to

predict transition trajectories and this increases its explanatory power. Resource-poor countries of

Eastern Europe and East Asia reformed faster than resource-rich countries like Kazakhstan or

Uzbekistan. The speed of reform does not guarantee the success of the transition. As an example,

the slow-reformer Uzbekistan avoided the growth collapse the baseline model predicted since the

Uzbek government used natural rents to ease the foreign exchange rate constraint. For Auty (2001),

it seems that transition was easier in crop-driven resource-abundant economies like Uzbekistan (the

main resource was cotton before discoveries of oil) than in a mineral-rich country like Kazakhstan.

Esanov, Raiser and Buiter (2001) work on oil-abundant transition economies (Azerbaijan, Kazakhstan,

Turkmenistan, Uzbekistan). They find that the probability of reform decreases as oil rent increases.

Incentives to implement reforms are reduced since the political elite view reform as a way to reduce

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its ability to appropriate rents. However, natural resources cannot be regarded as a pure curse for

these countries. Oil wealth attracted foreign direct investment (construction of pipelines,

prospection, oil transformation) which generates positive externalities to the whole economy. Some

other studies tend to show that natural resources are not a curse for the transition economies.

d. Are Natural Resources really a Curse for Transition Economies ?

Even in De Melo et al (1997), the impact of natural resources on economic growth is not clear. Weak

positive growth effects seem to appear but, among the cluster of initial conditions, it is difficult to

identify precisely the impact of natural resources. A positive effect is identified in other papers.

In her study of economic performances in different regions of Russia, Ahrend (2002) shows that

natural resource abundance (production of oil, natural gas and coal) has been good for growth at

least during the first few years of the transition. Commodities are easy to export and, thus, provide a

substantial stream of income. Since world prices of natural resources are higher than were internal

prices in the Soviet Union, resource-rich Russian regions benefit from a positive “terms of trade”

shock that can help to overcome the overall fall of production and promote economic growth.

Brunnschweiler (2009) studies the impact of oil on economic development in the transition

economies over the 1990-2006 period. She uses oil production and oil reserves to measure natural

resource abundance since these data are less subject to other factors (climate, price fluctuations)

than oil production. Data come from BP database and the US Energy Information Agency. Her results

tend to show that oil (production/reserves) has a strong positive effect on economic growth and this

effect is robust whatever the specification of the model is. Changing the measure of oil abundance

(exports per capita, exports/merchandise exports, exports/GDP) does not modify the main result.

Brunnschweiler (2009) also studies the issue of oil ownership. She considers four main ownership

structures: sate ownership with and without control, private domestic ownership and private foreign

ownership. The idea is that ownership structure affects institutional outcomes especially fiscal

regime (Luong and Weinthal, 2001, 2009). Her estimates suggest that the state ownership with

control has higher growth effects than the three other ownership structures. This result contradicts

Luong and Weinthal’s thesis that considers state ownership as the worst ownership structure since it

does not generate incentives for investing in an efficient and stable fiscal system.

Alexeev and Conrad (2010) provide a study in which they examine the impact of natural resource

endowment (oil wealth) on economic growth, on institutions and on welfare (infant mortality, life

expectancy at birth, …). They use cross-country regressions and introduce a dummy variable that

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equals 1 if the country is in transition. Their main results indicate that natural resources do not seem

to have affected economic growth between 1996 and 2005. They find some evidence that natural

resource abundance is associated with higher infant mortality in transition economies but, compared

to other economies in transition, resource-rich transition economies do not perform worse.

4. Determinants of Economic Growth in Transition Economies

The fall of the Iron Curtain and the explosion of the Soviet Union constitute one of the most

important events of the last century. Economists have tried to understand what can explain growth

in transition countries. In one of the earliest paper on this subject, De Melo, Denizer, Gelb and Tenev

(1996) show that initial conditions and economic policy can explain large differences in economic

performances observed across transition countries. This paper is also well known thanks to the index

of liberalization used by the authors to measure reforms. Among initial conditions, Krueger and

Ciolko (1998) consider that macroeconomic distortions are the most important. Some authors also

pay attention to the importance of financial markets in the transition (Campos and Coricelli, 2002).

The debate on the effects of liberalization speed and level emerges with Heybey and Murrell (1998)

and two theories oppose each other in the literature. According to the “shock therapy” theory, the

best solution is to liberalize and privatize very quickly. Going fast to transform a centrally planned

economy into a market economy will avoid any temptation to go back to the previous situation.

On the contrary, supporters of the “gradualism” theory consider that reforms have to be make

progressively. This is the way chosen by China. This last solution gives enough time to build

institutions that are necessary even in a market economy. The respective performances of these two

depend closely to the country. For example, the “shock therapy” was a success in Poland but a failure

in Russia. “Gradualism” worked very well in Hungary but it was not the case in Romania. A recent

work by Godoy and Stiglitz (2006) shows that initial conditions does not play anymore an important

role in explaining economic performance. According to the authors, countries that have chosen the

“shock therapy” exhibited lower economic growth rates during the first decade of transition.

5. Empirical Analysis

a. Sample

Our sample is composed by transition economies from Central and Eastern Europe (CEE) and from

the Former Soviet Union. The nine former communist countries have given birth to 28 transition

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countries (20 in Europe and 8 in Asia). Russian Federation, Ukraine, Belarus, Moldova, Estonia, Latvia,

Lithuania, Kazakhstan, Uzbekistan, Kirghizstan, Turkmenistan, Tajikistan, Azerbaijan, Armenia and

Georgia became independent when USSR fell. Czechoslovakia separated into Slovakia and the Czech

Republic in 1993. Five countries (and even six since the independence of Montenegro from Serbia in

2006) come from Yugoslavia : Serbia, Croatia, Slovenia, Macedonia and Bosnia-Herzegovina. Albania,

Bulgaria, Poland, Romania and Hungary have the same borders than during the communist period.

East Germany was taken over by West Germany. As other authors (Brunnschweiler, 2009), we

include Mongolia in our sample. Even if Mongolia was said to be an independent state, it was a

socialist country closely linked to USSR and started its transition at the beginning of the 1990.

Since these countries started their transition roughly on the same time (between 1990 and 1992)

with relatively similar initial conditions and external economic and political environment, working on

this sample will let us identify the effect of natural resource abundance on economic development

with more precision that we can do when we perform estimates on a more heterogeneous sample.

The observation period is necessarily short going from 1990 to 2003. A country enters the sample in

1990 even if the transition process has not started yet. Some authors like Brunnschweiler prefer to

take into account a country only after the official beginning of the transition (1990 for Central and

Eastern Europe and 1991 for the FSU). Since we introduce an indicator of the deepness of reforms

(coming from the European Bank for Reconstruction and Development), we consider that we control

for the fact that some countries didn’t start reforms before 1991-1992 (or even 1993-1994 if we take

into account the delay between the reform decision, its implementation and its effects).

b. Data Description and Estimation Strategy

The dependent variable is yearly per capita GDP growth (G), coming from the World Development

Indicators of the World Bank. We perform panel estimations with yearly data. We know that, in the

growth literature, it is common to use five or ten-averages to eliminate cyclical fluctuations.

However, if we apply this method to transition countries, it will reduce dramatically the number of

observations. We are going to perform estimates using the following equation :

where NR stands for our measure of natural resource abundance in country in period t.

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Since the seminal work of Sachs and Warner (1995), the share of natural resources in exports and/or

in GDP has been widely used but some authors (Brunnschweiler 2008) consider that it measures

more resource dependence than resource abundance. Here, our preferred indexes of natural wealth

are resource rents data coming from the World Bank. Rent is defined as the difference between the

world price of the resource and the local cost of production/extraction and expressed as a share of

GDP. Rents seem to be a better measure of natural resource abundance since it is the rent accruing

to governments that affects directly the behavior of the political elite and of the lobbies. We use five

different variables which let us to take account the different types of natural resources

- Rent from all natural resources : “rentgdp”

- Rent from oil and natural gas : “oilgdp”

- Rent from ten mining products : “mineralgdp”

- Rent from point resources (hydrocarbons, ores and metals, plantation crops) : “pointgdp”

- Rent from diffuse resources (forest and more familial crops like wheat) : “diffusegdp”

We also use natural resources exports as a share of GDP, agricultural land (as a % of total land area)

and forest area (as a share of total area) as robustness checks.

IC includes several measures of “initial conditions” that has been widely used in the literature. We

mainly use (log) of initial income per capita, urbanization rate and trade dependence. Initial income

and urbanization rates reflect the overall level of development (De Melo et al, 1997), with the first

controlling for convergence. Trade as a share of GDP is a measure of commercial dependence inside

the CMEA. For De Melo et al, 1997), the higher trade dependence is, the worse are the effects of

transition (rupture in commercial links). Data come from World Development Indicators.

X is a vector of control variables including several factors that can affect growth. Among them, we

find inflation (expected sign negative), population, investment, secondary school enrollment and

institutional quality (effect should be positive). Since we are working on transition countries, it is

necessary to control for the deepness of economic reforms. The EBRD provides each year transition

indicators concerning privatization, banking reform, price liberalization, competition policy, …

Transition indicators range between 0 and 5, where 5 denotes complete liberalization. We compute a

simple average of these indexes to have a measure of the overall deepness of the reform.

We concentrate on random effects estimations since all initial conditions and several covariates are

time-invariant. Taking account specific effects seem to be necessary and the Hausman test does not

reject the null hypothesis that the random effects model is consistent and convergent.

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6. Main Results

We start our estimations by using what we can call our “baseline model” (Column 1) in which we

regress the annual growth of GDP per capita on a measure of natural resource abundance, (log)

initial income, inflation, simple average of reform indexes urbanization rate. Then, we successively

control for trade (Column 2), for investment (Column 3) and for the secondary school enrollment rate

(Column 4). All these variables are widely used in the literature on economic transition.

We start by introducing resource rents as a share of GDP (Table 1). The main result is that natural

resources have a positive effect on economic growth in transition countries. This result is robust

when we introduce other additional variables (investment, trade, population, schooling). Changing

the measure of economic reform (price liberalization or level of privatization) do not alter results.

We further control for institutional quality by introducing a simple average of the indexes of “Political

liberties” and “Civil liberties” provided by Freedom House. The higher the index is, the less

important, liberties are. Our results indicate that institutional quality has a positive impact on

economic growth. This means that more autocratic countries have grown faster during the 1990-

2003 period (however, we have to be cautious when we are interpreting this relationship since we do

not address the endogeneity of institutions).

These results are consistent with the observation that non democracies like Turkmenistan or

Azerbaijan registered high growth rates and are oil-rich countries. We also tried to introduce an

interaction term between natural resources and institutions (results not shown). This interaction

term has a positive impact of growth (like institutions per se) whereas the coefficient associated to

natural resource abundance is now negative.

The other coefficients mostly have the expected signs. We find evidence of economic convergence

between countries since initial income has a negative effect on growth. Urbanization rate and reform

affect economic performance positively whereas inflation tends to reduce economic growth.

We continue by decomposing natural resources according to their type. Rents from “point resources”

(Table 2) still have a positive effect on growth rate. The same effect is identified if we consider only

rents from oil and natural gas (Table 3). When we look only at the rents coming from mining results

(Table 4), we see that natural resources are hardly never significant (even if the coefficient is

positive) For the moment, we do not find any evidence of a resource curse among transition

countries. Natural resources and especially “point resources” and oil are rather a blessing for

economic performance. Our results tend to confirm the previous conclusions of Brunnschweiler

(2009) and Ahrend (2002) who have seen in oil an important determinant of growth.

15

We now turn to “diffuse resource” (Table 5) and our results seem to indicate that they have a

negative effect on economic growth among transition countries. This result is not common in the

literature on the impact of natural resource abundance on economic growth. In fact, we generally

find that “diffuse” resources have less detrimental effects than oil and other “point resources”. Of

course, we know for a long time that agriculture does not necessarily promote growth especially

because of lack of productivity, of unskilled workers, of self-consumption and of other factors.

Furthermore, we have to be cautious with the definition of “diffuse resources”. To define this index,

we take into account forest, wheat, rice and maize which are the most traded agricultural products

among those identified as “diffuse” by Isham et al (2003). For transition economies, we have mainly

wheat and forest with a doubt for the latter that can also be regarded as a “point resource” (even if

wood is less easy to appropriate and to sell than diamonds or other mining products).

As a robustness check, we use natural resource exports as a share of GDP as a measure for natural

resource abundance (not shown). It is the most widely used measure of natural resources used in the

literature since the seminal work of Sachs and Warner (1995). Main results are unchanged, oil

exports still have a positive effect on economic growth whereas mining and agricultural exports have

a negative one. We also use forest land and agricultural land as a share of total area (not showsn) in

order to have a better measure of “diffuse resources” (since we do not have many plantation crops

in transition countries contrary to Latin America or even Africa). They both have a negative effect.

7. Conclusion

In this paper, we investigate the impact of natural resource abundance on economic growth among

transition countries. The literature on the “curse of natural resources” generally identifies a negative

effect of natural resources on economic performance. The most negative effects are generally

obtained with “point resources” that is to say hydrocarbons, mining products and plantation crops.

Transition economies provide an interesting case of study. In fact, these countries shared very similar

initial conditions at the beginning of the 1990’s when they started their transition from centrally

planned economies to market economies. During the next decade, they registered very different

growth records. Some recovered quickly whereas others have not yet overcome the initial shock.

They are a more homogeneous sample than those generally used in the literature. Thanks to that, we

are able to estimate more precisely the impact of natural resources on economic growth. In fact, we

16

consider that natural endowment can be one of the explanations of the divergence between

transition economies during the 1990’s since some are resource-rich and other resource-poor.

We further provide an empirical analysis using panel estimates over the 1990-2003 period. We

measure natural resources by resource rents as a share of GDP. Our main results do not support the

idea that there is a “curse of natural resources” in transition countries. We find a positive and robust

impact of natural resources on economic growth and this result holds even for “point resources” and

oil which are generally seen as having a negative effect on economic performances. On the contrary,

agriculture and forest (“diffuse” resources) seem to have detrimental effects on growth.

References

Ahrend R, 2002, Speed of Reform, Initial Conditions, Political Orientation or What ? Explaining Russian Regions’

Economic Performance, DELTA Working Paper 2002-10

Alexeev M, Conrad R, “The Natural Resource Curse and Economic Transition”, forthcoming in Economic Systems

Auty R.M, 2001, “Reforming Resource-Abundant Transition Economies : Kazakhstan and Uzbekistan”, in

Resource Abundance and Economic Development edited by R.M Auty, Oxford University Press, 260-276

Brunschweiler C.N, 2009, Oil and growth in Transition Countries, Working Paper 09/108, Center of Economic

Research at Swiss Federal Institute of Technology, Zurich

Campos N.F, Coricelli F, 2002, “Growth in Transition : Whet we Know, What we don’t and What we should”,

Journal of Economic Literature, 40, 793-836

De Melo M, Denizer C, Gelb A, Tenev S, 1997, Circumstance and Choice : The Role of Initial Conditions and

Policies in Transition Economies, World Bank Policy Research Working Paper 1866

Esanov A, Raiser M, Buiter W, 2001, Nature’s Blessing or Nature’s Curse : The Political Economy of Transition in

Resource-Based Economies, EBRD Working Paper 65

Godoy S, Stiglitz J.E, 2006, Growth, Initial Conditions, Law and Speed of Privatization in Transition Countries : 11

years later, NBER Working Paper 11992

Gylfason T, 2000, “Resources, Agriculture and Economic Growth in Economies in Transition”, Kyklos, 53 (4),

545-580

17

Gylfason T, 2001, Natural Resources and economic Growth : What is the Connection ?, Working Paper 530,

Center for Economic Studies & Ifo Institute for Economic research

Heybey B, Murrell P, 1999, “The Relationship between Economic Growth and the Speed of Liberalization during

Transition”, Policy Reform, 3, 121-137

Kronenberg T, 2004, “The Curse of Natural Resources in the Transition Economies”, Economics of Transition, 12

(3), 399-426

Krueger G, Ciolko M, 1998, “A Note on Initial Condition and Liberalization during Transition”, Journal of

Comparative Economics, 26, 718-734

Luong P, Weinthal E, 2001, “Prelude to the Resource Curse : Explaining Oil and Gas Development Strategies in

the Soviet Successor States and Beyond”, Comparative Political Studies, 34 (4), 367-399

Mehlum H, Moene K, Torvik R, 2006, « Institutions and the Resource Curse », The Economic Journal, 116 (508),

1-20

Sachs J.D, Warner A.M, 1995b, Natural Resource Abundance and Economic Growth, Development Discussion

Paper 517a, Harvard Institute for International Development

Sala-I-Martin X, Subramanian A, 2003, Addressing the Natural Resource Curse : An Illustration from Nigeria,

Working Paper WP/03/139, International Monetary Fund

18

Table 1 : Resource Rents (% of GDP) and Economic Growth in Transition Countries

Variables (1) (2) (3) (4) (5) (6) (7) (8)

Initial Income (log) -0.019** (-2.379)

-0.018** (-2.194)

-0.019** (-2.385)

-0.026*** (-3.135)

-0.009 (-1.363)

-0.006 (-0.821)

-0.015** (-2.035)

-0.020** (-2.508)

Urbanization 0.053* (1.721)

0.051 (1.627)

0.054* (1.732)

0.100*** (3.307)

0.076** (2.261)

0.033 (1.086)

0.073** (2.189)

0.060* (1.827)

Inflation -0.022***

(-7.816) -0.023*** (-7.617))

-0.023*** (-7.693)

-0.023*** (-8.114)

-0.022*** (-7.408)

-0.026*** (-10.803)

-0.022*** (-7.657)

-0.022*** (-7.738)

Reform 0.053*** (4.899)

0.051*** (4.516)

0.052*** (4.889)

0.045*** (3.581)

0.062*** (5.111)

0.053*** (4.904)

Trade 0.006

(0.405)

Rentgdp 0.091*** (5.535)

0.119*** (6.871)

0.087*** (5.039)

0.0823*** (3.817)

0.079*** (6.019)

0.079*** (5.546)

0.081*** (5.313)

0.092*** (5.566)

Investment

0.011

(0.230)

Secondary School Enrollment

-0.028

(-0.910)

Political & Civil Liberties

0.012***

(3.541)

Price

0.032***

(6.551)

Privatization

0.039***

(4.497)

Population

-0.002 (-0.767)

C -0.002

(-0.065) -0.015

(-0.433) -0.003

(-0.097) 0.071***

(3.736) -0.154*** (-3.217)

-0.068* (-1.807)

-0.017 (-0.511)

0.035 (0.600)

R

2 0.539 0.552 0.527 0.447 0.565 0.501 0.535 0.538

Observations

302 297 299 167 290 302 302 302

Estimations using random effects GLS panel estimations with GDP per capita growth as dependent variable. t-statistics given into brackets

(*), (**), (***) : statistically significant at 1%, 5% and 10% respectively

19

Table 2 : Rents from “Point Resources” (% of GDP) and Economic Growth in Transition Countries

Variables (1) (2) (3) (4) (5) (6) (7) (8)

Initial Income (log) -0.021***

(-2.628) -0.019** (-2.445)

-0.021*** (-2.627)

-0.025*** (-3.126)

-0.011* (-1.662)

-0.006 (-0.913)

-0.016** (-2.209)

-0.021*** (-2.772)


0.049* (1.646)

0.051* (1.683)

0.099*** (3.087)

0.073** (2.270)

0.033 (1.109)

0.070** (2.157)

0.058* (1.896)

Inflation -0.022*** (-10.117)

-0.023*** (-10.117)

-0.022*** (-9.961)

-0.023*** (-7.490)

-0.022*** (-8.624)

-0.026*** (-14.192)

-0.023*** (-9.374)

-0.022*** (-10.219)

Reform 0.053*** (4.781)

0.050*** (4.412)

0.052*** (4.731)

0.043*** (3.306)

0.061*** (4.639)

0.053*** (4.813)

Trade

0.011 (0.969)

Pointgdp 0.091*** (5.669)

0.118*** (6.737)

0.089*** (5.314)

0.090*** (3.686)

0.081*** (6.722)

0.078*** (5.674)

0.081*** (5.434)

0.092*** (5.692)

Investment

0.004 (0.096)


-0.028 (-0.878)


0.011*** (3.081)

Price

0.031*** (6.781)

Privatization

0.038*** (4.208)

Population

-0.002 (-0.752)

C 0.011

(0.334) -0.004

(-0.124) 0.011

(0.338) 0.072***

(3.783) -0.137*** (-2.856)

-0.061* (-1.758)

-0.004 (-0.148)

0.049 (0.837)

R

2 0.534 0.551 0.519 0.422 0.561 0.482 0.526 0.534

Observations

278 273 275 155 266 278 278 278



20

Table 3 : Rents from Oil and Natural Gas (% of GDP) and Economic Growth in Transition Countries

Variables (1) (2) (3) (4) (5) (6) (7) (8)

Initial Income (log) -0.019* (-1.816)

-0.018* (-1.752)

-0.021* (-1.837)

-0.023** (-1.988)

-0.006 (-0.806)

-0.006 (-0.753)

-0.016 (-1.615)

-0.029*** (-2.815)


0.067* (1.654)

0.069* (1.835)

0.132*** (3.417)

0.074** (1.972)

0.052 (1.505)

0.081** (1.965)

0.138*** (3.149)

Inflation -0.022***

(-8.918) -0.023*** (-9.010)

-0.022*** (-9.027)

-0.024*** (-8.467)

-0.022*** (-7.662)

-0.024*** (-11.512)

-0.022*** (-8.817)

-0.022*** (-9.717)

Reform 0.043*** (3.543)

0.040*** (3.543)

0.046*** (3.715)

0.038*** (2.801)

0.056*** (3.741)

(0.048)*** (4.131)

Trade

0.017 (1.229)

Oilgdp 0.080*** (6.131)

0.104*** (6.452)

0.084*** (5.141)

0.095*** (3.608)

0.074*** (5.489)

0.071*** (6.078)

0.074*** (3.365)

0.085*** (5.773)

Investment

-0.021 (-0.295)


-0.135*** (-2.782)


0.012** (2.500)

Price

0.026*** (5.429)

Privatization

0.033*** (5.602)

Population

-0.010** (-2.277)

C 0.020

(0.446) 0.003

(0.081) 0.028

(0.703) 0.144***

(4.954) -0.164** (-2.433)

-0.053 (-1.147)

0.003 (0.086)

0.208** (2.248)

R

2 0.475 0.494 0.485 0.431 0.539 0.442 0.500 0.515

Observations

229 226 226 126 222 229 229 229



21

Table 4 : Rents from Mining Products (% of GDP) and Economic Growth in Transition Countries

Variables (1) (2) (3) (4) (5) (6) (7) (8)


-0.019** (-2.097)

-0.019** (-2.125)

-0.027*** (-3.818)

-0.012 (-1.590)

-0.005 (-0.609)

-0.012 (-1.582)

-0.018** (-2.211)

Urbanization 0.002

(0.075) -0.005

(-0.158) 0.003

(0.103) 0.038

(1.021) 0.018

(0.511) -0.005

(-0.149) 0.015

(0.461) -0.009

(-0.251)

Inflation -0.021***

(-6.260) -0.020*** (-6.178)

-0.021*** (-6.241)

-0.024*** (-8.012)

-0.020*** (-5.894)

-0.025*** (-7.666)

-0.021*** (-6.662)

-0.021*** (-6.339)

Reform 0.059*** (5.955)

0.062*** (5.800)

0.059*** (5.483)

0.046*** (4.161)

0.068*** (7.035)

0.060*** (5.971)

Trade

-0.015* (-1.751)

Mineralgdp 0.057

(0.581) 0.086

(0.827) 0.061

(0.621) -0.050

(-0.769) 0.117

(1.106) 0.011

(0.107) 0.169* (1.766)

0.076 (0.675)

Investment

-0.018 (-0.232)


0.013 (0.293)


0.008** (2.273)

Price

0.034*** (8.106)

Privatization

0.046*** (6.273)

Population

0.003 (0.766)

C 0.006

(0.155) 0.013

(0.331) 0.009

(0.225) 0.071***

(2.619) -0.106** (-2.240)

-0.061 (-1.393)

-0.033 (-0.860)

-0.038 (-0.551)

R

2 0.533 0.527 0.527 0.394 0.549 0.455 0.545 0.533

Observations

236 233 235 134 225 236 236 236



22

Table 5 : Rents from Diffuse Resources (% of GDP) and Economic Growth in Transition Countries

Variables (1) (2) (3) (4) (5) (6) (7) (8)


-0.016** (-2.221)

-0.016** (-2.105)

-0.024** (-2.321)

-0.006 (-0.955)

-0.008 (-1.523)

-0.014** (-2.153)

-0.016** (-2.069)

Urbanization 0.048

(1.527) 0.047

(1.484) 0.049

(1.598) 0.094***

(2.821) 0.082** (2.337)

0.036 (1.189)

0.060* (1.795)

0.049 (1.406)

Inflation -0.027***

(-6.095) -0.027*** (-6.189)

-0.027*** (-6.158)

-0.024*** (-5.885)

-0.025*** (-6.266)

-0.029*** (-7.708)

-0.026*** (-6.598)

-0.027*** (-6.084)

Reform 0.026

(1.618) 0.024

(1.650) 0.026* (1.651)

0.028 (1.450)

0.048*** (3.143)

0.025 (1.618)

Trade

0.024 (1.334)

Diffusegdp -0.734***

(-2.894) -0.779*** (-2.914)

-0.736*** (-3.130)

-0.374 (-1.640)

-0.609*** (-2.753)

-0.700*** (-2.703)

-0.662*** (-2.663)

-0.733*** (-2.939)

Investment

0.113* (1.740)


-0.007 (-0.176)


0.016*** (3.799)

Price

0.019 (1.525)

Privatization

0.020** (1.994)

Population

-0.000 (-0.105)

C 0.071* (1.814)

0.053 (1.395)

0.047 (1.423)

0.092*** (5.076)

-0.139** (-2.470)

0.018 (0.329)

0.058 (1.473)

0.076 (1.281)

R

2 0.491 0.496 0.497 0.328 0.524 0.486 0.494 0.489

Observations

275 275 275 152 275 275 275 275



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