Is Natural Resource Abundance Beneficial or Detrimental to

Output Level and Growth?∗

Chi-Yung (Eric), Ng†

May 3, 2006

Abstract

The recent “resource curse” literature [e.g. Sach & Warner (1995, 2001)] indicates

that natural resource abundance has a negative impact on output growth. These studies

use natural resource dependence (e.g. share of resource exports in GDP) as a proxy

for resource abundance (resource endowments per worker), and focus on the impact

of natural resources on output growth. This paper addresses whether the distinction

between resource abundance and resource dependence is important, and whether the

impacts of natural resources on the level and growth rate of output are different. We find

that natural resource abundance and resource dependence exhibit different empirical

relationships with both the level and growth rate of output per worker. Using a simple

dynamic model, we show that cross-country differences in mineral resource abundance,

TFP levels in mining and non-mining sector, and relative prices of investment goods

can account quantitatively for the empirical relationships. Our results indicate that

the “resource curse” phenomenon reflects only a negative relationship between natural

resource dependence and output growth. Natural resource abundance per se is beneficial

to output level while not detrimental to output growth.

∗I am grateful to Jim MacGee and Igor Livshits for their advice and guidance throughout this project.

I also thank John Whalley for helpful suggestions, and Hiroyuki Kasahara and Martin Gervais for useful

discussions.†Contact Info: Department of Economics, University of Western Ontario, London, Ontario, Canada, N6A

5C2. Email: cng6@uwo.ca Telephone: (519)661-2111 ext.85886 Fax: (519)661-3666

1

1 Introduction

Is natural resource abundance a curse or a blessing? Recent findings of the “resource curse”

literature [e.g. Sachs & Warner (1995, 1997, 2001), Asea & Lahiri (1999), Leite & Weidmann

(1999), Gylfason (2001), Atkinson & Hamilton (2003), Papyrakis & Gerlach (2004), Isham

et al. (2005)] suggest that natural resource abundance is a “curse” for economic growth:

resource-abundant countries tend to grow more slowly than resource-poor countries. Using

cross-country growth regressions, these studies regress average growth rate of output per

capita on natural resource abundance, initial GDP per capita and other potential growth

factors (e.g. investment rate and institutional quality). A common finding is that natural

resource abundance is significantly negative, even controlling for other growth factors.

There are at least two potential caveats of recent work concluding that natural resources

are detrimental to economic performance. First, these studies often use natural resource

dependence measures as proxies for natural resource abundance. These resource dependence

measures include the share of export of natural resource goods in GDP (or total exports),

the share of mineral production (or resource rents) in GDP, and the share of natural cap-

ital in total national wealth. These resource dependency ratios may be poor proxies for

resource abundance since they reflect the endogenous responses of production and trade.

The endogeneity of resource dependency ratios implies there is a potential reverse causality

or omitted variable bias. A simple example can illustrate this point. Suppose there is an

exogenous time-invariant factor called institutional quality, which has a positive effect on

GDP growth but a negative impact on natural resource exports. Over a long time horizon,

countries with poor institutional quality will exhibit lower GDP levels and higher resource

exports than those with better institutional quality. Therefore, the resource dependency

ratio (measured by the ratio of nature resource exports to GDP) in the former countries

will be higher than that in the latter countries. If we use the resource dependency ratio as

a proxy for resource abundance, then we would tend to find a negative correlation between

output growth and resource abundance. But this negative relationship is driven by insti-

tutional quality, and not by natural resource abundance. Hence, the negative correlation

may reflect a reverse causality or omitted variable bias, and may not imply that natural

resource abundance is detrimental to output growth.

2

Second, this literature (see papers cited above) focuses on the relationship between out-

put growth and resource dependence, but not between output level and resource abundance.

This may be important, as natural resources may have different impacts on the growth rate

and level of output per worker. Countries with high endowments of natural resources may

have high share of natural resource sector (e.g. mining and quarrying) in GDP. Suppose that

the natural resource sector grows more slowly than the other non-resource sectors (perhaps

due to differences in productivity growth). These resource-abundant countries will exhibit

lower growth rates of aggregate GDP (due to compositional effect of natural resource sec-

tor), even though they may have high levels of GDP. In this case, even if natural resources

have a negative effect on growth, they may have a positive impact on development (in terms

of GDP per worker). In practice, oil-abundant countries (e.g. Qatar and Saudi Arabia) and

other resource-abundant economies (e.g. New Zealand) do exhibit low average growth rates

of per-worker GDP, but high levels of per-worker GDP. It is therefore important to examine

the impacts of natural resource abundance on both the level and growth rate of output.

This paper examines whether natural resource abundance is beneficial or detrimental

to both output level and growth taking into account the two potential caveats. The first

contribution is to document cross-country empirical facts on the respective relationships

of natural resource abundance (resource endowments per worker) and resource dependence

(share of resource exports in GDP) with output per worker. We show that the distinction

between natural resource abundance and resource dependence is important, as they exhibit

different empirical relationships with both the level and growth rate of output per worker.

The second contribution is to develop a multi-country two-sector growth model to investi-

gate the endogeneity of natural resource dependence and the impacts of natural resource

abundance on the level and growth rate of output per worker. We find that low produc-

tivity in the non-resource sector and high barriers to investment can result in high natural

resource dependence and low output growth. We also find that natural resource abundance

has a positive effect on output level but no significant impact on output growth. These find-

ings imply that one cannot simply use the regression of output growth/level on endogenous

resource dependence to infer the impacts of resource abundance on output growth/level.

In our empirical analysis, we divide natural resources into mineral and agricultural

3

resources. Our motivation is based on some studies documenting that countries with abun-

dant mineral resources tend to exhibit different economic and social development than other

resource-rich countries [e.g. Auty (2001), Isham et al. (2005)].1 For each group, we measure

resource abundance and resource dependence for a cross-section of countries.2 Our empiri-

cal findings indicate that: (1) mineral resource abundance is positively related to the level of

output per worker (both aggregate and non-mining GDP3), but not significantly related to

the growth rate of output per worker (Fact 1); (2) mineral resource dependence is negatively

related to the growth rate of output per worker (both aggregate and non-mining GDP), but

not significantly related to the level of output per worker (Fact 2); and (3) both agricultural

resource abundance and dependance exhibit no significant relationships with the level and

growth rate of output per worker (Fact 3). These facts imply that the finding of recent

“resource curse” literature is only a negative relationship between natural resource depen-

dence and output growth. Natural resource abundance pe se is not significantly related to

output growth.

We then develop a simple dynamic model to examine the endogeneity of mineral resource

dependence and the impacts of mineral resource abundance on output level and growth.4

We use the model to illustrate the potential bias in the regression of output growth on

endogenous resource dependence. We extend the standard two-sector neoclassical growth

model to a multi-country open-economy framework that allows for trade between countries.

Growth in each country comes from a common exogenous growth in the non-mining sec-

tor and capital accumulation. Countries may have different exogenous initial levels of total

factor productivity (TFP) in the mining and non-mining sector, and different exogenous bar-1See Section 2.1 for more details about the motivation of this division and the definitions of mineral and

agricultural resources.2We use different measures of resource abundance and dependence. All of these measures are in value

terms. There are 70 countries in our sample. See Section 2.1 for details.3Aggregate GDP refers to all sectoral value-added components of GDP as defined in International Stan-

dard Industrial Classification of All Economic Activities (ISIC Rev.2). Non-mining GDP refers to aggregate

GDP minus value-added in mining and quarrying (ISIC Rev.2: Major Division 2).4We focus on mineral resources since they exhibit contradictory empirical relationships with both the

level and growth rate of output per worker. We are interested in addressing whether standard growth or

trade theory can explain these empirical relationships.

4

riers to investment (relative prices of investment goods). These two features together with

country-specific endowments of mineral resources and labour determine possible linkages

between resource abundance/dependence and the level/growth rate of per-worker output in

each country.

In the model, there is a potential positive effect of mineral resource abundance on the

level of output. An increase in mineral resources, ceteris paribus, will increase the level of

mining output and so the level of aggregate output. The impact of mineral resource abun-

dance on the growth rate of output, however, depends on country-specific exogenous initial

sectoral TFP levels and barriers to investment. Ceteris paribus, if a resource-abundant

country has high (low) non-mining TFP level and low (high) barriers to investment, then it

will allocate more (less) existing physical capital into the non-mining sector and accumulate

more (less) new capital. The share of the non-mining sector in GDP will increase (decrease)

and there is an increase (decrease) in capital accumulation. As the non-mining sector grows

faster than the mining sector, the growth rate of aggregate output will increase (decrease).

Hence, the impact of mineral resource abundance per se on output growth is ambiguous. By

contrast, there is a potential negative relationship between mineral resource dependence and

output growth. If a country has low (high) non-mining TFP level and high (low) barriers

to investment, ceteris paribus, then it will allocate more (less) existing physical capital into

the mining sector and accumulate less (more) new capital. The share of the mining sector

in GDP will increase (decrease) and there is a decrease (increase) in capital accumulation.

The country will exhibit low (high) output growth and high (low) resource dependence.

This endogeneity of resource dependence implies that there is a potential reverse causality

(low output growth leads to high resource dependence) or omitted variable bias (low non-

mining TFP level leads to high resource dependence) in the regression of output growth on

resource dependence.

In our quantitative analysis, we simulate the model and find that cross-country differ-

ences in mineral resource abundance, sectoral TFP levels and relative prices of investment

goods can account quantitatively for the cross-country empirical relationships between min-

eral resource abundance/dependence and output level/growth (i.e. Fact 1-2). For reasonable

parameter values, the model implies that mineral resource abundance has a positive effect

5

on output level, but no significant impact on output growth. The model also predicts that

countries with high mineral resource dependence will exhibit low output growth. These

quantitative results suggest that one should be cautious of interpreting the finding of re-

cent “resource curse” literature. What the recent literature finds is a negative relationship

between natural resource dependence and output growth. Natural resource abundance per

se is beneficial to output level while not detrimental to output growth.

This paper is related to other recent studies that also find natural resources are not a

“curse” for output growth. Manzano and Rigobon (2001) show that the “resource curse”

might be due to the direct effect of “external debt overhang” in the 1970s for resource-

dependent countries. Stijns (2002) also finds energy and mineral reserves are not significant

factors for output growth rates. Using GMM estimation of dynamic panel data model,

Lederman and Maloney (2002) indicate that the “resource curse” finding is not robust

to the inclusion of country effects and correction of endogeneity. These studies, however,

focus on the impact of natural resources on the growth rate but not the level of output.

Besides, the analysis of panel data requires using a higher frequency growth data (e.g. 5-

year averages in the panel data instead of 30-year averages in the cross-sectional data).

Given the instability and volatility of growth rates in developing countries, the use of panel

data is unlikely to be informative [Pritchett (2000)].5 We differ from them on (i) focusing

the relationship between output level and natural resource abundance; (ii) distinguishing

between mineral and agricultural resource abundance, and examining their relationships

with both aggregate and non-mining GDP; and (iii) using a quantitative model to examine

these relationships.6

The rest of the paper is organized as follows. Section 2 presents the empirical relation-

ships between natural resources and output, and compares the findings with those of the

recent literature. Section 3 describes the model. Section 4 examines whether the model can

account quantitatively for the empirical facts, and also explores the link between institu-

tional quality and the quantitative results. Section 5 concludes.5Robert Solow (2001) also makes a cautious remark on the general practice of using cross-country growth

regressions to test growth theory.6The comparisons between quantitative modeling approach and econometric approach for addressing

growth and development issues can be found in King (1995), and McGrattan & Schmitz (1999).

6

2 Empirical Facts

This section provides cross-country empirical facts on (a) the relationships between natural

resource abundance (natural resource endowments per worker) and the level and growth rate

of output per worker (including aggregate and non-mining GDP), and (b) the relationships

between natural resource dependence (share of natural resource exports in GDP) and output

level and growth. We find that the distinction between resource abundance and resource

dependence is important, as they exhibit different empirical relationships with output level

and growth. We also compare our empirical findings with those of the recent “resource

curse” literature.

We divide natural resources into mineral and agricultural resources. Our motivation is

based on a number of studies documenting that countries with abundant mineral resources

tend to exhibit different economic and social development than other resource-rich coun-

tries.7 Mineral resources include fuel-related and other mineral resources such as oil, coal,

natural gas, metals and minerals. Agricultural resources include agricultural cropland, pas-

ture land, timber resources and non-timber forest resources. For each group, we measure

resource abundance and resource dependence for a cross-section of countries.

2.1 Measures of Natural Resource Abundance and Dependence

Natural resource abundance should capture exogenous endowments of natural resources.

In practice, however, even the estimation of reserve data (e.g. oil and mineral reserves)

depends on the available exploration technology and the incentive for exploration. Given

that no “perfect” data exist, we use three different measures of natural resources as proxies

for resource abundance, and examine whether they have consistent relationships with output

level and growth. These three measures are the stock value of natural capital, the export

value of natural resource goods and the value-added component of GDP in natural resource

sectors. Since one component of the stock value of natural capital (the subsoil capital stock)7For instance, Isham et al. (2005) indicate that counties with abundant point-source resources (i.e. oil,

mineral resources and plantation crops) tend to have weakened institutional capacity and lower economic

growth. Other studies [e.g. Collier & Hoeffler (2005), Fearon (2005) and Lujala, et al. (2005)] find that

oil-rich countries are more prone to civil war risks.

7

only exists for 70 countries, we restrict our attention to this group for the other measures

to allow consistent comparisons across the three measures.8 A list of country names and

their codes are shown in Table 1. All these abundance measures are converted to per-worker

terms. The labour force data (economically active population) are from World Development

Indicators.

The first measure of resource abundance is the stock value of natural capital estimated

by Kunte et al. (1998). They divide natural capital into subsoil and non-mineral natural

capital. Subsoil capital includes oil, coal, natural gas, metals and minerals. Non-mineral

natural capital (agricultural capital stock hereafter) includes agricultural cropland, pasture

land, timber resources, non-timber forest benefits and protected areas. For each type of

natural capital, they define the economic rent as the return on a commodity in excess of

the minimum inputs required to provide its services, and calculate the rental value as the

difference between market price and cost of production or extraction. The stock value of

each type of natural capital is then the present value of the stream of services it generates

over its life-time.

The second abundance measure is the export value of natural resource goods. Standard

Heckscher-Ohlin trade theory implies that the relatively resource-abundant countries tend

to produce and export more resource-intensive goods. We use the export value of mineral

goods as a proxy for mineral resource abundance, and the export value of agricultural

goods as a proxy for agricultural resource abundance. Specifically, exports of mineral goods

include fuels, ores and metals [Standard International Trade Classification (SITC) section 3:

mineral fuels, division 27: crude fertilizer and minerals nes, division 28: metalliferous ores

and scrap, and division 68: non-ferrous metals]. Exports of agricultural goods include food

and raw agricultural materials (SITC section 0: food and live animals, 1: beverages and

tobacco , 2: crude materials except fuels, 4: animal and vegetable oils and fats, excluding

division 27 and 28). The data are from World Development Indicators.

The third measure of resource abundance is the value-added in natural resource sec-

tors. Our logic is also based on standard Heckscher-Ohlin trade theory. Mineral resource8We also use the full sample for the other two measures to conduct similar empirical analysis in Section

2.2. The results are consistent with those using only these 70 countries.

8

and agricultural resource abundance refer to the value-added in mining and agriculture re-

spectively. In particular, the value-added in mining includes mining and quarrying (ISIC

Rev.2: Major Division 2), and the value-added in agriculture includes agriculture, hunt-

ing, forestry and fishing (ISIC Rev.2: Major Division 1). The data sources include United

Nation National Accounts Main Aggregates Database and World Tables.

To measure natural resource dependence, we refer to the share of exports of natural

resource goods in GDP. Again, we divide exports of natural resource goods into exports

of mineral and agricultural goods. Their shares in GDP are used as proxies for mineral

resource and agricultural resource dependence respectively. The definitions and sources of

export data are the same as those for the export value of natural resource goods described

above.

2.2 Key Facts

Our initial approach for examining the cross-country data is to report correlation statistics

between natural resource abundance/dependence and output level/growth.9 Correlation

statistics, however, may be spurious if there are other factors affecting both natural resources

and output. To address this issue, our second approach is to regress output level/growth on

natural resource variable (resource abundance or dependence) and other potential factors.

We examine whether the natural resource variable remains significant after controlling for

the other factors in the regression. For output growth regression, standard explaining factors

include initial GDP per worker (in 1970), physical investment rate (average over 1970-2000)

and institutional quality (average over 1986-1995).10 For output level regression, standard

factors include physical investment rate and institutional quality.9For all correlation analysis, we report both Pearson and Spearman rank correlation statistics. In the

sample, there are cases that the data are heavily skewed or contain some outliers. For instance, the distribu-

tion of mineral resource dependence is heavily skewed to the right since there are some countries that have

extremely high resource dependence. In this case, it is more appropriate to use the rank correlation since it

is less sensitive to the outliers than the Pearson correlation statistics.10These explaining factors are the core variables used in most of the “resource curse” literature mentioned

in Section 1. Institutional quality refers to an index of Government Antidiversion Policies (GADP) and is

an average of five measures of institutional quality during 1986-1995. See Section 4.4 for detailed definitions

and source of data.

9

We begin by examining the empirical relationships of mineral resource abundance with

the level and growth rate of output per worker. We use the three measures of mineral

resource abundance described above. All abundance measures are referred to their value in

1970 except the subsoil capital stock that we only have data in 1994. For output level and

growth, we refer to per-worker real GDP in 2000 and average growth rate of per-worker real

GDP over 1970-2000 respectively. Since aggregate GDP includes the sectoral value-added

in mining and quarrying, to isolate the compositional effect of this sector, we look at both

aggregate and non-mining GDP. Specifically, aggregate GDP refers to all sectoral value-

added components defined in ISIC Revision 2, and non-mining GDP refers to aggregate

GDP minus value-added in mining and quarrying (ISIC Rev.2: Major Division 2).11 Data

on aggregate GDP are from Penn World Tables 6.1, and data on mining share of GDP are

from United Nation National Accounts Main Aggregates Database.12

Fact 1: There is a significant positive relationship between mineral resource abundance

and the level of output per worker (both aggregate and non-mining GDP), but no significant

relationship between mineral resource abundance and the growth rate of output per worker

(both aggregate and non-mining GDP).

Figure 1 and 2 illustrate the respective scatter plots of the per-worker aggregate and

non-mining GDP level with different measures of mineral resource abundance. There seems

to be a positive relationship between mineral resource abundance and output level. The

correlation and regression analysis further confirms this point. Table 2 indicates that there

is a significant positive correlation between all mineral resource abundance measures and

the level of both aggregate and non-mining GDP per worker (ranging about 0.4-0.6). The

regression results in Table 3 also suggest that all mineral resource abundance measures

remain positively significant even including other standard factors in the regressions of

aggregate and non-mining GDP per worker. In contrast, Figure 3 and 4 illustrate that there11Mining and Quarrying include coal mining, crude petroleum and natural gas production, metal ore

mining, and other mining activities.12In addition to using the purchasing power parity (PPP) GDP data, we also use the GDP data in constant

price of US$ from World Development Indicators. We find that the results of both correlation and regression

analysis (in terms of coefficient estimates and their significance levels) are similar to the ones using PPP

GDP data.

10

seems to be no systematic relationship between mineral resource abundance and output

growth. Table 4 confirms that none of the three mineral resource abundance measures

is significantly correlated with the growth rate of either aggregate or non-mining GDP

per worker. Table 5 further indicates that all mineral resource abundance measures are

insignificant in both regressions of the growth rate of aggregate and non-mining GDP per

worker.

Next, we examine the empirical relationship between mineral resource dependence and

the growth rate and level of output per worker. For mineral resource dependence, we refer

to the GDP share of exports of mineral goods in 1970.

Fact 2: There is a significant negative relationship between mineral resource dependence

and the growth rate of output per worker (both aggregate and non-mining GDP), but no

significant relationship between mineral resource dependence and the level of output per

worker (both aggregate and non-mining GDP).

Figure 5 and 6 illustrate the respective scatter plots of the growth rate and level of per-

worker output with mineral resource dependence (including both aggregate and non-mining

GDP in each figure). Mineral resource dependence seems to exhibit a negative relationship

with output growth but no systematic relationship with output level. Table 6 confirms

that there is a significant negative correlation between mineral resource dependence and

the growth rate of both aggregate and non-mining GDP per worker (ranging from -0.31

to -0.46), but insignificant correlation between mineral resource dependence and the level

of output per worker (close to zero). The regression results also yield similar conclusion.

In Table 7, mineral resource dependence remains negatively significant in both regressions

of the growth rate of aggregate and non-mining GDP per worker. This finding is similar

to that of the recent “resource curse” literature that resource-dependent countries tend to

exhibit lower growth rates of aggregate output. In contrast, the regression results in Table

8 suggest that mineral resource dependence is an insignificant determinant of the level of

output per worker.

Finally, one can also examine the empirical relationships of agricultural resource abun-

dance and resource dependence with the level and growth rate of output per worker. We

also consider the three measures of agricultural resource abundance described above. All

11

abundance measures are referred to their value in 1970 except the agricultural capital stock

that we only have data in 1994. For agricultural resource dependence, we look at the GDP

share of exports of agricultural goods in 1970. For output level and growth, we also re-

fer to per-worker real GDP in 2000 and average growth rate of per-worker real GDP over

1970-2000 respectively.

Fact 3: Both agricultural resource abundance and resource dependance exhibit no sig-

nificant relationships with the level and growth rate of output per worker.

Figure 7 illustrates the scatter plots of the level of aggregate GDP per worker with

agricultural resource abundance and resource dependence (with Pearson and Spearman rank

correlation statistics). There seems to be a positive correlation between agricultural resource

abundance and output level, and a negative correlation between agricultural dependence and

output level (about 0.3 and -0.2 respectively). However, when other factors such as physical

investment rate and institutional quality are included in the output level regression, Table

9 indicates that both agricultural abundance and dependence are insignificant. Similarly,

Figure 8 shows that output growth seems to be uncorrelated with both agricultural resource

abundance and resource dependence (close to zero correlation). The regression results in

Table 9 further suggest that both agricultural resource abundance and resource dependence

are not significant determinants of output growth.

2.3 Comparison with Recent “Resource Curse” Literature

Our empirical findings differ sharply from the results of recent “resource curse” literature.

As mentioned in Section 1, the recent literature uses resource dependency ratios as proxies

for resource abundance, and finds a negative relationship between resource dependence and

output growth. Fact 1 and Fact 3 above point out that the distinction between resource

abundance and resource dependence matters. Natural resource abundance per se (either

mineral or agricultural resources) exhibits no significant relationship with output growth.

Also the recent literature does not examine the relationship between natural resource abun-

dance and output level. Fact 1 indicates that there is a positive relationship between mineral

resource abundance and output level.

Besides, the recent literature includes both agricultural and mineral resources in their

12

measurement of resource dependence. Fact 2 and Fact 3 illustrate that the distinction

between mineral dependence and agricultural dependence also matters. Only countries with

high mineral resource dependence tend to exhibit lower growth rates of output. Also the

recent studies do not examine the relationship between resource dependence and output

level. Fact 2 and Fact 3 indicate both mineral and agricultural dependence exhibit no

significant relationship with output level.

In sum, our empirical findings seem to suggest that natural resource abundance may be

beneficial to output level, but not detrimental to output growth. In next section, we use a

quantitative model to examine the impacts of natural resource abundance on output level

and growth.

3 The Model

In this and next section, we develop a simple dynamic model to investigate the endogeneity

of mineral resource dependence and the impacts of mineral resource abundance on out-

put level and growth. Our focus is on mineral resources since they exhibit contradictory

empirical relationships with both the level and growth rate of output per worker. We are in-

terested in addressing whether standard growth or trade theory can explain these empirical

relationships. We first describe the model in this section and then explore the quantitative

properties of the model in Section 4. We extend the standard two-sector neoclassical growth

model to a multi-country open-economy framework that allows for trade between countries.

3.1 Basic Features

In the model world, there are N countries. Each country has two production sectors:

mining (R) and non-mining (M) sectors. The mining sector produces mineral goods used

as intermediate goods in the non-mining sector, while the non-mining sector produces non-

mineral goods used for final consumption and investment. Countries can trade both mineral

and non-mineral goods with no trading costs across borders. There is no international

lending and borrowing, so the trade balance of each country is zero in each period.13

13In terms of empirical justification, this assumption is not unreasonable despite that enhanced financial

integration has increased financial capital flows across countries. First, the average current account during

13

Each country i (i = 1, ..., N) has initial endowments of natural capital (Ri), physical

capital (Ki0) and labour (Li). Natural capital and labour are the fixed factors of production

in the mining and non-mining sector respectively.14 Physical capital is mobile across sectors

within a country but is immobile across countries. The assumption of capital immobility

across countries is consistent with the fact that there is a large cross-country variation in

rental rate of physical capital.15 In each country, there is a representative household who

owns all factors of production.

Countries may have different exogenous initial total factor productivity levels (TFP)

in the mining and non-mining sector. All countries, however, are assumed to have a zero

TFP growth rate in the mining sector and a common exogenous positive TFP growth

rate in the non-mining sector. These assumptions are also consistent with the empirical

observation in G7 countries.16 In the quantitative analysis, we also calibrate most of the

1970-2000 (the periods that we consider in the empirical analysis and quantitative experiments) is not high

for the 70 countries that we study. The median current account is -2.46% of GDP. For OECD countries (20

in the sample), it is even lower at -0.85% while for non-OECD countries (50 in the sample) it is relatively

higher at -3.61%. Second, in terms of both gross and net amounts, international capital flows (especially

bank loans and portfolio flows) during the last 30 years have been concentrated in developed countries

even though there is a rising trend on capital flows to developing countries. In terms of computational

consideration, the assumption of a balanced trade makes it relatively easier to obtain numerical solutions

since there is no need to keep track of the current account dynamics for each country.14The assumption that labour is only used in the non-mining sector is based on the observation that a

number of mineral-resource abundant and dependent countries have very low labour share in the mining

sector (less than 10%). As the main objective of the model is to provide linkages between mineral resource

abundance/dependence and output level/growth, this assumption is not crucial for this objective. These

linkages depend mainly on exogenous sectoral TFP levels, which determine the allocation of mobile factor

across sectors. Since physical capital is the mobile factor, the inclusion of labour as another mobile factor is

not crucial for this feature.15Caselli and Feyrer (2005) finds substantial differences in marginal product of capital across countries.

On average, the marginal product of capital in developing countries is more than twice as large as in the

developed countries. The dispersion is even wider within developing countries, with the marginal product

of capital being three times as variable as within the developed countries.16Using the production functions in the model, we find that the average mining and non-mining TFP

growth rates during 1970-2000 in G7 countries are -0.54% and 1.13% respectively. Similar results for OECD

countries are also found in Kets and Lejour (2003). Under these assumptions, the model will predict a rising

trend in the relative price of mineral goods that is also consistent with data.

14

model parameters to match the average values or ratios in these countries. The source of

growth in each country is originated from the exogenous growth in the non-mining sector

and is generated through capital accumulation.

3.2 Production Sectors

The technologies for mining and non-mining sectors in each country are given by the fol-

lowing production functions:

YRit = ARiRαi K1−α

Rit (1)

YMit = AMiγtMLµ

i XθitK

1−µ−θMit (2)

where ARi and AMi are the exogenous initial country-specific TFP levels in the mining

and non-mining sector respectively; γM (> 1) is the exogenous TFP growth factor in the

non-mining sector and is common for all countries; Ri is the natural capital stock (mineral

resource endowments) in country i; Li is the labour employed in the non-mining sector of

country i; Kjit is the capital used in sector j of country i at time t (j = R,M); and Xit is

the mineral goods used in the non-mining sector of country i at time t.

Capital accumulation in each country follows a standard law of motion:

Ki,t+1 = (1− δ) Kit + Iit (3)

where Kit and Iit are aggregate capital stock and gross investment respectively in country

i at time t. Investment goods in each country are made of non-mineral goods only.

3.3 Firm Problem

Let YM be the numeraire goods. In each country, the mining sector (R) faces the following

firm problem in any period t:

max{KRit,Ri}

{pRtYRit − ritKRit − qRitRi} (4)

15

subject to (1), while the non-mining sector (M) faces the following problem in any period

t:

max{KMit,Xit,Li}

{YMit − ritKMit − pRtXit − witLi} (5)

subject to (2), where pRt is the world price of mineral goods (in unit of non-mineral goods)

at time t; rit is the rental price of physical capital (gross real interest rate) in country i at

time t; qRit is the rental price of natural capital in country i at time t; and wit is the labour

wage in the non-mining sector in country i at time t.

3.4 Household Problem

In each country, there is a representative household who owns all factors of production

and supplies labour inelastically. Each country may have different exogenous barriers to

investment (different relative prices of investment goods). The representative household in

each country solves the following problem:

max{Cit,Iit}

{ ∞∑t=0

βt (Cit/Li)1−σ

1− σLi

}(6)

subject to (3), and the following budget constraint in each period:

Cit + piIit = ritKit + qRitRi + witLi (7)

where pi is the exogenous country-specific relative price of investment goods (in terms of

consumption goods).

3.5 Market Clearing Conditions

In each country, there is a competitive market for physical capital. The physical capital

market clearing condition in each period is given by:

KRit + KMit = Kit (8)

Also, there are competitive world markets for mineral and non-mineral goods. The

world goods market clearing conditions in each period are given by:

16

N∑i=1

Xit =N∑

i=1

YRit (9)

N∑i=1

(Cit + piIit) =N∑

i=1

YMit (10)

3.6 Definitions of Key Aggregate Variables

The Gross Domestic Product (GDP) in each country at any period t is the sum of value-

added in mining and non-mining sector, and is given by:

GDPit = pRtYRit + (1− θ) YMit (11)

Each country can produce and trade mineral and non-mineral goods. Hence, exports of

mineral and non-mineral goods in any period t are defined respectively as follows:

EXRit = pRt (YRit −Xit) (12)

EXMit = YMit − Cit − piIit (13)

With above definitions, a country is exporting mineral goods (non-mineral goods) if EXRit >

0 (EXMit > 0) and is importing mineral goods (non-mineral goods) if EXRit < 0 (EXMit <

0).

In each country, the resource abundance (RAi) is defined as natural capital stock per

worker, while the resource dependence (RDit) at time t is defined as share of export of

mineral goods in GDP. Their definitions are given respectively by:

RAi =Ri

Li(14)

RDit =EXRit

GDPit(15)

17

3.7 Equilibrium Definitions

Competitive Equilibrium

A competitive equilibrium in the model world consists of:

(i) a set of prices: {pRt}t=0,...,∞, {rit, qRit,wit}t=0,...,∞; i=1,...,N

(ii) a set of allocation: {YRit, YMit, Xit,Cit,KRit,KMit,Ki,t+1}t=0,...,∞; i=1,...,N

such that given prices, the allocation solves:

(a) Firm Problem (3.3) for i = 1, ..., N (i.e. for all countries) and for t = 0, ...,∞

(b) Household Problem (3.4) for i = 1, ..., N

(c) Physical Capital Market Clearing Condition (8) for i = 1, ..., N and for t = 0, ...,∞, and

(d) World Goods Market Clearing Conditions [(9) and (10)] for t = 0, ...,∞.

Balanced Growth Path Equilibrium

There are two production sectors in each country. A balanced growth path therefore requires

the relative price of mineral goods to adjust so that the nominal output of mineral goods

grows at the same rate as the output of non-mineral goods. Hence, a balanced growth

path equilibrium with constant real interest rates (possibly different) for all countries, is a

competitive equilibrium defined above with the properties that (1) the output of non-mineral

goods, consumption and physical capital grow at a constant rate; and (2) the relative price

of mineral goods grows at a constant rate such that the nominal output of mineral goods

also grows at the same rate as the non-mineral goods. Specifically,

(i) {YMit, Cit,KRit,KMit,Ki,t}i=1,...,N grow at rate γ − 1,

(ii) {YRit, Xit} i=1,...,N grow at γ−1pR

γ − 1, and

(iii) pRt grows at γpR − 1,

where γ = γ1/(µ+αθ)M and γpR = γ

α/(µ+αθ)M .

3.8 Characterizing the Equilibrium

In any period of time t, the equilibrium in the model world can be characterized by the

production functions of mining and non-mining sector [Equation (1) and (2) for all i =

1, ..., N ], the market clearing condition for physical capital [Equation (8) for all i = 1, ..., N ],

the world market clearing condition for mineral goods [Equation (9)] as well as the following

conditions:

18

Xit =

(θAMiγ

tMLµ

i K1−µ−θMit

pRt

) 11−θ

i = 1, ..., N (16)

β

(Ci,t+1

Cit

)−σ

=pi

pi (1− δ) + ri,t+1i = 1, ..., N (17)

rit = (1− µ− θ) AMiγtMLµ

i XθitK

−µ−θMit i = 1, ..., N (18)

pRt (1− α) ARiRαi K−α

Rit = (1− µ− θ) AMiγtMLµ

i XθitK

−µ−θMit i = 1, ..., N (19)

Cit + piKi,t+1 − (1− δ) piKit = pRtYRt + (1− θ) YMit i = 1, ..., N (20)

Equation (16) captures each country’s optimal use of mineral goods (being intermediate

goods) in producing non-mineral goods. Equation (17) is the usual Euler’s equation, which

determines intertemporal optimal tradeoff between current and future consumption for the

representative household in each country. Equation (18) equates the real interest rate with

marginal product of physical capital in each country. The optimal allocation of physical

capital between mining and non-mining sectors in each country is captured by Equation

(19). Finally, Equation (20) ensures a balanced trade in each country.17

3.9 Model Mechanics

In each country, the linkages between mineral resource abundance/dependence and the

level/growth rate of per-worker aggregate and non-mining GDP depend crucially on its

exogenous initial sectoral TFP levels (ARi, AMi). Given competitive physical capital market

in each country and free trade in goods across countries, sectoral TFP levels determine

mainly each country’s sectoral allocation of physical capital and trade pattern. This feature

together with country-specific barriers to investment (relative price of investment goods)

and endowments of natural capital and labour then determine each country’s level and17For characterizing the equilibrium, the world market clearing condition for non-mineral goods [Equation

(10)] is redundant given Equation (9) and (20).

19

growth rate of per-worker output and resource dependence, along the transition to and on

the balanced growth path.

In the model, there is a potential positive effect of mineral resource abundance on the

level of output. An increase in mineral resources, ceteris paribus, will increase the level of

mining output and so the level of aggregate output. The impact of mineral resource abun-

dance on the growth rate of output, however, depends on country-specific exogenous initial

sectoral TFP levels and barriers to investment. Ceteris paribus, if a resource-abundant

country has high (low) non-mining TFP level and low (high) barriers to investment, then it

will allocate more (less) existing physical capital into the non-mining sector and accumulate

more (less) new capital. The share of the non-mining sector in GDP will increase (decrease)

and there is an increase (decrease) in capital accumulation. As the non-mining sector grows

faster than the mining sector, the growth rate of aggregate output will increase (decrease).

Hence, the impact of mineral resource abundance per se on output growth is ambiguous. By

contrast, there is a potential negative relationship between mineral resource dependence and

output growth. If a country has low (high) non-mining TFP level and high (low) barriers

to investment, ceteris paribus, then it will allocate more (less) existing physical capital into

the mining sector and accumulate less (more) new capital. The share of the mining sector

in GDP will increase (decrease) and there is a decrease (increase) in capital accumulation.

The country will exhibit low (high) output growth and high (low) resource dependence.

This endogeneity of resource dependence implies that there is a potential reverse causality

(low output growth leads to high resource dependence) or omitted variable bias (low non-

mining TFP level leads to high resource dependence) in the regression of output growth on

resource dependence.

The scenarios above can explain why a mineral resource-abundant country may have

high level of output and no systematic pattern on the growth rate of output, and why a

mineral resource-dependent country may exhibit a low growth rate of output. Whether or

not the model can generate these relationships in the cross-section of countries, however,

is a quantitative question. It depends crucially on the actual distribution on cross-country

differences in sectoral TFP levels, relative prices of investment goods, and natural capital

endowments. The next section will address this quantitative question.

20

4 Quantitative Analysis

We now examine whether the model can account quantitatively for the cross-sectional em-

pirical relationships of mineral resource abundance and resource dependence with output

level and growth. For reasonable parameter values, the model implies that mineral resource

abundance has a positive effect on output level, but no significant impact on output growth.

The model also predicts a negative relationship between mineral resource dependence and

output growth. We also explore the link between institutional quality and the quantitative

results.

4.1 Parameterization

Each time period represents a year. There are 70 countries which are identical to the ones

used in the empirical analysis (see Table 1 for a complete list of countries and their codes).

The empirical counterpart for the mining sector (R) in the model refers to the value-added

in mining and quarrying defined in ISIC Revision 2 (See Footnote 8 for details). On the

other hand, the empirical counterpart for the non-mining sector (M) refers to the aggregate

GDP minus the value-added in mining and quarrying.

Most of the parameters are set to match key values or ratios in the model to their

counterparts in the data of G7 averages.18 The labour share (µ) and mineral goods share

(θ) in non-mining sector are set to match their counterparts in G7 average in 1990, which

is 0.5 and 0.1 respectively.19 Using the steady state equilibrium conditions for detrended

economy, we set the depreciation rate (δ) to match G7 average physical investment rate

(23.51%) and capital-output ratio (2.87) over 1970-2000, and the utility discount factor

(β) to match G7 average capital-output ratio in non-mining sector (2.86) over 1970-2000.

Based on the balanced growth path equilibrium definition, the exogenous TFP growth rate

in non-mining sector (γM − 1) is set to match G7 average growth rate of GDP per worker

over 1970-2000 (1.81%).

Similar to the real business cycle literature, it is difficult to calibrate the intertemporal18G7 includes Canada, France, Germany, Italy, Japan, United Kingdom and United States.19We look at the Input-Output Tables in 1990 for G7 countries and calculate the average share of labour

compensation and share of use of mineral goods in non-mining gross output.

21

elasticity of substitution (σ) directly using the equilibrium conditions of our model. We set

σ equal to 1. This assumption of logarithmic utility function is also used in the growth

and development literature [e.g. Parente & Prescott (1994), Gollin et al. (2002), Restuccia

(2004)]. Finally, because of the lack of data on natural capital share in the mining sector

(α), we assume this sector has the same physical capital share as the non-mining sector.

This assumption together with the constant returns to scale in the mining sector allow us

to obtain the implied natural capital share.20 The following table summarizes the values of

parameters and their targets to match:

Parameters Value Target

µ 0.5 G7 average labour share in non-mining sector

θ 0.1 G7 average share of mineral goods used in non-mining sector

δ 0.0638 G7 average physical investment rate and capital-output ratio

β 0.95 G7 average capital-output ratio in non-mining sector

γM 1.0101 G7 average growth rate of GDP per worker

σ 1 logarithmic utility function

α 0.6 capital share in non-mining sector

4.2 Quantitative Experiment

In the quantitative experiment, we ask if exogenous cross-country differences in initial sec-

toral TFP levels (ARi, AMi), relative price of investment goods (pi), and endowments of

factors of production [natural capital (Ri), physical capital (Ki0) and labour (Li)] can ac-

count for the cross-sectional relationships between mineral resource abundance/dependence

and output level/growth. All exogenous factors are referred to their values in 1970 except

the subsoil capital stock that we only have data in 1994. The initial period (t = 0) in

the model thus corresponds to year 1970. We feed these exogenous factors in the model20In the Input-Output Tables for G7, the value-added measure for each sector is the sum of compensation

of employees, gross operating surplus and net indirect taxes. For the mining sector, the gross operating

surplus may include the rents of natural capital and physical capital. Since the Input-Output Tables only

provide the lump-sum figures of gross operating surplus, it is not possible to obtain directly the natural

capital share in this sector.

22

and solve numerically for the transition to the balanced growth path.21 Using the simulated

time series for each country during the first 31 periods (from 1970-2000), we conduct similar

correlation and regression analysis as in Section 2. We then compare the simulated results

with the corresponding empirical findings from the data.

We refer to various existing data sets to obtain cross-country data on natural capital,

labour and relative prices of investment goods. We also follow standard approaches to con-

struct data on physical capital and sectoral TFP levels. Natural capital refers to the subsoil

capital stock data (see Section 2.1 for details). Data on labour force (economically active

population) and relative prices of investment goods are from World Development Indicators

and Penn World Table 6.1 respectively. Aggregate physical capital stocks are constructed

by using the investment share data from Penn World Table 6.1 and following the perpetual

inventory method described in Klenow and Rodriguez-Clare (1997). To construct data on

sectoral TFP levels, a residual approach is used as follows. First, assuming equalization of

the rental price of capital across mining and non-mining sectors, we can use the aggregate

capital stock estimates to back out sectoral uses of capital stocks. Second, based on the

production function of mineral goods (Equation 1), we can estimate the mining TFP level.

Finally, we substitute the first order condition for optimal use of mineral goods in the non-

mining sector (Equation 16) into the production function of non-mineral goods (Equation

2), and then estimate the non-mining TFP level.

We first examine the predicted relationships of mineral resource abundance with out-

put level and growth. Figure 9 and 10 illustrate the respective scatter plots of predicted

level and growth rate of per-worker GDP (including both aggregate and non-mining GDP

in each figure) with exogenous mineral resource abundance. Mineral resource abundance

seems to exhibit a positive relationship with output level, but no systematic relationship

with output growth. Table 10 (Model A: endogenous price of mineral goods) confirms that

there is a significant positive correlation between mineral resource abundance and the level21To solve the transition to the balanced growth path, we first solve the transition to the steady state for

the detrended economy. We then add back the corresponding growth factors for detrended variables to obtain

the solutions for the original economy. Appendix 6.1 and 6.2 present in details the detrended equilibrium

conditions and steady state equilibrium conditions respectively. We use standard reverse shooting method

to solve the model. A brief outline of the algorithm is discussed in Appendix 6.3

23

of per-worker output, and an insignificant correlation between mineral resource abundance

and the growth rate of per-worker output (including both aggregate and non-mining GDP).

These predicted correlation statistics are close to those in the empirical data. Besides, the

simulated regression results are also consistent with the corresponding empirical findings.

Table 11 (Model A) reports the regression of predicted GDP level (including aggregate

and non-mining GDP per worker separately) on exogenous mineral resource abundance

and predicted average investment rate. Similarly, Table 12 (Model A) reports the regres-

sion of predicted GDP growth rate (including aggregate and non-mining GDP per worker

separately) on exogenous mineral resource abundance, predicted initial GDP and average

investment rate. Consistent with data, the model predicts that mineral resource abundance

has a significant positive effect on output level, but has an insignificant impact on output

growth.

Next, we examine the predicted relationships of mineral resource dependence with out-

put level and growth. Figure 11 and 12 illustrate the respective scatter plots of predicted

level and growth rate of per-worker GDP (including both aggregate and non-mining GDP

in each figure) with endogenous mineral resource dependence. Mineral resource dependence

seems to exhibit no systematic relationship with output level, but a negative relationship

with output growth. Similar conclusion can be drawn from Table 13 (Model A: endoge-

nous price of mineral goods). There is an insignificant correlation between mineral resource

dependence and output level, and a significant negative correlation between mineral re-

source dependence and output growth (both aggregate and non-mining GDP per worker).

These predicted correlation statistics are close to those in the empirical data. Besides, the

model also yields similar regression results compared with the empirical findings from the

data. The regression of predicted GDP level (including aggregate and non-mining GDP per

worker separately) on predicted mineral resource dependence and average investment rate

is shown in Table 14 (Model A). Similarly, the regression of predicted GDP growth rate

(including aggregate and non-mining GDP per worker separately) on endogenous mineral

resource dependence, predicted initial GDP and average investment rate is shown in Table

15 (Model A). Consistent with data, the model predicts that mineral resource dependence

has an insignificant effect on output level, but has a significant negative impact on output

24

growth.

The quantitative results above imply that one cannot simply use the regression of output

growth/level on endogenous resource dependence to infer the impacts of resource abundance

on output growth/level. In fact, one should be cautious of interpreting the finding of recent

“resource curse” literature. What the recent literature finds is a negative relationship

between natural resource dependence and output growth. Natural resource abundance per

se is beneficial to output level while not detrimental to output growth.

4.3 Robustness Check: Using Exogenous Mineral Goods Prices

In our multi-country model, the equilibrium relative price of mineral goods is endogenously

determined by the demand and supply conditions of mineral and non-mineral goods for

all countries in the sample. Furthermore, each country’s demand and supply conditions of

mineral and non-mineral goods depend mainly on its exogenous factors such as endowments

of natural capital. This implies that the simulated time series of relative price of mineral

goods may depend on the distribution of cross-country differences in exogenous factors. The

cross-country distribution then depends on sample selection. In the quantitative experiment

above, we restrict our attention to the 70 countries covered in the subsoil capital stock data.

This sample does not contain some mineral resource-rich countries such as Iraq and Russia

that may have large impact on the world price of mineral goods. Therefore, the simulated

results may be subject to sample selection.

To check the robustness of the quantitative results above, we take the relative price of

mineral goods as exogenous and conduct similar quantitative experiment. For the first 31

periods (from 1970 to 2000), the time series of relative price of mineral goods are taken

from the actual data over the corresponding periods. From the 32nd period (year 2001)

and onwards, the price is assumed to grow at the balanced growth rate of original model

(γpR − 1). We feed the relative price of mineral goods as well as other exogenous factors

in the model, and solve for the transition to the balanced growth path. We then conduct

similar correlation and regression analysis as in Section 4.2, and compare the simulated

results with the corresponding empirical findings from the data.

The exogenous relative price of mineral goods from 1970 to 2000 is computed as the ratio

25

of the price of mineral goods to the price of manufactured goods. The price of mineral goods

is the weighed average of petroleum price index and metal price index, with weights being

the average export earnings of the commodities over 1995-1997. Data on the commodity

price indices are from International Financial Statistics. The price of manufactured goods

refers to the unit value of manufactured goods exported by developed countries. The data

are from United Nations Conference on Trade and Development’s Handbook of Statistics.

Comparing with the original model with endogenous relative price of mineral goods, we

find that the model with exogenous price yields consistent results for the predicted rela-

tionships between mineral resource abundance and output level/growth. Table 10 (Model

B: exogenous price of mineral goods) reports the predicted correlation of mineral resource

abundance with output level and growth (including both aggregate and non-mining GDP

per worker). Table 11 and 12 (Model B) reports the respective simulated results of regress-

ing output level and growth (including both aggregate and non-mining GDP per worker)

on mineral resource abundance. Both correlation and regression analysis indicate mineral

resource abundance is beneficial to output level but not detrimental to output growth.

We also find that the model with exogenous relative price of mineral goods also generates

similar results as the original model for the predicted relationships between mineral resource

dependence and output level/growth. The predicted correlation statistics between mineral

resource dependence and output level/growth (including both aggregate and non-mining

GDP per worker) are shown in Table 13 (Model B: exogenous price of mineral goods). The

simulated results of regression of output level and growth (including both aggregate and

non-mining GDP per worker) on mineral resource dependence are reported respectively in

Table 14 and 15 (Model B). In sum, the model predicts that countries with high resource

dependence tend to exhibit lower growth rates of per-worker output and no systematic

pattern on their levels of per-worker output.

This alternative experiment suggests that the quantitative results of the original model

with endogenous mineral goods price are unlikely susceptible to sample selection bias.

26

4.4 Natural Resources and Institutional Quality

In this subsection, we explore the link between institutional quality and the quantitative

results in Section 4.2 and 4.3. Our approach is to examine the empirical relationships of

institutional quality with mineral resource abundance and dependence, and also the rela-

tionships of institutional quality with sectoral TFP levels and relative price of investment

goods. There are two motivation for this analysis. First, some of the recent “resource curse”

literature finds a negative effect of natural resources on institutional quality [e.g. Leite &

Weidmann (1999), Isham et al. (2005)]. These studies, however, do not distinguish be-

tween resource abundance and dependence, and use resource dependency ratios as proxies

for resource abundance. Second, there are empirical evidences that institutions and polices

affect productivity (TFP) and capital accumulation, which in turn determine long-run out-

put level and growth [e.g. Hall & Jones (1999), Knack & Keefer (1995), Mauro (1995)].

These two streams of studies suggest that natural resource abundance may affect institu-

tional quality, which in turn may affect productivity level (TFP). In our model both natural

resource abundance and sectoral TFP levels are treated as exogenous factors. Their poten-

tial linkages with institutional quality imply that institutional quality may matter for the

predicted relationships between resource abundance/dependence and output level/growth.

We use two different measures as proxies for institutional quality. The first measure

is an index of Government Antidiversion Policies (GADP) used in Hall & Jones (1999).

The original source of GADP data is from International Country Risk Guide. The GADP

is an average of five measures during 1986-1995, including law and order, bureaucratic

quality, corruption, risk of expropriation and government repudiation of contracts. The

index is measured from zero to one with a higher score representing a better institutional

quality. The second measure for institutional quality is the governance indicators created by

Kaufmann et al (2004). They provide a set of estimates of six dimensions of governance from

1996-2000. The index is measured from -2.5 to 2.5 with a higher score representing a better

institutional quality. We use four of the six measures, including government effectiveness

(GE), regulatory quality (RQ), rule of law (RL), and control of corruption (CC). Specifically,

GE measures the quality of public service provision, the quality of the bureaucracy, the

competence of civil servants, the independence of the civil service from political pressures,

27

and the credibility of the government’s commitment to policies. RQ measures the incidence

of market-unfriendly policies such as price controls or inadequate bank supervision, as well

as the perceptions of burdens imposed by excessive regulation in areas such as foreign trade

and business development. RL measures the extent to which agents have confidence in and

abide by the rules of society, including perceptions of the incidence of crime, the effectiveness

and predictability of the judiciary, and the enforceability of contracts. CC measures the

perceptions of corruption by public power for private gain.

We first examine the empirical relationships of institutional quality with mineral resource

abundance and dependence. For consistency, we use the same measures of mineral resource

abundance and dependence as in Section 2. Table 16 indicates that institutional quality

is positively correlated with resource abundance but negatively correlated with resource

dependence. These results suggest that resource-abundant countries tend to have better

institutional quality while resource-dependent countries tend to have poor institutional

quality. Hence, the claim from some of the recent “resource curse” literature [e.g. Leite

& Weidmann (1999), Isham et al. (2005)] that natural resource abundance results in poor

institutional quality is unwarranted. Their findings reflect only the negative relationship

between institutional quality and resource dependence but not resource abundance.

Next, we examine the empirical relationships of institutional quality with sectoral TFP

levels (including mining and non-mining sector) and relative price of investment goods,

using the same data sets in Section 4.2. We find that institutional quality is positively

correlated with non-mining TFP level but negatively correlated with relative price of in-

vestment goods (see Table 17). This finding is consistent with some studies documenting

a positive association between institutional quality and aggregate TFP level [e.g. Hall &

Jones (1999)].

The two findings above suggest that differences in institutional quality can explain

why mineral resource abundance and dependence may exhibit different relationships with

output level and growth respectively. Countries with high mineral resource abundance tend

to have better institutional quality and so higher non-mining TFP levels. In the model,

these countries will allocate more physical capital into the non-mining sector and will have

higher levels of both non-mining and aggregate output. On the other hand, countries with

28

poor institutional quality tend to have lower non-mining TFP levels and higher relative

prices of investment goods. They will allocate more physical capital into the mining sector

and accumulate less new capital. The share of the mining sector in GDP will increase and

there is a decrease in capital accumulation. Hence, these countries will exhibit lower growth

in output and higher resource dependence.

5 Conclusion

The “resource curse” phenomenon that countries with abundant natural resources tend

to grow more slowly than resource-poor countries has been widely accepted as one of the

stylized facts for modern economic growth.22 This paper, however, points out that the

“resource curse” finding of recent literature is subject to two potential caveats: (1) using

endogenous resource dependency ratios as a proxy for resource abundance; and (2) focusing

on the impact of resource dependence on output growth but not the impact of resource

abundance on output level. Hence, one should take cautious of interpreting this finding

before exploring any policy implications such as whether or not a resource-rich country

should exploit its natural resources.

We examine whether natural resource abundance is beneficial or detrimental to both

output level and growth taking into account the two potential caveats. We find that the

distinction between natural resource abundance and resource dependence matters since they

exhibit different empirical relationships with both the level and growth rate of output per

worker. In particular, mineral resource abundance is positively related to output level, but

not significantly related to output growth. On the contrary, mineral resource dependence

is negatively related to output growth, but not significantly related to output level.

We develop a simple dynamic model to investigate the endogeneity of natural resource

dependence and the impacts of natural resource abundance on output per worker. We use

the model to illustrate that one cannot simply use the regression of output growth/level

on endogenous resource dependence to infer the impacts of resource abundance on output

growth/level. Using the model, we find that cross-country differences in mineral resource22For instance, recent work like Sala-i-Martin (1997) and Doppelhofer et al. (2000) find natural resources

as one of the ten most robust variables in empirical analysis of economic growth.

29

abundance, TFP levels in mining and non-mining sector, and relative prices of investment

goods can account quantitatively for the empirical relationships. For reasonable parameter

values, the model implies that mineral resource abundance has a significant positive effect

on output level, but has an insignificant impact on output growth. The model also predicts

that countries with high mineral resource dependence tend to exhibit low output growth.

Hence, the “resource curse” phenomenon reflects only a negative relationship between natu-

ral resource dependence and output growth. Natural resource abundance per se is beneficial

to output level while not detrimental to output growth.

30

6 Appendix

6.1 Transitional Dynamics (Detrended Equilibrium Conditions)

To solve the model transition to the balanced growth path, we follow standard approach by

first detrending all variables with their corresponding growth factors, and then solving the

transition to the steady state for the transformed economy.23 Once we solve the transition

path for all variables of the transformed economy, we add back the growth factors for all

detrended variables to obtain the solutions for the original model. We denote all detrended

variables with small letters except for the detrended price of mineral goods which is de-

noted by a “hat” notation. Specifically, we detrend {YMit, Cit,KRit,KMit,Kit}i=1,...,N by

γt, {YRit, Xit} i=1,...,N by γ−tpR

γt, and pRt by γtpR

. The transitional dynamics of the trans-

formed (detrended) economy can be characterized by the following equations:

βγ−σ/(µ+αθ)M

(ci,t+1

cit

)−σ

=pi

pi (1− δ) + ri,t+1i = 1, ..., N (21)

rit = (1− µ− θ) θθ/(1−θ)A1/(1−θ)Mi L

µ/(1−θ)i p̂

θ/(θ−1)Rt k

−µ/(1−θ)Mit i = 1, ..., N (22)

(1− α) ARiRαi p̂

1/(1−θ)Rt k−α

Rit = (1− µ− θ) θθ/(1−θ)A1/(1−θ)Mi L

µ/(1−θ)i k

−µ/(1−θ)Mit i = 1, ..., N

(23)

cit+γ1/(µ+αθ)M piki,t+1−(1− δ) pikit = (1− θ) θθ/(1−θ)A

1/(1−θ)Mi L

µ/(1−θ)i p̂

θ/(θ−1)Rt k

(1−µ−θ)/(1−θ)Mit

+ARiRαi p̂Rtk

1−αRit i = 1, ..., N (24)

kRit + kMit = kit i = 1, ..., N (25)

p̂1/(1−θ)Rt =

N∑i=1

(θAMiL

µi k1−µ−θ

Mit

)1/(1−θ)

N∑i=1

ARiRαi k1−α

Rit

(26)

23Appendix 6.2 presents in details the steady state equilibrium conditions for the transformed economy.

31

After solving for the above equations, we can also obtain other relevant detrended ag-

gregate variables for any country i by the following equations:

yRit = ARiRαi k1−α

Rit (27)

xit =

(θAMiL

µi k1−µ−θ

Mit

p̂Rt

) 11−θ

(28)

yMit = AMiLµi xθ

itk1−µ−θMit (29)

gdpit = p̂RtyRit + (1− θ)yMit (30)

exRit = p̂Rt (yRit − xit) (31)

exMit = yMit − cit − γ1/(µ+αθ)M piki,t+1 − (1− δ) pikit (32)

6.2 Steady State Equilibrium Conditions for Detrended Economy

The steady state equilibrium conditions for any country i can be characterized by the

following equations:

ri = pi

[β−1γ

σ/(µ+αθ)M − (1− δ)

](33)

ri = (1− µ− θ) θθ/(1−θ)A1/(1−θ)Mi L

µ/(1−θ)i p̂

θ/(θ−1)R k

−µ/(1−θ)Mi (34)

(1− α) ARiRαi p̂

1/(1−θ)R k−α

Ri = (1− µ− θ) θθ/(1−θ)A1/(1−θ)Mi L

µ/(1−θ)i k

−µ/(1−θ)Mi (35)

ci+[γ

1/(µ+αθ)M − (1− δ)

]piki = ARiR

αi p̂Rk1−α

Ri +(1− θ) θθ/(1−θ)A1/(1−θ)Mi L

µ/(1−θ)i p̂

θ/(θ−1)R k

(1−µ−θ)/(1−θ)Mi

(36)

32

kRi + kMi = ki (37)

The steady state world price of mineral goods is given by:

p̂1/(1−θ)R =

N∑i=1

(θAMiL

µi k1−µ−θ

Mi

)1/(1−θ)

N∑i=1

ARiRαi k1−α

Ri

(38)

Substituting (33) into (34) and (35), the respective steady state kMi and kRi for country

i are given by:

kMi =(1− µ− θ)(1−θ)/µ θθ/µA

1/µMi Lip̂

−θ/µR

r(1−θ)/µi

(39)

kRi =(1− α)1/α A

1/αRi Rip̂

1/αR

r1/αi

(40)

Substituting (39) and (40) into (38), the steady state world price of mineral goods is

given by:

p̂(µ+αθ)/αµR =

[θ(µ+θ)/µ (1− µ− θ)(1−µ−θ)/µ

(1− α)(1−α)/α

]N∑

i=1

(A

1/µMi Li

r(1−µ−θ)/µi

)N∑

i=1

(A

1/αRi Ri

r(1−α)/αi

) (41)

The steady state values for other key aggregate variables in country i are given as follows:

yRi =(1− α)(1−α)/α A

1/αRi Rip̂

(1−α)/αR

r(1−α)/αi

(42)

yMi =(1− µ− θ)(1−µ−θ)/µ θθ/µA

1/µMi Lip̂

−θ/µR

r(1−µ−θ)/µi

(43)

gdpi =(1− α)(1−α)/α A

1/αRi Rip̂

1/αR

r(1−α)/αi

+(1− θ) (1− µ− θ)(1−µ−θ)/µ θθ/µA

1/µMi Lip̂

−θ/µR

r(1−µ−θ)/µi

(44)

exRi =(1− α)(1−α)/α A

1/αRi Rip̂

1/αR

r(1−α)/αi

− (1− µ− θ)(1−µ−θ)/µ θ(µ+θ)/µA1/µMi Lip̂

−θ/µR

r(1−µ−θ)/µi

(45)

33

6.3 Computational Algorithm for Solving Transition

To solve numerically the transition to the steady state for the detrended economy, we use the

standard reverse shooting method. The algorithm is outlined as follows. Denote the steady

state values of consumption and aggregate capital as css and kss respectively. Assume

the model will converge to its steady state at period T + 1 (T be sufficient large). Set

k(T + 1) = kss and c(T + 1) = λcss where λ is sufficiently close to 1. Using the equilibrium

conditions for the detrended economy (Equation 21-26), solve backward the system of non-

linear difference equations for all periods and obtain k(0). Denote k(0) as a function of λ,

i.e. k(0, λ). Solve zero for the function: k(0, λ)− k0 given a tolerance level, where k0 is the

given initial capital. More generic discussion on shooting methods can be found in Judd

(1998).

6.4 Data Sources

Data Sources

Subsoil Capital Kunte et al. (1998)

Non-mineral Natural (Agricultural) Capital Ditto

Export of Mineral Goods World Development Indicators

Export of Agricultural Goods Ditto

Labour Force Ditto

Value-added in Mining World Tables

Value-added in Agriculture UN National Accounts Main Aggregates

Mining Share of GDP Ditto

Aggregate Real GDP Penn World Tables 6.1

Investment Rate Ditto

Relative Price of Investment Goods Ditto

Institutional Quality (GADP) Hall & Jones (1999)

Institutional Quality (GE, RQ, RL, CC) Kaufmann et al. (2004)

Commodity Price Indices International Financial Statistics

Unit Value of Manufactured Goods UNCTAD Handbook of Statistics

34

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39

Table 1: List of Countries and their Codes Included in this Paper

Argentina ARG Mauritania MRTAustralia AUS Mexico MEXAustria AUT Morocco MARBangladesh BGD Mozambique MOZBenin BEN Namibia NAMBolivia BOL Nepal NPLBotswana BWA Netherlands NLDBrazil BRA New Zealand NZLCameroon CMR Niger NERCanada CAN Norway NORChile CHL Pakistan PAKChina CHN Papua New Guinea PNGColombia COL Peru PERCongo, Rep. COG Philippines PHLCote d'Ivoire CIV Portugal PRTDenmark DNK Rwanda RWADominican Republic DOM Saudi Arabia SAUEcuador ECU Senegal SENEgypt, Arab Rep. EGY Sierra Leone SLEFinland FIN South Africa ZAFFrance FRA Spain ESPGermany GER Sri Lanka LKAGhana GHA Sweden SWEGreece GRC Switzerland CHEGuatemala GTM Tanzania TZAHonduras HND Thailand THAIndia IND Togo TGOIndonesia IDN Trinidad and Tobago TTOIreland IRL Tunisia TUNItaly ITA Turkey TURJamaica JAM United Kingdom GBRJapan JPN United States USAJordan JOR Venezuela, RB VENKorea, Rep. KOR Zambia ZMBMalaysia MYS Zimbabwe ZWE

40

Table 2: Correlation between GDP Level and Mineral Resource Abundance

Pearson Rank Pearson RankMineral Resource Abundance Subsoil Capital per Worker (1994) 0.43* 0.37* 0.38* 0.30**

Export of Mineral Goods per Worker (1970) 0.50* 0.51* 0.48* 0.48*

Value-Added in Mining per Worker (1970) 0.65* 0.67* 0.62* 0.65*

*Statistically significant at 1% level**Statistically significant at 5% level

Table 3: Regression Analysis: GDP Level against Mineral Resource Abundance*

Explaining VariablesMineral Resource Abundance Subsoil Capital per Worker (1994) 0.111 0.091

(0.023) (0.025) Export of Mineral Goods per Worker (1970) 0.066 0.054

(0.029) (0.030) Value-Added in Mining per Worker (1970) 0.157 0.139

(0.033) (0.035)

Investment Rate (1970-2000, average) 0.451 0.581 0.437 0.428 0.535 0.404(0.156) (0.170) (0.156) (0.168) (0.175) (0.166)

Institutional Quality (1986-1995, average) 3.607 3.254 3.120 3.799 3.514 3.373(0.398) (0.462) (0.408) (0.429) (0.477) (0.433)

Adjusted R-square 0.799 0.750 0.799 0.774 0.743 0.782Observations 70 70 70 70 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.

Aggregate GDP Non-Mining GDPReal GDP per Worker Level (2000)

Aggregate GDP Non-Mining GDPDependent Variables: Real GDP per Worker Level (2000)

41

Table 4: Correlation between GDP Growth and Mineral Resource Abundance

Pearson Rank Pearson RankMineral Resource Abundance Subsoil Capital per Worker (1994) -0.04 -0.04 0.03 -0.05

Export of Mineral Goods per Worker (1970) -0.19 -0.20 -0.08 -0.09

Value-Added in Mining per Worker (1970) -0.14 -0.10 -0.01 0.01

Table 5: Regression Analysis: GDP Growth against Mineral Resource Abundance*

Explaining VariablesMineral Resource Abundance Subsoil Capital per Worker (1994) 0.113 0.054

(0.069) (0.063) Export of Mineral Goods per Worker (1970) -0.121 -0.096

(0.075) (0.068) Value-Added in Mining per Worker (1970) -0.043 -0.043

(0.117) (0.099)

Initial Aggregate GDP per Worker (1970) -1.552 -1.141 -1.264(0.251) (0.249) (0.302)

Initial Non-Mining GDP per Worker (1970) -1.076 -0.846 -0.910(0.254) (0.246) (0.283)

Investment Rate (1970-2000, average) 1.393 1.525 1.535 1.336 1.430 1.434(0.392) (0.386) (0.401) (0.378) (0.368) (0.381)

Institutional Quality (1986-1995, average) 6.020 5.140 5.126 4.430 4.000 3.923(1.313) (1.233) (1.306) (1.339) (1.257) (1.313)

Adjusted R-square 0.463 0.462 0.441 0.356 0.368 0.350Observations 70 70 70 70 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.

Non-Mining GDP

Dependent Variables: Real GDP per Worker Growth

Aggregate GDP

(1970-2000, average)Aggregate GDP

Non-Mining GDP

Real GDP per Worker Growth (1970-2000, average)

42

Table 6: Correlation between GDP Growth/Level and Mineral Resource Dependence

Pearson Rank Pearson Rank Pearson Rank Pearson RankMineral Resource Dependence Exports of Mineral Goods as % of GDP (1970) -0.46* -0.34* -0.31* -0.32* -0.12 -0.06 -0.16 -0.12

*Statistically significant at 1% level

Real GDP per Worker Level

Aggregate GDP Non-Mining GDP(2000)

Real GDP per Worker Growth (1970-2000, average)

Aggregate GDP Non-Mining GDP

43

Table 7: Regression Analysis: GDP Growth against Mineral Resource Dependence*

Explaining Variables Aggregate GDP Non-Mining GDPMineral Resource Dependence Exports of Mineral Goods as % of GDP (1970) -3.963 -2.634

(1.339) (1.252)

Initial Aggregate GDP per Worker (1970) -1.112(0.220)

Initial Non-Mining GDP per Worker (1970) -0.893(0.226)

Investment Rate (1970-2000, average) 1.538 1.445(0.370) (0.362)

Institutional Quality (1986-1995, average) 3.837 3.299(1.272) (1.286)

Adjusted R-square 0.507 0.390Observations 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.

Table 8: Regression Analysis: GDP Level against Mineral Resource Dependence*

Aggregate GDP Non-Mining GDPExplaining VariablesMineral Resource Dependence Exports of Mineral Goods as % of GDP (1970) 0.341 0.007

(0.602) (0.614)

Investment Rate (1970-2000, average) 0.615 0.569(0.176) (0.179)

Institutional Quality (1986-1995, average) 3.607 3.753(0.471) (0.480)

Adjusted R-square 0.732 0.730Observations 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.

Dependent Variables: Real GDP per Worker Growth(1970-2000, average)

Dependent Variables: Real GDP per Worker Level (2000)

44

Table 9: Regression Analysis: GDP Level/Growth against Agricultural Resource Abundance/Dependence*

Explaining VariablesAgricultural Resource Abundance Agricultural Capital per Worker (1994) 0.137 0.032

(0.090) (0.208) Export of Agricultural Goods per Worker (1970) 0.090 0.030

(0.058) (0.128) Value-Added in Agriculture per Worker (1970) 0.481 0.365

(0.104) (0.311)Agricultural Resource Dependence Exports of Agricultural Goods as % of GDP (1970) -1.131 -0.686

(1.198) (2.566)

Initial Aggregate GDP per Worker (1970) -1.349 -1.202 -1.505 -1.196(0.226) (0.226) (0.258) (0.221)

Investment Rate (1970-2000, average) 0.582 0.634 0.52 0.598 1.499 1.434 1.475 1.413(0.174) (0.174) (0.154) (0.178) (0.396) (0.380) (0.39) (0.383)

Institutional Quality (1986-1995, average) 3.485 3.162 2.361 3.542 5.273 4.654 4.902 4.761(0.455) (0.519) (0.476) (0.461) (1.256) (1.278) (1.279) (1.224)

Adjusted R-square 0.740 0.752 0.797 0.734 0.440 0.404 0.452 0.404Observations 70 69 70 69 70 69 70 69*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.

Real GDP per Worker Level(1970-2000, average)

Dependent VariablesReal GDP per Worker Growth

(2000)

45

Table 10: Model Prediction of Correlation of Mineral Resource Abundance with GDP Level and Growth

Pearson Rank Pearson Rank Pearson Rank Pearson RankMineral Resource Abundance#

Model A: endogenous price of mineral goods 0.49* 0.41* 0.36* 0.31* -0.10 -0.13 -0.03 -0.04

Model B: exogenous price of mineral goods 0.49* 0.42* 0.34* 0.30** -0.05 -0.07 -0.15 -0.16

Data 0.43* 0.37* 0.38* 0.30** -0.04 -0.04 0.02 -0.04

#Resource Abundance: exogenous subsoil capital per worker (1994, log)*Statistically significant at 1% level**Statistically significant at 5% level

Real GDP per Worker Growth(average over 1970-2000)

Aggregate GDP Non-Mining GDP

Real GDP per Worker Level(2000, log)

Aggregate GDP Non-Mining GDP

46

Table 11: Regression of GDP Level on Mineral Resource Abundance: Model Prediction vs Data*

Model A1 Model B2 Data Model A1 Model B2 DataExplaining VariablesMineral Resource Abundance Subsoil Capital per Worker (1994) 0.153 0.155 0.105 0.093 0.083 0.084

(0.035) (0.035) (0.035) (0.035) (0.035) (0.037)

Investment Rate (1970-2000, average) 0.885 0.878 1.362 1.073 1.107 1.388(0.150) (0.150) (0.177) (0.148) (0.149) (0.188)

Adjusted R-square 0.500 0.500 0.569 0.514 0.515 0.528Observations 70 70 70 70 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.1Model A: endogenous price of mineral goods2Model B: exogenous price of mineral goods

Table 12: Regression of GDP Growth on Mineral Resource Abundance: Model Prediction vs Data*

Model A1 Model B2 Data Model A1 Model B2 DataExplaining VariablesMineral Resource Abundance Subsoil Capital per Worker (1994) 0.001 0.017 0.002 -0.008 -0.067 -0.013

(0.045) (0.043) (0.073) (0.043) (0.048) (0.064)

Initial Aggregate GDP per Worker (1970) -0.396 -0.370 -0.808(0.126) (0.123) (0.218)

Initial Non-Mining GDP per Worker (1970) -0.283 -0.307 -0.496(0.138) (0.154) (0.197)

Investment Rate (1970-2000, average) 0.936 0.847 2.223 0.831 1.050 1.890(0.187) (0.183) (0.397) (0.212) (0.233) (0.364)

Adjusted R-square 0.289 0.253 0.330 0.190 0.257 0.291Observations 70 70 70 70 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.1Model A: endogenous price of mineral goods2Model B: exogenous price of mineral goods

Dependent Variables:Real GDP per Worker Level (2000)

Aggregate GDP Non-Mining GDP

Dependent Variables:Real GDP per Worker Growth (1970-2000, average)

Non-Mining GDPAggregate GDP

47

Table 13: Model Prediction of Correlation of Mineral Resource Dependence with GDP Level and Growth

Pearson Rank Pearson Rank Pearson Rank Pearson RankMineral Resource Dependence#

Model A: endogenous price of mineral goods 0.10 0.07 -0.16 -0.10 -0.52* -0.40* -0.40* -0.27**

Model B: exogenous price of mineral goods 0.13 0.08 -0.19 -0.12 -0.46* -0.31* -0.63* -0.44*

Data -0.12 -0.06 -0.16 -0.12 -0.46* -0.34* -0.31* -0.32*

#Resource Dependence: endogenous exports of mineral goods as % of GDP (1970)*Statistically significant at 1% level**Statistically significant at 5% level

Aggregate GDP Non-Mining GDP

Real GDP per Worker Growth(2000, log) (average over 1970-2000)

Aggregate GDP Non-Mining GDP

Real GDP per Worker Level

48

Table 14: Regression of GDP Level on Mineral Resource Dependence: Model Prediction vs Data*

Model A1 Model B2 Data Model A1 Model B2 DataExplaining VariablesMineral Resource Dependence Exports of Mineral Goods as % of GDP (1970) 0.993 0.830 -0.651 0.400 0.190 -1.025

(0.579) (0.776) (0.801) (0.576) (0.775) (0.825)

Investment Rate (1970-2000, average) 1.195 1.189 1.499 1.190 1.191 1.489(0.161) (0.159) (0.180) (0.160) (0.159) (0.186)

Adjusted R-square 0.457 0.464 0.516 0.467 0.475 0.503Observations 70 70 70 70 70 70*Each regression includes a constant term.

**Standard errors of coefficients are in parentheses.1Model A: endogenous price of mineral goods2Model B: exogenous price of mineral goods

Table 15: Regression of GDP Growth on Mineral Resource Dependence: Model Prediction vs Data*

Model A1 Model B2 Data Model A1 Model B2 DataExplaining VariablesMineral Resource Dependence Exports of Mineral Goods as % of GDP (1970) -1.859 -1.996 -5.491 -1.521 -4.495 -3.539

(0.667) (0.893) (1.313) (0.621) (0.832) (1.252)

Initial Aggregate GDP per Worker (1970) -0.234 -0.215 -0.689(0.120) (0.120) (0.180)

Initial Non-Mining GDP per Worker (1970) -0.233 -0.217 -0.508(0.127) (0.125) (0.177)

Investment Rate (1970-2000, average) 0.634 0.605 1.998 0.640 0.624 1.824(0.207) (0.206) (0.357) (0.218) (0.213) (0.344)

Adjusted R-square 0.364 0.304 0.470 0.257 0.470 0.367Observations 70 70 70 70 70 70*Each regression includes a constant term.**Standard errors of coefficients are in parentheses.1Model A: endogenous price of mineral goods2Model B: exogenous price of mineral goods

Dependent Variables:Real GDP per Worker Growth (1970-2000, average)Aggregate GDP Non-Mining GDP

Dependent Variables:Real GDP per Worker Level (2000)

Aggregate GDP Non-Mining GDP

49

Table 16: Correlation of Institutional Quality with Resource Abundance and DependenceGADP1 GE2 RQ3 RL4 CC5

Resource Abundance Subsoil Capital per Worker (1994) 0.30 0.26 0.30 0.25 0.27

Export of Mineral Goods per Worker (1970) 0.38 0.45 0.45 0.48 0.46

Value-Added in Mining per Worker (1970) 0.40 0.47 0.44 0.50 0.49

Resource Dependence Exports of Mineral Goods as % of GDP (1970) -0.29 -0.29 -0.25 -0.25 -0.25

1 GADP: Government Antidiversion Policies. See text for detailed description.2 GE: Government Effectiveness. See text for detailed description.3 RQ: Regulatory Quality. See text for detailed description.4 RL: Rule of Law. See text for detailed description.5 CC: Control of Corruption. See text for detailed description.

Table 17: Correlation of Institutional Quality with Sectoral TFP Levels and Relative Price of InvestmentGADP1 GE2 RQ3 RL4 CC5

TFP Level in Non-Mining Sector (1970) 0.61 0.64 0.65 0.60 0.65

TFP Level in Mining Sector (1970) -0.02 -0.01 -0.07 -0.01 -0.003

Relative Price of Investment (1970) -0.61 -0.58 -0.67 -0.58 -0.54

1 GADP: Government Antidiversion Policies. See text for detailed description.2 GE: Government Effectiveness. See text for detailed description.3 RQ: Regulatory Quality. See text for detailed description.4 RL: Rule of Law. See text for detailed description.5 CC: Control of Corruption. See text for detailed description.

50

Figure 1. Aggregate GDP Level and Mineral Resource Abundance across Countries

Pearson correlation: 0.43 Rank correlation: 0.37

CHN

TZA

MOZ RWA

NPLGHABENBGD

INDPAK

ZMB

BWA

IDN

COG

LKA

NER

CIVSEN

THA

SLE

DOM

TGO

HNDZWECMR

EGY

MRT

PNG

KOR

GTMPHLMAR

TURJOR

BOL

TUNBRACOLNAM

JAMECU

MYS

JPN FINESPPRT

AUT

GRC

IRL

GBR

ARG

PER

ZAFMEXCHLTTO

CHE FRA ITAGERSWE DNKNZL

NLDUSA

CANAUSNOR

VEN

SAU

6

7

8

9

10

11

12

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, 1994, log)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

Pearson correlation: 0.50 Rank correlation: 0.51

SAU

VEN

NORAUSCANUSA

NLD

NZLDNK SWE GERITAFRACHE

TTOCHLMEX

ZAF

PER

ARG

GBR

IRL

GRC

AUT

PRTESP

FINJPN

MYS

ECUJAM

NAMCOL

BRA TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMR ZWEHND

TGO

DOM

SLE

THA

SENCIV

NER

LKA

COG

IDN

BWA

ZMB

PAK IND

BGDBEN GHANPL

RWA MOZ

TZA

CHN

6

7

8

9

10

11

12

-7 -5 -3 -1 1 3 5 7 9

Resource Abundance (export of mineral goods per worker, 1970, log)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

Pearson correlation: 0.65 Rank correlation: 0.67

CHN

TZA

MOZ RWA

NPL GHABENBGD

INDPAK

ZMB

BWA

IDN

COG

LKA

NER

CIVSEN

THA

SLE

DOM

TGO

HND ZWECMR

EGY

MRT

PNG

KOR

GTMPHL MAR

TURJOR

BOL

TUNBRACOL

NAMJAM

ECU

MYS

JPNFINESP

PRT

AUT

GRC

IRL

GBR

ARG

PER

ZAFMEX

CHL TTO

CHEFRAITAGERSWEDNKNZL

NLDUSA

CANAUSNOR

VEN

SAU

6

7

8

9

10

11

12

-3 -1 1 3 5 7 9 11

Resource Abundance (value-added in mining per worker, 1970, log)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

51

Figure 2. Non-mining GDP Level and Mineral Resource Abundance across Countries

Pearson correlation: 0.38 Rank correlation: 0.30

SAU

VEN

NORAUSCANUSA

NLD

NZLDNKSWEGERITAFRACHE

TTOCHLMEXZAF

PER

ARG

GBR

IRL

GRC

AUT

PRTESP

FINJPN

MYS

ECUJAMNAM

COLBRATUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWEHND

TGO

DOM

SLE

THA

SENCIV

NER

LKA

COG

IDN

BWA

ZMB

PAKIND

BGDBENGHA

NPL

RWAMOZ

TZA

CHN

6

7

8

9

10

11

12

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, 1994, log)

Non

-min

ing

GD

P pe

r wor

ker (

2000

, log

)

Pearson correlation: 0.48 Rank correlation: 0.48

SAU

VEN

NORAUSCANUSA

NLD

NZLDNK SWE GERITAFRACHE

TTOCHLMEXZAF

PER

ARG

GBR

IRL

GRC

AUT

PRTESP

FINJPN

MYS

ECU JAMNAM

COLBRA TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMR ZWEHND

TGO

DOM

SLE

THA

SENCIV

NER

LKA

COG

IDN

BWA

ZMB

PAK IND

BGDBEN GHA

NPL

RWA MOZ

TZA

CHN

6

7

8

9

10

11

12

-7 -5 -3 -1 1 3 5 7 9

Resource Abundance (export of mineral goods per worker, 1970, log)

Non

-min

ing

GD

P pe

r wor

ker (

2000

, log

)

Pearson correlation: 0.62 Rank correlation: 0.65

SAU

VEN

NOR AUSCANUSA

NLD

NZLDNK SWEGERITAFRA CHE

TTOCHLMEXZAF

PER

ARG

GBR

IRL

GRC

AUT

PRTESPFINJPN

MYS

ECU JAMNAM

COLBRA TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMR ZWEHND

TGO

DOM

SLE

THA

SENCIV

NER

LKA

COG

IDN

BWA

ZMB

PAKIND

BGDBEN GHA

NPL

RWAMOZ

TZA

CHN

6

7

8

9

10

11

12

-3 -1 1 3 5 7 9 11

Resource Abundance (value-added in mining per worker, 1970, log)

Non

-min

ing

GD

P pe

r wor

ker (

2000

, log

)

52

Figure 3. Aggregate GDP Growth and Mineral Resource Abundance across Countries

Pearson correlation: -0.04 Rank correlation: -0.04

SAU

VEN

NOR

AUSCANUSA

NLD

NZL

DNKSWE

GERITAFRA

CHETTO

CHL

MEXZAF

PER

ARG

GBR

IRL

GRC

AUTPRTESP

FINJPN

MYS

ECU

JAM

NAMCOL

BRA

TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKACOG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-5

-3

-1

1

3

5

7

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, 1994, log)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

Pearson correlation: -0.19 Rank correlation: -0.20

SAU

VEN

NOR

AUSCANUSA

NLD

NZL

DNK SWE

GERITAFRA

CHETTO

CHL

MEXZAF

PER

ARG

GBR

IRL

GRC

AUTPRTESP

FINJPN

MYS

ECU

JAM

NAMCOL

BRA

TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKACOG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-5

-3

-1

1

3

5

7

-7 -5 -3 -1 1 3 5 7 9

Resource Abundance (export of mineral goods per worker, 1970, log)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

Pearson correlation: -0.14 Rank correlation: -0.10

SAU

VEN

NOR

AUSCANUSA

NLD

NZL

DNK SWE

GERITAFRA

CHETTO

CHL

MEXZAF

PER

ARG

GBR

IRL

GRC

AUTPRTESPFINJPN

MYS

ECU

JAM

NAMCOL

BRA

TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKACOG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-5

-3

-1

1

3

5

7

-3 -1 1 3 5 7 9 11

Resource Abundance (value-added in mining per worker, 1970, log)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

53

Figure 4. Non-mining GDP Growth and Mineral Resource Abundance across Countries

Pearson correlation: 0.03 Rank correlation: -0.05

SAU

VEN

NOR

AUSCAN

USA

NLD

NZL

DNKSWE

GERITAFRA

CHE

TTOCHL

MEX

ZAF

PER

ARG

GBR

IRL

GRC

AUTPRT

ESP

FINJPN

MYS

ECU

JAM

NAM

COL

BRA

TUN

BOL

JOR

TUR

MARPHLGTM

KOR

PNGMRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKA

COG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-3

-1

1

3

5

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, 1994, log)

Gro

wth

in n

on-m

inin

g G

DP

per w

orke

r (70

-00,

avg

)

Pearson correlation: -0.08 Rank correlation: -0.09

CHN

TZA

MOZ

RWA

NPL

GHA

BENBGD

INDPAK

ZMB

BWA

IDN

COG

LKA

NER

CIV

SEN

THA

SLE

DOM

TGO

HND

ZWECMR

EGY

MRTPNG

KOR

GTM PHLMAR

TUR

JOR

BOL

TUN

BRA

COL

NAM

JAM

ECU

MYS

JPN FIN

ESP

PRT AUT

GRC

IRL

GBR

ARG

PER

ZAF

MEX

CHL TTO

CHE

FRAITA GER

SWEDNK

NZL

NLD

USACAN

AUS

NOR

VEN

SAU

-3

-1

1

3

5

-7 -5 -3 -1 1 3 5 7 9

Resource Abundance (export of mineral goods per worker, 1970, log)

Gro

wth

in n

on-m

inin

g G

DP

per w

orke

r (70

-00,

avg

)

Pearson correlation: -0.01 Rank correlation: 0.01

SAU

VEN

NOR

AUSCAN

USA

NLD

NZL

DNK SWE

GERITAFRA

CHE

TTOCHL

MEX

ZAF

PER

ARG

GBR

IRL

GRC

AUTPRT

ESP

FINJPN

MYS

ECU

JAM

NAM

COL

BRA

TUN

BOL

JOR

TUR

MARPHLGTM

KOR

PNG MRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKA

COG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-3

-1

1

3

5

-3 -1 1 3 5 7 9 11

Resource Abundance (value-added in mining per worker, 1970, log)

Gro

wth

in n

on-m

inin

g G

DP

per w

orke

r (70

-00,

avg

)

54

Figure 5. GDP Growth and Mineral Resource Dependence across Countries

Pearson correlation: -0.46 Rank correlation: -0.34

SAU

VEN

NOR

AUSCANUSA

NLD

NZL

DNKSWE

GERITAFRA

CHETTO

CHL

MEXZAF

PER

ARG

GBR

IRL

GRC

AUTPRTESPFINJPN

MYS

ECU

JAM

NAMCOL

BRA

TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKACOG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-5

-3

-1

1

3

5

7

0 10 20 30 40 50 60 70

Resource Dependence (export of mineral goods as % of GDP, 1970)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

Pearson correlation: -0.31 Rank correlation: -0.32

SAU

VEN

NOR

AUSCANUSA

NLD

NZL

DNKSWE

GERITAFRA

CHE

TTOCHL

MEXZAF

PER

ARG

GBR

IRL

GRC

AUTPRT

ESP

FINJPN

MYS

ECU

JAM

NAM

COL

BRA

TUN

BOL

JOR

TUR

MARPHLGTM

KOR

PNG MRT

EGY

CMRZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKA

COG

IDN

BWA

ZMB

PAKIND

BGDBEN

GHA

NPL

RWA

MOZ

TZA

CHN

-3

-1

1

3

5

7

0 10 20 30 40 50 60 70

Resource Dependence (export of mineral goods as % of GDP, 1970)

Gro

wth

in n

on-m

inin

g G

DP

per w

orke

r (70

-00,

avg

)

55

Figure 6. GDP Level and Mineral Resource Dependence across Countries

Pearson correlation: -0.12 Rank correlation: -0.06

SAU

VEN

NORAUSCANUSA

NLD

NZLDNKSWEGERITAFRACHE

TTOCHLMEX

ZAF

PER

ARG

GBR

IRL

GRC

AUT

PRTESPFINJPN

MYS

ECUJAM

NAMCOLBRA TUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWEHND

TGO

DOM

SLE

THA

SENCIV

NER

LKA

COG

IDN

BWA

ZMB

PAKIND

BGDBENGHANPL

RWAMOZ

TZA

CHN

6

7

8

9

10

11

12

0 10 20 30 40 50 60 70

Resource Dependence (export of mineral goods as % of GDP, 1970)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

Pearson correlation: -0.16 Rank correlation: -0.12

CHN

TZA

MOZRWA

NPLGHABEN

BGD

INDPAK

ZMB

BWA

IDN

COG

LKA

NER

CIVSEN

THA

SLE

DOM

TGO

HNDZWECMR

EGY

MRT

PNG

KOR

GTMPHLMAR

TURJOR

BOL

TUNBRACOL

NAMJAM ECU

MYS

JPNFINESPPRT

AUT

GRC

IRL

GBR

ARG

PER

ZAFMEX CHL TTO

CHEFRAITAGERSWEDNKNZL

NLDUSA

CANAUSNOR

VEN

SAU

6

7

8

9

10

11

12

0 10 20 30 40 50 60 70

Resource Dependence (export of mineral goods as % of GDP, 1970)

Non

-min

ing

GD

P pe

r wor

ker (

2000

, log

)

56

Figure 7. GDP Level and Agricultural Resource Abundance/Dependence across Countries

Pearson correlation: 0.33 Rank correlation: 0.31

SAU

VEN

NOR AUSCANUSA

NLD

NZLDNK SWEGERITA FRACHE

TTO CHLMEX

ZAF

PER

ARG

GBR

IRL

GRC

AUT

PRTESP

FINJPN

MYS

ECUJAM

NAMCOL

BRATUN

BOL

JORTUR

MARPHLGTM

KOR

PNG

MRT

EGY

CMRZWE HND

TGO

DOM

SLE

THA

SENCIV

NER

LKA

COG

IDN

BWA

ZMB

PAK IND

BGDBENGHANPL

RWAMOZ

TZA

CHN

6

7

8

9

10

11

12

7 8 9 10 11 12 13

Resource Abundance (agricultural capital per worker, 1994, log)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

Pearson correlation: -0.21 Rank correlation: -0.26

CHN

TZA

MOZRWA

NPL GHABENBGD

INDPAK

ZMB

IDN

COG

LKA

NER

SEN

THA

SLE

DOM

TGO

HNDZWE CMR

EGY

MRT

PNG

KOR

GTMPHLMAR

TURJOR

BOL

TUNBRACOL

NAMJAM

ECU

MYS

JPN FINESP

PRT

AUT

GRC

IRL

GBR

ARG

PER

ZAFMEX

CHL TTO

CHEFRAITAGER SWE DNKNZL

NLDUSA

CAN AUSNOR

VEN

SAU

6

7

8

9

10

11

12

0 10 20 30

Resource Dependence (export of agricultural goods as % of GDP, 1970)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

57

Figure 8. GDP Growth and Agricultural Resource Abundance/Dependence across Countries

Pearson correlation: 0.01 Rank correlation: 0.02

CHN

TZA

MOZ

RWA

NPL

GHA

BENBGD

INDPAK

ZMB

BWA

IDN

COGLKA

NER

CIV

SEN

THA

SLE

DOM

TGO

HNDZWE

CMR

EGY

MRT

PNG

KOR

GTM PHLMAR

TURJOR

BOL

TUN

BRA

COLNAM

JAM

ECU

MYS

JPN FINESP

PRT AUT

GRC

IRL

GBR

ARG

PER

ZAFMEX

CHL

TTOCHE

FRAITAGER

SWEDNK

NZL

NLDUSA

CANAUS

NOR

VEN

SAU

-5

-3

-1

1

3

5

7

7 8 9 10 11 12 13

Resource Abundance (agricultural capital per worker, 1994, log)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

Pearson correlation: -0.06 Rank correlation: -0.15

CHN

TZA

MOZ

RWA

NPL

GHA

BENBGD

INDPAK

ZMB

IDN

COGLKA

NER

SEN

THA

SLE

DOM

TGO

HNDZWE

CMR

EGY

MRT

PNG

KOR

GTMPHLMAR

TURJOR

BOL

TUN

BRA

COLNAM

JAM

ECU

MYS

JPN FINESP

PRTAUT

GRC

IRL

GBR

ARG

PER

ZAFMEX

CHL

TTOCHE

FRAITAGER

SWE DNK

NZL

NLDUSA

CAN AUS

NOR

VEN

SAU

-5

-3

-1

1

3

5

7

0 10 20 30

Resource Dependence (export of agricultural goods as % of GDP, 1970)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

58

Figure 9. Predicted Aggregate and Non-Mining GDP Level against Resource Abundance

Pearson correlation: 0.49 Rank correlation: 0.41

CHN

TZA

MOZRWA

NPLGHA

BENBGD

INDPAK

ZMBBWA

IDN

COG

LKA

NER

CIV

SEN

THASLE

DOM

TGO

HND

ZWECMR

EGYMRT

PNG

KORGTM

PHL

MARTUR JOR

BOLTUN

BRACOL

NAM

JAMECU

MYS

JPN FINESP

PRT

AUTGRCIRLGBRARG

PER

ZAF MEXCHLTTO

CHEFRA ITA

GERSWE

DNKNZL

NLD

USACANAUS

NOR

VENSAU

7

8

9

10

11

12

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, log)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

Pearson correlation: 0.36 Rank correlation: 0.31

CHN

TZA

MOZRWA NPL

GHA

BENBGD

INDPAK

ZMB

BWA

IDN

COG

LKA

NER

CIV

SEN

THA

SLE

DOM

TGO

HND

ZWECMREGY

MRTPNG

KORGTM

PHLMAR

TUR JOR

BOLTUN

BRACOL

NAM

JAMECU

MYS

JPN FINESP

PRT

AUTGRC

IRLGBRARG

PERZAF

MEX

CHLTTO

CHE

FRA ITAGERSWE

DNKNZL

NLD

USA

CANAUS

NORVEN

SAU

7

8

9

10

11

12

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, log)

Non

-min

ing

GD

P pe

r wor

ker (

2000

, log

)

59

Figure 10. Predicted Aggregate and Non-Mining GDP Growth against Resource Abundance

Pearson correlation: -0.10 Rank correlation: -0.13

CHN

TZA

MOZ

RWA NPL

GHA

BENBGD

IND

PAK

ZMB

BWA

IDN

COG

LKA

NER

CIV

SEN

THA

SLE

DOM

TGO

HND

ZWE

CMR

EGY

MRT

PNG

KORGTM

PHL

MARTURJOR

BOLTUN

BRACOL

NAM

JAM

ECU

MYS

JPNFIN

ESPPRT

AUT

GRCIRL

GBRARG

PER

ZAF

MEXCHL

TTOCHE

FRAITA

GERSWEDNK

NZL

NLD

USA

CANAUS

NOR

VEN

SAU

-2

-1

0

1

2

3

4

5

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, log)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

Pearson correlation: -0.03 Rank correlation: -0.04

CHN

TZA

MOZ

RWA NPL

GHA

BEN

BGD

IND

PAK

ZMB

BWA

IDN

COG

LKA

NER

CIV

SEN

THA

SLE

DOM

TGO

HND

ZWE

CMREGY

MRT

PNG

KORGTM

PHL

MAR

TURJOR

BOLTUN

BRACOL

NAM

JAM

ECU

MYS

JPNFIN

ESP

PRT

AUT

GRCIRL

GBRARG

PER

ZAF

MEXCHL

TTO

CHE

FRAITA

GERSWEDNK

NZL

NLD

USA

CANAUS

NOR

VEN

SAU

-1

0

1

2

3

4

5

-3 -1 1 3 5 7 9 11 13 15

Resource Abundance (subsoil capital per worker, log)

Gro

wth

in n

on-m

inin

g G

DP

per w

orke

r (70

-00,

avg

)

60

Figure 11. Predicted Aggregate and Non-Mining GDP Level against Resource Dependence

Pearson correlation: 0.10 Rank correlation: 0.07

SAUVEN

NOR

AUSCANUSA

NLDNZL

DNKSWE

GERITAFRA

CHE

TTOCHLMEX ZAF

PER

ARGGBR

IRLGRCAUT

PRT

ESPFINJPN

MYSECU

JAM

NAM

COLBRA

TUNBOL

JORTUR MAR

PHL

GTMKOR

PNGMRT

EGY

CMR ZWE

HND

TGO

DOM

SLETHA

SEN

CIV

NER

LKA

COG

IDN

BWAZMB

PAKIND

BGDBEN

GHANPLRWA

MOZ

TZA

CHN

7

8

9

10

11

12

-20 -10 0 10 20 30 40 50 60 70 80 90

Resource Dependence (export of mineral goods as % of GDP)

Agg

rega

te G

DP

per w

orke

r (20

00, l

og)

Pearson correlation: -0.16 Rank correlation: -0.10

CHN

TZA

MOZRWANPL

GHA

BENBGD

INDPAK

ZMB

BWA

IDN

COG

LKA

NER

CIV

SEN

THA

SLE

DOM

TGO

HND

ZWECMREGY

MRTPNG

KORGTM

PHLMAR

TURJOR

BOLTUN

BRA COL

NAM

JAMECU

MYS

JPNFINESP

PRT

AUTGRC

IRLGBR

ARG

PERZAF

MEX

CHLTTO

CHE

FRAITAGER

SWEDNK

NZLNLD

USA

CANAUS

NOR VEN

SAU

7

8

9

10

11

12

-20 -10 0 10 20 30 40 50 60 70 80 90

Resource Dependence (export of mineral goods as % of GDP)

Non

-min

ing

GD

P pe

r wor

ker (

2000

, log

)

61

Figure 12. Predicted Aggregate and Non-Mining GDP Growth against Resource Dependence

Pearson correlation: -0.52 Rank correlation: -0.40

SAU

VEN

NOR

AUSCAN

USA

NLD

NZL

DNKSWEGER

ITAFRA

CHE TTO

CHLMEX

ZAF

PER

ARGGBR

IRLGRC

AUT

PRTESP

FINJPN

MYS

ECU

JAM

NAM

COLBRA

TUNBOL

JORTUR MAR

PHL

GTMKOR

PNG

MRT

EGY

CMR

ZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKA

COG

IDN

BWA

ZMB

PAK

INDBGDBEN

GHA

NPL RWA

MOZ

TZA

CHN

-2

-1

0

1

2

3

4

5

-20 -10 0 10 20 30 40 50 60 70 80 90

Resource Dependence (export of mineral goods as % of GDP)

Gro

wth

in a

ggre

gate

GD

P pe

r wor

ker (

70-0

0, a

vg)

Pearson correlation: -0.40 Rank correlation: -0.27

SAU

VEN

NOR

AUSCAN

USA

NLD

NZL

DNKSWEGER

ITAFRA

CHE

TTO

CHLMEX

ZAF

PER

ARGGBR

IRLGRC

AUT

PRT

ESPFINJPN

MYS

ECU

JAM

NAM

COLBRA

TUNBOL

JORTUR

MAR

PHL

GTMKOR

PNG

MRT

EGYCMR

ZWE

HND

TGO

DOM

SLE

THA

SEN

CIV

NER

LKA

COG

IDN

BWA

ZMB

PAK

IND

BGD

BEN

GHA

NPL RWA

MOZ

TZA

CHN

-1

0

1

2

3

4

5

-20 -10 0 10 20 30 40 50 60 70 80 90

Resource Dependence (export of mineral goods as % of GDP)

Gro

wth

in n

on-m

inin

g G

DP

per w

orke

r (70

-00,

avg

)

62