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 capital 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 institutional 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 output 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 sector), 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 investigate 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 productivity 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 findings 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 abundant 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 empirical 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 dependence 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 sector 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 bar1
See Section 2.1 for more details about the motivation of this division and the definitions of mineral and
agricultural resources.
2
We 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.
3
Aggregate GDP refers to all sectoral value-added components of GDP as defined in International Standard 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).
4
We 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 abundance 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 nonmining 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 differences in mineral resource abundance, sectoral TFP levels and relative prices of investment
goods can account quantitatively for the cross-country empirical relationships between mineral 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 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.
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 resourcedependent 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. 5year 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 relationships 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 institutional quality and the quantitative results. Section 5 concludes.
5
Robert Solow (2001) also makes a cautious remark on the general practice of using cross-country growth
regressions to test growth theory.
6
The 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 countries.7 Mineral resources include fuel-related and other mineral resources such as oil, coal,
natural gas, metals and minerals. Agricultural resources include agricultural cropland, pasture 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)
7
For 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 sectors. Our logic is also based on standard Heckscher-Ohlin trade theory. Mineral resource
8
We 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 respectively. 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, hunting, 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.
9
For 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 distribution 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.
10
These 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 valueadded 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 there
11
Mining and Quarrying include coal mining, crude petroleum and natural gas production, metal ore
mining, and other mining activities.
12
In 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 perworker 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 abundance 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 refer 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 significant 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 abundance 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 output 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 interested 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 nonmineral 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
13
In 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.
14
The 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.
15
Caselli 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.
16
Using 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 following production functions:
1−α
YRit = ARi Riα KRit
(1)
1−µ−θ
t
YM it = AM i γM
Lµi Xitθ KM
it
(2)
where ARi and AM i 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 }
{pRt YRit − rit KRit − qRit Ri }
15
(4)
subject to (1), while the non-mining sector (M ) faces the following problem in any period
t:
max
{KM it ,Xit ,Li }
{YM it − rit KM it − pRt Xit − wit Li }
(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
(∞
X
{Cit ,Iit }
(Cit /Li )1−σ
βt
Li
1−σ
t=0
)
(6)
subject to (3), and the following budget constraint in each period:
Cit + pi Iit = rit Kit + qRit Ri + wit Li
(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 + KM it = 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
X
Xit =
N
X
i=1
N
X
YRit
(Cit + pi Iit ) =
i=1
3.6
(9)
i=1
N
X
YM it
(10)
i=1
Definitions of Key Aggregate Variables
The Gross Domestic Product (GDP) in each country at any period t is the sum of valueadded in mining and non-mining sector, and is given by:
GDPit = pRt YRit + (1 − θ) YM it
(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)
EXM it = YM it − Cit − pi Iit
(13)
With above definitions, a country is exporting mineral goods (non-mineral goods) if EXRit >
0 (EXM it > 0) and is importing mineral goods (non-mineral goods) if EXRit < 0 (EXM it <
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:
Ri
Li
(14)
EXRit
GDPit
(15)
RAi =
RDit =
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 , YM it , Xit, Cit , KRit, KM it , 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) {YM it , Cit , KRit, KM it , Ki,t }i=1,...,N grow at rate γ − 1,
(ii) {YRit , Xit }
i=1,...,N
γ − 1, and
grow at γp−1
R
(iii) pRt grows at γpR − 1,
1/(µ+αθ)
where γ = γM
3.8
α/(µ+αθ)
and γpR = γM
.
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 =
β
t Lµ K 1−µ−θ
θAM i γM
i
M it
pRt
Ci,t+1
Cit
−σ
=
!
1
1−θ
pi
pi (1 − δ) + ri,t+1
−µ−θ
t
rit = (1 − µ − θ) AM i γM
Lµi Xitθ KM
it
i = 1, ..., N
(16)
i = 1, ..., N
(17)
i = 1, ..., N
−µ−θ
−α
t
pRt (1 − α) ARi Riα KRit
= (1 − µ − θ) AM i γM
Lµi Xitθ KM
it
Cit + pi Ki,t+1 − (1 − δ) pi Kit = pRt YRt + (1 − θ) YM it
i = 1, ..., N
i = 1, ..., N
(18)
(19)
(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 , AM i ). 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 and
17
For 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 abundance 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 nonmining 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 empirical 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 intertemporal
18
19
G7 includes Canada, France, Germany, Italy, Japan, United Kingdom and United States.
We 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:
4.2
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
Quantitative Experiment
In the quantitative experiment, we ask if exogenous cross-country differences in initial sectoral TFP levels (ARi , AM i ), relative price of investment goods (pi ), and endowments of
factors of production [natural capital (Ri ), physical capital (Ki0 ) and labour (Li )] can account 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 model
20
In 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 construct 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 nonmining 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 output 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 level
21
To 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 regression 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 output 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: endogenous price of mineral goods). There is an insignificant correlation between mineral resource
dependence and output level, and a significant negative correlation between mineral resource 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 relationships 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 regressing 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 relationships 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 between 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 output 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 institutional 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 potential 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 investment 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 resource
22
For 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 natural 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 denoted by a “hat” notation. Specifically, we detrend {YM it , Cit , KRit, KM it , Kit }i=1,...,N by
γ t , {YRit , Xit }
i=1,...,N
γ t , and pRt by γpt R . The transitional dynamics of the transby γp−t
R
formed (detrended) economy can be characterized by the following equations:
−σ/(µ+αθ)
βγM
ci,t+1
cit
−σ
=
pi
pi (1 − δ) + ri,t+1
i = 1, ..., N
1/(1−θ) µ/(1−θ) θ/(θ−1) −µ/(1−θ)
Li
p̂Rt
kM it
rit = (1 − µ − θ) θθ/(1−θ) AM i
1/(1−θ) −α
kRit
(1 − α) ARi Riα p̂Rt
(21)
i = 1, ..., N
1/(1−θ) µ/(1−θ) −µ/(1−θ)
Li
kM it
= (1 − µ − θ) θθ/(1−θ) AM i
(22)
i = 1, ..., N
(23)
1/(µ+αθ)
cit +γM
1/(1−θ) µ/(1−θ) θ/(θ−1) (1−µ−θ)/(1−θ)
Li
p̂Rt
kM it
pi ki,t+1 −(1 − δ) pi kit = (1 − θ) θθ/(1−θ) AM i
1−α
+ARi Riα p̂Rt kRit
kRit + kM it = kit
N P
1/(1−θ)
p̂Rt
=
i=1
i=1
23
i = 1, ..., N
1−µ−θ
θAM i Lµi kM
it
N
P
i = 1, ..., N
(24)
(25)
1/(1−θ)
(26)
1−α
ARi Riα kRit
Appendix 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 aggregate variables for any country i by the following equations:
1−α
yRit = ARi Riα kRit
1−µ−θ
θAM i Lµi kM
it
p̂Rt
xit =
1
1−θ
(28)
1−µ−θ
yM it = AM i Lµi xθit kM
it
(29)
gdpit = p̂Rt yRit + (1 − θ)yM it
(30)
exRit = p̂Rt (yRit − xit )
(31)
1/(µ+αθ)
(32)
exM it = yM it − cit − γM
6.2
!
(27)
pi ki,t+1 − (1 − δ) pi kit
Steady State Equilibrium Conditions for Detrended Economy
The steady state equilibrium conditions for any country i can be characterized by the
following equations:
h
σ/(µ+αθ)
ri = pi β −1 γM
i
− (1 − δ)
(33)
1/(1−θ) µ/(1−θ) θ/(θ−1) −µ/(1−θ)
kM i
Li
p̂R
ri = (1 − µ − θ) θθ/(1−θ) AM i
1/(1−θ) −α
kRi
(1 − α) ARi Riα p̂R
h
1/(µ+αθ)
ci + γM
1/(1−θ) µ/(1−θ) −µ/(1−θ)
Li
kM i
= (1 − µ − θ) θθ/(1−θ) AM i
i
(34)
(35)
1/(1−θ) µ/(1−θ) θ/(θ−1) (1−µ−θ)/(1−θ)
Li
p̂R
kM i
1−α
− (1 − δ) pi ki = ARi Riα p̂R kRi
+(1 − θ) θθ/(1−θ) AM i
(36)
32
kRi + kM i = ki
(37)
The steady state world price of mineral goods is given by:
N P
1/(1−θ)
p̂R
=
i=1
1−µ−θ
θAM i Lµi kM
i
1/(1−θ)
(38)
N
P
i=1
1−α
ARi Riα kRi
Substituting (33) into (34) and (35), the respective steady state kM i and kRi for country
i are given by:
−θ/µ
1/µ
(1 − µ − θ)(1−θ)/µ θθ/µ AM i Li p̂R
kM i =
(39)
(1−θ)/µ
ri
1/α
kRi =
1/α
(1 − α)1/α ARi Ri p̂R
(40)
1/α
ri
Substituting (39) and (40) into (38), the steady state world price of mineral goods is
given by:

"
(µ+αθ)/αµ
p̂R
=
#
(1−µ−θ)/µ
θ(µ+θ)/µ (1 − µ − θ)
(1 − α)(1−α)/α
N
P
1/µ
AM i Li

(1−µ−θ)/µ

 i=1 ri
 N 1/α  P ARi Ri




(41)
(1−α)/α
i=1
ri
The steady state values for other key aggregate variables in country i are given as follows:
1/α
yRi =
(1−α)/α
(1 − α)(1−α)/α ARi Ri p̂R
1/µ
yM i =
(43)
(1−µ−θ)/µ
ri
+
(1−α)/α
−θ/µ
1/µ
1/α
(1 − α)(1−α)/α ARi Ri p̂R
(1 − θ) (1 − µ − θ)(1−µ−θ)/µ θθ/µ AM i Li p̂R
(1−µ−θ)/µ
ri
(44)
ri
1/α
exRi =
−θ/µ
(1 − µ − θ)(1−µ−θ)/µ θθ/µ AM i Li p̂R
1/α
gdpi =
(42)
(1−α)/α
ri
1/α
(1 − α)(1−α)/α ARi Ri p̂R
(1−α)/α
1/µ
−
−θ/µ
(1 − µ − θ)(1−µ−θ)/µ θ(µ+θ)/µ AM i Li p̂R
(1−µ−θ)/µ
ri
ri
33
(45)
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 k ss respectively. Assume
the model will converge to its steady state at period T + 1 (T be sufficient large). Set
k(T + 1) = k ss 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 nonlinear 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
Australia
Austria
Bangladesh
Benin
Bolivia
Botswana
Brazil
Cameroon
Canada
Chile
China
Colombia
Congo, Rep.
Cote d'Ivoire
Denmark
Dominican Republic
Ecuador
Egypt, Arab Rep.
Finland
France
Germany
Ghana
Greece
Guatemala
Honduras
India
Indonesia
Ireland
Italy
Jamaica
Japan
Jordan
Korea, Rep.
Malaysia
ARG
AUS
AUT
BGD
BEN
BOL
BWA
BRA
CMR
CAN
CHL
CHN
COL
COG
CIV
DNK
DOM
ECU
EGY
FIN
FRA
GER
GHA
GRC
GTM
HND
IND
IDN
IRL
ITA
JAM
JPN
JOR
KOR
MYS
Mauritania
Mexico
Morocco
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Niger
Norway
Pakistan
Papua New Guinea
Peru
Philippines
Portugal
Rwanda
Saudi Arabia
Senegal
Sierra Leone
South Africa
Spain
Sri Lanka
Sweden
Switzerland
Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
United Kingdom
United States
Venezuela, RB
Zambia
Zimbabwe
40
MRT
MEX
MAR
MOZ
NAM
NPL
NLD
NZL
NER
NOR
PAK
PNG
PER
PHL
PRT
RWA
SAU
SEN
SLE
ZAF
ESP
LKA
SWE
CHE
TZA
THA
TGO
TTO
TUN
TUR
GBR
USA
VEN
ZMB
ZWE
Table 2: Correlation between GDP Level and Mineral Resource Abundance
Real GDP per Worker Level (2000)
Aggregate GDP
Non-Mining GDP
Pearson
Rank
Pearson
Rank
Mineral 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*
Dependent Variables: Real GDP per Worker Level (2000)
Aggregate GDP
Non-Mining GDP
Explaining Variables
Mineral 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
(0.156)
0.581
(0.170)
0.437
(0.156)
0.428
(0.168)
0.535
(0.175)
0.404
(0.166)
Institutional Quality (1986-1995, average)
3.607
(0.398)
3.254
(0.462)
3.120
(0.408)
3.799
(0.429)
3.514
(0.477)
3.373
(0.433)
0.799
70
0.750
70
0.799
70
0.774
70
0.743
70
0.782
70
Adjusted R-square
Observations
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
41
Table 4: Correlation between GDP Growth and Mineral Resource Abundance
Real GDP per Worker Growth
(1970-2000, average)
Non-Mining GDP
Aggregate GDP
Pearson
Rank
Pearson
Rank
Mineral 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*
Dependent Variables: Real GDP per Worker Growth
(1970-2000, average)
Aggregate GDP
Non-Mining GDP
Explaining Variables
Mineral 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
(0.251)
-1.141
(0.249)
-1.264
(0.302)
Initial Non-Mining GDP per Worker (1970)
-1.076
(0.254)
-0.846
(0.246)
-0.910
(0.283)
Investment Rate (1970-2000, average)
1.393
(0.392)
1.525
(0.386)
1.535
(0.401)
1.336
(0.378)
1.430
(0.368)
1.434
(0.381)
Institutional Quality (1986-1995, average)
6.020
(1.313)
5.140
(1.233)
5.126
(1.306)
4.430
(1.339)
4.000
(1.257)
3.923
(1.313)
0.463
70
0.462
70
0.441
70
0.356
70
0.368
70
0.350
70
Adjusted R-square
Observations
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
42
Table 6: Correlation between GDP Growth/Level and Mineral Resource Dependence
Real GDP per Worker Growth
(1970-2000, average)
Non-Mining GDP
Aggregate GDP
Pearson Rank Pearson Rank
Mineral Resource Dependence
Exports of Mineral Goods as % of GDP (1970)
-0.46*
-0.34*
-0.31*
-0.32*
*Statistically significant at 1% level
43
Real GDP per Worker Level
(2000)
Aggregate GDP
Non-Mining GDP
Pearson Rank Pearson Rank
-0.12
-0.06
-0.16
-0.12
Table 7: Regression Analysis: GDP Growth against Mineral Resource Dependence*
Dependent Variables: Real GDP per Worker Growth
(1970-2000, average)
Aggregate GDP
Non-Mining GDP
Explaining Variables
Mineral 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
(0.370)
1.445
(0.362)
Institutional Quality (1986-1995, average)
3.837
(1.272)
3.299
(1.286)
0.507
70
0.390
70
Adjusted R-square
Observations
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
Table 8: Regression Analysis: GDP Level against Mineral Resource Dependence*
Dependent Variables: Real GDP per Worker Level (2000)
Aggregate GDP
Non-Mining GDP
Explaining Variables
Mineral 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
(0.176)
0.569
(0.179)
Institutional Quality (1986-1995, average)
3.607
(0.471)
3.753
(0.480)
0.732
70
0.730
70
Adjusted R-square
Observations
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
44
Table 9: Regression Analysis: GDP Level/Growth against Agricultural Resource Abundance/Dependence*
Dependent Variables
Real GDP per Worker Level
Real GDP per Worker Growth
(2000)
(1970-2000, average)
Explaining Variables
Agricultural 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
(0.226)
-1.202
(0.226)
-1.505
(0.258)
-1.196
(0.221)
Investment Rate (1970-2000, average)
0.582
(0.174)
0.634
(0.174)
0.52
(0.154)
0.598
(0.178)
1.499
(0.396)
1.434
(0.380)
1.475
(0.39)
1.413
(0.383)
Institutional Quality (1986-1995, average)
3.485
(0.455)
3.162
(0.519)
2.361
(0.476)
3.542
(0.461)
5.273
(1.256)
4.654
(1.278)
4.902
(1.279)
4.761
(1.224)
0.740
70
0.752
69
0.797
70
0.734
69
0.440
70
0.404
69
0.452
70
0.404
69
Adjusted R-square
Observations
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
45
Table 10: Model Prediction of Correlation of Mineral Resource Abundance with GDP Level and Growth
Real GDP per Worker Level
Real GDP per Worker Growth
(2000, log)
(average over 1970-2000)
Aggregate GDP
Aggregate GDP
Non-Mining GDP
Non-Mining GDP
Pearson Rank Pearson Rank Pearson Rank Pearson Rank
Mineral 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
46
Table 11: Regression of GDP Level on Mineral Resource Abundance: Model Prediction vs Data*
Dependent Variables:
Real GDP per Worker Level (2000)
Aggregate GDP
Non-Mining GDP
Model A1 Model B2
Model A1 Model B2
Data
Data
Explaining Variables
Mineral 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)
Adjusted R-square
Observations
0.885
(0.150)
0.878
(0.150)
1.362
(0.177)
1.073
(0.148)
1.107
(0.149)
1.388
(0.188)
0.500
70
0.500
70
0.569
70
0.514
70
0.515
70
0.528
70
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
Model A: endogenous price of mineral goods
1
2
Model B: exogenous price of mineral goods
Table 12: Regression of GDP Growth on Mineral Resource Abundance: Model Prediction vs Data*
Dependent Variables:
Real GDP per Worker Growth (1970-2000, average)
Non-Mining GDP
Aggregate GDP
Model A1 Model B2
Model A1 Model B2
Data
Data
Explaining Variables
Mineral 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
(0.126)
-0.370
(0.123)
-0.808
(0.218)
Initial Non-Mining GDP per Worker (1970)
Investment Rate (1970-2000, average)
Adjusted R-square
Observations
-0.283
(0.138)
-0.307
(0.154)
-0.496
(0.197)
0.936
(0.187)
0.847
(0.183)
2.223
(0.397)
0.831
(0.212)
1.050
(0.233)
1.890
(0.364)
0.289
70
0.253
70
0.330
70
0.190
70
0.257
70
0.291
70
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
Model A: endogenous price of mineral goods
1
2
Model B: exogenous price of mineral goods
47
Table 13: Model Prediction of Correlation of Mineral Resource Dependence with GDP Level and Growth
Real GDP per Worker Level
Real GDP per Worker Growth
(2000, log)
(average over 1970-2000)
Aggregate GDP
Non-Mining GDP
Aggregate GDP
Non-Mining GDP
Pearson Rank Pearson Rank Pearson Rank Pearson Rank
Mineral 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
48
Table 14: Regression of GDP Level on Mineral Resource Dependence: Model Prediction vs Data*
Dependent Variables:
Real GDP per Worker Level (2000)
Aggregate GDP
Non-Mining GDP
Model A1 Model B2
Model A1 Model B2
Data
Data
Explaining Variables
Mineral 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)
Adjusted R-square
Observations
1.195
(0.161)
1.189
(0.159)
1.499
(0.180)
1.190
(0.160)
1.191
(0.159)
1.489
(0.186)
0.457
70
0.464
70
0.516
70
0.467
70
0.475
70
0.503
70
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
Model A: endogenous price of mineral goods
1
2
Model B: exogenous price of mineral goods
Table 15: Regression of GDP Growth on Mineral Resource Dependence: Model Prediction vs Data*
Dependent Variables:
Real GDP per Worker Growth (1970-2000, average)
Aggregate GDP
Non-Mining GDP
Model A1 Model B2
Model A1 Model B2
Data
Data
Explaining Variables
Mineral 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
(0.120)
-0.215
(0.120)
-0.689
(0.180)
Initial Non-Mining GDP per Worker (1970)
Investment Rate (1970-2000, average)
Adjusted R-square
Observations
-0.233
(0.127)
-0.217
(0.125)
-0.508
(0.177)
0.634
(0.207)
0.605
(0.206)
1.998
(0.357)
0.640
(0.218)
0.624
(0.213)
1.824
(0.344)
0.364
70
0.304
70
0.470
70
0.257
70
0.470
70
0.367
70
*Each regression includes a constant term.
**Standard errors of coefficients are in parentheses.
Model A: endogenous price of mineral goods
1
2
Model B: exogenous price of mineral goods
49
Table 16: Correlation of Institutional Quality with Resource Abundance and Dependence
GE2
RQ3
RL4
GADP1
Resource Abundance
Subsoil Capital per Worker (1994)
0.30
0.26
0.30
0.25
CC5
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
-0.29
-0.29
-0.25
-0.25
-0.25
Resource Dependence
Exports of Mineral Goods as % of GDP (1970)
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 Investment
GE2
RQ3
RL4
CC5
GADP1
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
Aggregate GDP per worker (2000, log)
12
11
USA
IRL
NOR
AUT
FRA
AUS
DNK NLD CAN
JPN FINITAGER
GBR
SWE
ESP
NZL
GRC
KOR PRT
ARG
TTO
CHL
MYS
MEX
ZAF
TUN
VEN
BRA
TUR BWA
DOM
PER COL
THA
EGYJOR NAM
GTM
ECU
MAR
PHL
IDN JAM
CHN BOL PNG
IND
ZWE
HND
PAK
CMR
CIV
COG
SEN
NPLBGD
MRT
GHA
BEN
SLE ZMB
TGO
CHE
10
9
LKA
8
MOZNERRWA
7
SAU
TZA
6
-3
-1
1
3
5
7
9
11
13
15
Resource Abundance (subsoil capital per worker, 1994, log)
Pearson correlation: 0.50 Rank correlation: 0.51
Aggregate GDP per worker (2000, log)
12
USAIRL
NLD
NOR
AUT
FRA
AUSCAN
CHE
ITA
DNK
FIN
GBR SWE GER
ESP
NZL
SAU
KOR PRTGRC
ARG
MYS CHL TTO
MEX
TUN ZAF
VEN
TUR BRABWA
COL
DOM
PER
EGYTHA JOR
GTM
NAM
ECU
PHLMAR
LKA
JAM
IDN
BOL
CHN
PNG
IND
ZWE
HND
PAK
CIV CMR
COG
SEN
11
JPN
10
9
BGD
8
NPL
BEN
NER
GHA
RWA
7
MRT
TGO SLE
MOZ
ZMB
TZA
6
-7
-5
-3
-1
1
3
5
7
9
Resource Abundance (export of mineral goods per worker, 1970, log)
Pearson correlation: 0.65 Rank correlation: 0.67
Aggregate GDP per worker (2000, log)
12
USA
IRL
NLD
NOR
AUT
FRA
CAN
CHE
ITA
FIN
GER AUS
JPN
GBR
SWE
ESP
NZL
PRT KOR GRC
ARG
MYS CHL TTO
MEX
ZAF
VEN
BRA TURBWATUN
COL JOR
PER
THADOM
EGY
GTM
NAM
ECU
MAR
PHL
JAM
LKA
IDN
BOL
CHN PNG
ZWE
HND
PAKIND
CMR
CIV
COG
SEN
BGD
MRT
BEN
GHA
SLE
TGO
ZMB
MOZ
NER
RWA
11
DNK
10
9
8
NPL
7
SAU
TZA
6
-3
-1
1
3
5
7
9
Resource Abundance (value-added in mining per worker, 1970, log)
51
11
Figure 2. Non-mining GDP Level and Mineral Resource Abundance across Countries
Pearson correlation: 0.38 Rank correlation: 0.30
Non-mining GDP per worker (2000, log)
12
11
IRL
USA
AUT
FRA
DNK NLD CAN
AUS
JPN FINITAGER
GBR
SWE
NOR
ESP
NZL
GRC
KOR PRT
ARG
TTO
MEX
MYS
CHL
CHE
10
9
LKA
NPLBGD
BEN
GHA
8
ZAF
TUN
BRA
TUR
DOM
PER
THA
JOR COL
GTM
EGY
BWA
MAR
NAM
PHL
ECU
IDN JAM
CHN BOL
IND
ZWE
HND
PAK
CMR
CIV
PNG
SEN
SLE
TGO
MOZNERRWA
SAU
VEN
MRT
ZMB COG
7
TZA
6
-3
-1
1
3
5
7
9
11
13
15
Resource Abundance (subsoil capital per worker, 1994, log)
Pearson correlation: 0.48 Rank correlation: 0.48
Non-mining GDP per worker (2000, log)
12
11
USAIRL
NLD
AUT
CHE
FRA
ITA
DNK
FIN
GER
GBR SWEAUSCAN
NOR
ESP
NZL
KOR PRTGRC
JPN
ARG
10
MEX
SAU
TTO
MYS CHL
TUN ZAF
BRA
DOM
PER
THACOL
JOR
EGY
BWA
NAM
PHLMAR
JAM
IDN
BOL
CHN
ZWE
HND
CIV CMR
PNG
SEN
TUR
GTM
9
LKAECU
PAK
BGD
8
NPL
IND
BEN
GHA
RWA
COG
NER
MOZ
TGO
VEN
MRT
ZMB
SLE
7
TZA
6
-7
-5
-3
-1
1
3
5
7
9
Resource Abundance (export of mineral goods per worker, 1970, log)
Pearson correlation: 0.62 Rank correlation: 0.65
Non-mining GDP per worker (2000, log)
12
11
IRL
USA
NLD
AUT
FRA
CHE
ITA
FIN
CAN
GER AUS
JPN
GBR
SWE
NOR
ESP
NZL
PRT KOR GRC
ARG
MEX CHL TTO
MYS
DNK
10
9
8
SAU
ZAF
TUN
BRA TUR
DOM
COL JOR
PER
THA
VEN
GTM
BWA EGY
MAR
NAM
LKA ECU PHL
JAM
IDN
BOL
CHN
ZWE
HND
PAKIND
CMR PNG
CIV
SEN
BGD
BEN
GHA
MRT
ZMB
SLE
RWA
COG
TGO
NERMOZ
NPL
7
TZA
6
-3
-1
1
3
5
7
9
Resource Abundance (value-added in mining per worker, 1970, log)
52
11
Figure 3. Aggregate GDP Growth and Mineral Resource Abundance across Countries
Growth in aggregate GDP per worker (70-00, avg)
Pearson correlation: -0.04 Rank correlation: -0.04
7
BWA
5
KOR
CHN
IRL
MYS
IDN
IND
PAK
EGY TUN
NPL JPN FINDOM
NOR
PRT
AUT
COG
TUR
ITAGERGBR
FRA ESP
USA CHL
GRC DNK
JOR
BRA
NLD CAN
AUS
BGD
CMR
SWE
MAR
ECU TTO
BEN
PHLZWE
GTM
MEX
ARG NZLCOL
PNG
SEN
BOL ZAF
GHA
NAM
HND
CIV
PER MRT
JAM
ZMB
TGO
SLE
THA
3
LKA
1
CHE
RWA
TZA
-1
MOZ
NER
VEN
-3
SAU
-5
-3
-1
1
3
5
7
9
11
13
15
Resource Abundance (subsoil capital per worker, 1994, log)
Growth in aggregate GDP per worker (70-00, avg)
Pearson correlation: -0.19 Rank correlation: -0.20
7
BWA
5
KOR
CHN
IRL
MYS
THA
IDN
3
IND
PAK
COG
LKA
NPL
BGD
1
EGYJPN
DOMPRT
TUN
FIN
NOR
AUT
TUR
GER
ITA
FRA
GBR
ESP USA
CHL
GRC DNK
BRA JOR
NLD
CMR
SWEAUSCAN
MAR
ECU
TTO
PHLZWE
GTM
COL
MEX
ARG
CHE
PNG
RWA
NZL
SEN
BOL
GHA
ZAF NAM
HND
TZA
CIV
MRT
PER
JAM
ZMB
MOZ
TGO
SLE
BEN
-1
NER
VEN
-3
SAU
-5
-7
-5
-3
-1
1
3
5
7
9
Resource Abundance (export of mineral goods per worker, 1970, log)
Growth in aggregate GDP per worker (70-00, avg)
Pearson correlation: -0.14 Rank correlation: -0.10
7
BWA
5
KOR
CHN
IRL
THA
IDN
3
MYS
IND
PAK
JPN
EGY
DOM
TUN
FIN
NOR
PRT
AUT
COG
TUR
GERUSA
ITA
LKA
FRA
GBR
ESP
CHL
BRA DNK JORGRCNLD
CAN
BGD CMR
SWE AUS
MAR
TTO
GTM BEN ECU PHLCOL ZWEMEX
ARGNZL CHE
RWA PNG SEN
BOL
GHA
HND
ZAF NAM
TZACIV
MRT JAM
PER
ZMB
MOZ
TGO
NER
SLE
NPL
1
-1
VEN
-3
SAU
-5
-3
-1
1
3
5
7
9
Resource Abundance (value-added in mining per worker, 1970, log)
53
11
Figure 4. Non-mining GDP Growth and Mineral Resource Abundance across Countries
Growth in non-mining GDP per worker (70-00, avg)
Pearson correlation: 0.03 Rank correlation: -0.05
5
BWA
KOR
CHN
IRL
THA
3
LKA
1
RWA
CHE
MYS
IDN
IND
TUN
PAKEGY
NPL JPN FINDOM
AUT
PRT
TUR
FRA ITAGERGBR
NOR
USA
ESP
CHL
BRA
GRC DNK
TTO
NLD
JOR
NAM CAN
AUS
BGD
CMR
MARSWE
BEN
PHLZWE ZMB
GTM
MEX
ECU
ARG NZLCOL
SEN
BOL ZAF
GHA
COG
HND
TZA
CIV
PER
-1
MOZ
NER
PNG
MRT
JAM
SAU
SLE
TGO
VEN
-3
-3
-1
1
3
5
7
9
11
13
15
Resource Abundance (subsoil capital per worker, 1994, log)
Growth in non-mining GDP per worker (70-00, avg)
Pearson correlation: -0.08 Rank correlation: -0.09
5
BWA
KOR
CHN
IRL
MYS
THA
IDN
3
IND
PAK
NPL
FIN
AUT
ITA
GER
FRA
NOR
GBR
ESP USA
CHL
BRA JOR
GRC DNK
CAN TTO
NLD
NAM
AUS
SWE
CMR
MAR
ZMB
PHLZWE
GTM
MEX
COL
RWA
CHE
ECU
ARG
SEN
NZL
BOL
ZAF
GHA
COG
HND
TZA
CIV
PNG
MRT
PER
JAM
SAU
MOZ
TGO SLE
BGD
1
TUN
EGYJPN
DOM
PRT
TUR
LKA
BEN
-1
NER
VEN
-3
-7
-5
-3
-1
1
3
5
7
9
Resource Abundance (export of mineral goods per worker, 1970, log)
Growth in non-mining GDP per worker (70-00, avg)
Pearson correlation: -0.01 Rank correlation: 0.01
5
BWA
KOR
CHN
THA
3
IND
PAK
NPL
DOM
PRT
TUR
LKA
BGD CMR
BEN
GTM
RWA
ECU
1
TZA
CIV
IRL
IDN
MYS
TUN
EGY
JPN
FIN
AUT
ITA
GER
FRA
NOR
GBR
USA
ESP
CHL TTO
BRA DNK JORGRCNLD
CAN
SWE AUS NAM
MAR
ZMB
ZWE
PHL
MEX
COL
CHE
ARG
SEN
BOL NZL ZAF
GHA
COG
HND
PNG
MRT
PER
JAM
-1
MOZ
NER
SAU
SLE
TGO
VEN
-3
-3
-1
1
3
5
7
9
Resource Abundance (value-added in mining per worker, 1970, log)
54
11
Figure 5. GDP Growth and Mineral Resource Dependence across Countries
Growth in aggregate GDP per worker (70-00, avg)
Pearson correlation: -0.46 Rank correlation: -0.34
7
BWA
5 KOR
CHN
IRL
THA
3 IND
IDN
MYS
PAK
JPN
EGY
DOM
NPL
FIN
TUN
PRT
AUT NOR
COG
TUR
GER
ITA
LKA
FRA
GBR
USA
ESP
CHL
GRC
BRA
JOR
NLD
DNK
C
AN
AUS
CMR
SWE
MAR
1BGD
ECU
BEN PHL
GTM
COL
MEX ZWE
ARG
CHE
RWA
NZL
SEN
BOL
GHA
NAM
ZAF
HND
TZA
CIV
PER
JAM
-1 MOZ
TGO
NER
SLE
TTO
PNG
MRT
ZMB
VEN
-3
SAU
-5
0
10
20
30
40
50
60
70
Resource Dependence (export of mineral goods as % of GDP, 1970)
Pearson correlation: -0.31 Rank correlation: -0.32
Growth in non-mining GDP per worker (70-00, avg)
7
5
BWA
KOR
CHN
IRL
THA
IDN
MYS
3 IND
TUN
EGY
PAK
JPN
DOM
NPL
FIN
AUT
PRT
TUR
ITA
GER
FRA
GBR NOR
LKA
USA
ESP
CHL
BRA
GRC
CAN
NLD
DNK
JOR
NAM
AUS
BGD
SWE
CMR
MAR
1BEN
ZWE
PHL
GTM
MEX
COL
RWA
CHE
ECU
ARG
SEN
NZL
BOL
GHAZAF
HND COG
TZA
CIV
PER
JAM
-1
MOZ
SLE
TGO
NER
TTO
ZMB
PNG
MRT
SAU
VEN
-3
0
10
20
30
40
50
60
Resource Dependence (export of mineral goods as % of GDP, 1970)
55
70
Figure 6. GDP Level and Mineral Resource Dependence across Countries
Pearson correlation: -0.12 Rank correlation: -0.06
Aggregate GDP per worker (2000, log)
12
IRL
11 USA
NOR
NLD
AUT
FRA
AUS
CHE
CAN
ITA
DNK
FIN
GER
JPN
GBR
SWE
ESP
NZL
GRC
KOR
PRT
ARG
CHL
MYS
10 MEX
ZAF
TUN
BRA
BWA
TUR
COL
DOM
PER
THA JOR
EGY
GTM
NAM
ECU
MAR
9 LKA PHL
IDNJAM
BOL
CHN
IND
ZWE
HND
PAK
CMR
CIV
SEN COG
NPL
8BGD
BENGHA
SLE
TGO
MOZ
NER
RWA
7
SAU
TTO
VEN
PNG
MRT
ZMB
TZA
6
0
10
20
30
40
50
60
70
Resource Dependence (export of mineral goods as % of GDP, 1970)
Pearson correlation: -0.16 Rank correlation: -0.12
Non-mining GDP per worker (2000, log)
12
IRL
11 USA
NLD
AUT
FRA
CHE
ITA
DNK
CAN
FIN
AUS
GER
JPN
GBR
SWE
NOR
ESP
NZL
GRC
KOR
PRT
ARG
10 MEX
MYS
CHL
TUN
BRA ZAF
TUR
DOM
COL
PER
THA JOR
GTM
EGY BWA
NAM
MAR
9 LKA PHL
ECU
IDNJAM
BOL
CHN
IND
ZWE
HND
PAK
CMR
CIV
SEN
NPL
8BGD
BENGHA
RWA
MOZ COG SLE
TGO
NER
SAU
TTO
VEN
PNG
MRT
ZMB
7
TZA
6
0
10
20
30
40
50
60
Resource Dependence (export of mineral goods as % of GDP, 1970)
56
70
Figure 7. GDP Level and Agricultural Resource Abundance/Dependence across Countries
Pearson correlation: 0.33 Rank correlation: 0.31
Aggregate GDP per worker (2000, log)
12
11
USA IRL
AUT
FRANOR FIN
DNK
SWE
ESP
SAU
PRT
KOR GRC
ARG
TTO
CHL
MYS
MEX
ZAF
TUN
VEN
BRA
BWA
TUR
COL
DOM
PER
JOR GTM
THA
EGY
NAM
ECU
MAR
PHL
LKA
IDN
BOL
CHN
PNG HND
ZWEIND
PAK
CIVCMR
COG SEN
BGD
NPL
GHA MRT
BEN
SLE
ZMB
TGO
MOZ
NER
RWA
NLD ITA
GER
JPN CHE
GBR
10
9
JAM
8
7
AUS
CAN
NZL
TZA
6
7
8
9
10
11
12
13
Resource Abundance (agricultural capital per worker, 1994, log)
Pearson correlation: -0.21 Rank correlation: -0.26
Aggregate GDP per worker (2000, log)
12
USA
AUTNOR
FRA
CHE
CAN AUS
ITA
DNK
FIN
GER
JPN
GBR
SWE
ESP
SAU KORGRCPRT
ARG
TTO
CHL
10
MEX
ZAF
TUN
VEN
TUR BRA
PERCOL
JOR
EGYTHA
MAR
9
JAM
IDN
BOL CHN
IND
ZWE
PAK
11
NLD
NZL
MYS
DOM
GTM
NAM
ECU
PHL
LKA
PNG
CMR
COG
BGD
8
ZMB
7
NPLMRT
SLE
MOZ
RWA NER
IRL
HND
SEN
GHA
BEN
TGO
TZA
6
0
10
20
Resource Dependence (export of agricultural goods as % of GDP, 1970)
57
30
Figure 8. GDP Growth and Agricultural Resource Abundance/Dependence across Countries
Growth in aggregate GDP per worker (70-00, avg)
Pearson correlation: 0.01 Rank correlation: 0.02
7
BWA
5
KOR
CHN
THA
IDN
3
IRL
MYS
IND
PAK
EGY
DOM
NPL
FIN
TUN
PRTTUR
AUT NOR
COG
GER
ITA
GBR LKAESPFRA
USA
CHL
GRC
BRA
JOR
NLD
DNKCMR
BGD
SWE
MAR
ECU
BENGTM
TTO
PHL MEX COL
ZWE
ARG
CHE
PNG SEN
RWA
BOL
GHA
NAM
ZAF HND
TZA
CIV
MRT
PER
ZMB
MOZ
TGO
NER
SLE
JPN
1
JAM
-1
CAN
AUS
NZL
VEN
-3
SAU
-5
7
8
9
10
11
12
13
Resource Abundance (agricultural capital per worker, 1994, log)
Growth in aggregate GDP per worker (70-00, avg)
Pearson correlation: -0.06 Rank correlation: -0.15
7
5
KOR
CHN
IRL
THA
3
1
-1
-3
MYS
IDN
IND
JPNPAK NPL
EGY
DOM
TUN FIN
AUT
PRT
COG
TURNOR
GER
ITA
LKA
GBR
FRA
USA
CHLESP
GRC
BRAAUS
JORCAN
DNK
NLD
BGD SWE
CMR
ECU
TTO MAR BEN GTM
COL
MEX
ZWE PHL
CHE
PNGNZL
RWA ARG
SEN
BOL
GHA
NAM
ZAF
TZA
MRTJAM PER
ZMB
MOZ
TGO
NER
SLE
HND
VEN
SAU
-5
0
10
20
Resource Dependence (export of agricultural goods as % of GDP, 1970)
58
30
Figure 9. Predicted Aggregate and Non-Mining GDP Level against Resource Abundance
Pearson correlation: 0.49 Rank correlation: 0.41
12
SAU
VEN
Aggregate GDP per worker (2000, log)
USA
CHE
DNK NLD
ITA SWE
GBR
GRCARG NZL
GER
AUT
ESP
IRL
JPN FIN
ZAF
PRT
FRA
11
TTO
NOR
MEX
CHL
PER
COL
BRAJAMMYS
ECU
JOR
GTM
KORMAR
TUR
10
CAN
AUS
DOM
HND
PHL
TUNNAM
CIV
EGYIDN
BOL
THA
MRT
SLE
ZWE
PNG
INDCMR
TGO
BGD SEN PAK
ZMB
BEN
BWA
GHA
NPL
CHN
LKA
9
MOZNER
RWA
8
COG
TZA
7
-3
-1
1
3
5
7
9
11
13
15
Resource Abundance (subsoil capital per worker, log)
Pearson correlation: 0.36 Rank correlation: 0.31
Non-mining GDP per worker (2000, log)
12
USA
CHE
DNK NLD CAN
AUS
FRA ITA SWE
GBR NZL
GRC
ARG
GER IRL
AUT
JPN FINESP
MEXNOR
VEN
PRT
ZAF
CHL
TTO
PER
GTM
CJAM
OL
BRA
KOR TUR
ECU
JOR
MYS
MAR
DOM
PHL
HND
CIV
THA
EGY TUN
BOL PNG
IDN
INDCMR MRT
ZWE
PAK
BGD
TGO
SEN
SLE
BEN
NAM
BWA
NPL
CHN
GHA
11
10
LKA
9
MOZ
NERRWA
8
SAU
ZMB
COG
TZA
7
-3
-1
1
3
5
7
9
11
Resource Abundance (subsoil capital per worker, log)
59
13
15
Figure 10. Predicted Aggregate and Non-Mining GDP Growth against Resource Abundance
Pearson correlation: -0.10 Rank correlation: -0.13
Growth in aggregate GDP per worker (70-00, avg)
5
4
3
LKA
2
IDN
IND
ECU
THA
GTM
BGDKOR
MAR
PAK
DOM
TUR
GRCIRL
BEN
HND
JOR
NPL
USA
MEX
CJAM
OL MYS
BRA
PRT GBR MRT
CHL
ITA CHN
CMR
ARG
JPN
PHL
FRA
ESP
DNK
ZAF CAN
CIV TGO
FIN GER
AUS
SWE
NLD
EGY
AUT
VEN
SLE
PER
BWA
TTO
SEN
NZL
ZWE
NOR
PNG
BOL
TUN
ZMB
GHA
RWA
CHE
MOZ
NER
1
TZA
0
NAM
COG
-1
SAU
-2
-3
-1
1
3
5
7
9
11
13
15
Resource Abundance (subsoil capital per worker, log)
Pearson correlation: -0.03 Rank correlation: -0.04
Growth in non-mining GDP per worker (70-00, avg)
5
IDN
4
3
RWA
LKA
2
ECU
IND
THA
GTM
MAR
MYS
BGDKOR
PAK
JAM
DOM
TUR
GRCIRL MRT
HND
BEN
JOR COL
CHL
BRAUSA MEX
NPL
PRT GBR
VEN
ITA CHN
CMR
ZAF
TGO
ARG
CAN
FRA
JPN
ESP
DNK
TTO
CIV PHL
SLE EGY
AUS
FIN AUT
GER
SWE
NLD
PER
BWA
CHE
MOZ
SEN
NER
1
ZWE
NZL
BOL PNG
ZMB
TUN
NOR
GHA
TZA
0
NAM
COG
SAU
-1
-3
-1
1
3
5
7
9
11
Resource Abundance (subsoil capital per worker, log)
60
13
15
Figure 11. Predicted Aggregate and Non-Mining GDP Level against Resource Dependence
Pearson correlation: 0.10 Rank correlation: 0.07
12
Aggregate GDP per worker (2000, log)
SAU
USA
CHE
AUS CAN
NLD
DNK
ITA
FRA
GBR
SWE
NZL
GRC
GER
AUT
ARG
ESP
IRL
JPN
FIN
NOR
MEX
PRT
11
VEN
PER
GTMBRA COLJAM
ECU
KOR
TURJOR
MAR
10
DOM
HND
PHL
CIV
THA
LKA
9
TTO
ZAF
CHL
MYS
NAM
TUN
EGY
BOL IDN
MRT
SLE
ZWE
TGO
PNG
CMR
IND
PAK
BGD
SEN
BENMOZ
BWA NER
RWA
GHA
NPL CHN
8
ZMB
COG
TZA
7
-20
-10
0
10
20
30
40
50
60
70
80
90
Resource Dependence (export of mineral goods as % of GDP)
Pearson correlation: -0.16 Rank correlation: -0.10
Non-mining GDP per worker (2000, log)
12
USA
CHE
DNK
NLD AUS CAN
SWEITA
FRA
GBR
NZL
GRC
ARG
GER
ESP
AUT
IRL
FIN
JPN
MEX
NOR
PRT
11
VEN
ZAF
CHL
GTMBRA PER
COLJAM
KOR
TUR
ECU
JOR
MYS
MAR
DOM
PHL
HND
CIV
THA
LKA
EGY TUN
BOL IDN
PNG
CMR
IND
MRT
ZWE
PAK
BGD
TGO
SEN
SLE
BEN
MOZ
BWA
NER
RWA
NPL CHN
GHA
10
9
8
TTO
SAU
NAM
ZMB
COG
TZA
7
-20
-10
0
10
20
30
40
50
60
70
Resource Dependence (export of mineral goods as % of GDP)
61
80
90
Figure 12. Predicted Aggregate and Non-Mining GDP Growth against Resource Dependence
Pearson correlation: -0.52 Rank correlation: -0.40
Growth in aggregate GDP per worker (70-00, avg)
5
4
IDN
IND
ECU
THA
GTM
BGD
KOR
MAR
PAK
DOM
TUR
GRC
BEN
IRL
HND
MYS
JOR
NPL
LKA
MEX
USACOLJAM
RWA
BRA
PRT
MRT
GBR
CHL
CHN
ITA
CMR
ARG
JPN
PHL
FRA
ESP
DNK
CAN TGOZAF
CIV
FIN
SWE
GAUT
ER AUS
NLD
EGY
SLE
PER
BWA
CHE
MOZ
SEN
NZL
ZWE
NOR
PNG
NER
BOL
TUN
3
2
1
VEN
TTO
ZMB
GHA
TZA
0
NAM
COG
-1
SAU
-2
-20
-10
0
10
20
30
40
50
60
70
80
90
Resource Dependence (export of mineral goods as % of GDP)
Pearson correlation: -0.40 Rank correlation: -0.27
Growth in non-mining GDP per worker (70-00, avg)
5
IDN
4
ECU
IND
THA
GTM
MAR
BGD
MYS
KOR
PAK
JAM
DOM
TUR
GRC
HND
IRL JOR
BEN
MRT
COL
USA
CHL
RWA
MEX
LKA
BRA
NPL
PRT
GBR
CHN
ITA
CMR
ARG
CAN TGOZAF
FRA
JPN
ESP
PHL
DNK
CIV
EGY
SLE
AUS
FIN
GER
SWE
AUT
NLD
PER
BWA
CHE
MOZ
SEN
ZWE
NOR
NZL
PNG
BOL
NER
TUN
3
2
1
VEN
TTO
ZMB
GHA
TZA
0
NAM
COG
SAU
-1
-20
-10
0
10
20
30
40
50
60
70
Resource Dependence (export of mineral goods as % of GDP)
62
80
90