DEPARTMENT OF ECONOMICS
OxCarre (Oxford Centre for the Analysis of
Resource Rich Economies)
Manor Road Building, Manor Road, Oxford OX1 3UQ
Tel: +44(0)1865 281281 Fax: +44(0)1865 281163
reception@economics.ox.ac.uk www.economics.ox.ac.uk
OxCarre Research Paper 81
_
Natural Resource Dependence and Economic
Performance in the 1970-2000 Period
George Mavrotas*
Global Development Network
Syed Mansoob Murshed
University of Birmingham
&
Sebastian Torres
Universidad Católica del Uruguay
*OxCarre External Research Associate
Direct tel: +44(0) 1865 281281
E-mail: celia.kingham@economics.ox.ac.uk
Review of Development Economics, 15(1), 124–138, 2011
DOI:10.1111/j.1467-9361.2010.00597.x
Natural Resource Dependence and Economic
Performance in the 1970–2000 Period
rode_597
124..138
George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres*
Abstract
We look at the type of natural resource dependence and growth in developing countries. Certain natural
resources called point-source, such as oil and minerals, exhibit concentrated and capturable revenue patterns,
while revenue flows from resources such as agriculture are more diffused. Developing countries that export
the former type of products are regarded prone to growth failure due to institutional failure. We present an
explicit model of growth collapse with micro-foundations in rent-seeking contests with increasing returns.
Our econometric analysis is among the few in this literature with a panel data dimension. Point-source-type
natural resource dependence does retard institutional development in both governance and democracy,
which hampers growth. The resource curse, however, is more general and not simply confined to mineral
exporters.
1. Introduction
The purpose of this paper is to explore the link between certain types of natural
resource dependence and poor recent economic performance in certain developing
countries—the resource curse. The term natural resources can include agricultural and
mineral assets, as well as resources associated with water and forests. Among these,
certain types of natural resources such as oil and minerals have a tendency to lead to
production and revenue patterns that are concentrated, while revenue flows from other
types of resources such as agriculture are more diffused throughout the economy.
Following the classification in Auty (1997), countries rich in the former category may be
called point-sourced economies, while nations abundant in the latter type may be
referred to as diffuse. Sometimes, agricultural commodities such as coffee/cocoa are
also considered point-sourced, if they are produced or marketed in a manner akin to
the concentrated conditions prevalent with minerals. Isham et al. (2005) find that
point-sourced economies identified as exporters of oil, mineral, and plantation-based
crops have lower growth rates compared to other diffuse and manufactured exporters
between 1975 and 1997 because of the poorer governance engendered by a fuel,
mineral, or plantation-dependent economy. The challenge is to extend the pure crosssectional econometric analysis so that it has a time dimension, and this is what we do in
this paper.
* Mavrotas (corresponding author): Chief Economist, Global Development Network, 2nd Floor, West Wing,
ISID Complex, Plot No. 4, Vasant Kunj Institutional Area, New Delhi 110 070, India. Tel: (+91)-11-2613
9494/2613 6885; Fax: (+91)-11-2613 6893; E-mail: gmavrotas@gdnet.org. Murshed: Institute of Social Studies
(ISS), PO Box 29776, 2502 LT The Hague, Netherlands; Birmingham Business School, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. E-mail: murshed@iss.nl. Torres: Departamento de
Economía, Universidad Católica del Uruguay, Montevideo, Uruguay. E-mail: setorres@ucu.edu.uy. Mansoob
Murshed is grateful to the International Institute for Environment and Development (IIED) in London for
financial support. The authors thank the Editor and an anonymous referee for their insightful comments and
suggestions; Nicky Pouw and Admasu Shiferaw for excellent research assistance; as well as Jörg Mayer for
comprehensive comments on previous drafts. Any remaining errors or omissions remain the responsibility of
the authors.
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
125
With regard to the channels between a rich point-sourced resource dependence and
growth failure two separate avenues can be identified. The first is to do with “Dutch
disease” and the fact that a temporary but substantial increase in the price of minerals
(or remittances, as in Vargas-Silva, 2009) can cause resource misallocation via the
mechanism of relative prices; see the survey in Murshed (2004). There is a switch from
internationally traded goods to nontraded goods. Collier and Goderis (2007), for
example, find that commodity booms have a positive short-term effect on output, but
adverse long-term effects.
The second mechanism is to do with the political economy of resource rents.
Increases in the availability of resource rents following a boom in their world prices can
increase the appetite for resource rents amongst certain individuals or groups within
society. This is known as a voracity effect (Lane and Tornell, 1996), or the rentier effect
(Ross, 2001), and it can instigate serious diversion from normal productive activities.
Competition over lootable natural resource rents can sometimes descend in to outright
civil war (Murshed, 2002). A wealth of mineral resources or plantation-based production can spawn extractive and nondevelopmental institutions that eventually become
entrenched (Sokoloff and Engerman, 2000).
Acemoglu et al. (2005) argue that good economic institutions (property rights and
the rule of law) are required for growth in the longer term. They assert that these are
more likely to materialize in the context of settings that constrain the arbitrary exercise
of political power, where there is a broad-based interest in property rights and if there
are fewer rents that can be appropriated by a small political elite. Collier and Hoeffler
(2009) unpack democracy into (i) electoral competition and (ii) checks and balances,
and examine their interaction with natural resource rents in determining GDP growth.
The blend between resource rents and strong electoral competition is growth reducing;
while the mix of resource rents and strong checks and balances yield growth-enhancing
outcomes. Granted, good institutions can be the key to future economic progress. This
raises the question as to what actually determines good institutions. The Acemoglu
et al. (2005) thesis is that it is whether or not Europeans settled a colony. Our, alternative, explanation is that a nation’s endowments or economic dependence are that
which is important in determining future institutional quality.
The rest of the paper is organized as follows: section 2 constructs a theoretical model
of macroeconomic growth collapse embedded within micro-foundations of rentseeking behavior and contests, linked to the institutional environment. The major
theoretical innovation is the incorporation of increasing returns to scale in rent seeking.
Section 3 is concerned with empirical findings on the link between natural resource
dependence and growth, where the type of resource dependence exerts a positive or
negative impact on growth via institutional quality (the Fraser index of governance) or
the quality of democracy (the Polity index). The second innovation in our paper lies in
the fact that our empirical investigation is based on panel data models, unlike the purely
cross-sectional regressions of most of this literature. The third innovation in our paper
is that we employ a variety of econometric techniques to establish the robustness of our
results. Finally, section 4 concludes.
2. A Dynamic Model of Growth Collapse Combined with Rent Seeking
We begin with a competitive game of rent seeking in the spirit of Tullock (1967).
Several agents compete for rents and the competition to capture this entails a cost, be
that bribery or lobbying expenditure. Let P represent the winner-take-all prize of
resource rents that each agent is attempting to seize. Each agent’s probability of success
© 2011 Blackwell Publishing Ltd
126
George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
will depend on their own rent-seeking expenditure relative to others. The expected
utility (E) of an agent (i) in a symmetrical setting can take the form:
Ei = π i P − ci ,
(1)
where p is the probability of winning based upon the contest success function,
and c represents lobbying costs or expenditures. The contest success function is
given by:
π i ( ci , c j , s ) =
cis
; i = 1, 2; j ≠ i.
c1s + c2s
(2)
In this example above there are only two agents, i = 1, 2. The crucial parameter s
represents the “efficiency or productivity” of lobbying expenditure or bribery; if s > 1,
there are increasing returns to scale in such expenditure. If that is so, under weak
institutions of governance, lobbying expenditure is even more productive as far as rent
seekers are concerned. In many ways, s can be characterized to be negatively related to
good governance and institutional quality, with s > 1 being a sign of very poor institutional environment. Our analysis has some similarities to the grabber-friendly
institutions described in Mehlum et al. (2006), but we incorporate increasing returns to
scale to that activity, and the possibility of an attrition game. It is not only the total
available prize (P) that determines rent seeking, but also the institutional environment
parameterized by s in our model.
Substituting (2) into (1) and maximizing with respect to ci we find that:
ci =
sP
; i = 1, 2.
4
(3)
Equation (3) gives us the Cournot–Nash equilibrium level of lobbying spending by
each agent. The substitution of (3) into (1) yields the following expected utility:
Ei =
P sP
−
.
2 4
(4)
The above expression becomes negative if s > 2. If this is so, it will lead to an even more
socially wasteful war of attrition game, where the object is to make one’s opponents
exit the rent-seeking game because an opponent’s very presence yields negative
expected utility. Lobbying or rent-seeking expenditure is wasteful and detracts from
the capital stock. Total lobbying expenditures may cause a decline in the capital stock,
as investment in capital declines.At this juncture we introduce two definitions which we
intend to utilize in the macro model of growth collapse:
∑ ci = z0
and
∑ P = z1.
(5)
We now turn to the macro model, akin to the Ramsey growth model (see Murshed,
2004, for details). All variables are given in per capita values. The equilibrium level of
the capital stock in the steady state implicitly defines growth rates. An “infinitely”-lived
individual maximizes utility at each period (t):
∞
max U ( t ) = ∫t u (C ( t )) exp ( −it ) dt,
(6)
where utility (U or u) depends on consumption, C; exp is the exponential operator; and
the real interest rate is i. Maximization is subject to two budget constraints at t:
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
127
( t ) = C ( t ) + I ( t ) + iD ( t ) − f ( k ( t )) ,
D
(7)
k ( t ) = I ( t ) .
(8)
We ignore the rate of depreciation. Output subject to constant returns, Y is given by:
Y ( t ) = f ( k ( t )) .
(9)
The stock constraint (8) tells us that the rate of capital accumulation at time t, is equal
to investment (I ) at time t. Equation (9) is the production function for Y (output)
written in per capita fashion, k is the capital/labor ratio. Equation (7) is the flow
constraint in an open economy. It informs us that the rate of accumulation of international debt is given by the excess of consumption (C), investment (I ), and debt servicing
(iD, where i is the interest rate and D is debt stock), over output (f(k)).
The current value Hamiltonian (H) is:
⎧
⎡
⎛ I (t ) ⎞ ⎫
⎤
H ( t ) = u (C ( t )) − μ ( t ) ⎢C ( t ) + I ( t ) ⎨1 + g ⎜
⎟⎠ ⎬ + iD ( t ) − f ( k ( t ))⎥ + μ ( t ) pk I ( t ) .
⎝
(
)
k
t
⎩
⎣
⎦
⎭
(10)
Here pk is the shadow price of capital. The function inside g(·) gives us the cost of
installing capital per unit of existing capital, g′ > 0. The two costate variables are: m(t)
and m(t)pk.
Maximization yields the following, among other, first-order conditions:
δ H (t )
= u ′(C ( t )) − μ ( t ) = 0,
δ C (t )
(11)
δ H (t )
I (t )
g ′(⋅) = pk .
= 1 + g (⋅) +
δ I (t )
k (t )
(12)
Equation (11) tells us that the optimizing agent will equate the marginal utility of
consumption, u′(C(t)) to the shadow price of consumption, m (the Ramsey rule). The
consumption and investment decisions are separable. Equation (12) informs us that the
ratio of investment to the existing stock of capital is equated to the shadow price of
capital (pk). We may write the ratio of investment to capital as a function of the shadow
price of capital (pk).This allows us a steady-state differential equation in k, with g(·) = 0:
k = I ( t ) = k ( t )ϕ ( pk ( t )) ; ϕ ′ > 0, ϕ (1) = 0,
(13)
indicating that investment is an increasing function of the shadow price of capital (pk).
The picture is incomplete unless we postulate an equation determining pk:
p k = pk i − f ′ ( k ( t )) .
(14)
Equations (13) and (14) can be utilized to describe the dynamics and steady-state
equilibrium of the system. A resource boom is incorporated by an additive and multiplicative effect to the production function in (9):
Y ( t ) = (1 − z 0 ) f ( k ( t )) + z1.
(15)
Here z0 represents the diversion of a part of the capital stock from ordinary production
to rent-seeking activities, and z1 is the revenue component. They are described by
© 2011 Blackwell Publishing Ltd
128 George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
pk
F′
SS2
E1
E2
.
k=0
F
.
pk1 = 0
.
pk 0 = 0
k
Figure 1. Growth Collapse
equation (5). The additive component, z1 has no effect on the marginal product of
capital, and therefore no effect on investment and the capital stock, but only on
consumption. Note that the resource rents (z1) are exogenous in the sense that they are
like a pure transfer or manna from heaven. The costs to the economy (the multiplicative term z0) are, however, an endogenous outcome of rent-seeking activities described
above in equations (1) to (4). We postulate that rent seeking will reduce the effective
marginal product of capital, due to the diversion of productive investment away from
normal activities towards rent seeking.
Following Murshed (2004), the events related to investment and capital accumulation are depicted in Figure 1 in k- and pk-space. In Figure 1, the k = 0 schedule is a
horizontal line; the optimal capital stock is related to marginal productivity and not pk.
The p k = 0 line is negatively sloped as a rise in pk increases the rate of investment, which
in turn raises the capital stock (k). However, with a fixed marginal productivity of
capital, the capital stock is given at its optimal level, k*, such that pk = 1 in the steady
state; hence k will decline.
When there is a negative effect on the capital stock, the economy jumps from the
initial equilibrium at E1 to the new saddle-path (SS2) at point F. The p k = 0 schedule
will then shift to the left, and the final equilibrium is at E2. There is an initial, but not
steady-state, fall in the shadow price of capital. This makes the rate of investment
negative between F and E2, which, in turn, causes the capital stock to decline, prompting negative growth. The economy comes to a rest with a lower steady-state capital
stock and growth rate at E2. The growth collapse occurs between F and E2. Net
output declines in the new steady state due to the combined effect of the diversion
of output to rent seeking and a lower capital stock. Rent-seeking expenditure per se
is greater the higher the prize, P in (1) to (4), or z1 in (15). This implies a
large amount of resource rents. But, more importantly, total rent-seeking expenditure
for any level of natural resource rents will be greater when the polity is more
predatory, oligarchic, and poor institutions abound. This means that z0 is large in (5)
and s > 1.
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
129
3. Empirical Analysis
The Data
We investigate the link between natural resources, institutional development, and
economic growth in 56 developing countries (excluding industrialized nations and
former socialist economies) over the period 1970–2000. For each country the first two
major export items were identified based on UNCTAD (2002) and is utilized as an
indicator of resource dependence in each nation. The export commodities were then
grouped as originating from one of the following four sources: (i) point-source natural
resources; (ii) diffuse-source natural resources; (iii) coffee/cocoa; or (iv) manufacturing.
Our definition of “point” includes both proper point-source commodities such as
minerals, as well as coffee and cocoa. This is because various authors including Isham
et al. (2005) have argued in favor of aggregating coffee/cocoa economies along with
point-source goods due to similar conditions of production and distribution. The data
span the 1970–2000 period over which the structure of exports is allowed to vary, unlike
in previous studies where export structure is held fixed. A case can be made for the
GDP share of natural resources, as in Sachs and Warner (1999); however, a nation’s
principal exports are both indicative of its competitive and comparative advantage,
which has to be related to its endowments over time. Brunnschweiler and Bulte (2008)
differentiate between resource dependence and resource abundance. Our measure is
closer to a definition of resource dependence.
Many datasets on democracy exist. Perhaps the best known is the Polity dataset that
gives a democracy score of between 0 (lowest) and 10 (highest), compiled by Gurr and
Jaggers (1996).A similar autocracy dataset gives an autocracy score of between -10 and
0, with -10 being the worst score. The Polity 2 score is a combination of both autocracy
and democracy, and a reflection, both, of a country’s democratic and nondemocratic
credentials. Table 1 (descriptive statistics) indicates that point-sourced economies have
worse democracy scores and lower growth rates than diffuse economies, and diffuse
economies perform more poorly on these two counts compared to manufacturing
exporters.
We use a second data source for institutions, the Fraser chain-linked index: see
Gwartney and Lawson (2005) for details. This dataset pertains more to governance,
and is perhaps closer to what Acemoglu et al. (2005) have in mind when they refer
to “economic” institutions. It covers the following: (a) the size of government expenditure and tax structure, (b) the legal structure and property rights, (c) access to
sound money, (d) the freedom to trade internationally (the trade policy stance), and
(e) the regulatory structure. The maximum score in this index is 10. Besides being
comprehensive in its coverage of economic governance, the greatest advantage of this
dataset is that these data are available going back to 1970, in five-yearly intervals.
This allows its application to panel data estimates. Human capital is measured following the Barro and Lee dataset as an estimate of educational attainment (average
schooling years) for the population over age 15. For macroeconomic variables like
per capita GDP, investment, and real effective exchange rates, the main source was
the World Bank (2002).
Econometric Analysis
The empirical model Consider the following model that consists of equations for
institutions and growth.
© 2011 Blackwell Publishing Ltd
130 George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
Table 1. Descriptive Statistics
Variable
Base sample
Point
Diffuse
Manufactures
Polity 2
-0.58
(6.73)
408
-0.99
(6.82)
241
-0.69
(6.63)
131
2.69
(5.61)
36
5.18
(1.13)
371
5.05
(1.18)
219
5.18
(0.95)
119
6.02
(1.05)
34
1.31
(3.48)
390
0.87
(3.46)
230
1.57
(3.26)
125
3.12
(3.82)
35
6.72
(0.95)
384
6.76
(0.92)
229
6.68
(0.99)
119
6.65
(0.95)
36
0.56
(0.96)
360
0.56
(0.78)
208
0.64
(1.00)
119
0.38
(1.46)
33
2.97
(0.39)
401
2.97
(0.41)
236
2.94
(0.35)
129
3.08
(0.35)
36
Number of observations
Fraser index
Number of observations
Growth GDP per capita
Number of observations
Log income
Number of observations
Log human capital
Number of observations
Investment
Number of observations
Note: Variable sample means and standard deviations (in parentheses).
Institutionsit = θ 1 + θ 2 Point + θ 3 Diffuse + φ1Zit + ui + eit ,
(16)
Growthit = β1 + β 2 Predicted Institutionsit + ϕ i Zit ηi + υit .
(17)
The econometric model is motivated by the theory outlined in section 2. Our
econometric methodology addresses both the indirect effect of resource dependence
on growth, as well as the potential endogeneity problem that runs between institutions and growth, because not only do institutions promote growth, but increased
prosperity also affects institutional quality. One issue relates to the fact that the
impact of resource dependence on economic prosperity is indirect, working through
the institutional quality channel. Ultimately the political process determines institutional quality, and the Polity 2 index could be a proxy for the political conditions that
reproduce well-functioning economic institutions. Alternatively, we could have direct
measures of governance affected by the resource dependence type, such as with the
Fraser index. We try both, because good governance is also associated with certain
types of autocracies. The second point is related to the Lipset (1960) hypothesis,
which argues that democratic institutions automatically emerge when countries
become more affluent, because the body politic becomes more educated and conscious of their rights. Thus, there is a problem of reverse causality between
institutions and growth. We therefore need to estimate institutions separately in (16)
as a function of resource-dependence type, and use predicted values for institutions
in the growth equation, (17).
The first equation, (16), is a reduced form of the rent-seeking effect where Institutionsit
represent either the polity or governance score for country i at time t as a proxy for
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
131
rent-seeking activities. This is in line with equations (3) and (5) above, where rentseeking expenditure is a function of total available rents (P, z0, z1) and the institutional
environment (s).This in turn depends on the type of resource dependence, where we can
expect a priori that point-sourced economies fare worst.Thus, we are hypothesizing that
institutions are determined by resource dependence and not geography, culture, or
colonial heritage. Our proxies for a country’s resource dependence are its two principal
exports. Pointit and Diffuseit first reflect actual percentages of a nation’s principal exports;
we also later utilize dummy variables coded 1, if a country’s major export in a particular
year is point-source and 0 otherwise in (16); similarly for the variable diffuse. The latter
procedure is likely to magnify any negative effect of point-source or diffuse export
dependency on institutions. The point and diffuse variables also act as instruments that
correct for any endogeneity between growth and institutions. The model has two error
components: ui which is unobserved country specific and time invariant, and eit which is
normally distributed with zero mean and constant variance.
Equation (17) is the reduced form of the growth collapse theoretical equation (15)
with proxies for the reduction in the marginal product of capital via already estimated
institutions in the first equation and other standard features of growth regressions that
include human and physical capital (measured by gross fixed capital formation) as well
as the effects of resource booms via terms of trade and real exchange rate movements.
Zi is a vector of these growth regressors, also utilized in (16), including lagged investment as a ratio to GDP, initial stocks of human capital (this is justified by the fact that
human capital levels take longer gestation periods before yielding returns), indicators
of macroeconomic instability, and a control for initial levels of per capita GDP. Macroeconomic instability is proxied by standard deviations of the terms of trade and real
exchange rate on a three-year moving average basis. These are important, as these
macroeconomic indicators point to Dutch disease effects; that is the effect of relative
prices and exchange rates following resource booms on growth, in contrast to political
economy effects. By including them we are controlling for the other (nonpolitical
economy) channel through which resource booms could adversely affect growth. Our
model, therefore, has more control variables than is usual in this literature. Equation
(17) also has two error terms; a country-specific term (hi) and a normally distributed
disturbance (vit).
Unlike previous studies (Isham et al., 2005, for example) our estimates are over the
1970–2000 period for a five-year average panel of 56 developing countries where
changes in the pattern of exports and comparative advantage are therefore taken into
account. In order to test the robustness of our empirical specifications, and in addition
to using two new variables to measure Institutions, we depart from previous studies by
using not one, but several different econometric techniques, some of which take care of
unobserved country fixed effects.
First, in order to test the direction of causality in the institutions–growth correlation,
we follow the Acemoglu et al. (2005) strategy and estimate both an instrumental
variables pooled regression (Table 2) and an instrumental variables panel regression
(Table 3), where our institutional measure is instrumented with our point and diffuse
indices. The Sargan and Basmann tests for overidentifying restrictions suggest that our
instruments are valid (Table 2).
Several alternatives were then available. The random effects model has a conceptual
appeal in large datasets since it provides a more general specification for the sources of
errors and uses up fewer degrees of freedom. However, a crucial assumption in the
random effects model is that the two error components (time and nontime invariant)
are homoskedastic and serially uncorrelated. To address this issue and obtain efficient
© 2011 Blackwell Publishing Ltd
132 George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
Table 2. Instrumental Variables Pooled Regressions of GDP Per Capita Growth Rate Using Point
and Diffuse as Instruments
Point and diffuse as %
of two main exports
Fraser chain-link
index
Polity
2
Point and diffuse
as dummies
Fraser chain-link
index
Polity
2
Panel A: Instrumental Variables Two-Stage Least Squares Regression
Institutions
2.26***
0.54***
2.99***
(0.74)
(0.25)
(1.00)
Exchange rate SD
0.00***
0.00
0.00***
(0.00)
(0.00)
(0.00)
Terms of trade SD
-0.09***
-0.10***
-0.07**
(0.03)
(0.04)
(0.04)
Investment
0.78
2.40***
0.30
(0.76)
(0.77)
(0.93)
Initial human capital
0.60***
0.08
0.61***
(0.28)
(0.45)
(0.31)
Initial income level
-1.16***
-1.47***
-1.38***
(0.35)
(0.57)
(0.43)
0.69**
(0.34)
0.00
(0.00)
-0.09**
(0.05)
2.26***
(0.89)
-0.16
(0.56)
-1.73**
(0.72)
Panel B: First-Stage Regression for Institutions
Point
-1.00***
(0.23)
Diffuse
-0.98***
(0.24)
Exchange rate SD
-0.00***
(0.00)
Terms of trade SD
-0.02*
(0.01)
Investment
0.57***
(0.18)
Initial human capital
0.02
(0.08)
Initial income level
0.31***
(0.08)
-3.13**
(1.24)
-2.72**
(1.31)
-0.00*
(0.00)
-0.04
(0.06)
0.14
(1.05)
1.23***
(0.49)
1.76***
(0.49)
R-squared
Number of observations
Sargan test
Basmann test
0.23
270
0.72
0.73
-3.94***
(1.34)
-3.48***
(1.44)
-0.00
(0.00)
-0.04
(0.06)
-0.01
(1.06)
1.14***
(0.50)
1.82***
(0.49)
0.17
287
0.72
0.73
-0.78***
(0.22)
-0.67***
(0.23)
-0.00***
(0.00)
-0.02*
(0.01)
0.59***
(0.18)
0.02
(0.09)
0.31***
(0.08)
0.21
270
0.74
0.74
0.17
289
0.91
0.91
Notes: Panel A reports the two-stage least squares estimates instrumenting for the Fraser chain-link index
and for Polity 2 using point and diffuse; Panel B reports the corresponding first stage.
***, **, * Estimate is significant at the 1%, 5%, 10% level, respectively. Standard errors are in parentheses.
p-Values reported for the Sargan and Basmann overidentification tests.
estimates we deployed the feasible generalized least squares (FGLS) method and
proceed first by correcting for autocorrelation and then for heteroskedasticity in Tables
4 and 5, respectively. Finally, we also utilized the instrumental variables generalized
method of moments (GMM) estimator to take into account the possible autocorrelation between the dependent variable and our regressors (Table 6). We first use actual
point and diffuse exports as a share of the first two exports for our definition of point
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
133
Table 3. Instrumental Variables Panel Regressions of GDP Per Capita Growth Rate Using Point
and Diffuse as Instruments
Point and diffuse as %
of two main exports
Fraser chain-link
index
Polity
2
Point and diffuse
as dummies
Fraser chain-link
index
Polity
2
Panel A: Instrumental Variables Two-Stage Least Squares Regression
Institutions
1.55**
0.26
2.51**
(0.83)
(0.17)
(1.21)
Exchange rate SD
0.00
0.00**
0.00*
(0.00)
(0.00)
(0.00)
Terms of trade SD
-0.10***
-0.12***
-0.10***
(0.03)
(0.03)
(0.03)
Investment
1.02
2.17***
0.40
(0.84)
(0.73)
(1.06)
Initial human capital
0.63
0.42
0.67
(0.46)
(0.71)
(0.55)
Initial income level
-0.96**
-0.99
-1.27**
(0.54)
(0.80)
(0.69)
0.50**
(0.25)
0.00
(0.00)
-0.12***
(0.03)
1.78**
(0.82)
0.39
(0.64)
-1.49**
(0.78)
Panel B: First-Stage Regression for Institutions
Point
-0.83***
(0.23)
Diffuse
-0.89***
(0.24)
Exchange rate SD
-0.00*
(0.00)
Terms of trade SD
-0.00
(0.01)
Investment
0.57***
(0.21)
Initial human capital
0.00
(0.14)
Initial income level
0.29**
(0.15)
-2.81***
(1.08)
-3.18***
(1.08)
-0.00*
(0.00)
0.07*
(0.04)
-0.02
(1.06)
0.99
(1.10)
1.88*
(1.16)
Prob. > chi2
Number of observations
0.00
270
-3.62***
(1.15)
-4.33***
(1.16)
-0.00**
(0.00)
0.06
(0.04)
-0.70
(1.06)
0.90
(1.00)
1.94**
(1.05)
0.00
287
-0.62***
(0.22)
-0.59***
(0.23)
-0.00**
(0.00)
-0.00
(0.01)
0.60***
(0.21)
-0.01
(0.15)
0.31**
(0.16)
0.00
270
0.00
289
Notes: Panel A reports the two-stage least squares estimates instrumenting for the Fraser chain-link index
and for Polity 2 using point and diffuse; Panel B reports the corresponding first stage.
***, **, * Estimate is significant at the 1%, 5%, 10% level, respectively. Standard errors are in parentheses.
and diffuse in (16) for the regressions reported in the first two columns of Tables 2–6,
followed by regressions using dummy variables for point (0, 1) and diffuse (0, 1) in the
last two columns of Tables 2–6.
Empirical results First, in all but one of the 20 cases of different estimation procedures, two types of data definitions of point and diffuse export dependence, and
two definitions of institutions, reported in Tables 2–6, the impact of the institutional
variable upon growth is positive and significant. The exception is for Polity 2 (or
© 2011 Blackwell Publishing Ltd
134 George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
Table 4. Feasible Generalized Least Squares Panel Data Regressions of GDP Per Capita Growth,
Using Point and Diffuse to Predict Institutions
Point and diffuse as %
of two main exports
Fraser chain-link
index
Polity
2
Point and diffuse
as dummies
Fraser chain-link
index
Panel A: Dependent Variable—GDP Per Capita Growth Rate (Equation (17))
Predicted institutions
2.09***
0.54***
2.57***
(0.67)
(0.17)
(0.68)
Initial income level
-1.16***
-1.49***
-1.30***
(0.32)
(0.40)
(0.32)
Initial human capital
0.66***
0.09
0.66***
(0.25)
(0.31)
(0.25)
Investment
1.18*
2.46***
0.84
(0.69)
(0.53)
(0.69)
Exchange rate SD
0.00***
0.00*
0.00***
(0.00)
(0.00)
(0.00)
Terms of trade SD
-0.09***
-0.10***
-0.08***
(0.03)
(0.03)
(0.03)
Prob. > chi2
Number of observations
0.00
287
0.00
287
Panel B: Dependent Variable—Institutions (Equation (16))
Point
-1.00***
-3.96***
(0.23)
(1.31)
Diffuse
-0.98***
-3.50**
(0.24)
(1.41)
Initial income level
0.31***
1.86***
(0.08)
(0.48)
Initial human capital
0.02
1.12**
(0.08)
(0.49)
Investment
0.57***
-0.11
(0.18)
(1.03)
Exchange rate SD
-0.00*
-0.00*
(0.00)
(0.00)
Terms of trade SD
-0.02*
-0.02
(0.01)
(0.06)
Prob. > chi2
0.00
0.00
0.00
287
-0.86***
(0.20)
-0.88***
(0.23)
0.31***
(0.08)
0.02
(0.08)
0.59***
(0.18)
-0.00***
(0.00)
-0.02*
(0.01)
0.00
Polity
2
0.78***
(0.21)
-1.93***
(0.45)
-0.16
(0.33)
2.41***
(0.53)
0.00**
(0.00)
-0.10***
(0.03)
0.00
287
-2.88**
(1.17)
-2.64**
(1.34)
1.83***
(0.48)
1.08**
(0.49)
-0.05
(1.03)
-0.00**
(0.00)
-0.02
(0.06)
0.00
Notes: Panel A reports the two-stage least squares estimates instrumenting for the Fraser chain-link index
and for Polity 2 using point and diffuse; Panel B reports the corresponding first stage.
***, **, * Estimate is significant at the 1%, 5%, 10% level, respectively. Standard errors are in parentheses.
democracy) in the instrumental variables panel regression reported in Table 3.
Second, governance seems to be more important for growth, as it is always
significant, with a larger coefficient compared to democracy. Third, both types of
resource dependence, point and diffuse, exert a negative influence on growth via
institutions when compared to manufactured goods exporting economies. Resource
dependence of any kind appears to be bad for institutional development in developing countries.
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
135
Table 5. Feasible Generalized Least Squares with Heteroskedastic Error Structure Panel Data
Regressions of GDP Per Capita Growth, Using Point and Diffuse to Predict Institutions
Point and diffuse as %
of two main exports
Fraser chain-link
index
Polity
2
Point and diffuse
as dummies
Fraser chain-link
index
Panel A: Dependent Variable—GDP Per Capita Growth Rate (Equation (17))
Predicted institutions
1.33***
0.32***
1.52***
(0.40)
(0.10)
(0.40)
Initial income level
-1.05*
-1.48***
-1.12***
(0.22)
(0.30)
(0.22)
Initial human capital
0.59***
0.20
0.60***
(0.14)
(0.16)
(0.14)
Investment
2.34***
3.00***
2.19***
(0.47)
(0.38)
(0.47)
Exchange rate SD
0.00***
0.00
0.00***
(0.00)
(0.00)
(0.00)
Terms of trade SD
-0.08***
-0.09***
-0.08***
(0.02)
(0.02)
(0.02)
Prob. > chi2
Number of observations
0.00
287
0.00
287
Panel B: Dependent Variable—Institutions (Equation (16))
Point
-1.05***
-4.48***
(0.10)
(0.88)
Diffuse
-1.01***
-3.47***
(0.10)
(0.95)
Initial income level
0.34***
2.75***
(0.04)
(0.42)
Initial human capital
0.01
1.23***
(0.04)
(0.45)
Investment
0.47***
0.06
(0.11)
(0.85)
Exchange rate SD
-0.00***
-0.00**
(0.00)
(0.00)
Terms of trade SD
-0.00
0.02
(0.01)
(0.05)
Prob. > chi2
0.00
0.00
0.00
287
-0.94***
(0.08)
-0.95***
(0.09)
0.34***
(0.04)
-0.00
(0.03)
0.49***
(0.11)
-0.00***
(0.00)
-0.00
(0.01)
0.00
Polity
2
0.52***
(0.13)
-1.98***
(0.37)
-0.08
(0.20)
2.89***
(0.38)
0.00
(0.00)
-0.09***
(0.02)
0.00
287
-2.83***
(0.75)
-2.08**
(0.88)
2.59***
(0.42)
1.29***
(0.46)
0.21
(0.83)
-0.00**
(0.00)
0.02
(0.04)
0.00
Notes: Panel A reports the two-stage least squares estimates instrumenting for the Fraser chain-link index
and for Polity 2 using point and diffuse; Panel B reports the corresponding first stage.
***, **, * Estimate is significant at the 1%, 5%, 10% level, respectively. Standard errors are in parentheses.
Fourth, and most importantly, it is not always the case that a point sourced
export structure is worse than diffuse resource dependence as far as institutional
development is concerned (Tables 3 and 5). It depends upon which institution we are
looking at. Here we must again reiterate the differences between governance, as measured by the Fraser index, and democracy captured by Polity 2. The first is a picture
of how well a country is run, and the latter an indication of the right to choose
leaders and executive constraints. Often democracies, especially weak and embryonic
© 2011 Blackwell Publishing Ltd
136 George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
Table 6. Instrumental Variables Generalized Method of Moments Estimation of GDP Per Capita
Growth Rate Using Point and Diffuse as Instruments
Point and diffuse as %
of two main exports
Fraser chain-link
index
Polity
2
Point and diffuse
as dummies
Fraser chain-link
index
Polity
2
Panel A: Instrumental Variables Two-Stage Least Squares Regression
Institutions
2.27***
0.53*
3.00**
(0.86)
(0.30)
(1.12)
Exchange rate SD
0.00***
0.00
0.00***
(0.00)
(0.00)
(0.00)
Terms of trade SD
-0.09***
-0.10**
-0.07*
(0.03)
(0.04)
(0.04)
Investment
0.76
2.43**
0.27
(1.21)
(0.98)
(1.39)
Initial human capital
0.60***
0.10
0.61**
(0.24)
(0.44)
(0.27)
Initial income level
-1.17***
-1.46**
-1.39***
(0.38)
(0.65)
(0.46)
0.70*
(0.76)
0.00
(0.00)
-0.10*
(0.05)
2.25**
(1.08)
-0.17
(0.58)
-1.74**
(0.83)
Panel B: First-Stage Regression for Institutions
Point
-1.00***
(0.19)
Diffuse
-0.98***
(0.20)
Exchange rate SD
-0.00***
(0.00)
Terms of trade SD
-0.02
(0.01)
Investment
0.57***
(0.20)
Initial human capital
0.02
(0.07)
Initial income level
0.31***
(0.09)
-3.13***
(1.14)
-2.72**
(1.19)
-0.00***
(0.00)
-0.04
(0.06)
0.14
(1.06)
1.23**
(0.53)
1.76***
(0.53)
Prob. > F
Number of observations
Excluded inst. test
Anderson test
Cragg–Donald test
0.00
270
0.00
0.00
0.00
-3.94***
(1.30)
-3.48**
(1.38)
-0.00***
(0.00)
-0.04
(0.06)
-0.01
(1.05)
1.14**
(0.52)
1.82***
(0.52)
0.00
287
0.09
0.08
0.07
-0.78***
(0.20)
-0.67***
(0.20)
-0.00***
(0.00)
-0.02
(0.01)
0.59***
(0.20)
0.02
(0.07)
0.31***
(0.09)
0.00
270
0.00
0.00
0.00
0.00
289
0.21
0.06
0.05
Notes: Panel A reports the two-stage least squares estimates instrumenting for the Fraser chain-link index
and for Polity 2 using point and diffuse; Panel B reports the corresponding first stage.
***, **, * Estimate is significant at the 1%, 5%, 10% level, respectively. Standard errors are in parentheses.
p-Values reported for the test of excluded instruments and for the underidentification tests (Anderson and
Cragg–Donald).
democracies, are badly governed. The point is that a weak democracy (those with
Polity scores consistently below 8) may also be poorly governed. Our finding is
that good governance is more important for growth when compared to democracy.
This is explained by the fact that many of the democracies in our sample are rather
© 2011 Blackwell Publishing Ltd
RESOURCE DEPENDENCE AND ECONOMIC PERFORMANCE
137
imperfect and new democracies that may be poorly governed. Note that both
types of resource dependency retard both categories of institutional development.
With regard to governance, point resources are worse than diffuse, except very marginally in two cases (Tables 3 and 5). As far as democracy is concerned, a diffuse
export pattern appears to retard democratic development by more in only two
cases (Table 3).
Finally, the magnitudes of the macroeconomic control variables are either small or
insignificant, suggesting that the political economy channels dominate traditional
Dutch disease effects that manifest themselves in terms of trade and exchange rate
volatility. Investment and the level of initial human capital are significant in some cases,
but in other instances are insignificant with the wrong signs, particularly when associated with Polity 2.This serves to underscore the explanatory power of institutions, as far
as growth is concerned.
4. Conclusions
Our theoretical model encapsulates macroeconomic growth collapse as a consequence
of resource dissipation resulting from a wasteful rent-seeking contest, where the object
of the contest is the capture of resource rents. The novelty here is that the institutional
environment can make rent-seeking outlays subject to increasing returns to scale, and
even more profitable. Our econometric analysis also contains major innovations. First,
it has theoretical foundations. Second, it is one of the few econometric investigations
into the resource curse that includes analysis over time, as it is a panel data estimation.
Our proxies for institutional quality are the degree of democracy and the quality of
governance, thus we have attempted to capture both the effects of how a country is
ruled as well as its polity; governance seems to have a greater impact on development.
Finally, our results are more robust than in other cases in the literature, as a variety of
econometric models have been used to test the same specification. Our general results
suggest that both point-source and diffuse-type natural resource dependence retard the
development of democracy and good governance, which in turn hampers economic
growth. So there is a more widespread resource curse, valid for both types of dependence. Point-sourced economies have a worse impact on governance, which is more
important for growth.
The resource curse in point-sourced economies depends on the quality of institutions, and the quality of institutions may be endogenously affected by resource
dependence. Historically, however, rich mineral resource endowments did not
prevent economic growth in Australia, Canada, and the USA a century ago, as
pointed out by Findlay and Lundahl (1994). Brunnschweiler and Bulte (2008) challenge the notion of resource curse in their cross-sectional analysis. They distinguish
between resource dependence and resource abundance, finding that resource dependence has no significant effect on growth. By contrast, they find that resource
abundance has significantly positive effects on growth. A resource-abundant
nation may not be very resource dependent, if it has a diversified production structure. Indeed, resource dependence may be a reflection of the failure to grow and
develop good economic and political institutions, along with the associated poverty,
inequality, and poor human development outcomes. Finally, institutional improvement is like administering an antibiotic to a patient curing him of a serious infection;
changes in policy are more akin to the ingestion of aspirin that works like a temporary palliative.
© 2011 Blackwell Publishing Ltd
138 George Mavrotas, Syed Mansoob Murshed, and Sebastian Torres
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© 2011 Blackwell Publishing Ltd