NATURAL RESOURCE CURSE AND
EXTERNALITIES FROM NATURAL RESOURCE
EXPORTS
Author: FREDERICO ROCHA
e-mail: [email protected]
Associate Professor at the Instituto de Economia – Universidade Federal do Rio de Janeiro
Address: Av. Pasteur, 250 Rio de Janeiro – RJ – Brazil CEP: 22.290-240
Tel: +(55) 21 38725242; Fax: +(55) 21 25418148
Visiting Fellow at University of Siena (January 2010-December 2010)
Abstract
Some economists have argued that natural resources provide low level of externalities and, as
a consequence, that resource dependent economies tend to grow their GDP per capita at a
slower rate. This paper addresses the issue of externality generation from natural resource
exports. Using panel data regressions, the paper shows that natural resource exports generate
positive externalities that are at least as high as those generated by manufacture exports and,
thus, resource rich countries that manage to grow their exports at a fast rate do not suffer
from the natural resource curse.
Keywords: natural resource; growth; externalities; exports; resource curse
JEL: O13; Q33; Q32
November 2010
1
1
INTRODUCTION
The development economics literature has found evidence that resource rich economies have
been outperformed by resource poor economies in terms of GDP per capita rates of growth
(Sachs and Warner 1997). The literature has followed two trajectories in dealing with this
apparent paradox. On the one hand, there have been studies that challenge or reproduce
Sachs and Warner’s results through the use of alternative measures and techniques. On the
other hand, there have been attempts to find the transmission channels for the curse, that is,
to explain how the abundance of natural resources may have a negative impact on growth.
Many transmission mechanisms have been examined. One of the earliest discussed
mechanisms is the lack of externalities provided by natural resources, due to feeble linkages
backward and forward linkages or low demand of skills and infrastructure (Singer 1950,
Hirschman 1958). This argument has been questioned recently (Maloney 2008, Wright and
Czelusta 2008). Singer (1950) and Hirschman (1958) argued for the connections that
manufacturing would have with the rest of the economy and that would be absent in natural
resource staples. This would be responsible for the structural heterogeneity of some resource
rich developing economies, where a high productivity exporting sector does not provide
positive spillovers for domestic producers, increasing the productivity distance among the
sectors (Furtado 1957, 1974 examining the Venezuelan case). This kind of hypothesis has also
been present in Dutch disease models. Dutch disease theorists (Van Winjberger 1984, Torvik
2001) have elaborated models in which an external foreign currency shock over evaluates
domestic currency and shrinks the tradable manufacturing sector that is unable to enjoy
increasing returns from additional production and therefore in the next period presents a
smaller size that damages domestic economy. The external shock may be a consequence of a
price increase in the natural resource exporting product. However, no attention has been
given to the hypothesis that the foreign currency premium is result of a production movement.
2
Therefore, extensions of the Dutch Disease paradigm take for granted that natural resource
exports show low level of externalities (Sachs and Warner 1999). More recent work argues
however that there is no reason for the natural resource tradable sector to provide less
externality than the manufacturing sector (Maloney 2008 and Wright and Czelusta 2008). They
argue that, on the contrary, successful resource rich economies have been able to grow
through the externalities provided by these activities.
This paper aims to contribute to this literature arguing that there are as much externalities
from the natural resource exports sector as there are from the manufacture exports sector.
The point also attempts to show that the real curse is on some resource rich economies that
fail to expand their production. These tasks are carried out through the testing of two
hypotheses. First, the paper tests the hypothesis that the great problem about resource rich
economies is the lack of dynamism of its external sector. Second, using a model elaborated by
Feder (1982) that measures spillovers from the tradable sector, this paper compares spillovers
provided by the natural resource and the manufacture exports sector.
The remaining of this paper is composed of four sections. Section 2 provides the analytical
background for the paper, highlighting previous literature contributions and formulating the
two hypotheses to be tested. Section 3 is dedicated to the formulation of the models to be
used in the empirical section and to the presentation of the database. Section 4 presents and
comments the empirical results. The final section drives the main conclusions from the paper.
2
ANALYTICAL BACKGROUND
Natural resource curse literature has gained large attention from scholars and policy makers
since the publication of Sachs and Warner (1995, 1997, 1999, 2001) series of papers. In these
papers, using cross-section OLS regressions, the authors show thorough evidence for the 19701990 period that countries that were specialized in natural resources presented lower average
3
rates of growth of GDP per capita. They use four distinct measures for natural resource
abundance: (i) the ratio of natural resource exports to GDP, (ii) the share of natural resource
exports in total exports, (iii) the ratio of mineral production to total GNP, and (iv) per capita
agricultural land extent. Though the careful examination of the authors, a large literature has
followed these papers trying to reproduce or challenge their initial findings. The main strategy
has been to use alternative measures of resource abundance in order to show different
results. Stijns (2005) measures natural resource abundance by the present value of mineral
reserves and finds no correlation between this variable and economic growth. Lederman and
Maloney (2008) use the Leamer index (natural resources net exports/labor force) and, in a
panel data setting, find it not significant with respect to GDP per capita growth.
A second stream of challenges on Sachs and Warner’s tests arises from the use of cross-section
regressions. Measures of resource abundance usually maintain some endogeneity with respect
to growth variables. It is difficult to address the issue of causality whenever using cross-section
settings. Some other country specific characteristics may be influencing the result. Panel data
settings appear to be more adequate for the treatment of these issues. Some recent work has
tried to overcome this empirical shortcoming with results that do not allow for the
generalization of the resource curse hypothesis (Lederman and Maloney 2008, Manzano and
Rigobón 2008 and Bravo-Ortega and De Gregorio 2008).
Literature has also been concerned about investigating the transmission channels for the
resource curse. In Sachs and Warner (1997) they established some lines of work that should be
undertaken in order to investigate the issue a bit further. First, they argued for the role played
by currency over evaluation, explored later in Sachs and Warner (1999, 2001), which conduct
to high wages and de-industrialization as argued in the Dutch Disease literature (Corden and
Neary 1982) and by Furtado (1974) in the analysis of the Venezuelan case. Though there is
evidence that natural resource rich economies suffer from high prices and, in some cases, of
4
factor re-alocation, what may also be transformed in a crowding out argument, the literature
shows little evidence over its impact on long run growth.
Second, part of the literature has been dedicated to show the interaction between natural
resource curse and institutions. They argue that natural resource rich countries are more likely
to present weak institutions that promote rent-seeking behavior and mismanagement that
would displace efforts towards less productive activities. Mehlum et al. (2006) show that when
one includes in Sachs and Warner (1997) regressions a variable composed by the interaction of
a institutional quality variable and the resource abundance variable the direct effect of the
resource abundance variable becomes stronger but the interaction variable has the opposite
(positive) sign that offsets the effect of the resource abundance variable, that is, natural
resource intensive countries that have good institutions do not suffer from the curse. Studying
the isolated case of Norway, Larsen (2005, 2006) shows how strong institutions have helped
Norway to overcome the natural resource curse and surpass its neighbors’ level of growth and
GDP per capita.
Third, Gylfasson et al. (1999) and Gylfasson (2001) argue that the lack of investment in
education is a transmission mechanism for the natural resource curse. This would be a
consequence of the low requirement for human capital in resource intensive activities that
would drive to a reduction on long term growth. Gylfasson (2001) shows evidence that natural
resource intensive nations spend less and have a lower average level of education in their
population. Nonetheless, he fails to show causality. Bravo-Ortega and De Gregorio (2008)
gather some evidence that interacting an education variable with the resource abundance
variable, in a panel data setting, the interaction variable would offset the resource abundance
variable whenever the average years of schooling for over 25 years old population would be
around three. They show that in a 60 country sample, 2/3 of the countries would be free from
resource curse.
5
Fourth, some argue that the problem may be on the structure of exports. They have
emphasized the role played by concentration of exports in a few products (Lederman and
Maloney 2008). Others have directed their attention to what they argue is the wrong
specialization into activities that show little technological spillovers (ECLAC 2007) as the main
causes of the curse.
The problems associated with the concentration of exports have been treated before. Furtado
(1957, 1974) deals with this issue when analyzing performance of the Venezuelan oil
dependent economy. He argues that over dependence on one product exposes the country to
periodical balance of payments crises due to price fluctuations. Furthermore, Furtado stresses
that the productivity difference between the resource sector and the non-resource sector
would cause wages and shrink the low productivity non-resource sector. This process would be
even more radical when resources prices are high. This view anticipates the Dutch Disease
mechanism (Medeiros 2007). Furtado argues that the generation of externalities from the
natural resource exporting sector is low and, therefore, structural heterogeneity tends to
persist. According to Furtado, the main reason for this structural problem was in the use of the
rents from oil production that would go into the import of luxury consumption goods rather
than into machinery, which also anticipates discussions on institutions. Implicitly, therefore,
Furtado was arguing that there were little spillovers from production in the resource sector
and little spillovers from the use of oil rents.
The lack of externalities was also treated by Singer (1950:476) that stated that manufactures
“provide the growing points for increased technical knowledge, urban education, the
dynamism and resilience that goes with urban civilization”. Therefore, trade specialization in
natural resources would provide low spillovers, compared to trade specialization in
manufacturing. This position is coupled by ECLAC (2007) that argues that the striking
difference between high and low growth countries is the share of what they call engineering
6
intensive sectors in total manufacturing. These sectors would play the role of technological
diffusers to the rest of the economy. Furthermore, comparing the cases of Latin American
countries with a group of successful resource rich economies – Australia, Canada, Finland,
Ireland, Norway and New Zealand – they show that the latter group has a much higher share
of engineering sector, a more similar industrial structure to what they classify as the leading
economy, US, measured by the Krugman index, finally, they show that the share of exports in
fast growing sectors in the latter group is much larger than in Latin America. However, when
multivariate analysis is undertaken, they do not introduce a resource abundant variable,
though the share of engineering sectors and the Krugman index show respectively positive and
negative significant impact on catch-up.
The issue about externalities (increasing returns) and natural resources has been dealt with by
Dutch disease theorists. They have built models that consider learning by doing as a
transmission mechanism, mostly associated with the tradable sector. van Winjberger (1984)
builds a two-period model with a tradable and a non-tradable sector. The tradable sector is
subject to learning by doing from production. Therefore, the level of production in the first
period may affect the outcome of the second period. He shows that with a foreign currency
premium that implies a real valuation of the country’s currency, the production of tradables in
the first period will be smaller, generating negative effects in the second period, which
damages welfare. He proposes therefore a subsidy or the use of the foreign currency premium
to accumulate foreign assets. Sachs and Warner (1999) propose a model with similar
mechanics but with learning spillovers from the tradable sector affecting the non-tradable
sector as well. Torvik (2001) adds learning by doing in the non-tradable sector. All these papers
depart from the premise that trade in manufacturing has spillovers through learning by doing
and that there is little (less) learning in natural resource exporting sector.
7
Maloney (2008) argues that there is nothing special about manufactures and that technical
change, education and externalities in natural resource sector are the same. Using data from
Maddison database and the ratio of net natural resources exports to worker as natural
resource abundance indicator, he finds that, in the 1820-1950 period, natural resource rich
countries grew at faster rates than others (Auty 2000 makes the same point using descriptive
statistics). For the post war period, he finds that the resource abundance variable lacks
significance whenever variables indicating the degree of a country’s openness to trade and the
level of the country’s technological capabilities are introduced. Therefore, he goes on to
reverse the causality proposed by Singer, indicating that the lack of absorptive capacity and
the reliance on import substitution industrialization were the main causes for the Latin
America feeble performance in post-war years. As ECLAC (2007), he searches for empirical
support in the successful resource abundant nations, as Australia, Canada, Sweden and the US.
He argues that, previous to Second World War, these countries already had a well established
education system that allowed for absorptive capacity, while in Latin American countries1 the
education system was precarious which may have undermined absorptive capacity and
learning. Maloney (2008) also puts that the establishment of property rights and free trade
were distinguished factors of these countries’ cases and that those features may have allowed
for the higher rates of growth showed by the former countries.
David and Wright (1997) show evidence that the development of the US was based on natural
resource industries that were capable to introduce technological innovations in their processes
and generate spillovers for the rest of the economy. On the one side, this was a consequence
of the country’s territorial expansion. On the other side, this was a result of well organized
mineral research and of the introduction of technical innovations that made viable the
processing and use of mineral reserves that previous technology did not allow. This success is
1
Some of which had the same GDP per capita as those successful nations in the late XIX century.
8
attributed to a well developed legal environment, infrastructure of public knowledge and
education in mineral and metallurgy disciplines.
Based on the same arguments, Wright and Czelusta (2008) defend that US resource abundant
economy was not a gift from nature, but was a conquest of technical change. Natural resource
reserves were expanded during the country’s development. In this case, they agree with
Krautkraemer (1998) on the endogeneity of natural resources. One point that these economic
historians stress is that technological improvements allowed for the development of the US
mineral industries were able to use and supply technological knowledge and, most
importantly, to expand mineral production.
The correlation between exports and economic growth has been widely tested and its
beneficial spillover effect has been stated in many studies, though some doubts seem to arise
on the direction of causality (see Edwards 1998). Nonetheless, literature seems to have
collected little evidence about the distinction made by Dutch Disease theorists that only the
manufacturing tradable sector provides positive externalities through learning by doing. What
Dutch Disease theorists seem to model is the occurrence of price effects that have influence
on the exchange rate, they throw little attention on the expansion of production of natural
resources. In fact, many resource abundant countries that have been stagnated in the last
forty years seem to be characterized by the stagnation of their production of natural resource.
Manzano and Rigobón (2008) show evidence that the world per capita production of fuel and
mineral ores has decreased from 1978 to 1996,2 however, the per capita production of these
fuel and mineral ores in resource abundant countries has shown higher rates of decrease. This
may suggest that it is not low spillover from natural resource but slow growth in exports the
main reason for the slow growth in resource abundant countries.
2
This should not hold for more recent years due to the China effect.
9
However, as stressed by Wright and Czelusta (2008), the highly successful resource abundant
economies managed to expand their production/sales of natural resources through the
introduction of technological innovations, whereas the most striking and known case of
resource rich stagnant economies – the OPEC countries – have suffered from imposed
production rationing to achieve price increases or prevent price decreases. These features
raise the first question this paper intends to address: if it is true that the tradable sector
provides positive externalities to the economy, then, do resource abundant countries perform
as well as non-resource abundant economies whenever they have good export performance?
The second question derives from the frequently hypothesized learning by doing effects of
manufacturing. There are good reasons to think that trade provides positive externalities.
Some are associated with learning by doing, as the Dutch Disease literature has stressed,
others are associated with balance of payment effects such as those presented in the two-gap
models (Chenery and Bruno 1962) and the export-led growth models (Thirlwall 1979,
McCombie 1997). There are no reasons to suppose that the balance of payment externality
effects of the growth of manufacture exports outperform those of the rate of growth of
natural resource exports. However, though some have argued for positive effects of
manufacturing on infrastructure, technological knowledge and the production chain, there are
good reasons to suppose that these effects are also present in the natural resource sector. So,
the paper attempts to address this question: do the externalities that arise from the growth of
natural resource exports match those that arise from manufacture exports expansion?
3
3.1
EMPIRICAL STRATEGY
Effects of Exports Dynamism on GDP per Capita Growth
In order to answer the first question, the paper departs from the basic cross-section equation
tested by Sachs and Warner (1997), originated in the Solow growth model:
10
(1)
where y is per capita GDP, t is the final year, t-1, the initial year, T, the number of years from
the initial to the final year, I/Y, the average investment to GDP ratio from the initial to the next
to initial year, NR a natural resource abundance variable and Z is a vector of economic
characteristics that determine the country’s growth. This paper will use the ratio of natural
exports to GDP (sxp) as the natural resource abundance indicator. This indicator is chosen for
two reasons. First, it has been the most tested and preferred measure of resource abundance
in the literature. Second, and most importantly, it fits better in growth accounting modeling as
will be shown. This paper will introduce an interaction variable between NR and a export
dummy (EXPDUM), that assumes value one, whenever the country showed above median
rates of export growth, and zero, otherwise.
Sachs and Warner (1997) have used cross-section OLS estimation for equation (1). Crosssection regressions show two problems in this case. They are sensitive to omitted variables
that may be associated with country specific characteristics and, furthermore, they do not
account for endogeneity that may occur in the case of the sxp indicator. In order to deal with
these problems, the paper uses panel data models (see Manzano and Rigobón 2008 for
comments and justification).
3.2
Externalities
In order to test for the spillover effects of natural resource and non-natural resource exports,
this paper uses a model specification elaborated by Feder (1982). Feder uses a two-sector
model that this paper extends to three sectors. Denote N for the non-exporting sector, NR for
the natural resource exports sector and MX for the manufacture exports sector.
(2)
(3)
11
(4)
And
Feder departs from the same hypothesis used in most of the above surveyed literature that
the exports sectors have higher productivity than the non-exports sector. Therefore, let the
ratio of the marginal productivity of capital and labor in the two exporting sectors to the
marginal productivity in the non-exports sector equal (1+δ):
(5)
Taking
, where
(6)
where I, stands for investment and
,
(7)
(8)
(9)
Suppose a linear relationship between the marginal product of labor and the average product
per worker, such that:
(10)
Then, (10), (9), (8) and (7) into (6) and dividing by Y, we have:
(11)
12
Where the coefficients
and
and
gather respectively the spillover effects,
and the productivity differential effects,
and
. The dots over the
variables mean rates of growth.
In order to separate spillovers from productivity differential effects, one further hypothesis is
introduced: as Feder (1982), it is necessary to suppose a constant elasticity function.
Therefore, introducing a constant elasticity hypothesis of the effect of natural resource and
manufacturing exports on N, from equation (2), we obtain
(2’)
and, therefore,
and
and substituting into (11):
(12)
where θ and μ are the externality parameters and off course,
and
the productive differential and xn, the share of natural resource exports in total exports. If
or
there is no productivity differential.
In order to deal with the direct effect of the rate of growth of natural resource and
manufacture exports, the paper addresses equations (11) and (12) in two different forms. First,
equations are run against GDP growth as dependent variable. Then, the external trade sector is
excluded from the dependent variable by subtracting from GDP the difference between
exports and imports.
3.3
The Data
As has been stated above, this paper adopts the ratio of natural resources exports to GDP (sxp)
as a measure of resource abundance. This variable has been built using the percentage of
13
natural resource exports and exports in US$ current value and GDP in US$ current value. All
measures have been taken from World Development Indicators, 2009 (WDI).
The catching-up variable is the initial period per capita GDP. The paper uses the chained per
capita GDP in constant prices for 2005 variable from the Penn World Tables 6.3 (rgdpch). In
order to cope with high variability of the data, the paper uses a five year average to obtain the
variable, two years before and two years after the base year.3 Per capita GDP rates of growth
have been obtained from the local currency constant prices GDP per capita of the WDI. The
use of this measure avoids problems with transformation into PPP, but may affect results
when compared to other papers. Anyway, the use of different versions of PWT 6.3 tends to
show variability of data in similar dimensions. The investment to GDP ratio has been obtained
from ten year average investment to gross GDP ratio of the PWT 6.3. Data for exports growth
have been obtained from WDI in current prices (see Table 1 and Table 2).
Data have been collected for four years: 1970, 1980, 1990 and 2000. The panels will have as
reference three ten year growth periods: 1970-80, 1980-90, 1990-2000. Cross-section
regressions will adopt 1970-2000 period. The database is composed of 74 countries that had
data for all the years. The countries are listed in annex. In the case of the use of grgdpdom,
only 61 countries are used in the estimation due to data shortages.
3
One of the first concerns of the paper was to match results with Sachs and Warner (1997). However,
due to the need to find data for more recent years, new versions of the Penn World Tables had to be
used. One empirical consequence of this search was the high variability of data according to the version
of PWT. As has been put by Johnson et al. (2009) problems associated with the PPP measure make
annual data change more than it would be desirable. The authors find that studies that use five year
averages show greater robustness of their results.
14
Table 1
Variable
gea
Variables Calculation and Data Source
Method for Calculation
Rate of growth of per capita GDP, calculated by
) where, T is the number of years from t to t0. In this
case, the paper uses the GDP per capita at constant local currency
for 2005 for calculation of the gea
lgdp
Natural logarithm of five year average of the real GDP per capita
in PPP at 2005 prices in the PWT chained series, the rgdpch
variable
Linv
Natural logarithm of the average share of investments in GDP for
the period considered in the gea variable, the ci variable from
PWT
xrn
Percentage of natural resource exports in total exports. It is
composed by the sum of fuel, mineral ore and metals, food and
agricultural shares in total exports.
grexp
Export growth in current US$,
expdum
A dummy variable that assumes value one, whenever grexp is
above its median value for the period, 0, otherwise
sxp
The natural resource exports to GDP ratio
, where GDP is
obtained in current US$ from WDI
grgdp
Rate of growth of GDP at local currency constant prices, 2005,
calculated in the same way as gea
grgdpdom Rate of growth of GDP of the domestic sector at local currency
constant prices, 2005, obtained by GDP-(X-M). Exports (X) and
imports (M) are measured at constant local currency, calculated
in the same way as gea
sxpexp
Interaction variable: sxp*expdum
grpop
Rate of growth of the population
grn
Rate of growth of natural resource exports
grm
Rate of growth of manufacture exports
special
grn-grm
sxpgrn
sxp*grn
sxmgrm
sxp*grm
Source
WDI
PWT 6.3
PWT 6.3
WDI
WDI
WDI
WDI
WDI
WDI
WDI
WDI
15
Table 2
Descriptive Statistics
Variable
gea
lgdp
linv
xrn
grexp
expdum
sxp
grgdp
sxpexp
grpop
grn
grm
special
sxpgrn
sxmgrm
4
4.1
Obs
222
222
222
222
222
222
222
222
222
222
222
222
222
222
222
Mean
1.817
8.812
3.031
60.527
0.097
0.505
0.136
3.511
0.065
1.694
0.079
0.122
-0.043
0.010
0.010
Std. Dev.
2.187
0.969
0.505
30.997
0.076
0.501
0.123
2.178
0.113
1.072
0.081
0.102
0.097
0.021
0.019
Min
-5.983
6.430
1.209
2.600
-0.090
0.000
0.002
-2.269
0.000
-0.316
-0.099
-0.167
-0.462
-0.064
-0.021
Max
8.828
10.580
3.970
99.800
0.383
1.000
0.761
12.493
0.761
5.338
0.382
0.493
0.319
0.180
0.133
EMPIRICAL RESULTS
Do Exports Matter?
Table 3 shows the results of eight regressions that have the rate of growth of GDP per capita as
the dependent variable. Equations 1.1 to 1.4 present regressions that reproduce Sachs and
Warner (1997) equations for the data set used in this paper. Equation (1.1) shows the results
for the cross-section regression with rates of growth of GDP per capita from 1970 to 2000 as
dependent variables. All variables have the expected sign and significance. However, the
coefficient for the sxp variable is much smaller (in module) than the one found in Sachs and
Warner (1997) equations. Three factors contribute for this difference. First, the samples are
not the same. Second, the period is larger. Third, and most importantly, there are differences
in the database as put in section 3.3.
Equations (1.2) to (1.4) show the result for panel data settings. Variables maintain the
expected sign and significance. However, the magnitudes of coefficients vary according to the
16
model. The coefficient for sxp assumes its lower value in the pooled data equation and its
higher value in the fixed effects panel data model. According to this model, an increase of one
standard deviation in the sxp variable implies a 1.1 percentage point decrease in the rate of
growth of the GDP per capita. In the cross-section model, this impact is of only minus 0.28
percentage point.4
Equations (1.5) to (1.8) add an interaction variable formed by the product of sxp by the export
dummy. When the interaction variable sxpexp is added, the coefficient sxp increases in
absolute value and its level of significance also augments in all equations. However, the
interaction variable assumes a positive and significant value. In all equations with the
exception of the fixed effects model, the coefficient of the interaction variable is larger than
the coefficient of the sxp variable, that is, a resource abundant country that manages to grow
its exports at a fast rate does not suffer from the natural resource curse. In the fixed effects
model, however, the interaction variable coefficient is in module a little lower than the
coefficient of sxp, ending up in a net negative value even for natural resource economies that
have grown their exports fastly. Yet, the overall negative effect of an increase of a standard
deviation in sxp is reduced from 1.1 percentage point to 0.27 percentage point.5 This means
that in a thirty year period, the resource rich economy would have grown its per capita income
by less 8.4%, which is very far away from the disaster proposed by the results obtained by the
natural resource curse literature.
It could be that resource rich countries that show greater rates of growth of their exports are
those that diversify towards manufacturing activities. Equations (1.9) to (1.11) attempt to
address this question. It introduces a variable for the direction of specialization, formed by the
difference between the rate of growth of natural resources exports and the rate of growth of
4
The F test for the significance of the intercepts and the Hausman test suggest the acceptance of the
fixed effects model.
5
Again, the F test for ui=0 and the Hausman test suggest the acceptance of the fixed effects model.
17
manufacturing exports. The variable is insignificant and the coefficients and significance of the
remaining variables are not affected by the introduction of this variable.
Table 4 splits the sample into those economies that have shown low exports growth rates and
those that show high exports growth rates for each ten year period. Equations (2.1) to (2.3)
deal with the former case, while equations (2.4) to (2.6) deal with the latter. In all equations
for the low exports growth case, sxp has a negative and significant effect on growth rates. The
sxp coefficients in these equations are also higher than those presented in Table 3. Again, the
highest value is in the fixed effects model, which has the best fit as well. In that model the
coefficient for sxp is -12.57. This means that if we increase sxp by one standard deviation, the
economy will grow less -1.55 percentage point per year, which would result an accumulated
rate of growth of -63% in a thirty year period, which is clearly relevant. However, when we
take the sample for fast exports growth economies, in equations (2.4) through (2.6), sxp does
not have a statistically significant coefficient.
Thus, the lack of dynamism of the tradable sector of resource abundant economies seems to
affect their long run performance. Resource rich economies that are able to grow their exports
at a fast rate do not suffer from the curse.
18
Table 3
sxp
lgdp
linv
Regressions – Dependent Variable gea (growth in GDP per capita)
Crosssection
(1.1)
-2.28*
Pooled
(1.2)
-3.35***
Fixed
Effects
(1.3)
-8.93***
Random
Effects
(1.4)
-4.91***
Crosssection
(1.5)
-5.13***
-8.21***
Fixed
Effects
(1.7)
-9.77***
(-1.75)
-0.37*
(-1.94)
2.16***
(5.79)
(-3.15)
-0.47***
(-2.83)
2.40***
(7.5)
(-3.6)
-3.27***
(-6.97)
0.12***
(3.47)
(-3.82)
-0.64***
(-3.35)
0.14***
(7.14)
(-3.49)
-0.32*
(-1.80)
1.88***
(5.25)
5.74***
(3.42)
(-6.96)
-0.42***
(-2.76)
1.87***
(6.26)
9.43***
(7.09)
(-4.30)
-2.98***
(-6.89)
0.08**
(2.39)
7.61***
(5.44)
-3.64**
(-2.39)
-0.83
(-0.68)
29.02***
(6.81)
4.99***
(3.20)
-2.83*
(-1.97)
0.34
(0.31)
27.05***
(6.91)
0.38
32.87
0.47
31.80
sxpexp
Pooled
(1.6)
special
_cons
R-square
0.35
0.09
0.36
0.23
0.45
F
12.67
21.11
27.16
13.87
Wald Chi2
58.42
Obs
74
222
222
222
74
Groups
74
74
F test for
2.81
ui=0
Hausman
48.1
LM Breusch
7.98
and Pagan
t-statistics in parenthesis. * 10% significance **5% significance ***1% significance
222
74
222
74
2.70
Random
Pooled
Fixed
Effects
(1.9)
Effects
(1.8)
(1.10)
-8.93***
-8.30*** -9.81***
(-6.81)
(-6.93)
(-4.19)
-0.55***
-0.41*** -2.97***
(-3.09)
(-2.74)
(-6.83)
0.10***
1.87***
1.77**
(5.56)
(6.25)
(2.49)
8.97***
9.48***
7.56***
(6.86)
(7.09)
(5.36)
-0.55
-0.10
(-0.45)
(-0.08)
4.93***
0.30 23.47***
(3.43)
(0.27)
(5.21)
0.35
117.66
222
74
0.38
26.24
0.47
25.48
222
222
74
Random
Effects
(1.11)
-8.60***
(-6.55)
-0.63***
(-3.51)
2.11***
(6.03)
9.13***
(7.05)
0.04
(0.04)
1.56
(1.15)
0.36
124.63
222
74
2.66
40.9
8.87
30.2
8.45
19
Table 4
Sample
sxp
lgdp
linv
_cons
Regressions – Dependent Variable gea (growth in GDP per capita), samples low and high export growth
Low Exports Growth
Pooled Data
Fixed Effects
Random Effects
-7.16***
-12.57***
-8.01***
(-4.78)
(-2.99)
(-4.88)
-0.30
-4.29***
-0.48*
(-1.38)
(-4.28)
(-1.95)
1.37***
0.98
1.61***
(3.41)
(0.88)
(3.50)
0.54
37.24***
1.52
(0.34)
(3.65)
(0.84)
R2
0.24
0.46
0.32
F
11.12
12.97
Wald Chi2
32.64
Obs
183
183
183
Groups
61
61
F test for ui=0
2.3
Hausman
22.4
LM Breusch and Pagan
2.33
t-statistics in parenthesis. * 10% significance **5% significance ***1% significance
High Exports Growth
Pooled Data
Fixed Effects
Random Effects
0.32
-2.31
0.24
(0.25)
(-0.67)
(0.16)
-0.52**
-2.67***
-0.56**
(-2.47)
(-4.81)
(-2.43)
2.46***
2.17
2.32***
(5.19)
(1.47)
(4.52)
-0.44
19.93
0.31
(-0.28)
(3.25)
(0.17)
0.2118
9.68
183
0.35
8.11
183
61
1.9
0.15
21.15
112
64
19.2
0.65
20
21
4.2
Externalities
Table 5 and Table 6 tackle the problem of externalities testing equations (11) and (12) with
two different dependent variables. In Table 5, the dependent variable is the rate of growth of
GDP, while, in Table 6, the dependent variable is the rate of growth of the GDP with the
exclusion of the trade sector, that is, subtracting from the GDP the difference between exports
and imports.
Equations (3.1) to (3.3) test the neoclassical growth equation and both the capital and labor
variables have positive and significant signs. The F test for the fixed effects dummies is
significant inducing to accept the pooled data regression as a better specification. The
Hausman test also rejects the fixed effects model. Therefore, we should take the strong
significance of the variables in the pooled data and random effects models.
However, when we introduce the export sector variables formed by the interaction of the
product of the natural resources exports to GDP ratio by the rate of growth of natural
resources exports (sxpgrn) and by the product of the manufacture exports to GDP ratio by the
rate of growth of manufacture exports (sxmgrm) in equations (3.4) to (3.6) both capital and
labor variables lose significance. In the pooled data model, capital and labor still maintain
significance, but in the fixed effects model only the labor variable is significant and in the
random effects model, only the capital variable is significant.6 One possible explanation for the
erratic behavior of the capital variable may be found in Bond and Malik (2010) that shows that
a greater weight of fuel exports in the economy implies a higher level of investment.
In all three models, sxpgrn is the most significant explicative variable and its coefficient is
greater than the one for sxmgrm. This leads to the conclusion that
large as
is at least as
, that is, the sum of the excess of productivity of the natural resource
6
It should be stated that the F test for the fixed effects dummies and the Hausman value suggest the
acceptance of the fixed effects model.
22
trade sector over the domestic sector,
trade sector over the domestic sector,
, and the externality effect of the natural resource
, is at least as large as the same sum for the
manufacturing sector.
Equations (3.7) to (3.9) in Table 5 attempt to separate the externality effect from the
productivity effect. In these equations the coefficient for grn, θ, and grm, , represent
respectively, the externality effect of the natural resource exports and the externality of
manufacture exports over the domestic sector. In all three equations, the externality effect for
the natural resource exports sector is significant at the 1% level, while the manufacture
exports sector is significant at the 1% level only in the pooled and the random effects models.
In the fixed effects, it only achieves a 10% level of significance.7 In all three equations, θ> . In
the fixed effects model, an increase of one standard deviation in the rate of natural resource
exports growth results is an increase of 0.48 percentage point in the rate of growth of the GDP
due to the externality effect, while an increase in one standard deviation of the manufacturing
sector, in the same model, provides an increase of 0.21% in the rate of growth of the GDP.
The coefficients ,
and
are also positive and significant in all three
equations. These coefficients are a measure of the impact of the higher relative productivity of
each of the two external sectors to the domestic sector. In the fixed effects model, the
coefficient for the natural resource exports sector is larger than the one for the manufacture
exports sector. In the other two models the reversal is true. These results suggest that the two
exports sector show greater productivity than the domestic sector.
Table 6 reproduces equations (3.4) to (3.9) with the use of GDP minus trade surplus as the
dependent variable. The results confirm the conclusions obtained from the equations in Table
5. Again the investment variable lacks significance in most equations. The labor variable is
7
Again the F and the Hausman tests suggest the acceptance of the fixed effects model.
23
positive and significant in all equations with exception of equation (4.5). Most importantly, the
coefficients of the natural resource exports externalities are positive and significant in all
equations. In equations (4.1), (4.2) and (4.3),
larger than
is the most relevant variable and is
. In equations (4.4) to (4.6), the externality coefficient for natural
resource, θ, is positive and significant. The externality variable for manufacture exports is
significant in equations (4.4) and (4.6), but lacks significance in equation (4.5).
The results from these two tables seem to confirm the hypothesis made by the literature that
the tradable sectors have higher productivity than the non-tradable sector. In the two sets of
equations that analyze separately the productivity effect and the externality effect, the
productivity effects of the natural resource and the manufacturing tradable sectors are
positive and achieve at least 10% of significance.
24
Table 5
Linv
grpop
Regressions – Dependent Variable grgdp
Pooled
(3.1)
0.09***
(5.55)
0.81***
(5.85)
Fixed
(3.2)
0.13***
(2.98)
0.95***
(2.69)
Random
(3.3)
0.09***
(5.26)
0.83***
(5.52)
Pooled
(3.4)
0.78***
(2.81)
0.60***
(4.76)
42.03***
(7.33)
32.30***
(5.14)
Fixed
(3.5)
1.05
(1.40)
0.74***
(2.65)
50.98***
(9.56)
45.06***
(3.09)
Random
(3.6)
0.03**
(2.06)
0.54***
(4.38)
42.60***
(7.32)
32.47***
(4.98)
0.16
(0.31)
-1.10
(-1.03)
0.05
(0.08)
-0.60
(-0.64)
-1.91
(-0.88)
1.13**
(2.45)
sxpgrn
sxmgrm
grn (θ)
grm (μ)
_cons
R2
0.40
0.48
0.1728
0.1226
0.12
F
36.76
32.99
22.88
10.2
Wald Chi2
41.8
Obs
222
222
222
222
222
Groups
74
74
74
F test for ui=0
2.2
1.49
Hausman
1.6
LM Breusch and Pagan
3.94
t-statistics in parenthesis. * 10% significance **5% significance ***1% significance
0.48
161.01
222
74
21.53
15.8
Pooled
(3.7)
0.57**
(2.26)
0.53***
(4.52)
17.21***
(2.57)
27.53***
(4.73)
8.20***
(4.52)
3.68***
(3.09)
-0.68
(-0.80)
Fixed
(3.8)
0.65
(0.88)
0.39
(1.39)
30.33***
(4.06)
25.70*
(1.74)
6.01***
(3.12)
2.10*
(1.68)
-0.41
(-0.19)
Random
(3.9)
0.56**
(2.01)
0.51***
(4.05)
21.16***
(3.28)
27.32***
(4.28)
7.52***
(4.35)
3.19***
(2.80)
-0.52
(-0.56)
0.50
36.52
0.53
26.73
0.52
222
222
74
1.8
221.03
222
74
29.07
8.04
25
Table 6
Linv
grpop
sxpgrn
sxmgrm
Regressions – Dependent Variable grgdpdom
Pooled Data
(4.1)
0.60*
(1.71)
0.49***
(2.81)
67.90***
(6.62)
38.57***
(4.13)
Fixed Effects
(4.2)
-0.12
(-0.11)
0.81*
(1.76)
85.36***
(8.20)
44.25**
(2.07)
Random Effects
(4.3)
0.58
(1.57)
0.48***
(2.65)
71.32***
(7.19)
38.93***
(3.94)
-0.32
(-0.27)
1.10
(0.35)
-0.29
(-0.23)
grn (θ)
grm (μ)
_cons
R2
0.32
0.42
0.42
F
21.27
21.78
Wald Chi2
90.81
Obs
183
183
183
Groups
61
61
F test for ui=0
1.53
Hausman
27.4
LM Breusch and Pagan
3.12
t-statistics in parenthesis. * 10% significance **5% significance ***1% significance
Pooled Data
(4.4)
0.35
(1.08)
0.39**
(2.40)
31.05**
(2.54)
25.29***
(2.81)
9.21***
(3.67)
4.23**
(2.50)
-0.23
(-0.21)
Fixed Effects
(4.5)
-0.36
(-0.34)
0.35
(0.74)
55.87***
(3.85)
21.72
(0.98)
7.22**
(2.47)
1.43
(0.73)
2.29
(0.75)
Random Effects
(4.6)
0.35
(1.08)
0.39**
(2.40)
31.05**
(2.54)
25.29***
(2.81)
9.21***
(3.67)
4.23**
(2.50)
-0.23
(-0.21)
0.40
21.6
0.46
16.79
0.43
183
183
61
1.21
129.57
183
61
27.2
0.8
26
4.3
Discussion
The results suggest a revision of the natural resource curse assertions. It does not seem that
the specialization in natural resources is negatively correlated to growth, but to the lack of
growth of exports in resource dependent economies. In this case, as put by Wright and
Czelusta (2008:208), “the danger of the resource-curse thesis is that countries may be
discouraged from pursuing this reasonable and potentially fruitful avenue for economic
progress”. The high level of externalities showed by natural resource exports in this paper
recommends that countries that are able to use their natural resources should be encouraged
to do so.
The paper also argues for the risk of extending Dutch Disease model implications to the whole
set of natural resource rich economies. The hypothesis of an exogenous foreign currency
premium is associated with a price increase in existing exports (in a small open economy) and
therefore is suitable when these situations are present. It should not be assumed as a plausible
hypothesis in situations where natural resource production is increasing and, as shown in this
paper, providing positive externalities to the economy.
As a consequence of this line of argument, one should be wary about policies that aim to
diminish periodical price decreases or reverse price decrease trends by the establishment of
quotas or production restrictions. In most cases, these policies are more likely to generate
market share losses in the long run and displace possible positive externalities that would be
gained through productivity growth in the affected sectors.
The arguments raised in this paper recommend caution in the use of dual economy with
structural heterogeneity models. These models are based in the incapacity of the high
productivity sector to generate positive spillovers to the low productivity sector, straightening
the gap between them. The results from this paper do not invalidate structural heterogeneity
models but suggest that if there is structural heterogeneity it is unlikely that its origin should
27
be associated with the type of trade specialization of the country. In this case, more general
causes, such as the lack of absorptive capacity, should be pursued.
The paper does not address some issues that may be important. First, it does not tackle the
type of externalities generated by each exporting sector. Second, it does not deal with possible
problems of production or exports growth that may arise from the specific specialization in
natural resource. Third, the paper does not address problems associated with excessive
specialization of the export basket.
In the first case, Esfahani (1991) argues that exports affect balance of payment and therefore
the capacity to import inputs for production and to acquire capital goods for other sectors. His
results show that balance of payments type of externalities are quite important. It is unlikely
that any balance of payment externality would be uneven depending on the sectors that
generated the currency. Thus, in what matters for this paper, this type of externality could not
be the origin of asymmetries. The other main externalities would be through capability
accumulation generated by learning by doing type of effect or backward and forward linkages.
This paper advances in this respect stating that if there are across sectors differences between
these two types of externalities, they seem to be counterbalanced or are in favor of the
natural resource sector.
The second cavity of the paper questions whether the previously identified natural resource
curse isn’t really the reflection of a stagnation of exports problem in an export dependent
economy. Therefore, the real question to be answered would be the reasons why some
natural resource rich countries grow their exports slowly and why some resource rich
economies manage to grow their exports at a fast rate. Natural resource friendly literature
conjectures that the main driver of expansion is technical progress (Maloney 2008, Wright and
Czelusta 2008). In this case, learning and capability accumulation processes should be the most
important drivers of success and failure and should affect indistinctly any sector. Problems in
28
the expansion of production front would be overcome by the introduction of technical
progress, which is why they understand natural resources reserves as being intrinsically
endogenous. However, some critics of natural resource specialization may argue that
production or export expansion may be related to other situations. Low income elasticity and
deterioration of the terms of trade may appear as one of the main obstacles, though Prebisch
and Singer theses have failed to demonstrate their empirical pertinence (Cuddington et al.
2008). Other limitations may arise by production quota administration as the OPEC cartel does
in the case of oil.
The third cavity persists and, in fact, may be an important cause for poor performance of some
of the slow export growth and GDP growth economies. Lederman and Maloney (2008) address
this issue and find that the inclusion of a Herfindahl concentration index reduces the
significance and the coefficient of the natural resource variable suggesting that the
concentration of exports is the problem.
It should be added that the proposition that slow exports growth is an important transmission
mechanism does deny importance of other mechanisms. In fact, some of the mechanisms,
such as institutional fragility, may help to explain why some countries have grown their
exports at a slower rate.
5
CONCLUSIONS
This paper has shown that when one controls for export performance, only slow export growth
resource rich economies grow significantly slower than the sample’s average. Thus, it argues
that the problem with natural resource curse may be associated with slow export growth.
Countries that are specialized in natural resources grow slower because their export base
shows lower growth. Resource rich countries that overcome slow exports growth manage to
engage in fast growth.
29
Literature has dedicated much attention to the role that would be played by asymmetric
externalities that would arise from manufacture and natural resource industries as a
transmission mechanism for the resource curse. This paper has raised evidence that this
asymmetry is unlikely to occur. The results from the estimates presented suggest three
important features: (i) natural resource export sector has higher productivity than the
domestic sector; (ii) natural resource exports provide positive spillovers to the domestic
sector; and (iii) its productivity and spillovers to the economy are at least as high as those
provided by the manufacture exports sector.
One can invert the order of presentation of the results and state that natural resources exports
show positive externalities at least as high as those that arise from manufacture exports and,
thus, resource rich countries that manage to grow their exports at a fast rate are able to grow
as fast as non-resource rich economies.
30
6
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34
35
7
ANNEX
Country Sample
Argentina
Australia
Austria
Belgium
Belize
Bolivia
Brazil
Barbados
Canada
Switzerland
Chile
Cote d'Ivoire
Cameroon
Colombia
Costa Rica
Germany
Denmark
Algeria
Ecuador
Egypt, Arab Rep.
Spain
Finland
Fiji
France
United Kingdom
Greece
Guatemala
Hong Kong, China
Honduras
Hungary
Indonesia
India
Ireland
Iceland
Israel
Italy
Jamaica
Japan
Korea, Rep.
Morocco
Madagascar
Mexico
Mali
Malta
Malawi
Malaysia
Nicaragua
Netherlands
Norway
Nepal
New Zealand
Pakistan
Panama
Peru
Philippines
Papua New Guinea
Portugal
Paraguay
Saudi Arabia
Senegal
Singapore
El Salvador
Sweden
Seychelles
Syrian Arab Republic
Togo
Thailand
Trinidad and Tobago
Tunisia
Turkey
Uruguay
United States
Venezuela, RB
South Africa
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