examining sachs and warner’s model of natural …
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EXAMINING SACHS AND WARNER’S MODEL OF NATURAL RESOURCE CURSE: IMPLICATIONS AND LESSONS FOR NATURAL RESOURCE-RICH
COUNTRIES
A Thesis submitted to the Faculty of the
Graduate School of Arts & Sciences in partial fulfillment of the requirements for
the degree of Master of Public Policy in the Georgetown Public Policy Institute
By
Anne Hong, A.B.
Washington, D.C. April 8, 2009
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EXAMINING SACHS AND WARNER’S MODEL OF NATURAL RESOURCE CURSE: IMPLICATIONS AND LESSONS FOR NATURAL RESOURCE-RICH COUNTRIES
Anne Hong, A.B.
Thesis Advisor: Tobias Pfutze, Ph.D.
ABSTRACT
This thesis explores the model of natural resource curse proposed by Jeffrey Sachs and
Andrew Warner (1995) in two parts. First, it re-creates the model to determine if the results are
time-specific. In other words, by extending the time period beyond 1970 to 1989, this thesis will
attempt to ascertain whether natural resource exports do, in fact, result, in slowed growth, as
measured by GDP in a cross-country analysis. Secondly, it will attempt to develop arguments for
why differences in time-specific results are produced, testing theories regarding human capital
investment, debt overhang, and country size effects on the so-called “resource curse.”
Essentially, the theory behind Dutch Disease and natural resource curse has two
purported effects that limit growth. One hypothesis argues that natural resources result in trade
shocks because commodity markets are naturally volatile. Consequently, appreciation in
exchange rates leaves the natural resource economy in a depressed state following such boom and
bust cycles. A second hypothesis is that the investment in the booming sector (natural resource
sector) produces small contributions to overall growth as lagging sector resources move to the
booming sector.
Prior research has demonstrated that the effects of a “natural resource curse” do, to some
extent, exist, however, the defined time period by which such a curse persists (or the time period
in which it can be overcome) has never really been defined. Emulating the original model as
closely possible, this thesis attempts to ascertain whether Dutch Disease is a temporary problem
or one that persists over longer time periods. The question becomes, then, whether countries
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learn to correct for the problems of over-investment in the booming sector as well as rent-seeking
over time. While the period that Sachs and Warner (1995) examine is comprehensive (measuring
about 19 years of data), the effects for a recent time period (1990 to 2003) and for an extended
time period (1970 to 2003), demonstrate the effects of the natural resource curse are not, in fact,
constant. While the extended time model demonstrates a substantial effect of primary resource
exports on GDP growth, the most recent time period model does not. With this in mind, what are
the policy implications of natural resource curse? Is it a time-specific curse that can only be
observed over specific time periods? Or is there something inherent in the economy of the 1990s
that addresses why share of exports in natural resources is not statistically significant for growth?
In addition to attempting to ascertain the reasons behind time-specific results for natural
resource curse, the thesis will examine three potential theories: the argument that export
concentration is the cause of natural resource curse (as opposed to actual natural resources
serving as the root cause of the problem), the argument that human capital investment can often
mitigate the negative effects of natural resource dependence, and finally, debt overhang as the
causal mechanism behind the resource curse.
With these theories in mind, this thesis will attempt to answer some of the questions
about policy-making in light of Dutch Disease and Natural Resource Curse if it is, in fact, an
intractable problem.
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TABLE OF CONTENTS
I. Introduction........................................................................................................................1-5
II. Background and Literature Review.................................................................................5-14
III. Conceptual Framework and Hypothesis.........................................................................14-16
IV. Data and Methods...........................................................................................................16-17
V. Summary Statistics.........................................................................................................17-19
VI. Regression Results.........................................................................................................20-28
VII. Discussion and Conclusion............................................................................................28-31
VIII. References......................................................................................................................32-34
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I. Introduction
This thesis explores the phenomenon known as Dutch Disease by utilizing the model of
Jeffrey Sachs and Andrew Warner (1995) as a basis for understanding potential theories
surrounding Dutch Disease and Natural Resource Curse. The concept of the Dutch Disease
gained momentum following the post-World War II era as numerous Latin American countries
suffered from economic problems following slumps in commodity prices. While there is cursory
evidence of this “curse” (as a brief examination of a scatter plot of commodity-rich countries and
GDP growth generally plots a negative relationship), this thesis utilizes an econometric model
developed by Sachs and Warner (1995) in addressing the time conundrum of the curse, that is to
say, to discern whether the findings hold up for a different time period altogether and whether the
effects of slow growth are not as significant when dealing with longer time periods than that
which was originally utilized in their study. Upon emulating the model and subsequently testing
it for the time period 1970 to 2003 and 1990 to 2003, one finds that the results are not statistically
significant for the 1990 to 2003 period. With this said, there may be implications for re-assessing
the original Sachs and Warner (1995) model and testing additional variables to explain the tested
effects.
In attempting to determine why the discussed effects are significant for 1970 to 2003 and
not for 1990 to 2003, three possible arguments are examined: human capital investment as a
policy measure to fight the resource curse, natural resource as a measure of export concentration,
and natural resource curse as debt overhang.
Specifically, because unsatisfactory growth performance is often associated with low
levels of human capital as well as high risk (which limits public and private investment), the
model should go beyond simply controlling for “rule of law” and also employ human capital
indicators to ascertain whether a high proportion of natural resource exports does contribute to
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slower growth when controlling for those factors which often accompany the macroeconomic
environments of Less Developed Countries (LDCs).
Birdsall, Pinckney, and Sabot (2001) have argued that countries can achieve equitable
growth through investments in human capital as it has positive effects on investments in savings,
investments, and increases productivity. Of course, the direction of causality, a priori, is not
entirely clear. Cross-country growth regressions show a general positive relationship between
educational attainment and growth rates. The problem, with resource-rich economies, then, is
that because resource rents are controlled by only a few firms, those owners (whether they are
private or public) capture rents rather than investing them. Secondly, because income is
concentrated in the booming sector, incomes for other tradables fall, reducing the incentives for
educational attainment.
Sachs and Warner (1995) do not fully examine the effects of human capital investment on
GDP growth; and while it is unlikely that the addition of such a variable could have a hugely
dramatic impact on the model’s results, it is a question which has important policy implications.
Consequently, the addition of the variable for human capital seems to be theoretically supported.
The Sachs and Warner (1995) model evinces additional questions regarding the effects of
Dutch Disease on large versus small countries. From a non-causal perspective, it is clear that
agriculture and natural resource exports comprise a declining proportion of GDP as GNP per
capita increases. Syrquin and Chenery (1975, 1989) conducted exhaustive studies to determine
the relationships between several sectors (manufacturing, services, agriculture, and utilities) and
GDP and GNP per capita. In examining the structure of value added in GDP by country resource
endowment (large country with manufacturing sector, small country with primary sector, large
country with primary sector, and small country with manufacturing sector), the relationships for
the sectors and share of GDP hold across the different country samples. In general, as GNP per
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capita increases (from USD 250 to 4,000), the share of GDP for agricultural sector declines while
the share for utilities, manufacturing, and services increases, albeit at differing rates.1 What these
relationships indicate, generally, is that the benefits of primary exports decrease as GNP per
capita increases; and furthermore, it indicates that development of the manufacturing and/or
services sector is more beneficial relative to other sectors for overall economic growth.
A secondary implication of the Syrquin and Chenery (1975, 1989) study is that country
size may matter. Auty and Kiiski (2001) bolster the argument, purporting that large resource-
abundant countries have “two advantages over the small ones for sustaining economic growth.
First, the probability of depending on one or two primary exports is lower for large countries than
for small ones...second, trade accounts for a smaller share of GDP in large economies so that they
are more self-contained and therefore less vulnerable to external shocks.”2 It may be argued,
then, that the effects of Dutch Disease are not constant; in other words, it affects countries with
fewer exports in a negatively more dramatic way and furthermore, that price shocks then have far
more detrimental effects on GDP growth for smaller countries. As such, this thesis will build on
the initial Sachs and Warner (1995) model to ascertain a better understanding of how natural
resources can impact growth and if it is environment-specific (i.e. when commodity price
fluctuations are high versus low, etc.)
A final and third concern of the thesis involves the question of natural resources as a
proxy for debt overhang. If results are time-specific, what can we glean from environmental
impacts and the effects on this natural resource curse phenomenon? The 1970s were marked by
high commodity prices which then underwent a bust in the 1980s. In contrast, the 1990s were
marked by similar boom and bust but on a substantially smaller scale (with less volatility). If that
1 Auty, R.M. and S. Kiiski. “Natural Resources and Welfare.” Resource Abundance and Economic Development. Ed. R.M. Auty. Oxford: Oxford University Press, 2001, 21. 2 Auty, Kiiski, 28-29.
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is the case, then perhaps the Dutch Disease phenomenon is actually the result of a separate causal
mechanism: because countries use natural resources as collateral, the volatility in prices reduces
a countries ability to pay external debt when bust cycles occur. If this is assumed, the Dutch
Disease can only be observed over periods of time in which the real price appreciation increases
were dramatic and volatile (perhaps more so than those observed in the 1990s.)
Of the studies conducted on Dutch Disease, one might ask why, specifically, the model of
Sachs and Warner was chosen. While a very simple regression study demonstrates that negative
relationships exist between natural resource endowments and GDP growth, Sachs and Warner do
have some hesitations in accepting models of other authors. The study of Doppelhofer et al.
(2000), for example, has omitted variable bias, as it did not control for a number of other possible
factors which might have explained the negative relationship between natural resource
endowments and growth. An illustration of this concern, as pointed out by Sachs and Warner,
was geography. Paul Collier and other economic development theorists have argued that there is
some relationship between geography and growth (especially with countries that are land-locked),
and Sachs and Warner tackle this issue through their liberal use of other trade “openness” factors
which can serve as indicators for such non-quantifiable factors such as geography.
Furthermore, Sachs and Warner were the first to confirm the theory of Dutch Disease on
a comparative, worldwide level.3 Given that it is the most comprehensive econometric study, to
date, with regard to the natural resource curse, this thesis will utilize the model and methods as a
basis for determining the effects of natural resource exportation for a period of time beyond 20
years.
3 Sachs, Jeffrey and Andrew Warner. “Natural Resource Abundance and Economic Growth.” Center for International Development and Harvard Institute for International Development, Working Paper. Harvard University, November 1997, 3.
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Counterexamples have not been particularly compelling in providing substantial
econometric evidence to refute the study of Sachs and Warner, though some authors have pointed
out that countries such as Finland, Sweden, and Canada continue to rely heavily on natural
resource exports but do not suffer from economic stagnation. While these studies do serve as
strong counter-examples of natural resource exports and GDP growth, they do not, in and of
themselves, wholly produce robust enough findings to generally refute the theory of Dutch
Disease.
II. Background and Literature Review
The Natural Resource Curse is characterized, primarily, by the theory of Dutch Disease
which expounds upon the chief problems associated with high concentration in exports in the
natural resource sector. The term Dutch Disease refers to the adverse effects on Dutch
manufacturing following the natural gas discoveries in the 1960s. The increase in natural gas
exports led to appreciation of the Dutch real exchange rate from high earnings from the export of
gas; but that increase in exchange rate was subsequently deleterious to the previously competitive
exporters. Production of non-gas exports thereby decreased, resulting in slowed economic
growth overall.
The core model of Dutch Disease assumed by this thesis is presented by Corden and
Neary (1982) as a Booming Sector Model, in which three assumed sectors, namely, the Booming
Sector (B), Lagging Sector (L) and Non-Tradeables (N), produce goods facing world prices. The
resource boom then results in three effects: a spending effect, a relative price effect, and a
resource movement effect. Output is determined by economic inputs and labor. With regard to
the spending effect, a boom raises aggregate incomes of the factors initially employed (the causes
of the boom being either technical, windfall discoveries, or exogenous price increases). This
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boom, then, subsequently results in a spending effect as the extra income from sector B is
invested directly or indirectly. Following from this, provided the income elasticity of demand for
N is elastic, the price of N relative to the prices of tradables will rise. This effect is known as real
appreciation.4 From drawing resources out of B and L and into N and drawing demand to B and
L.
In addition to the spending effect and real price appreciation, there is a resource
movement effect.5 As demand in labor in sector B increases, labor moves from L and N to B.
This result lowers output of the lagging sector, resulting in de-industrialization. These two effects
can have substantial impacts on the economies of primary export countries.
Finally, the third effect, the relative price effect, results as there is an appreciation in the
currency resulting from the boom. This effect reduces the domestic prices of exports and of
imports, which are competing with domestic output, reducing the rents of the booming sector.
The domestic prices of non-tradables rise with increased demand, so resources shift from
tradables to non-tradables, with a consequent reduction in exports and an increase in imports.6
The Dutch Disease phenomenon has become commonplace in economic development
literature, as numerous countries that have been heavily dependent on commodity exports have
experienced stagnated or stunted development. A cursory examination of heavy commodity-
exporting countries may seem to legitimate the claim, but a more thorough examination of the
effects of primary exports over a substantial period of time is crucial to understanding the impact
of commodity exports on GDP.
4 Corden, W.M. “Booming Sector and Dutch Disease Economics: Survey and Consolidation.” Oxford Economic Papers. New Series, Vol. 36, No. 3., (Nov., 1984), 360. 5 Ibid. 6 Auty, R.M. Resource Abundance and Economic Development. Ed. R.M. Auty. Oxford: Oxford University Press, 2001, 7.
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Fluctuations in international primary markets, resulting from high levels of natural
resource exports, make investment, both in the public and private sector, relatively difficult.
Secondly, the “secular movements in the terms of trade” of many primary products can leave
producers in a weak position.7 While manufactured goods have increasingly comprised a higher
percentage of total good traded, those countries that do trade primary sector goods tend to have
higher percentage of total trade in primary goods. As a result, there are concerns about the effects
that such high levels of commodity exporting can have on those respective economies. For
countries with a large share of exports in natural resources, the combination of these effects has a
compounding effect to produce a lessened future potential for exporting manufactured goods and
diversifying the production base.8
Sachs and Warner (1995 and 2001) effectively summarize the “curse of natural
resources” through a country-level econometric study that utilizes share of primary exports as a
key independent variable to determine effects on GDP growth over a nearly twenty year period.
Sachs and Warner summarize the studies of Auty (1990), Gelb (1988), and Sachs and Warner
(1995 and 1999) to show alternative studies in demonstrating the effect of natural resource
exports on economic growth and find that the inclusion of alternative variables and methods
supports the “natural resource curse” theory.
So, then, with the aforementioned problems with natural resource exports, what are the
potential treatments for mitigating the negative impact of resource endowment(s)? In dealing
with the question of savings, Collier and Gunning (1999) do a cross-country examination of a
handful of studies, examining disparities in results from private savings versus public savings and
the length of the specific commodity price booms in question. While they theorize that public 7 Abrams, F. Gerard and Jere R. Behrman. Commodity Exports and Economic Development. Lexington: Lexington Books, 1982, 3. 8 Murshed, S.M. “Contrasting Natural Resource Endowments.” Resource Abundance and Economic Development. Ed. R.M. Auty. Oxford: Oxford University Press, 2001, 115.
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savings may help mitigate the effects of trade shocks, a corresponding outcome would then be
that Dutch Disease effects would be mitigated. The problem with examining the effects of
savings rates in numerous natural resource-rich countries is that many of those countries,
particularly in Africa, lack the infrastructure, transparency, and accountability to ensure that the
windfall gains benefit the entire population as opposed to just government officials. Windfall
gains in these countries have been wasted in many cases, but the development of a Stabilization
Account, through the help of the World Bank, has been heralded as a potential solution for the
misuse of windfall gains.9 While one cannot econometrically test the effects of an externally
created savings account (for lack of data and relative youth of the institution of such programs),
the examination of domestic savings in light of price shock expectations may be an important
variable to control for, given that it has been theorized to help smooth production cycles and price
volatility in commodity markets. Many economists point to the case of Norway as a government
that successfully instituted transparency and savings to overcome the effects of the natural
resource curse; and if this is the case, perhaps the question is not whether natural resource curses
are intrinsically linked to poor growth but whether the treatment of such endowments results in
poor economic results.
Having considered the limitations of including the effects of specific Stabilization
Accounts on GDP growth (because of a paucity of data in adequately measuring those effects),
the thesis will accept the Sachs and Warner (1995) model’s inclusion of domestic investment as a
proxy for potentially measuring the effects of investment on natural resource export
concentration.
9 Jerome, Afeikhana. “Unit 1: Practical Proposals for Lifting the Oil Curse in Nigeria.” Africa – Commodty Dependence, Resource Curse, and Export Diversification. Ed. Karl Wohlmuth, Chicot Eboue, Achim Ugtowski, Afeikhena Jerome, Tobias Knedlik, Mareike Meyn, Touna Mama. New Brunswick: Transaction Publishers, 2007, 105.
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The comprehensive inclusion of variables (rule of law, domestic investment, trade
openness, and terms of trade) that Sachs and Warner (1995) employ provided a compelling
reason to use the Sachs and Warner (1995) model as a basis for this thesis. The model was
chosen for its comprehensive scope and full inclusion of variables that had previously not been
included in such a panel study. However, what Sachs and Warner (1995 and 2001) do not answer
is how long the effect of Dutch Disease really takes in terms of time. Is it a phenomena that is
eventually ‘recoverable’ in some sense? Or are countries that suffer from such a disease destined
to continue down the path of stagnated growth? The question of time is an important one, as it
may have substantial policy effects. For countries suffering from Dutch Disease, is a concerted
effort away from natural resources sector the solution? Or can stagnated growth be expected for a
temporary period of time and eventually, the country will compensate for those losses in other
areas? By extending the time period and also replicating the study in a more recent period, this
thesis will attempt to ascertain what Dutch Disease effects are in terms of time.
Additionally, while the Sachs and Warner model is effective in addressing trade openness
and domestic investment in the economy, it does not address three policy questions that have
implications for Dutch Disease theory, namely, human capital investment, country size, and
environmental influences of commodity price volatility. This thesis will address the theory by
examining ideas posited by a number of authors with regard to attempts to “correct” for Dutch
Disease through policies aimed to mitigate the effects of the curse.
Human Capital Investment as a Mitigating Factor for Natural Resource Curse
An interesting theory with regard to the theory of Dutch Disease and natural resource
curse is that it may not be the natural resource sector itself that is the problem, but rather, the
macro-economic and societal ways in which natural resource abundance is handled. Having said
this, controlling for such factors that would mitigate certain effects of the “curse,” namely,
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education and savings rates as well as country size, could lead to some insights about how to
handle such situations. Furthermore, as Botswana has often been cited as an example that has
escaped the curse, to some degree, and coincidentally has higher levels of investment in human
capital, there could be lessons gleaned from the non-fiscal policy treatment of the curse. While
countries have aggressively pursued attempts at protectionism, liberalization, tariffs, and
openness to help deter Dutch Disease effects, case studies regarding the use of savings to smooth
production or investment in education have not been exhausted by any means.
Going beyond the application of economics, society itself can manage some of the
negative aspects of the natural resource curse. As argued by Wohlmuth, natural resources can
enhance growth, but only if “negative effects on corruption on investment on openness to trade
and on human capital formation can be controlled.”10 While Sachs and Warner do effectively
address trade openness and corruption through the use of a Rule of Law Index, they do not
acknowledge the productivity or employment effect, that is to say, the resource sector will
employ labor and may often build an incentive to draw labor from other sectors (specifically in
service sectors or manufacturing) so incentives for education may then decrease. Additionally,
this result could potentially be offset by the development of technology in the natural resource
sector, increasing productivity and thereby producing incentives for human capital and education.
The problem, however, is that either way, the Sachs and Warner (1995) model does not control
for this variable. While Manzano and Rigobon (2001) have added human capital to a fixed-
effects model of Sachs and Warner, simply adding it to a non fixed-effects model may also
produce differing results from that which was originally produced.
10 Wolhmuth, Karl. “An Introduction: Abundance of Natural Resources and Vulnerability to Crises.” Africa – Commodity Dependence, Resource Curse and Export Diversification. Ed. Karl Wohlmuth, Chicot Eboue, Achim Gutowski, Afeikhena Jerome, Tobias Knedlik, Mareike Meyn, Touna Mama. New Brunswick: Transaction Publishers, 2007, 11.
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Gylfason (2001) also explores the relationship between large natural resource
endowments and education, arguing that natural resource abundance has been theorized to reduce
public and private incentives to improve human capital because of high levels of non-wage
income. On the other hand, there are cases such as Botswana, where the rents from natural
resources have resulted in high education expenditures. Despite conflicting cases that do serve as
anomalies, the general trend is that across countries, public expenditures on education relative to
national income “are all inversely related to natural resource abundance.”11 Sachs and Warner
(1995) do not effectively examine this variable – and the consequential policy implications that
investment in education could have over particularly extended time periods (such as that
examined in this thesis), could be extremely enlightening in determining how to mitigate the
curse. Gylfason (2001) does preliminary cross-country examinations of public expenditures on
education and natural resource abundance, but does not extend the analysis to a full econometric
model that controls for a host of other variables. His argument hinges on a four-part effects
model of natural resource curse, arguing that the exchange rate effects, rent seeking, and
overconfidence produces stagnations in economic development, but additionally, that public and
private incentives for human capital are reduced.
Country Size and Dutch Disease: the “Staple Trap”
Large resource-abundant countries have been demonstrated to have certain advantages
over the smaller countries with regard to sustaining economic growth. First, the likelihood of
staple dependence (that is, dependence on a few exports) is smaller for larger countries and
secondly, because large countries likely have more diversified natural resources, it may have a
higher likelihood of diversifying into the manufacturing sector because large markets allow for
11 Gylfason, Thorvaldur. “Lessons from Dutch Disease: Causes, Treatments, and Cures.” The Paradox of Plenty (STATOIL- ECON conference volume), 22 March 2001. http://www3.hi.is/~gylfason/pdf/statoil22.pdf
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economies of scale. Additionally, trade accounts for a smaller share of GDP in large economies
and thus, they are less vulnerable to trade shocks. Syrquin and Chenery (1989) did an extensive
study on the effects of agriculture as a share of GDP in large countries versus smaller countries
and found that the share of agriculture in GDP declined more slowly in large countries than in
any other endowment category.12
In addition to the increased susceptibility of smaller countries to trade shocks as well as
the lack of diversification in exports, smaller countries also have been observed, historically, to
take a longer time to diversify into production and manufactured goods. While Syrquin and
Chenery (1989) specifically explore the differences in trends between different primary exports
(agricultural products versus mineral products), this thesis does not differentiate between the
different sectors. Rather, it will attempt to address this issue of country size as a potential control
variable for the effects of Dutch Disease.
Lederman and Maloney (2006) argue that export concentration is the reason behind
econometric models that have demonstrated the effect of this resource curse. As such, this
concentration “of export revenues reduces growth by hampering productivity and...[export
concentration], rather than natural resources, per se, drives...[the] negative impact of natural
resource exports over total exports, a proxy that we, in the end see as measuring concentration.”13
Dependence on a single export, which is usually the case for resource-rich countries, can thereby
make a country increasingly susceptible to sharp declines in terms of trade. Using estimation
techniques that use differing methods of natural resource endowments (specifically Leamer’s
calculation of net natural resource exports per worker) shows significance but positive
relationship with growth as opposed to negative. 12 Auty, R.M. and S. Kiiski, 29. 13 Lederman, Daniel and William F. Maloney. “Trade Structure and Growth.” Natural Resources: Neither Curse nor Destiny. Ed. Daniel Lederman and William F. Maloney. Palo Alto: Stanford University Press, 2007, 16.
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Natural Resource Curse as a mechanism of Debt Overhang
A third, and relatively newer theory, questioning the natural resource curse is that
proposed by Manzano and Rigobon (2007). Manzano and Rigobon do not attempt to refute the
natural resource curse in its entirety, but rather, attempt to describe a specific causal mechanism
that may be producing the results found in the Sachs and Warner (1995) study that could be
operating outside of simply the Dutch Disease phenomenon.
Manzano and Rigobon (2007) complete both fixed-effects and cross-sectional models and
find that the natural resource curse effect persists in cross-sectional studies but not in those using
fixed effects. So, then, in an attempt to determine omitted variables that may be causing such
results, they pinpoint credit constraints and debt overhang as a possible explanation. In showing
debt growth and its correlation with resource abundance, the relationship is relatively strongly
positive. And interestingly, those countries with very negative growth and high level of resources
also demonstrated large jumps in debt-to GDP ratios.
However, this relationship between debt and natural resources is not uni-dimensional.
Examining the commodity prices in the 1970s and 1980s, Manzano and Rigobon make a
compelling argument that perhaps large swings in nominal commodity prices of coal, copper,
iron, and oil all experienced great booms in the early part of the period between 1970 and 1990
only to suffer large drops by the 1980s (all of them as much as 30%). Given such price volatility,
the findings result in disparate results when dividing the regression between 1970-1980 and 1980-
1990. With the addition of credit constraints as a variable on a sliding scale of debt to GNP, the
authors find that non-agricultural exports (the variable of interest) becomes insignificant and the
debt variable becomes strongly significant.
Manzano and Rigobon (2007) do take liberty in altering the core structure of the model;
and as such, this thesis will attempt to maintain the core of the Sachs and Warner (1995) model
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rather than altering the calculation of natural resource endowment or model specification, and
instead, attempt to address why the results of the Sachs and Warner (1995) study are, in fact, time
dependent.
With these three primary theoretical questions in mind, this thesis will proceed as
follows: emulating the original Sachs and Warner (1995) model to ensure sound econometric
methods of emulation, then extending the time period beyond 1989 to 2003 and subsequently
testing for the period 1990 to 2003 along with a re-examination of the original model utilizing
additional variables.
III. Conceptual Framework and Hypothesis
The question of Dutch Disease and natural resource curse is an interesting one with
regard to development; moreover, it is crucial to understand whether its effects are real, in the
sense that so much of the developing world is rich in commodities, and yet, so many of these
countries have been unable to harness the advantages of booming commodity prices (especially in
oil), to create sustainable development and economic growth. Consequently, the research
question poses the question first posed by Sachs/Warner in that it asks whether Dutch Disease
does, in fact, exist.
The hypothesis, then, is do the effects of Dutch Disease continue beyond 20 years? Or
more specifically, does natural resource abundance have a stagnating effect on economic
development over a time period as long as 33 years (1970 to 2003)? In addition, while this
natural resource curse has been evident for historical periods, is it still relevant when examining
natural resource endowments from the period 1990 to 2003?
Furthermore, the question of the combination of macroeconomic factors as well as
societal and governance factors need to be controlled for in order to ascertain exactly what the
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effects of natural resource endowments are. Specifically, the problem with some empirical
studies that do not involve econometric models have adequately demonstrated that numerous
LDCs lack development and growth despite large natural resource endowments; however, many
of those countries have been enmeshed in civil war or have extremely corrupt governments that
do not handle the windfall gains from natural resources in an effective manner. With that said,
the conceptual framework will examine the different sort of factors that could result in explaining
natural resource curse by controlling for such things as human capital, rule of law, terms of trade,
etc.
The following page illustrates the conceptual framework in a graphic depiction, to
illustrate how the different factors can then determine whether natural resource exports are, in
fact, a curse or a blessing.
The basic estimation technique employs the following model that has been employed by most
theorists who have examined cross-sectional studies of Natural Resource Curse:
Y hat = lnyt – lnyt-z = γ lnyt-z + βXi,t + αNRX i,t-z +εI,t’
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This stylized model utilizes growth rate of GDP per capita as the dependent variable. This is
measured as the differences in the natural logarithms of income per capita between the year t and
the initial year t-z. Utilizing the Barro (1991) specification, Sachs and Warner (1995) regress this
on the initial log of income per capita.
While this model has incited criticism from Lederman and Maloney (2006) among others
for endogeneity problems, specifically with regard to the initial level of income and its high
correlation with growth and the interdependence of growth-related variables, as pointed out by
Knight, Loayza, and Villanueva (1993), the core model did theoretically support the purported
theory of Natural Resource Curse. And because this thesis is essentially testing time effects of
the original Sachs and Warner (1995) model, the fundamental OLS model will not be altered,
save for the addition of new independent variables.
IV. Data and Methods Qualitative descriptions of the key variables used are below:
Variable Description Share of Primary Exports Share of exports of primary products as measured in
Gross National Product on the base year (so 1970 or 1990). Primary products include minerals, metals, ores, and fuels. This variable comes from the World Development Indicators for the period 1995 to 2003 and for the period prior to 1995 is captured by the “primary exports” variable in the World Data 1994 CD-Rom.
Outward Orientation The fraction of years during the period 1970 to 2003 or 1990 to 2003 in which a country is rated as open as defined by Sachs and Warner (1995). This openness variable is determined by threshold levels on import tariffs as well as by examining Black Market Exchange Premiums and whether a country is considered Socialist. Import tariffs were taken from World Bank Data: Trade, Production, and Protection Data Set; and Black Market Premiums were taken from World Bank Data: Global Development Network Growth Database. For those years which were missing, a general average was taken for those years that were available.
17
Variable Description Rule of law Political Risk Services Rule of Law Index which
gives countries a score of 1-6 in terms of governance and rule of law.
Gross Domestic Investment Natural log of the ratio of real gross domestic investment which includes both public and private source to real GDP, averaged over the period in question. Data is derived from Penn World Tables.
External Terms of Trade External terms of trade is the ratio of an export price index to an import price index. This ratio is then averaged over the period in question to determine relative prices for the economy (or country) in question. Data is derived from WDI and World Data 1994 CD-Rom.
Human Capital Measured by secondary school gross enrollment ratio. Because average level of educational attainment reduced sample size to only 46 countries, this measure, while perhaps a sub-par measure of human capital, results in a larger sample size. Data is derived from WDI online.
Long-Term Debt Total External Long-Term Debt as measured in USD. Data is derived from WDI online.
V. Summary Statistics Variable: Economically Active Population (as percentage of total population, measured as those aged 15-64.)
Observations Mean Standard Deviation
Minimum Maximum
Econ active population in 1970 179 55.35 5.64 45.22 68.90
Econ active population of 1990
166 42.78 13.32 18.2 73.8
Econ active population of 2003 180 58.62 6.86 46.28 72.95
18
Variable: GDPEA (GDP *100/economically active population). Because real GDP from World Penn Tables is given in per capita terms, one must use the aforementioned methodology to calculate GDP per economically active population.
Observations Mean Standard Deviation
Minimum Maximum
LGDPEA 1970
147 8.69 1.06 6.02 11.68
LGDPEA 1990 163 8.39 1.14 5.96 10.37
LGDPEA 2003
159 9.46 1.38 6.58 12.15
Variable: Share of exports of primary products in GNP in 1970, 1990, and 2003. Primary products or natural resource exports include minerals, metals, and fuels.
Observations Mean Standard Deviation
Minimum Maximum
SXP 1970 109 .16 .16 .006 .885 SXP 1990 101 .26 .29 -0.17 .997 Variable: Openness as measured by the fraction of years during the period 1970 to 1990 in which the country is rated as an open economy based on black market exchange premiums and trade tariffs on imports
Observations Mean Standard Deviation
Minimum Maximum
SOPEN 1970 to 1990 109 .34 .44 0 1 SOPEN 1970 to 2003 109 .42 .40 0 1 SOPEN 1990 to 2003 109 .55 .41 0 1 Variable: Natural log of ratio of real gross domestic investment to real GDP, averaged over the period in question.
Observations Mean Standard Deviation
Minimum Maximum
LInvestment of 1970 to 1990 141 2.67 .69 .31 4.0
LInvestment of 1970 to 2003
154 2.49 .62 .82 3.8
LInvestment of 1990 to 2003 169 2.47 .62 .84 3.6
19
Variable: Rule of Law Index. This index “reflects the degree to which the citizens of a country are willing to accept the established institutions to make and implement laws and adjudicate disputes.” Sachs and Warner use the 1982 Index measures for purposes of the OLS model.
Observations Mean Standard Deviation
Minimum Maximum
RL 1982 82 2.96 2.08 0 6 RL 1990 121 3.02 1.71 1 6
Variable: DTT 7090. Average annual growth as measured by the log of external “terms of trade” between the time periods in question.
Observations Mean Standard Deviation
Minimum Maximum
DTT 7090 128 .04 3.15 -6.45 8.62 DTT 7003 114 -.04 1.87 -3.30 5.29 DTT 9003 115 -.13 1.19 -3.87 4.83
Variable: Human Capital as measured by secondary school enrollment Ratio
Observations Mean Standard Deviation
Minimum Maximum
Secondary School Enrollment 1970
125 31.71 26.40 1 102
Secondary School Enrollment 1990 98 52.82 31.69 4 117
Variable: Long-Term Debt Outstanding
Observations Mean Standard Deviation
Minimum Maximum
Long-Term Debt Outstanding 1970
93 6.36e+08 1.35e+09 0 7.94e09
Long Term Debt Outstanding 1990
115 9.23e+09 1.64e+10 3.07e+07 9.04e+10
Variable: Country Size by Land Area
Observations Mean Standard Deviation
Minimum Maximum
Land area in square km. 185 624,087 1,503,880 28 9,326,410
20
VI. Regression Results
The first regression (1a) involves re-creating the results found by Sachs and Warner
(1995). In so doing, the subsequent models for the extended time periods can be assuredly based
on the same calculation techniques and methods. The table below describes the results for the
regression analysis for the time period 1970 to 1990. The subsequent models (1b), (1c), (1d), and
(1e) will be discussed in the latter pages of this section. The core model of Sachs and Warner
(1995) is included below as well:
Log GDP growth (1970 to 1990)= β0 + β1GDP in 1970 β2Share prim exports + + β3Outward Orientation + β4Rule of Law 1982 + β5Log(real gross domestic investment/real GDP) + β6Average annual growth in log of external terms of trade + ε
Variable (1a) (1b) (1c) (1d) (1e)
Lgdpea 1970 -1.141** (.242)
-1.36** (0.34)
-0.96** (0.37)
-1.09** (0.30)
-1.27** (0.46)
SXP 1970 -5.99** (1.68)
-5.40** (2.38)
-5.60** (2.63)
-6.03** (1.80)
-8.74* (4.58)
Openness 1970 1.48** (0.48)
1.66** (0.64)
1.90** (0.79)
1.80** (0.56)
2.57** (1.03)
Log of Investment 0.796* (0.457)
0.27 (0.58)
0.49 (0.61)
0.82* (0.47)
-0.03 (0.78)
Rule of Law in 1982
0.205 (0.13)
0.09 (0.16)
0.16 (0.22)
0.11 (0.15)
0.25 (0.27)
DTT 7090 0.04 (0.054)
0.04 (0.06)
0.07 (0.08)
0.03 (0.06)
0.07 (0.09)
Secondary School Enrollment Ratio 0.02**
(0.01) 0.02
(0.02) 0.00
(0.01) 0.02
(0.01) Long-Term Debt Outstanding (Total) -2.25e-11
(1.41e-10) -1.68e-10 (2.16e-10)
Country Size by Land Area -4.45e-08
(8.59e-08) 1.84e-07
(2.12e-07)
Adjusted R2 .46 .38 .32 .48 .46
Sample Size 70 52 42 63 34
Standard Errors are depicted in parentheses below the respective coefficients ** indicates p value of at least p=.05 * indicates p value of at least p=.10
21
Per the specification of the Sachs and Warner (1995) model, the significant variables are
share of exports and log of initial gdp. For purposes of this thesis, the SXP is the variable which
is of utmost concern. Share of exports (SXP) is measured as a share of GDP; and thus, based on
the regression results, one can argue that a one unit increase in share of exports as a percentage of
GDP decreases GDP growth by 5.99% in real GDP. This effect is statistically significant at the
p=.001 level. In terms of other statistically significant factors, the degree of trade openness is
statistically significant at the p=.003 level. Its coefficient implies that an increase of one unit in
the fraction of years a country is considered “open to trade” (or 1/20 in this case, as it is captured
over a 20 year time period), implies a 1.48% increase in GDP.
Concerns:
Given the differences in the regression results between the model above and those
produced by Sachs and Warner (1995), a comparison of summary statistics was produced to
determine if the calculations involved in measuring certain variables was, in fact, disparate. The
comparisons with the Sachs and Warner (1995) summary statistics reveal that the general
summary statistics are, in fact, for the most part, akin to those of the authors. Consequently,
differences in data calculation and sources are not likely. Rather, their inclusion of several
countries using alternative data sources, namely, for Bangladesh, Bahrain, Botswana, Cape
Verde, China, Cyprus, Jordan, Iran, Myanmar, Taiwan, South Africa, Uganda, Singapore,
Trinidad, Zimbabwe, may be responsible for the different outcomes.
Because the level of significance matches the original Sachs and Warner, the model is
extended for the time period 1970-2003 in model (2a) to determine whether the results indicate
similar levels of significance for the variables in question.
22
Log GDP growth (1970 to 2003) = β0 + β1Share prim exports + β2GDP in 1970 + β3Outward Orientation + β4Rule of Law + β5Log(real gross domestic investment/real GDP) + β6Average annual growth in log of external terms of trade + ε
Variable (2a) (2b) (2c) (2d) (2e)
Lgdpea 1970 -0.90** (2.78)
-1.07** (0.33)
-1.04** (0.38)
-1.03** (0.33)
-1.04** (0.39)
SXP 1970 -7.74** (2.06)
-7.52** (2.19)
-6.63** (2.95)
-7.92** (2.21)
-6.65** (3.00)
Openness 1970 1.71** (0.72)
1.77** (0.82)
1.32 (1.01)
1.69** (0.81)
1.30 (1.03)
Log of Investment
1.29** (0.47)
1.24** (0.49)
0.73 (0.64)
1.30** (0.49)
0.75 (0.65)
Rule of Law in 1982
0.065 (0.14)
-0.04 (0.17)
0.25 (0.26)
-0.003 (0.17)
0.26 (0.27)
DTT 7090 0.149 (0.11)
0.14 (0.11)
0.20 (0.13)
0.13 (0.11)
0.20 (0.14)
Secondary School Enrollment Ratio
0.01 (0.01)
0.04* (0.02)
0.01 (0.01)
0.04* (0.02)
Long-Term Debt Outstanding (Total)
-5.19e-11 (1.59e-10) -3.25e-11
(1.98)e-10
Country Size by Land Area -1.27e-07
(1.03e-07) -4.01e-08 (2.34e-07)
Adjusted R2 .47 .48 .49 .49 .34
Sample Size 64 58 40 58 40
Standard Errors are depicted in parentheses below the respective coefficients ** indicates p value of at least p=.05 * indicates p value of at least p=.10
Based on the results from the regression above, openness, share of primary exports, and
log of initial GDP remain statistically significant. Thus, it may be implied that the Sachs and
Warner (1995) model is not time-specific.
Finally, a third time-specific model is examined for the period 1990 to 2003, model (3a).
Here, the same variables produced in the former two models are included, with the exception of
23
the years in question for the share of exports, Rule of Law Index, the original GDP, the terms of
trade, the log of domestic investment, and fraction of years of openness.
Log GDP growth (1990 to 2003) = β0 + β1Share prim exports + β2GDP in 1990 + β3Outward Orientation + β4Rule of Law + β5Log(real gross domestic investment/real GDP) + β6Average annual growth in log of external terms of trade + ε
Variable (3a) (3b) (3c) (3d)
Lgdpea 1990 1.67* (.97)
-1.79 (1.54)
-2.36 (1.56)
-1.81 (1.52)
SXP 1990 0.002 (0.18)
0.008 (0.02)
0.002 (0.02)
.008 (0.02)
Openness 1990 2.79 (2.15)
3.59 (2.16)
3.38 (2.32)
3.68* (2.15)
Log of Investment -0.26 (1.64)
-0.39 (1.92)
1.00 (1.98)
-0.51 (1.91)
Rule of Law in 1990
-0.48 (0.47)
0.47 (0.67)
-0.51 (0.55)
0.70 (0.70)
DTT 9003 -0.26 (0.41)
-0.66 (0.39)
-0.59 (0.41)
-0.63 (0.39)
Secondary School Enrollment Ratio 0.13**
(0.04) 0.13
(0.04) 0.14** (0.05)
Long-Term Debt 7.89e-11* (4.04e-11) 4.12e-11
(5.31e-11) Country Size (Land Area in Sq km) 5.41e-07
(6.32 e-07) 1.20E-06
(1.11E-06)
Adjusted R2 .22 .66 .30 .49
Sample Size 46 25 33 25
Standard Errors are depicted in parentheses below the respective coefficients ** indicates p value of at least p=.05 * indicates p value of at least p=.10
There are multiple concerns with the 1990 to 2003 regression results. First and foremost,
very few variables are statistically significant, in stark contrast to the previous two models for the
other time periods. Consequently, it is unlikely that the model can produce such disparate results
from the original regression model; and as such, a review of the possible theoretical explanations
must be pursued.
24
Possible Explanations:
It is possible that the notion of natural resource curse is time-dependent. Going back in
history, R. M. Auty has pointed out that natural resources, in the nineteenth century, were not
considered ‘curses’ for development; and that up until the 1960s, those countries that were rich in
natural resources, in fact, experienced higher average GDP than countries not-resource rich. In
effect, this presents a very real quagmire in relation to the theory of Dutch Disease and natural
resource curse: Is it time-specific? In other words, is it environment-specific? Given the oil
shocks of the 1970s, it is possible that the natural resource curse model of Sachs and Warner
(1995) is adequately demonstrating repercussions from the global oil shocks of the 1970s and
early 1980s rather than an inherent tendency for natural-resource countries to truly suffer from a
‘curse’ of sorts.
In recent years, the growth trajectory of resource-abundant countries has been positive,
with sustained rapid economic growth. What is interesting is that the effects of natural resources
on GDP are not equal across the sample of countries. Auty argues that smaller countries tend to
suffer from the effects of “natural resource curse” more so than larger countries. This effect
results as larger countries tend to have less dependency on just one or two primary exports
whereas smaller ones are more vulnerable to collapses.14 Per Auty, the effects of smaller
countries that have natural resources as a primary export may be dependent on only a handful of
exports whereas larger countries have more diverse exports.
In models (1d) and (1e) for the 1970 to 1990 period and models, (2d) and (2e) for the
1970 to 2003 period, an attempt to control for country size is made by including the land area in
square miles for the data set. The significance of the key variable, SXP, does not change given
the addition of the control. 14 Auty, R.M. Resource Abundance and Economic Development. Ed. R.M. Auty. Oxford: Oxford University Press, 2001, 7.
25
Other theorists have posited that demographic cycles play a substantial role in
determining outcomes of growth. Changing dependency ratios have been argued to accompany
changes in the demographic transition, which then, have implications for economic growth and
domestic investment and savings. The problem with this theory, however, is that a cursory
examination of demographic transitions and country-level GDP may, in fact, demonstrate such a
relationship, however, it is a difficult concept to prove causation in terms of direction of causality.
Looking at other potential policy explanations, there is a growing volume of literature
which argues that human capital investment plays a substantial role in lowering the growth rates
of resource-abundant countries. While Lederman and Maloney (working paper, 2002), have
addressed the question of human capital in a fixed-effects model, the addition of human capital
and risk index measures to the basic Sachs and Warner model (1995) may lead to differing results
when controlling for those variables. Birdsall, Pinckney, and Sabot make the astute observation
that resource-abundant countries tend to invest less in education. Governments in resource-rich
countries may attempt to increase education efforts, but because the labor market in such
countries do not reward education, there is likely little investment beyond the natural resource
sector.15 Consequently, raising the rates of return for human capital can induce higher savings
and more investment, in addition to increased productivity. Many point to the investment in
human capital as one of the key factors in aiding the rapid growth of the Asian Tigers.
Determining the direction of causality with regard to education and GDP growth has been
a challenge. Barro-style cross-country regressions regarding growth have generally demonstrated
that education has significant impacts on growth rates; education increases wages but it
importantly also increases productivity (in resources sectors and otherwise). In addition to the
15 Birdsall, Nancy, Thomas Pinckney and Richard Scott. “Natural Resources, Human Capital, and Growth.” Resource Abundance and Economic Development. Ed. R.M. Auty. Oxford: Oxford University Press, 2001, 58.
26
direct impact that education has on output, it also has an indirect effect. Education generally
reduces inequality, which subsequently perpetuates growth. Education has also been
demonstrated to improve child health, which can then aid a country to transition to a different
demographic transition with lower fertility rates and increased savings and investment.
With this concept in mind, an additional regression was run utilizing the human capital as
measured by secondary school enrollment ratios (models (1b), (1c), (1d), (1d), (2b), (2c), (2d),
and (2e)). The problem with the primary school enrollment values was that the sample size was
reduced substantially when the secondary school enrollment ratio was included in the regression
model. As a result, this variable was included as an alternative to school enrollment rates.
It is clear from the results in regression 1 and 2 that the addition of the human capital
variable does not change the results dramatically for the original Sachs and Warner (1995) model
or the extended model from 1970 to 2003. The education variable is, however, statistically
significant, which demonstrates some effect of educational attainment on GDP growth. However,
because the sample size is reduced when adding the education variable, there is some concern that
the model may be biased because of small sample size.
So, then, how can one explain these curious results from 1990 to 2003? One thought is
that commodity prices underwent a boom and bust cycle between 1970 and 1990 as well as 1970
to 2003 that was less drastic or not present during the latter time period in the 1990s. However,
between 1990 to 2003, commodity prices did experience a boom and slight bust effect.
Consequently, it may be possible that fluctuations in commodity prices could provide a key to
determining why the resource curse existed from 1970 to 1990 and not between 1990 and 2003.
A cursory examination of the CRB commodity spot price index does not seem to hold any
answers.
27
CRB Spot Index (1967=100)
(monthly close) January 1947 - January 2009
50
100
150
200
250
300
350
400
450
500
550
1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007
© Commodity Research Bureau
Index Value
source: http://www.crbtrader.com/crbindex/
The level of commodity price volatility is somewhat controlled by the “terms of trade”
variable which Sachs and Warner (1995) originally employed. While the above Commodity
Research Bureau (CRB) index demonstrates more volatility over the 1970 to 2003 period
(partially by virtue of the fact that the time period is longer), there is no clear trend which may
provide answers as to why the 1990 to 2003 period does not demonstrate the Dutch Disease
effects which were evident in the previous time period(s).
With no clear answers to demonstrate why the natural resource curse does not appear to
be significant for the period 1990 to 2003, and additional theory is proposed: it is possible that
the results from the original Sachs and Warner study (1995) were capturing debt overhang and
not natural resource curse effects from primary exports.
In an attempt to discern whether the Sachs and Warner model is measuring a debt
overhang in lieu of actual natural resource curse, the variable for long-term debt at the country
level (external debt only) is included in models (1c), (1e), (2c), and (2e) as means of potentially
explaining how the natural resource share of exports is actually a proxy variable for debt
overhang. How can this be the case? Manzano and Rigobon propose a theory that in the 1970s,
28
the high commodity prices led to developing countries to utilize those commodities as collateral
for debt. When those prices fell in the 1980s, the significant fall left a majority of countries with
a considerable debt overhang. After exhaustive re-evaluation of the original Sachs and Warner
model using fixed effects models with panel data, the authors find that the natural resource curse
disappears. A plausible explanation for this result is that there is correlation with omitted
variables – and they propose this to be those with high debt (incidentally, those countries with the
most negative growth and large natural resources were those that also had significant increases in
their debt-to-GDP ratio between 1975 and 1985.16) While the models ((1c), (1e), (2c), and (2e))
utilized in this thesis did not seem to eradicate the negative significance of the SXP variable,
given the addition of the long-term debt variable, it differs from the Manzano and Rigobon (2007)
study in that regular cross-sectional analysis was used (in lieu of the panel data and models which
they employed). The model was based upon the original Sachs and Warner (1995) structure with
the addition of Long-Term Debt Outstanding.
These curious results lead us to question whether there is some intrinsic, inherent factor
that was present in the 1970s which allowed for natural resources to negatively impact GDP
growth that was not present at later periods of time. The possible answers, namely, education,
country size, and debt, do not seem to hold answers to this enigma. With that said, the following
section will delve more into theories to help ascertain what makes this model time-specific.
VII. Discussion and Conclusion
The curious results from the 1990 to 2003 period indicate that perhaps natural resource
curse is time specific. Lederman and Maloney test the results for historical periods prior to 1970
16 Manzano, Ozmel and Roberto Rigobon. “Resource Curse or Debt Overhang?” Natural Resources: Neither Curse nor Destiny. Ed. Daniel Lederman and William F. Maloney. Palo Alto: Stanford University Press, 2007, 41.
29
and find that the results do not hold up when looking at other time periods. Utilizing a simplified
version of the Sachs and Warner (1995) model, Lederman and Maloney find that the share of
natural resources as a percentage of exports actually has a positive effect for 1820 to 1870 (they
did not include all other variables given lack of data) and also for 1913 to 1950.17
An interesting theory proposed by Basedau (2005) is that the type of resource may
matter. In other words, “the banana curse is different from the oil curse.”18 Moreover,
macroeconomic vulnerability and the likelihood of susceptibility to price fluctuations differ from
resource to resource. Historically, metals, for example, have been demonstrated to be less
volatile in prices than oil. Secondly, Basedau points out that location and the manner of
exploitation are two other crucial factors in explaining the effects of natural resource exports.
Furthermore, certain resources can be easily smuggled (such as diamonds), which could produce
high prices on the global market as a result of the nature of the illegality of the resource in
question. “When rebel groups are willing to cooperate with criminal networks or run their
networks abroad, they can greatly benefit from this trade. On the other hand, governments will
rather try to avoid illicit commodities as a source of income since most of them are concerned
with their public image.”19 Consequently, grouping natural resources as a “share of total exports”
as was done in the aforementioned models may not be representative of the actual natural
resource abundance a country may have. Furthermore, grouping them into one category may also
confuse the effects that such exports have on the overall GDP growth.
17 Lederman, Daniel and William Maloney. “Open Questions about the Linke between Natural Resources and Economic Growth: Sachs and Warner Revisited.” Central Bank of Chile Working Paper. No. 141, February 2002. 18 Basedau, Matthias. “Context Matters – Rethinking the Resource Curse in Sub-Saharan Africa.” Working Paper: Global and Area Studies. German Oversease Institute Responsible Unit: Institute of African Affairs, May 2005. www.duei.de/workingpapers, 24. 19 Ibid, 25.
30
Given this quagmire of trying to make sense of differing results, a number of policy
implications are clear: natural resources cannot be definitively thought of as a “curse” as a whole
and secondly, resources should not be used as collateral for debt. There are few definitive
answers that may be gleaned from the results discussed herein; however, repeated studies by
Lederman and Maloney and Manzano and Rigobon have demonstrated that when fixed-effects
models are utilized, the negative impact of such a natural resource “curse” are mitigated, if not
eradicated. So, then, one must approach the issue of Dutch Disease (and the curse) with caution.
Domestic savings and investment have not been demonstrated to necessarily eradicate the curse
but the question of time is important. Perhaps the 1990 to 2003 period is too short a time period
to adequately measure the negative (or positive) impacts of natural resource abundance. Without
much fluctuation in commodity prices, the effects of any real exchange rate appreciation and
subsequent bust effects may be difficult to discern. Furthermore, the debt overhang question may
be useful in creating policies to avoid using commodities as collateral wherever possible.
Further research regarding the time effect of Dutch Disease must be studied. Perhaps a
better evaluation of the time effect would be to study individual commodities within the model
along with a commodity index price volatility measure that could better account for how much
the boom and busts can impact a country’s growth. Without more specific information on the
extent to which commodity prices fluctuate, it is difficult to definitively argue that the curse is
non-existent, and therefore, the conclusion is that context matters. Aside from investments in
human capital and rule of law, domestic savings/investments and openness to trade can all help to
alleviate the effects of a potential curse, but those measures have a dynamic interplay with the
type of resource in question as well as the global commodity market(s) itself. Attempting to
simplify such a curse in a cross-sectional study does not aid in formulating specific policies to
prevent such deleterious effects; however, they do aid policy-makers in understanding that
31
generalizations regarding commodities can be dangerous, as fixed-effects models by other authors
have demonstrated. Country context beyond those measurable factors, as well as the role of
illegal markets, global commodity price volatility and demand are crucial to understanding how
natural resources can affect a given economy.
Perhaps the overarching conclusion is that a country-level study of Dutch Disease may be
dangerous in that it generalizes a problem that involves far more inputs than just those variables
captured in the original Sachs and Warner (1995) model. Subsequent studies which account for
commodity price levels as well as volatility must be evaluated to understand the economic aspects
of the phenomenon.
32
VIII. References
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Adams, F. Gerard and Jere R. Behrman. Commodity Exports and Economic Development.
Philadelphia: Lexington Books, 1983. Asiedu, Elizabeth. “On the Determinants of Foreign Direct Investment to Developing
Countries: Is Africa Different.?” World Development Vol. 30, No.1, (2002), 107-119. Auty, Richard M. and Sampsa Kiiski. “Natural Resources, Ccapital Accumulation, Structural
Change, and Welfare.” Resource Abundance and Economic Development. Ed R.M. Auty. New York: Oxford University Press, 2001, 19-35.
Auty, Richard M. and Alan H. Gelb. “Political Economy of Resource-Abundant States.”
Resource Abundance and Economic Development. Ed R.M. Auty. New York: Oxford University Press, 2001, 126-143.
Basedau, Matthias. “Context Matters – Rethinking the Resource Curse in Sub-Saharan
Africa.” German Overseas Institute. Responsible Unit: Institute of African Affairs. Working Papers: Global and Area Studies. www.duei.de/workingpapers.
Birdsall, Nancy, Thomas Pinckney and Richard Sabot. “Natural Resources, Human Capital
and Growth.” Resource Abundance and Economic Development. Ed R.M. Auty. New York: Oxford University Press, 2001, 58-75.
Bresinger, Clemens and James Thurlow. “Asian-Driven Resource Booms in Africa:
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Corden, W.M. “Booming Sector and Dutch Disease Economics: Survey and
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Collier, Paul and Jan Willem Gunning and Associates. Trade Shocks in Developing Countries –
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Vol. 13. No. 3 (Summer, 1999), 23-40. De Gregorio, Jose. “The Role of Foreign Direct Investment and Natural Resources in
Economic Development.” Central Bank of Chile Working Papers. No. 196, Enero 2003. http://www.bcentral.cl/estudios/documentos-trabajo/pdf/dtbc196.pdf
33
Manzano, Ozmel and Roberto Rigobon. “Resource Curse or Debt Overhang?” Natural Resources: Neither Curse nor Destiny. Ed. Daniel Lederman and William F. Maloney. Palo Alto: Stanford University Press, 2007, 41-70.
MacBean, Alasdair I. and D.T. Nguyen. Commodity Policies: Problems and Prospects. New York:
Croom Helm, 1987. Murshed, S.M. “Contrasting Natural Resource Endowments.” Resource Abundance and
Economic Development. Ed. R.M. Auty. Oxford: Oxford University Press, 2001. Gylfason, Thorvaldur. “Lessons from Dutch Disease: Causes, Treatments, and Cures.” The
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