tilburg university the return of the dutch disease?
TRANSCRIPT
Tilburg University
THE RETURN OF THE DUTCH DISEASE?
Bachelor Thesis Economics
Student: W.J.A. van Benthem Supervisor: prof. dr. J.A. Smulders
Date: 06/06/2011 Word Count: 7892
Table of Contents
Introduction ...........................................................................................................................1
What is the Dutch Disease? ..................................................................................................3
Models Explaining the Dutch Disease ......................................................................4
Empirical Work on the Dutch Disease ......................................................................8
Methodology and Data ..........................................................................................................10
Methodology .............................................................................................................10
Data ...........................................................................................................................11
Results ...................................................................................................................................15
Potential Problems and Pitfalls .................................................................................18
Conclusion ............................................................................................................................24
Appendix 1 Description of Variables and their Sources .......................................................25
Appendix 2 Countries Used in the Dataset ...........................................................................26
Appendix 3 Correlation Matrices..........................................................................................27
Reference List .......................................................................................................................29
1
Introduction
The striking observation that many countries abundant with natural resources have
experienced little or no economic growth in the last century has been studied extensively in
the last decades. This phenomenon is generally referred to as the resource curse. The study of
the resource curse is relevant because many of the world’s poorest countries possess huge
amounts of natural resources. With this in mind it is especially disturbing that resource
abundance, in most cases, does not seem to translate directly into economic growth.
There is a variety of possible causes studied in the literature. Recently there is a big
emphasis on the role of institutions in explaining the resource curse. For example Mehlum et
al. (2006) introduce the concept of grabber friendly institutions, which in combination with
resource abundance should lead to low growth, and producer friendly institutions, which
should help turning abundance of natural resources into a blessing. They find empirical
evidence for their hypothesis that ‘institutions determine whether countries avoid the resource
curse or not’. Boschini et al. (2007) confirm the finding that institutions play a vital role in
determining whether resource abundance is a curse or a blessing, and in addition show
evidence that the type of resource is a decisive factor as well.
In addition, van der Ploeg and Poelhekke (2009) find that the most important cause of
the resource curse comes from macroeconomic volatility. The volatility that comes with
natural resources prices creates volatility in the output per capita in the countries that depend
heavily on their natural resources.
Earlier studies of the resource curse date back from the late 1970s and early 1980s.
Contrary to the contemporary views, in these days there was much focus on the so-called
Dutch disease explanation of the resource curse. In short this theory states that due to a
windfall of natural resources there is an appreciation of the real exchange rate which leads to
de-industrialization and a decrease in the sector for traded goods (Corden and Neary, 1982).
Stijns (2003) made an empirical investigation on the Dutch disease hypothesis using a gravity
model of trade. He found negative relationships between a net energy exporting country’s net
energy exports and manufacturing exports, and between energy world prices and
manufacturing exports. Further, Harding and Venables (2010) have written an article about
the effects of changes in net resource exports on non-resource exports, non-resource imports,
and net non-resource exports which is the difference between the former and the latter. One
2
important result they find is a fall of 65 cents in net non-resource exports for a dollar increase
in natural resource exports.
The relationship I am trying to study for my thesis tries to integrate the effects of
institutions in explaining the Dutch disease. The main research question is: Does the Dutch
disease really exist and what is the role of a country’s institutional quality on the impact of
the disease. To investigate the existence of the Dutch disease, and to shed light upon the role
of institutions on the Dutch disease, I use empirical data to carry out regression analysis. The
remainder of the thesis is constructed as follows: section two describes the theoretical
framework and some empirical studies, section three elaborates on the methodology and data
used for the research, section four discusses the results, and section five concludes.
The results show that most of the times a negative relationship occurs between a
windfall in natural resource wealth and manufacturing exports, albeit it that there could be
significant omitted variables bias. Furthermore, in some cases the relationship turns out to be
insignificant. Additionally, the role of institutions turns out to be insignificant as well.
3
What is the Dutch Disease?
In an article published as early as 1977 in the Economist, the bad macroeconomic
situation in the Netherlands compared to other European countries, was given the name the
Dutch disease. The article argues that part of the bad performance can be explained by the
post OPEC-recession that occurred at that time. This recession caused other countries to
perform badly as well. However the reason behind the fact that the Netherlands was having
more problems than the other European countries is being linked to the amounts of gas
reserves the country was exploiting during that period. The article argues that the cause of the
Dutch disease consisted out of three parts.
First of all, the currency at the time was too strong. When the gas was discovered in
1959, the Dutch government chose for quick exploitation of the gas, because at that time oil
was cheap and nuclear energy was expected to be abundant within twenty years. The quick
implementation took the form of low prices for domestic use of the gas, and long term export
contracts. By means of this way the current account improved accordingly, with an average
of $2 billion in the period 1972-1976. However, on the other hand the improving capital
account was partially offset by capital outflows. In the period 1967-1971 the country was a
recipient of foreign investment, whereas in the period 1972-1976 it became a net investor
abroad. This capital outflows even helped to prevent, to a certain degree, the Dutch industry
from losing its attractiveness to importers from abroad. Although these outflows have slowed
it down, they could not prevent an increase in the value of the Guilder.
Second, there was a significant increase in industrial costs which was caused by three
main reasons. Minimum wage legislation imposed upward pressure on all wages in the
economy. In addition, the social security payments were substantial and risen significantly
compared to earlier years. And in that period, in the Netherlands the regulations for
environmental and employment standards where tightened.
Finally, the proceeds from the gas reserves were not used in a sound way. It seems
most appropriate to invest the revenues from an asset which is diminishing, in capital1.
However the Dutch government chose to use it for government spending. The majority of this
spending took the form of transfer payments. For example: pensions and unemployment
benefits.
1 Generally known as Hartwick’s rule.
4
Models Explaining the Dutch Disease
Corden and Neary (1982) provide a clear and understandable analysis of the Dutch
disease. Their framework is a small open economy which produces three goods. Two of them
are traded goods, called energy and manufacturing (XE and XM respectively). The prices of XE
and XM are exogenously determined at the world markets. And the other good is non-traded,
called services (XS). The price of XS is determined by domestic supply and demand. Two
important assumptions are made. First, the models presented are in real terms so that
monetary considerations play no role, and national output is always equal to expenditure.
Second, the markets for the factors of production, in this case labor and capital are perfect.
This means that the real wages are perfectly flexible and that full employment is maintained
at all times. It is further important to know that the real exchange rate in this article is defined
as the relative price of non-traded to traded goods.
An important distinction this article makes is between the resource movement effect
and the spending effect. The resource movement effect is defined as a reallocation of mobile
factors of production, caused by an increase in the marginal products of those factors in the
sector experiencing a boom. On the other hand the spending effect is the increase in spending
on XS caused by the higher real income resulting from the windfall in natural resources. This
effect raises the price of services and thus a real appreciation which leads to further
adjustments in the structure of the economy.
The models put forward in the article are distinguished from each other by different
assumptions about the economy. The assumptions concern the mobility of the different
factors of production between the three sectors. For example, one model assumes that labor is
the only mobile factor of production in the economy, while another one assumes that next to
the mobility of labor, capital is mobile between the manufacturing and services sectors, and
that the energy sector uses its own specific factor of production. Other important factors
influencing the effects of the boom are the relative intensities of the factors of production.
Due to the different situations presented in the article the outcomes differ from each other.
To give a basic understanding of the dynamics, the simplest of the models is
presented. To begin with it is assumed that labor is the only mobile factor between the three
different sectors (XE, XM, XS) in the economy, and that each sector has its own specific capital
factor. The total labor supply of the economy is shown by the distance OSOT in figure 1.
Labor input into services is measured by the distance from OS, and likewise the distance from
OT measures the total labor supply into the traded goods sector. Further, the labor demand
5
schedules for the services sector (LS), the manufacturing sector (LM), and the total traded
sector (LT) are shown.
Figure 1
The effect of the boom can be analyzed by looking at the resource movement effect
and the spending effect separately. Through the resource movement effect the labor demand
schedule of the energy sector shifts upwards and thus the total traded labor demand schedule
shifts upwards as well. The new labor demand schedule for traded goods is shown by the
dashed LT’ curve in the figure. The position of the LT’ curve causes a new equilibrium to
form at point B, and labor is drawn out of the manufacturing sector (m � m’). The new
equilibrium thus gives rise to direct de-industrialization. In addition, the resource movement
effect leads to an excess demand for services. In order to restore equilibrium between the
traded goods and services markets, shown in figure 2, there must be an appreciation of the
real exchange rate (the price of non-traded goods relative to traded goods increases). As a
result of the higher prices in the services sector, the labor demand schedule for services shifts
upward. This in turn causes a final equilibrium to form a point G.
Next the effects of the boom are analyzed by looking at the spending effect. The
boom increases the production possibilities of the economy, shown by the new curve T’S in
figure 2. The point b corresponds to the highest possible indifference curve which can now be
reached. The curve ON shows the demand for services at the initial exchange rate, it is
OS m m’ m’’
LM
B
G
A
Wage
OT
LS
LS’
LT’ LT
w2
w1
w0
Labor
6
assumed that demand for services rises with income. This curve intersects the T’S to the right
of point b. Hence, there is again an excess demand for services and an appreciation of the real
exchange rate must take place to restore equilibrium. To summarize, the resource movement
effect causes a decrease in the output of services, and the spending effect leads to an
increased demand for services. Both effects lead to a real appreciation of the exchange rate,
and in turn to indirect de-industrialization.
According to van Wijnbergen (1984) the Dutch disease cannot simply be ignored,
since the structure of the economy has a big influence on the performance of the economy.
For example, after the Second World War most of the economies that experienced rapid
growth benefited from strong traded sectors. Furthermore, in the economic literature it is a
stylized fact that technological progress is much faster in the traded sector.
In his article Van Wijnbergen sets out a model for the Dutch disease which
incorporates learning by doing. The reason for this is that one of the factors affecting
technological progress is accumulated experience. Further, the article assumes that learning
by doing mostly occurs at the traded sector, so that in the model learning by doing effects
only affect the traded sector. The learning by doing implies that production in the second
period depends positively on the production in the first period.
The economy modeled is a two period model with a traded and a non-traded sector. A
subsidy is introduced which induces firms in the traded sector to produce at the social optimal
S
a
b
n T
T’
O
Services
Traded
Goods
Figure 2
7
level. Without the subsidy this would not occur since the return on experience is assumed to
be industry specific. For this reason firms cannot appropriate all the returns on experience.
Therefore the optimal level for the firm and the socially optimal level are not the same, and
government intervention is needed. The effects of a temporary increase in oil revenues are
analyzed in two situations: one with an exogenous current account, and another with the
possibility to accumulate assets or borrow from abroad.
In the model with the assumption that the current account is exogenous, the results are
unambiguous. The temporary increase in oil revenues in the first period is at least partially
spent on non-traded goods. This means that a real appreciation of the price of non-traded
good in terms of traded goods has to occur, for the demand for non-traded goods increases.
This real appreciation causes resources to shift out of the traded sector into the non-traded
sector. Because learning by doing takes place only in the traded sector, the shift means that
production in the second period will be lower. Therefore a higher subsidy in the first period is
needed to ensure that firms produce at the social optimal level. Due to the higher subsidy
more traded goods are produced in the first period and as a result in the second period as
well. The excess supply of traded goods pushes up the second period real exchange rate. To
conclude, without the subsidy the second period welfare will decline because of the loss of
learning by doing, induced by lower production of traded goods in the first period.
The second model introduces foreign borrowing and foreign asset accumulation. This
allows income in one period to be distributed over the two periods. The outcome of this
model is however ambiguous unfortunately. Again, the higher oil revenues will lead to a real
appreciation exactly similar to the previous model, and thus to less learning by doing in
period one and lower output in period two2. On the other hand, the real exchange rate which
appreciates in the first period will gradually depreciate over time. This causes a higher cost of
borrowing in the first period, and therefore there will be a shift of expenditure from the first
to the second period. The effect of the shift in expenditure is an upward pressure on the real
exchange rate of tomorrow3. In addition, the learning by doing effect increases the first period
optimal production subsidy, and the cost of borrowing effect decreases the subsidy. So the
question is: which of these effects really dominates.
2 Learning by doing effect. 3 Cost of borrowing effect.
8
Empirical Work on the Dutch Disease
Stijns (2003) investigates the phenomenon of the Dutch disease by using a gravity
model of trade. This model helps to take out the other macroeconomic effects faced by the
home economy. For example, an energy boom that takes the form of an increase in the world
price of energy tends to accompanied by economic recessions.
In the paper, four different testable hypotheses are being identified; an appreciation of the
real exchange rate; an increase in non-traded output; a decrease in manufacturing sector
production; and a decrease in manufacturing exports. The last hypothesis is being tested,
because for the second and third hypotheses there is not enough data, and the first hypothesis
is already being tested by Chen and Rogoff (2003).
Three approaches are being used. First, only the world price of energy is used as the
independent variable. Second, net energy exports is being used for this purpose. And third,
net energy exports and the world price of energy are used together to capture the benefits of
both approaches. In his investigation Stijns makes use of the world price of energy because
he is of the belief that it is safe to assume that the world price of energy is exogenous to
manufacturing trade, whereas net energy exports has the potential to be endogenous to
manufacturing trade.
The results Stijns finds are very interesting. In all the three approaches mentioned
above, there seems to be significant evidence which supports the Dutch disease theory: the
world price of energy and net energy exports both have a negative effect on manufacturing
exports. According to the results of the article: ‘a one percent increase in the price of energy
will, ceteris paribus, decrease a net energy exporter’s real manufacturing exports by half a
percent.’ In addition, ‘a one percent increase in net energy exports will, ceteris paribus,
decrease a net energy exporter’s real manufacturing exports by one eight of a percent.’
As mentioned before, Chen and Rogoff (2003) have tested the relationship between
natural resource booms and the real exchange rate. In their article they test the relationship
between the real exchange rates and world commodity prices. The relationship is tested for
three OECD countries: Canada, Australia, and New Zealand. The focus is on these three
countries in particular because they have a large share of commodity exports in their total
exports, and because of their relatively small size, which disables them to influence the world
price of commodities. The results of the study show a significant positive relationship
between the real exchange rate and world commodity prices for Australia and New Zealand.
However, the relationship is not significant for Canada after the trend of declining world
commodity prices is being removed out of the relationship. This investigation confirms the
9
Dutch disease, for a positive relationship is found between the commodity price shock and
the value of the real exchange rate. However, the data is compiled only out of three
developed countries. For this reason it may not be very useful for policy-makers in the
developing world, which have to deal with the resource curse the most.
More recently, Harding and Venables (2010) have studied the effects of foreign
exchange windfalls on the balance of payments of a country. The balance of payments
accounts studied are non-resource exports, total imports, and the net accumulation of foreign
assets. This accumulation takes the form of ‘government accumulation of a sovereign wealth
fund’, and ‘foreign debt reduction’. Two different forms of the foreign exchange windfalls
are studied; net resource exports, defined as the net exports of fuels, metals, and ores; and
foreign aid, defined as inflows of aid. For their investigation they use a data set consisting of
133 countries, over the period 1960 to 2000.
The results of their study lead to the conclusion that roughly per dollar of resource
exports, there is a 50 cent decline in non-resource exports, a 15 cent increase in imports, and
35 cents are saved. The effect of one dollar of foreign aid is an increase imports by 40 cents,
while exports decline only slightly.
What this study shows for my own research is that an increase in resource exports
tends to decrease non-resource exports. The latter could lead to a symptom of the Dutch
disease, namely a decrease in outputs of the non-resource tradable sector.
10
Methodology and Data
Methodology
In order to lay bare the relationship between manufacturing exports4 and natural
resource wealth. I will make use of regression analysis. The regression analysis is based on
panel data. Because panel data varies over two dimensions, in this case time and individual
countries, it is often more accurate than single time series or cross-sectional data (Verbeek,
2008). The data is described in the section below this section.
The model studied is constructed as shown by equation 1. This model is a fixed
effects model, based on the first-differencing technique. An advantage of this technique is
that it eliminates the time invariant effects from the model. First-differencing suits my dataset
well, for I have two time periods available, as will be explained in the section below. This
makes it easy to subtract the values for the two periods for each variable.
∆��� � ∆�������� ∆��������� ∆������������� ∆����� ∆��� (eq. 1)
∆Yit is the dependent variable which represents the difference in total manufacturing
exports per capita of country i at period t compared to period t-1. ∆NatCapit is the variable
representing the difference in the World Bank’s (1997, 2006) natural resource wealth per
capita estimate of country i at the same periods, whereas ∆InstQit represents the difference in
institutional quality of a country i at these periods. To discover the combined effect of
institutional quality and natural resource wealth, I have included an interaction term as well
in the model. ∆Xit is a vector of the differences in control variables. These controls are
constructed following Gourdon (2009), however not all variables are the same because I
could not collect precisely the same data. In his article Gourdon investigates the determinants
of trade. The variables are made up out of produced capital per capita, intangible (or human)
capital per capita, openness to trade, total factor productivity growth5 , GDP per capita,
population, weighted growth rates of trading partners per country, number of patent
applications, total kilometers of road network, and number of internet users per 100 people.
∆uit is the error term capturing the random components.
4 For reasons of simplicity I use the term manufacturing goods to denote non-natural resource traded goods, similar to section two. 5 I use total factor productivity growth instead of total factor productivity. The reason for this is that I was not able to find data on total factor productivity for the countries used in my dataset.
11
The variables for the endowments are not constructed in the same way as Gourdon
(2009). Gourdon uses the ratio of a country’s per capita endowment of a factor to the world
per capita endowment of that factor. This is because the relative advantage compared to other
countries is used as an explanatory variable in Gourdon’s model. I am interested in
explaining the relationship between a country’s natural resource endowments and
manufacturing exports. Because the theory described in section two does not directly take
into consideration a country’s comparative advantage in factor endowments I have chosen to
take the per capita values of the factor endowments. By this way I can test whether windfalls
in other factors have an effect on manufacturing exports as well. For a full description of the
control variables and their sources I refer to appendix 1.
When the Dutch disease would really exist I expect the estimate of β1 to be negative,
for a windfall in natural resource endowments would cause a decrease in manufacturing
exports. Next, the estimate of β2 could be either positive because generally better institutional
quality would lead to better economic conditions, and therefore better export performance, or
negative, because of the possibility that the richer a country becomes, the more it will
specialize in services rather than manufactured goods. β3 is a very interesting term in the
model. This is the interaction term composed of institutional quality and natural resource
wealth. The estimate of the coefficient for this interaction term provides information about
the role of institutions in the Dutch disease. It tests whether the relationship between natural
resource abundance and manufacturing exports is different for different levels of institutional
quality. One would maybe expect that there would be an influence of institutions on the
Dutch disease. On the other hand however, the Netherlands and the UK, two countries with
good institutional quality, have experienced Dutch disease symptoms in the past.
Data
The mechanisms discussed in section two have to be translated into models consisting
of measurable variables. The effects highlighted in the theories of van Wijnbergen (1984) and
Corden and Neary (1982) suggest several variables, and as mentioned before Stijns (2003)
identifies four different hypotheses. First of all, what is observed is an increase in the real
exchange rate. In both articles this is defined as the price of non-traded goods in terms of
traded goods in a country. To construct a variable for the real exchange rate, one could use a
price index of non-traded goods in a country divided by the price index of traded-goods in a
country. Chen and Rogoff (2003) use the nominal exchange rates of countries expressed in
terms of a basket composed of foreign currencies. Their results are described in section two.
12
Second, non-traded output per country could be chosen as the dependent variable.
According to the theory this variable should increase with a natural resource boom. However,
the question is whether data is widely available for many countries. Especially it will be hard
to collect data on this for the developing countries, and for these countries data is particularly
useful since most of the countries that are endowed with large amounts of natural resources
are developing countries.
Third, output in the manufacturing sector could be taken as the dependent variable.
An accurate measure for this would be a variable which measures tradable goods output. For
instance it could be created by aggregating output in the sectors not falling under the
classification of natural resources or non-tradables.
Nevertheless, most studies use non-resource exports as the dependent variable
(Harding and Venables, 2010), (Stijns, 2003), because of data-availability. Using exports as
the dependent variable could lead to a misinterpretation of the results. An increase in exports
should lead to an increase in output, but a decrease in exports should not necessarily lead to a
decrease in output, for the demand at home could have risen more than the decline in exports.
However (Stijns, 2003) finds that manufacturing exports are too much affected by the
resource boom, for the latter to be plausible. For data-availability reasons I follow Stijns’
(2003) approach by taking manufacturing exports as the dependent variable. The data on
manufacturing exports for each country is extracted from the dataset compiled by Feenstra et
al. (2005). This database covers international trade for almost all of the world’s countries.
Further, the database disaggregates all trade according to the Standard International Trade
Classification (SITC) Revision 2 (UN, 1975). Table 1 lists the SITC categories I have
selected to represent manufacturing exports. I have divided the manufacturing exports levels
by the total number of the population in a country, for the natural capital wealth estimates are
also measured per capita.
Table 1
SITC Section Code Section Heading
5 Chemicals and Related Products, N.E.S.
6 Manufactured Goods
7 Machinery and Transport Equipment
8 Miscellaneous Manufactured Articles
13
Next, we turn to the explanatory variables. As mentioned in Stijns (2003), Corden
(1984) describes three possible forms a natural resource boom can take: ‘a once–and-for-all
exogenous technical improvement, a windfall discovery of new resources, or an exogenous
rise in the world price of the natural resource, relative to import price.’ The technological
improvement would be hard to measure, because one would have to gather data on
technological improvements in a specific sector of the economy. This would be quite difficult
to do, as gathering data on the state of the technology in the overall economy is difficult
already. For the latter sometimes a proxy is taken in the form of resource and development
expenditures. Because this data is hard to find for many countries, I would expect that
resource and development expenditures data for the manufactured sector is even harder to
find.
As mentioned above Stijns (2003) uses the world price of energy and net energy
exports. This data is easier to collect. Furthermore, windfall discoveries of new resources are
interesting to use, for developing countries regularly find new amounts of natural resources.
For example, in Ghana (2007 and 2009) and Uganda (2009) huge amounts of oil were found.
Most studies use the share of natural resource exports in total exports or GDP, to quantify
resource abundance, for example Arezki and van der Ploeg (2010), Sachs and Warner (1997),
and Boschini et al (2007).
Nonetheless, other studies, for example, Fum and Hodler (2010), Brunnschweiler and
Bulte (2009), and Bond and Malik (2009) use the value of natural capital calculated by the
World Bank (1997, 2006). This measure is based on agricultural lands; pasture lands; forests;
protected areas, metals and minerals; and coal, oil, and natural gas. The advantage of these
estimates is that they are more accurate than the share of natural resource exports, since the
latter are amounts traded and not the endowment of a country. The disadvantage of the
natural capital estimates of the World Bank compared to natural resource exports is that the
data is estimated for only two years. Nevertheless, I will use these natural capital estimates to
test the existence of the Dutch disease, because to my knowledge this data is never been used
before to explore the empirical relationship between manufacturing exports and natural
resource abundance. Further, any possible relationship found can be compared, to some
degree at least, with the results of Stijns (2003) who made use of natural resource exports.
Because natural resource abundance is one of the most important explanatory variables, I
have chosen to base the countries and time periods in the dataset on those used for these
estimates, as reported by the World Bank (1997, 2006). A full list of the countries used in the
dataset can be found in appendix 2.
14
In addition, data on institutional quality is gathered from Kaufmann et al. (2009). This
database contains country-level information about six different indicators of institutional
quality. These indicators are: voice and accountability, political stability and absence of
violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of
corruption. All six indicators are measured on a scale of -2.5 to 2.5, where the higher the
value the better the quality is. I have chosen to take the average of these six indicators to
construct a single index number for institutional quality in a country. Unfortunately, this
database covers the period 1996-2008. For this reason I have chosen to take the 1996 average
value as a proxy for the 1994 average value, under the assumption that in two years time
institutional quality in a country does not change significantly.
15
Results
In this section the results of the regression analysis are described. The regression
analysis is conducted according to section three. Before the results of this analysis are
presented, a scatter plot is made to illustrate the relationship graphically. In figure 3 the y-
axis represents the change in manufacturing exports per capita, and the x-axis the change in
natural capital per capita. This scatter plot suggests that the relationship between natural
resource windfalls and manufacturing exports is negative, and thus that the Dutch disease
does exist. To investigate the relationship further we turn to regression analysis.
Figure 2
The results of the regression analysis are shown in table 2. The analysis is conducted
in five steps, according to the correlations between the different explanatory variables in the
model. This correlation matrix can be found in appendix three. I have chosen to completely
exclude the changes in patent applications, internet users per 100 people, and total roads
network, from the model. The reason for this is that the data on these three variables has
16
many missing values, and high correlations with other variables which I assume are more
important for the determination of manufacturing exports.
The first regression includes the changes in a country’s factor endowments,
population, trade openness, weighted average growth of a country’s trading partners, and the
growth of total factor productivity. Further, the model omits the change in GDP of a country
because this variable has a significant correlation with the change in intangible capital and the
change in openness. Because of missing observations the total number of observations used
in the model is 62. From the table can be read that the model explains 44.5% of the variation
in the data. Furthermore, the only significant explanatory variable in the model is ∆open. This
result is easy to understand, for an improvement in a country’s openness to trade will increase
its exports. Especially because this variable is measured as the sum of a country’s exports and
imports divided by the country’s GDP. The estimator for ∆nat has a negative sign but is not
significant at a 10% significance level. However, the smallest significance level where the
variable is significant is equal to 11%.
Regression two incorporates institutional quality into the model to see if this brings
more significance to the model. Unfortunately R2 is decreased, as well as the t-value of ∆nat.
Although, the estimator for ∆nat still has a negative sign, this means that incorporating ∆iq in
the first model is not going to help us any further in proving the existence of the Dutch
disease.
Regression three starts from another viewpoint. This model includes, amongst others,
∆gdp to control for change in the domestic market size. According to the correlation matrix,
∆gdp is significantly correlated to ∆open and ∆int. For this reason these two variables are
omitted in this model. When looking at the results, the first thing that strikes is that after
introducing the new variable, the estimator for the change in natural capital is significant,
even at the 5% significance level. Further, the estimators for ∆pro and ∆gdp are significant at
the 5% significance level as well. This result confirms the Dutch disease, albeit that there is
not a radical effect on manufacturing exports. An increase of $1 in a country’s natural wealth
estimate per capita decreases a country’s manufacturing exports per capita with 6.4 cents. In
addition, the model now explains 61.4% of the variation in the data.
17
Table 2
**,* significant at 5% and 10% respectively, the number within brackets denotes the value of the t-
statistic
To test again for the influence of institutional quality on the Dutch disease, regression
four builds on regression three by including ∆iq, similar to regression two. Unfortunately,
again institutional quality is not significant in explaining the Dutch disease. This
insignificance may be caused by the fact that there is correlation between institutional quality
and GDP. Nevertheless, the significance of the estimators for ∆nat and ∆pro, as well as the
(1) (2) (3) (4) (5)
∆nat -0.048
(-1.627)
-0.40
(-1.321)
-0.064**
(-2.641)
-0.070**
(-2.764)
-0.067**
(-2.610)
∆int 0.002
(1.226)
∆pro 0.014
1.068
0.011
(0.879)
0.026**
(2.393)
0.028**
(2.501)
0.026**
(2.279)
∆gdp 0.434**
(8.726)
0.446**
(8.594)
0.458**
(8.424)
∆pop -6.742E-06
(-0.620)
-7.669E-06
(-0.704)
-5.121E-06
(-0.577)
-5.831E-06
(-0.653)
-6.195E-06
(-0.690)
∆open 69.388**
(5.312)
72.563**
(5.688)
∆iq 830.565
(0.878)
-692.608
(-0.856)
-539.390
(-0.644)
∆tfp 0.115
(0.319)
0.003
(0.007)
0.168
(0.565)
0.238
(0.770)
0.229
(0.739)
∆gtp -2.315
(-1.182)
-2.075
(-1.061)
-1.331
(-0.848)
-1.317
(0.837)
-1.401
(-0.884)
∆pat
∆trn
∆inu
∆natiq 0.102
(0.747)
R2 0.445 0.438 0.614 0.619 0.623
observations 62 62 62 62 62
18
significance of the entire model is increased slightly, and the significance of ∆gdp is
decreased slightly. A $1 increase in natural resource abundance per capita now leads to a
decrease of 7 cents in manufacturing exports per capita.
Regression five includes the interaction term between natural capital wealth and
institutional quality as described in section three, ∆natiq, to test whether the influence of a
change in natural capital is different for different levels of institutional quality. The inclusion
of this interaction term slightly increases the R2 of the model to 0.623. Nonetheless this
interaction term is not significant in the model. The significance of the estimators for ∆nat,
∆pro, and ∆gdp are all decreased slightly but they remain significant at the 5% significance
level. A $1 windfall in natural resource wealth per capita leads to a decrease of 6.7 cents in
manufacturing exports per capita.
Potential Problems and Pitfalls
One of the big issues arising from using regression analysis to empirically test
economic relationships is the omitted variable problem. The estimators may be biased
because important variables are left out of the model. For instance, variables can be left out of
the model because of collinearity or simply because the data is not available. The problem
described can be an important issue for my research. For instance, due to the significant
correlation between institutional quality and GDP, it is better not to use both variables in the
same regressions. Probably the change in institutional quality is endogenous to the change in
GDP. When one of the variables is left out of the model, its effects on the model are
incorporated in the error term ∆uit. When one of the explanatory variables is correlated to the
error term, omitted variable bias can arise (Hill, Griffiths, and Lim, 2008).
In order to solve this problem one could make use of an instrumental variable to replace
the problematic variable. Instrumental variables must satisfy the following conditions:
1. The variable does not have an effect on the dependent variable
2. The variable is not correlated with the error term in the model
3. The variable must be strongly correlated with the variable that it replaces
Some of the empirical literature on the resource curse makes use of an instrumental
variable for institutional quality (Boschini et al. 2007), (Arezki and van der Ploeg, 2010).
This literature follows Acemoglu et al. (2001), by using the log of the European settler
mortality risk. However, using this variable as an instrument for institutional quality in my
19
research is not possible since I am using the first-differencing technique. The fact that this
technique makes use of the change in a variable over time means that the single value for
settler mortality risk cannot be used. On the other hand however, the advantage of using first-
differencing is that it removes omitted variable bias arising from time invariant variables, for
the simple reason that they do not change over time. For example geographical variables do
not change over time6.
To test for any misspecification I will use the RESET test (REgression Specification
Error Test) as described in Hill, Griffiths, and Judge (2001). This test uses the predicted
values of the model. The predicted values are the ∆y����s that can be computed by plugging in
the explanatory variables in the regression equations found by running the regressions. With
these predicted values the following model can be constructed:
∆��� � ∆���� � ������
� �������∆��� (eq. 2)
In this model ∆Yit is again the change in a country’s manufacturing exports, and ∆X’it
is a vector containing all the explanatory variables used in the regressions. The procedure of
the RESET test is to test the hypothesis H0:γ1= γ2=0 against H1: γ1≠0 or γ2≠0. For this testing
procedure an F-test7 is required. Rejection of H0 means that the original model may not be
correctly specified and can be improved. A failure to reject H0 means that the test was not
able to detect any misspecification. The results of the RESET test conducted on the five
models are shown in table 3.
As can be read from the table, all five models may be subject to model
misspecification and can be improved. The fact that the coefficients of the predicted values
do not appear to be zero according to these tests, means that the effects of omitted variables
may be picked up by these variables.
Since institutional quality is one of the main explanatory variables of my research, I
have chosen to adopt an alternative strategy to be able to test for its effects on the Dutch
disease. The approach is simple. The dataset is made up out of a wide range of countries, both
from the developed and developing world. Assuming that institutions are one of the main
6 At least not in the period used for the research. 7 The F-test is conducted according to the follow five steps:
1. Test H0:γ1= γ2=0 against H1: γ1≠0 or γ2≠0
2. Test statistic: �!""#$%""#&'/!)%*'
""#&/!+%!),�''
3. Reject H0 if - . /;)%*;+%!),�'
4. Calculate value of F 5. Draw conclusion
20
driving forces behind GDP growth, one could say that the developing countries have worse
institutions compared to the developed countries. Following this reasoning I have classified
the countries in the dataset according to the classification by income developed by the World
Bank. I have chosen to use the low income countries8 and lower middle income countries.
Although this shrinks the dataset to 41 countries, now inferences can be made on the Dutch
disease for developing countries. These countries are shown in bold in appendix two; the
correlation between the variables can be found in the second table of appendix three.
Table 3
(1) (2) (3) (4) (5)
SSEr 1,035E+08 1,049E+08 7,203E+07 7,108E+07 7,036E+07
SSEc 1,863E+07 1,881E+07 1,329E+07 1,312E+07 1,149E+07
K 9 9 8 9 10
G 7 7 6 7 8
N 62 62 62 62 62
Α 0,05 0,05 0,05 0,05 0,05
FINV 3,175 3,175 3,172 3,175 3,179
VAL 118,444 118,997 117,126 114,860 130,651
Conclusion Reject H0 Reject H0 Reject H0 Reject H0 Reject H0
α denotes the significance level of the test, k and g denote the number of explanatory variables with
and without the predicted values respectively
The results of the regressions based on the dataset compiled of low income and lower
middle income countries can be found in table 4. I have chosen the variables for the different
regressions according to their mutual correlations. The sixth regression starts with including
∆nat, alongside ∆int, ∆open, and ∆pop. In the model ∆pro is omitted because of a significant
correlation with ∆int. A model that includes intangible capital instead of produced capital
proves to be more significant. With this regression 37.3% of the variation in the data is
explained. The sign of the estimator for ∆nat is now positive, albeit that the estimator is not
significant at a 0.05 or 0.10 significance level. The only significant variable is ∆int, with a
negative sign for the estimator.
8 This classification is based on 2009 GNI per capita. Low income countries are countries with a GNI per capita of $995 or less, lower middle income countries have GNI per capita of $996 - $3,945.
21
Regression seven includes ∆tfp in the analysis, controlling for the effect of more
efficient use of inputs. This decreases the amount of observations to 27 because of missing
values. The sign of the estimator for ∆nat is still positive and its significance is increased,
though the estimator is still not significant. In addition, the significance of the model is
increased from 0.373 to 0.567.
Adding the change in a country’s GDP per capita decreases the significance of the
model to 0.494, as shown in regression eight. In this model ∆pop is omitted because of
significant correlation with ∆gdp. The influence of the change in a country’s natural capital
per capita is about the same in this regression compared to the previous one.
Table 4
(6) (7) (8) (9)
∆nat 0.001
(0.280)
0.004
(0.696)
0.004
(0.707)
-0.001
(-0.030)
∆int -0.003**
(-3.535)
-0.003**
(-3.539)
-0.003**
(-3.010)
-0.003*
(-1.854)
∆pro
∆gdp 0.027
(0.652)
∆pop 1.241E-7
(0.879)
8.582E-8
(0.112)
1.891E-7
(0.165)
∆open 0.694
(0.155)
0.409
(0.249)
0.469
(0.289)
1.157
(0.335)
∆tfp -0.531
(-0.161)
-0.487
(-0.149)
0.032
(0.313)
∆gtp
∆oil 0.024
(0.539)
R2 0.373 0.567 0.494 0.546
observations 41 27 27 16
**, * significant at 0.05 and 0.10 significance level respectively
As a final step in my research I have chosen to include a variable measuring the
change in a country’s proven reserves of oil, ∆oil. Different types of natural resources may
have different effects on the economy. For instance, Bond and Malik (2009) find a positive
22
relationship between fossil fuel exports and investment, and Boschini et al. (2007) show that
the type of resources a country possesses are important in determining whether natural
resource abundance is beneficial for economic development. The data for oil reserves per
country are gathered from the U.S. Energy Information Administration. I have chosen to
include the variable ∆oil into the model with the highest significance (for developing
countries). Including this variable, changes the sign of the estimator for ∆nat, but its
significance is decreased dramatically. In addition, the proportion of variance explained by
the model decreases to 54.6%.
What is remarkable is that the only significant variable in the models, ∆int, remains
negative. This result indicates that an increase in the amount of intangible capital per capita
will have a negative effect on manufacturing exports. Possibly this result shows the
possibility that the more educated a country becomes, the more it specializes in services
rather than manufactured goods.
The RESET test is being conducted as well for the last four regressions, in the same
way as described above. The results of this test are shown in table 4.4. For these four
regressions, the test fails to detect any misspecification.
Table 5
(6) (7) (8) (9)
SSEr 3,428E+05 182642,088 179278,582 1.549E+05
SSEc 3,211E+05 160209,481 144773,463 1.403E+05
K 6 7 7 8
G 4 5 5 6
N 41 27 27 16
Α 0,05 0,05 0,05 0.05
FINV 3,276 3,522 3,522 4.737
VAL 1,146 1,330 2,264 0.365
Conclusion Don't reject H0 Don't reject H0 Don't reject H0 Don't reject H0
α denotes the significance level of the test, FINV denotes the rejection value, VAL denotes the value
of the test statistic
To summarize, filtering out the richer countries to test for the effects of less
institutional quality does not yield significant estimators for ∆nat. Nonetheless, what is
interesting to see is that for regressions six, seven and eight the sign of ∆nat is positive. If the
23
estimators were significant this would mean that developing countries experience less of the
symptoms of the Dutch disease than richer countries do.
24
Conclusion
My research tried to investigate one of the earlier explanations of the resource curse
by testing the Dutch disease hypothesis. What distinguishes this research from other research
is that the differences in the variables in two moments in time are used to detect the effects of
a change. Although my research finds mostly negative relationships between a windfall in
natural resource wealth and manufacturing exports, the usability of the results is limited due
to some of the problems described above. Furthermore, the effects of institutions on the
Dutch disease could not be shown to exist. To further understand the mechanics of the Dutch
disease additional research has to be done.
Further research could include the natural capital estimates used in this paper in a
gravity model of trade, similar to the one used by Stijns (2003). Consequently, the effects of a
windfall in natural resource wealth estimates can be compared with a windfall in net energy
exports and world energy prices, to see if the Dutch disease still persists.
Another strategy would be to disaggregate the total measure of natural resource
wealth into different types of resources in the same fashion as Boschini et al. (2007), and use
these different types of resources to test for the Dutch disease rather than differences in GDP
growth.
In addition, an econometric model could be used which is not based on differences in
two points in time. By this way, it will be possible to include the instrument for institutional
quality as proposed by Acemoglu et al. (2001). The omitted variable problem can be
decreased by this method.
Additionally, even if a significant and persisting Dutch disease relationship is laid
bare, this does not necessarily mean that an economy suffers from it. For example, the fact
that a country’s manufacturing exports decrease does not necessarily imply a decrease in the
growth rate of the economy, for the increase in natural resource exports can more than offset
the aforementioned decrease, or the country can specialize in other sectors. After a Dutch
disease relationship is found, one should think of a way to test the relationship between the
Dutch disease and the growth of a country’s economy, and possibly include also the role of
the institutions.
25
Appendix 1 Description of Variables and their Sources
Table 6
variable description source
Mxp Manufacturing exports composed of goods categorized under the headings 5, 6, 7 and 8 by SITC
Feenstra et al. (2005)
Nat Natural capital estimates by World Bank World Bank (1997, 2006)
Int Intangible (or human) capital estimates by World Bank
World Bank (2006) and Kunte et al. (1998)
Pro Produced capital estimates by World Bank World Bank (2006) and Kunte et al. (1998)
Gdp Real GDP per capita (constant prices, chain series) Pen World Table Version 6.3 by Heston et al. (2009)
Opn Openness of a country for trade. Measured by exports plus imports divided by real GDP
Pen World Table Version 6.3 by Heston et al. (2009)
Pop Population of a country Pen World Table Version 6.3 by Heston et al. (2009)
Iq Institutional quality. Measured by the unweighted average of the indices of voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption
Kaufmann, Kraay, and Mastruzzi (2009)
Tfp Percentage growth of total factor productivity. The Conference Board Total Economy Database (2011)
gtp9 Weighted growth rates of trading partners of a country
Calculated by author by using Feenstra et al. (2005)
Pat Number of patent applications World Development Indicators World Bank (2011)
Trn Total kilometers of road network World Development Indicators World Bank (2011)
Inu Number of internet users per 100 people World Development Indicators World Bank (2011)
Oil Proven oil reserves, billions of barrels U.S. Energy Information Administration (2011)
All monetary measures are converted to the 2005 dollar value using the U.S. Bureau of
Economic Analysis GDP deflator.
9 This value was calculated by the following procedure. First, all of a country’s exports to a specific country were aggregated. Second, this value was divided by a country’s total exports. Third, the previously calculated ratio was multiplied by the importing country’s GDP growth. And finally, the calculated values were aggregated to yield the weighted growth of trading partners.
26
Appendix 2 Countries Used in the Dataset
Table 7
Countries Used in the Dataset
Argentina Greece Niger
Australia Guatemala Norway
Austria Guinea-Bissau Pakistan
Bangladesh Haiti Panama
Belgium Honduras Paraguay
Benin India Peru
Brazil Indonesia Philippines
Burkina Faso Ireland Portugal
Burundi Italy Rwanda
Cameroon Jamaica Senegal
Canada Japan South Africa
Chad Jordan Spain
Chile Kenya Sri Lanka
China Korea, Rep. Of Sweden
Congo, Rep. Of Madagascar Switzerland
Costa Rica Malawi Thailand
Cote d'Ivoire Malaysia Trinidad and Tobago
Denmark Mali Tunisia
Dominican Rep. Mauritania Turkey
Ecuador Mauritius United Kingdom
Egypt, Arab Rep. Of Mexico United States
El Salvador Morocco Uruguay
Finland Mozambique Venezuela
France Nepal Zambia
Gambia, The Netherlands, The Zimbabwe
Germany New Zealand
Ghana Nicaragua
27
Appendix 3 Correlation Matrices
Table 8 Correlation Matrix for all Countries
∆nat ∆int ∆pro ∆gdp ∆pop ∆open ∆iq ∆tfp ∆gtp ∆pat ∆trn ∆inu
∆nat 1
∆int 0.015 1
∆pro 0.095 -0.104 1
∆gdp 0.074 0.553** -0.203 1
∆pop -0.016 -0.123 0.080 -0.101 1
∆open -0.068 0.197 -0.051 0.354** -0.023 1
∆iq -0.173 0.239* 0.002 0.272* -0.032 0.151 1
∆tfp 0.084 -0.090 0.023 -0.102 -0.019 -0.096 0.212 1
∆gtp -0.075 0.132 0.012 0.006 -0.007 0.206 0.092 0.037 1
∆pat -0.007 0.077 0.539** 0.094 0.071 -0.004 0.127 0.027 -0.239 1
∆trn -0.013 -0.044 0.071 -0.058 0.933** 0.023 -0.011 -0.059 0.032 -0.003 1
∆inu -0.188 0.634** -0.177 0.619** -0.224 0.259 0.361* -0.104 -0.010 0.320* -0.192 1
**,* correlation is significant at 0.01 and 0.05 level respectively
28
Table 9 Correlation Matrix for Low and Lower Middle Income Countries
∆nat ∆int ∆pro ∆gdp ∆pop ∆open ∆tfp ∆gtp ∆oil
∆nat 1
∆int -0,012 1
∆pro -0,178 0.621** 1
∆gdp 0,232 -0,237 -0,183 1
∆pop 0,005 -0,067 0,069 0,391* 1
∆open -0,146 -0,110 0,001 -0,003 0,030 1
∆tfp 0,066 0,067 0,104 -0,061 -0,090 -0,297 1
∆gtp -0,320* -0,052 -0,019 0,154 0,000 0,187 -0,004 1
∆oil 0.164 -0.035 0.497* -0.311 -0.185 0.378 0.057 -0.340 1
**,* correlation is significant at 0.01 and 0.05 level respectively
29
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