1. SCHOOL OF ECONOMICS
UNIVERSITY OF NOTTINGHAM
L14009 ECONOMIC DATA ANALYSIS
Black Gold & Blood Diamonds: Does the Resource Curse
Exist?
An Investigation into the Existence of the Resource Curse in
Point-Source Economies using Macro Panel Analysis
Oliver Haskins
Student ID: 4228781
Word Count: 1,964
Thispaperaims to addressthe currentissue of the resource curse using macro panel data alongside
the common correlated effects mean group estimator developed by Pesaran 2006. The research
paperfindsnoevidence of the resource curse amongst seventeen resource dependent economies
overa thirty-nine yearperiod.Thisisin line with the research carried out by Cavalcanti, Mohaddes
and Raissi in 2011 but contrary to a wealth of cross sectional research previously undertaken.

2. 1
Contents
1. Introduction....................................................................................................................... 2
2. Literature Review................................................................................................................ 2
3. Data Collection and Sources.............................................................................................. 4
4. Model Estimation and Methodology................................................................................. 4
Table 4.1.................................................................................................................................5
5. Empirical Analysis............................................................................................................... 6
Table 5.1: Results of resource curse model using CCEMG..................................................... 7
6. Conclusions........................................................................................................................ 8
7. References.......................................................................................................................... 9
8. Appendix.......................................................................................................................... 11

3. 2
1. Introduction
This research paper aims to investigate whether or not there is an apparent ‘Resource Curse’
adversely affecting growth of GDP in those economies which are heavily dependent on
Point-Source resources. The ‘Resource Curse’ is a widely studied topic and has raised
contentious debate surrounding its existence ever since the publication of “Oil Windfalls: A
Blessing or a Curse?” on the topic by Gelb in 1988 and later expanded upon by the more
commonly cited Sachs & Warner 1995 paper. The research has primarily focused on cross-
sectional analysis and has thus limited itself to a single time period (Van Der Ploeg, 2011).
Contrary to the common cross-sectional studies, this paper aims to uncover the existence of
a resource curse using time-series macro panel data, covering the period between 1974 and
2012 across 17 countries that have a dependency on point-source resource production.
Point-source natural resources are defined as those which have a direct single source, are
non-renewable and must be mined or extracted, such as oil, minerals and precious metals.
The alternative is known as a diffuse resource, mainly agriculture (Woolcock et.al., 2001).
This paper will focus heavily on those economies falling into the former category. Point-
source dependency is measured using fuel (as a proxy for oil and gas) or mineral and ore
exports as a percentage of total merchandise exports. A notable feature of this piece of
research is the use of dependency rather than abundance when it comes to resources. The
active choice to use dependency arises from the fact that some countries may be naturally
well endowed with point-source resources but choose not to export these as a focus of
trade. These countries are thus not as susceptible to the adverse effects commonly seen with
point-source exports. It is the dependency upon these resources which I wish to test for the
‘Resource Curse’.
2. Literature Review
As previously mentioned, the majority of research in this area focuses on a cross-sectional
analysis and has surprisingly contrasting conclusions, some finding strong evidence of a
resource curse (Sachs & Warner, 1995) and others finding no such evidence at all (Gylfason,
2001; Andersen, 1993; Larsen, 2006; Fasano, 2002). However, it is the few papers that have
analysed this subject using time series methods and panel cointegration methods in
particular that will be the focus of this section. Raddatz (2007) uses a panel vector auto-

4. 3
regression (VAR) approach to establish whether low-income countries perform poorly
economically due to external shocks. The findings have some consistency with outlined
hypotheses but deduce that external shocks can only account for a very small proportion of
the volatility of real GDP. Following on from this research, Collier & Goderis (2007) adopt a
panel cointegration methodology which allows them to explore a longer time horizon than
the aforementioned VAR approach. Their findings strongly support those of Sachs & Warner
in finding evidence of a resource curse. In order to address cross-section dependence they
use an error-correction model with country-specific fixed effects and a vector of regional
time dummies. This method assumes that the dependence is homogenous across all
countries, affecting each in an identical manner. Lee, Pesaran and Smith (1997) show that
there is an ubiquitous heterogeneity, across a panel of 102 countries, in terms of their
growth rates and speed of convergence. With homogeneity still present in Collier & Goderis
paper, their results may be subject to significant biases. Cavalcanti, Mohaddes & Raissi
(2011), (CMR) address this issue by allowing the cross-sectional dependence to vary across
countries. Their approach is known as a multi-factor error structure and is the approach that
will be used in this paper. Why this is an appropriate approach is discussed in the following
section on methodology and model estimation. CMR find that oil abundance has a strong
positive effect on real income. This contradicts the previous findings and suggests that
simply an abundance of oil does not cause a resource curse.
This paper aims to address the resource curse issue using Macro-Panel analysis, more
specifically common correlated effects mean group estimators (CCEMG), and focusing on
point-source resource dependant countries. It will look at countries that have a dependency
on either; fuel exports (as a proxy for oil and gas exports) or mineral and ore exports. A list of
countries studied can be found in appendix 1.

5. 4
3. Data Collection and Sources
Data has been collated from a variety of sources predominately using World Bank’s Data
Bank and the IMF statistics for GDP data (World Development Indicators, 2014), fuel and
mineral exports. Data found on the quality of governance was collected using the Polity IV
annual time-series data set and gives a score ranging from -10 to +10. The score uses an
amalgamation of democratic or autocratic regimes and how they are structured, run and
their commitment to a democratic regime. The data is then standardized and a score is
calculated. The polity IV data is used as a proxy for strong governance and democratic
regimes – a variable that has been proven to influence the growth of a nation, especially
when it is linked to the use of natural resources (Woolcock et. al. 2004).
Conflict data was collected from the Democracy Time Series Data made available on
Harvard’s Education website. (Norris, 2009)
After pulling together the variables from different sources into one data sheet, it was then
possible to narrow the selection and focus on those countries that are important to this
research. From such a large dataset it was possible to focus on countries where the data was
complete and therefore using a balanced sample. Annual data from a sample of 17 point-
source resource dependant countries over 39 years has been collected and will be analysed.
Between 1974 (the initial data point collected) and 1990 Germany was split into East and
West during the Cold War. As a proxy for the country as a whole, West Germany data has
been used between these dates as this closely follows that of the unified Germany from 1990
onward.
4. Model Estimation and Methodology
The resource curse has long been debated and tested using cross-sectional analysis.
However, very few papers have chosen to analyse the issue using macro-panel data. The
inconsistency surrounding the results of previous studies shows that the topic is still
contentious and prevalent in modern literature. This paper is at the forefront of discussion
surrounding the resource curse and is one of the first to test resource dependence amongst
a panel of point-source exporting countries.

6. 5
There are still issues which need to be understood and dealt with regarding macro-panel
data, cross-section dependence and the issue of group heterogeneity and endogeneity in
the errors. A multi-factor error structure and use of the common-correlated effects estimator
help address these aforementioned issues. These problems are allowed for using the
following model specification:
lnGDPjt = αj + β1jFjt + β2jMjt + β3jTTjt + β4jGjt + γjt + εjt
whereby the unobservable characteristics of GDP growth are captured by the constant term
αj and the country-specific trend γjt. A full description of the variables analysed is presented
in table 4.1 below. The model will then be estimated using the Common-Correlated-Effects
Mean Group estimator hereby known as CCEMG. In order to estimate this model we need to
include cross-section averages for each of the variables included in the model and then
implement these using the MG estimator.
The aim of the estimation of such a model is to see the effect of a change in resource
dependency on GDP and to test the significance of each channel.
Table 4.1
Variables Definitions
lnGDP Natural logarithm of GDP in current US$
F Fuel export as a percentage of total merchandise exports
M Ore and metals export as a percentage of total merchandise exports
TT Total trade as a percentage of GDP
G Polity IV score as a proxy for Governance (see section 3 for a detailed
description)

7. 6
5. Empirical Analysis
Before we can estimate the model we first need to determine the co-integration of the panel
and variables along with both the cross-section dependence and parameter heterogeneity.
Addressing cross-section dependence first, using Pesaran’s Cross-section Dependence (CD)
test we find that all variables reject the null of independence (see Appendix 5.1). Thus, we
model the dependence to arise from a series of unobservable common factors which affect
each country in a different manner and to a different degree. This multi-factor error structure
allows the coefficient to be driven by a common factor (ft ) with a different factor loading
dependent upon the country or panel group.
The Pesaran (2007) CIPS panel unit root test is utilised to analyse the time series properties
associated with each variable. From this, it is not possible to reject the null of non-
stationarity in the data. To counter both the aforementioned non-stationarity and cross-
section dependence issues this paper will adopt the common correlated effects mean group
estimator, hereby known as CCEMG. This estimator augments the standard OLS estimators
with cross-sectional averages of the dependant and independent variables and are then
estimated using the mean group estimator. Together these estimates account for the
unobservable common factor prescribed in the equation above. From the model outlined
above, the CCEMG utilises the parameter, γjt, a set of unobservable common factors with
factor loadings that can differ across the set of seventeen countries. The coefficient estimates
are shown in table 5.1.
The coefficients shown below represent weighted means across countries (groups) and the
cross-sectional averaged regressors are suffixed by an underscore. The estimates of the
cross-sectional averages are not directly interpretable and are instead used to blend out
effect of the unobservable factor loadings by country. This allows for consistent estimates of
the observable parameters in the model. As shown in the table, the effect of both oil and gas
and natural mineral reserves on GDP growth is insignificant. This suggests that using macro
panel data and focusing purely on resource dependant countries over a period of 39 years
shows no evidence for the resource curse - a consistent finding with that of Cavalcanti,
Mohaddes and Raissi (2011).

8. 7
Table 5.1: Results of resource curse model using CCEMG
Variables Coefficients (standard errors)
Fuel Exports
(% of merchandise trade)
-0.0028
(0.0094)
Ore & Mineral Exports
(% of merchandise trade)
0.0139
(0.0119)
Total Trade 0.0149
(0.0027)
Governance Polity IV 0.00605
(0.0061)
_lngdp 0.9926
(0.0558)
_fuelexp 0.0025
(0.0042)
_oreexp 0.0029
(0.0089)
_trade 0.0075
(0.0027)
_govt 0.0056
(0.0161)
The 10% significant coefficients are shown in bold.
The two significant variables, total trade and the governance polity IV score are again
consistent with previous literature. The former suggests that a point percentage increase in
total trade results in 1.5% increase in GDP. The latter suggests that a point increase in polity
score and thus an improvement in the governance of a country results in a 0.6% increase in a
countries GDP. These results are both of the magnitude and direction that we would expect
prior to analysis.
Testing the cross-section dependence of the residuals we can see that the model successfully
eliminates any previous cross-sectional dependence through failing to reject the null of cross
section independence (see appendix 5.2). Test on the stationarity of the data show that it no

9. 8
longer contains a unit root. Both of these results suggest that the model used is correctly
specified and yields unbiased results.
6. Conclusions
Upon testing a panel of 17 resource dependant countries over a period of 39 years this
paper finds no evidence for the previously defined resource curse. Despite this analysis
conforming to that of Cavalcanti et. al. the results should not be used widely to eliminate any
possibility of a resource curse. The results still suggest that government institutions remain a
vital force in controlling the growth of GDP and thus, in turn, the resource curse. But the data
used is not widespread enough to cause a major change in thought on the phenomena. By
using only resource dependant countries the sample is limited to those that are most directly
impacted by the apparent resource curse however, as a control it would be wise to include a
wider variety of economies and a larger sample size. It is a common thread of thought in the
literature that it is not resource dependence that should be measured but resource
availability. This would include those economies that have a high level of resources but do
not necessarily utilise those resources to fuel economic growth. Despite these apparent
issues with the paper, this research can be used in conjunction with other literature as a
supporting paper, finding that using macro panel analysis shows no evidence of a resource
curse.

10. 9
7. References
Andersen, S. S. (1993). The Struggle over North Sea Oil and Gas: Government Strategies in
Denmark, Britain and Norway. Scandinavian University Press, Oslo.
Cavalcanti, T. V. D. V., Mohaddes, K., & Raissi, M. (2011). Growth, development and natural
resources: New evidence using a heterogeneous panel analysis. The Quarterly Review of Economics
and Finance, 51(4), 305-318.
Collier, P., & Goderis, B. (2008). Commodity prices, growth, and the natural resource curse:
reconciling a conundrum. Growth, and the Natural Resource Curse: Reconciling a Conundrum (June
5, 2008).
Eberhardt, M., & Teal, F. (2011). Econometrics For Grumblers: A New Look At The Literature On
Cross‐Country Growth Empirics. Journal of Economic Surveys, 25(1), 109-155.
Eberhardt, M., & Teal, F. (2012). Structural change and cross-country growth empirics. The World
Bank Economic Review, lhs020.
Fasano, U. (2002). With open economy and sound policies, UAE has turned oil “curse” into a
blessing, IMFSurvey. October, 21, 330-332.
Gelb, A. H. (1988). Oil windfalls: Blessing or curse?. Oxford University Press.
Gylfason, T. (2001). Natural resources, education, and economic development.European Economic
Review, 45(4), 847-859.
Haber, S., & Menaldo, V. (2011). Do natural resources fuel authoritarianism? A reappraisal of the
resource curse. American Political Science Review, 105(01), 1-26.
Isham, J., Woolcock, M., Pritchett, L., & Busby, G. (2002). The varieties of rentier experience: how
natural resource endowments affect the political economy of economic growth. Unpublished mimeo
dated January, 8.
Larsen, E. R. (2006). Escaping the resource curse and the Dutch disease?.American Journal of
Economics and Sociology, 65(3), 605-640.
Lee, K., Pesaran, M. H., & Smith, R. P. (1997). Growth and convergence in a multi-country empirical
stochastic Solow model. Journal of applied Econometrics, 12(4), 357-392.
Murshed, S. M. (2004). When does natural resource abundance lead to a resource curse? (No.
24137). International Institute for Environment and Development, Environmental Economics
Programme.
Norris, P. (2009). Democracy Timeseries Data Release 3.0, January 2009, shared datasets.
Retrieved from http://www.hks.harvard.edu/fs/pnorris/Data/Data.htm
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor
error structure. Econometrica, 74(4), 967-1012.
Ross, M. L. (2001). Does oil hinder democracy?. World politics, 53(03), 325-361.
Raddatz, C. (2007). Are external shocks responsible for the instability of output in low-income
countries?. Journal of Development Economics, 84(1), 155-187.

11. 10
Sachs, J. D., & Warner, A. M. (1995). Natural resource abundance and economic growth (No.
w5398). National Bureau of Economic Research.
Woolcock, M., Pritchett, L., & Isham, J. (2001). The social foundations of poor economic growth in
resource-rich countries. Resource Abundance and Economic Development, Oxford University Press,
Oxford, 76-92.
World Development Indicators (2014) retrieved from http://databank.worldbank.org/data/views/reports
Van der Ploeg, F. (2011). Natural resources: Curse or blessing?. Journal of Economic Literature, 366-
420.

12. 11
8. Appendix
1 Table of countries studied
Algeria Ecuador Panama
Australia Egypt Singapore
Bolivia Germany Tunisia
Brazil Indonesia United Kingdom
Colombia Malaysia Venezuela
Denmark Mexico
5.1 Pesarans Cross-section dependence test
Average correlation coefficients & Pesaran (2004) CD test
Variables series tested: lngdp Fuel_Exp Ore_Exp Total_Trade Govt eFE
Group variable: RefNumber
Number of groups: 17
Average # of observations: 41.44
Panel is: unbalanced
---------------------------------------------------------
Variable | CD-test p-value corr abs(corr)
-------------+-------------------------------------------
Lngdp | 68.24 0.000 0.937 0.937
-------------+-------------------------------------------
Fuel_Exp | 21.72 0.000 0.298 0.451
-------------+-------------------------------------------
Ore_Exp | 8.83 0.000 0.121 0.330
-------------+-------------------------------------------
Total_Trade | 28.47 0.000 0.391 0.438
-------------+-------------------------------------------
Govt | . . . .
-------------+-------------------------------------------
eFE | 42.86 0.000 0.589 0.601
---------------------------------------------------------
Notes: Under the null hypothesis of cross-section
independence CD ~ N(0,1)

13. 12
5.2 Pesarans CD test on the residuals post CCEMG estimation
Average correlation coefficients & Pesaran (2004) CD test
Variables series tested: eCMG
Group variable: RefNumber
Number of groups: 17
Average # of observations: 41.44
Panel is: unbalanced
---------------------------------------------------------
Variable | CD-test p-value corr abs(corr)
-------------+-------------------------------------------
eCMG | -0.81 0.416 -0.011 0.209
---------------------------------------------------------
Notes: Under the null hypothesis of cross-section
independence CD ~ N(0,1)