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EFFECTS OF RESOURCE BASED SOVEREIGN WEALTH FUNDS ON
FINANCIAL DEVELOPMENT: EMPIRICAL EVIDENCE USING OLS AND
QUANTILE REGRESSIONS
ABSTRACT
This paper employs Pooled OLS and Quantile Regression methods to investigate the
association between natural resource based sovereign wealth funds and financial
development. Supplementary model in the form of Barro-regression is used to
validate the use of financial development as measurement factor for economic
growth. The Barro-regression results indicate that financial development is critical
for economic growth regardless of resource endowment and resource dependence.
The Pooled OLS and Quantile regression results reveal that there is statistically
significant but negative association between resource funds and financial
development. This is contrary to few recent literatures in support of resource funds,
but supplements the overall position of mixed views on their uses and effectiveness.
The results suggest that more detailed studies may reveal as to why such negative
relationship exists. It may also be helpful for governments to directly channel
resource revenues to policies aimed at developing their financial system for the
benefit of long term economic growth, rather than concentrating resource revenues
in one central location. The results also indicate that resource funds may need to
adapt more transparent operating practices as apparent lack of quality data on
resource funds presents considerable challenges for investigating their socio-
economic implications.
Keywords: Resource fund, financial development, natural resources, developing
countries
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Table of Contents
I. INTRODUCTION ......................................................................................................... 4
II. LITERATURE REVIEW ........................................................................................... 8
1. Resource Curse ......................................................................................................... 8
2. Resource Funds ....................................................................................................... 11
3. Financial Development ........................................................................................... 12
III. METHODOLOGY AND DATA ............................................................................... 14
a. Rationale and Criterion .......................................................................................... 15
b. Testing and Robustness .......................................................................................... 16
c. Pooled OLS .............................................................................................................. 18
d. Quantile Regression ................................................................................................ 19
e. Supplementary Barro-regression ........................................................................... 21
f. Data and Variables ................................................................................................. 22
g. Comparative Analysis on Sample Data .................................................................. 27
IV. RESULTS ................................................................................................................ 29
a. Pooled OLS and Quantile Regression..................................................................... 29
b. Barro-regression ..................................................................................................... 45
V. CONCLUSION AND POLICY IMPLICATIONS ................................................... 48
BIBLIOGRAPHY ............................................................................................................... 52
APPENDICES ................................................................................................................... 56
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List of Tables and Figures
Figure 1: The Research Question ........................................................................................ 7
Figure 2: Institutional Quality and Resource abundance (Mehlum, Moene and Trovik
2006) .................................................................................................................................. 11
Figure 3: Quantile regression coefficients for the sample of resource rich countries ...... 58
Figure 4: Quantile regression coefficients for the sample of global set of countries ........ 58
Table 1: Selection of resource funds around the world ....................................................... 6
Table 2: Description of variables ....................................................................................... 22
Table 3: Sources of explanatory variables ........................................................................ 26
Table 4: Resource fund and financial development using Pooled OLS (global sample) .. 31
Table 5: Resource fund and financial development using Pooled OLS (resource rich
sample) ............................................................................................................................... 32
Table 6: Private Credit and Resource Fund using Quantile Regression (global sample) 39
Table 7: Private Credit and Resource Fund using Quantile Regression (resource rich
sample) ............................................................................................................................... 40
Table 8: Liquid Liability and Resource Fund using Quantile Regression (global sample)
........................................................................................................................................... 41
Table 9: Liquid Liability and Resource Fund using Quantile Regression (resource rich
sample) ............................................................................................................................... 42
Table 10: Market Capitalization and Resource Fund using Quantile Regression (global
sample) ............................................................................................................................... 43
Table 11: Market Capitalization and Resource Fund using Quantile Regression
(resource rich sample) ....................................................................................................... 44
Table 12: Financial Development and Economic Growth controlling for resource
dependence ........................................................................................................................ 46
Table 13: Summary statistics for the sample of resource rich countries ......................... 56
Table 14: Summary statistics for the sample of global set of countries ........................... 56
Table 15: Correlation matrix for the sample of resource rich countries .......................... 57
Table 16: Correlation matrix for the sample of global set of countries ............................ 57
Table 17: Sample of global set of countries ....................................................................... 59
Table 18: Sample of resource rich countries ..................................................................... 59
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I. INTRODUCTION
Natural resources sector is one the most commercially volatile industries in
the world while being economically significant to many countries. Prices of
energy products such as oil and gas, and bulk commodities including coal and
iron ore change on an almost daily basis. The financial and socio-economic
impacts of these boom-bust cycles may lead to adverse circumstances as
evidenced by the current state of matters in Venezuela, Mongolia and many
other countries that depend on natural resources. Yet, price volatility is just one
of many issues faced by countries that are endowed with natural resources. It
has been empirically proven that countries ―blessed‖ with abundant natural
resources perform poorly, turning the ―resource blessing‖ into a ―resource curse‖
(see Prebisch, 1950; Sachs and Warner, 1995 and T. Beck, 2010 among others) as
the commodity sector increasingly became volatile with unpredictable cycles and
price fluctuations. Consequences of such boom-bust cycles are particularly
noticeable for developing nations with exhaustible natural resources such as
fuels, ores, minerals and metals. Common explanations to the resource curse
include resistance to economic diversification, concentration of control over
natural resources through state owned enterprises, increased corruption and
misallocated revenues. More specific accounts such as (Corden 1984) argues that
abrupt windfall gains from sudden discovery of large natural resource deposit
leads to short-term appreciation in commodity prices which results in excess
inflow of foreign currency and supressing of non-resource sectors through
appreciated local currency. While (Richard M Auty 1998) suggests that intense
concentration of human and financial capital to the resource sector deteriorates
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the non-resource sectors thus negatively affecting growth in other manufacturing
sectors.
Equally high number of literary sources investigated possible solutions for
resolving the ―resource curse‖ offering solutions such as special taxation regime,
sustainable extraction practices, and allocation of resource revenues (see for
instance Hotelling, 1931 and Hossain, 2003 ). Among these is the relatively new
approach of establishing special purpose sovereign wealth funds (resource fund)
using the excess revenues from natural resources sector. Such resource funds, in
almost all cases, are managed and owned by the government. Purposes of
establishing resource funds are generally within the framework of managing
budget deficits and ensuring macroeconomic stability (Andrew Bauer 2014).
Majority of these funds were established during the past few decades, but
regardless of their short history resource funds control over US$ 7.2 trillion in
assets spanning over 30 countries as of December 2015 (Sovereign Wealth
Center 2016). However, literary evidence on the performance and usefulness of
these resource funds are rather mixed. Findings by (Jeffrey Davis 2001) suggest
that resource funds lead to duplication of government expenditures and further
states that proper fiscal policies may as well replace the functions of resource
funds. On the other hand, arguments in support of resource funds include
positive correlation between resource funds and institutional quality as
evidenced by (S. Tsani 2012) while (Tsalik 2003) finds that resource funds are
helpful in implementing good revenue management based on evidences from
funds in Azerbaijan and Kazakhstan. Table 1 summarizes a selection of
sovereign wealth funds established exclusively by using funding from natural
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resource revenues. Majority of these funds are established within the past 10-20
years making it a relatively recent approach in managing the ―resource curse‖.
Table 1: Selection of resource funds around the world
Source: Sovereign Investor Institute, National Resource Governance Institute, Funds’ individual websites
Country Funds based on resource revenues Est. Source
Assets Under
Management
(AUM)
USD Billion
Algeria Revenue Regulation Fund 2000 Oil 34.7
Azerbaijan State Oil Fund of Azerbaijan (SOFAZ) 1994 Oil 33.6
Bahrain Future Generations Reserve Fund 2006 Oil 0.4
Botswana The Pula Fund 1993 Diamonds,
Minerals 6.9
Brunei Brunei Investment Authority 1983 Oil 40
Canada Alberta Heritage Savings Trust Fund 1976 Oil 16.4
Chile Social and Economic Stabilization Fund 2007 Copper 15.2
Pension Reservation Fund 2006 Copper 7.0
Gabon Fund for Future Generations 1998 Oil 0.38
Iran National Development Fund of Iran 2011 Oil 54
Iraq The Development Fund for Iraq (DFI) 2003 Oil 18
Kazakhstan
The National Fund of the Republic of
Kazakhstan (NFRK) 2000
Oil, gas,
metals 68.9
National Investment Corporation of
National Bank 2012 Oil 20
Kuwait Kuwait Investment Authority 1953 Oil 410
Libya Libyan Investment Authority 2006 Oil 65
Mauritania Mauritania National Fund for
Hydrocarbon Reserves (MNFHR) 2006 Hydrocarbons 0.3
Mexico The Oil Revenues Stabilization Fund of
Mexico 2000 Oil 6
Mongolia Mongolia’s Fiscal Stability Fund 2011 Minerals 0.3
Nigeria Nigeria Sovereign Investment Authority 2011 Oil 1
Norway The Government Pension Fund (GPFG) 1990 Oil 818
Oman Oman Investment Fund 2006 Oil 6
Oman State General Reserve Fund 1980 Oil & Gas 8.2
Qatar Qatar Investment Authority 2005 Oil & Gas 170
Russia Russia National Welfare Fund 2008 Oil 88
Russia Reserve Fund 2008 Oil 86.4
Saudi Arabia Saudi Arabian Monetary Agency 1974 Oil 675.9
Timor-Leste Timor-Leste Petroleum Fund 2005 Oil & Gas 14.6
Trinidad and
Tobago
The Heritage and Stabilization Fund
(HSF) 2000 Oil 5.0
Turkmenistan Foreign Exchange Reserve Fund 1995 Hydrocarbons N/A
United Arab
Emirates
Abu Dhabi Investment Authority (ADIA) 1976 Oil 773
Emirates Investment Authority 2007 Oil 10
International Petroleum Investment
Company 1984 Oil & Gas 65.3
Investment Corporation of Dubai 2006 Oil 70
USA Alabama Trust Fund 1985 Oil & Gas 2.5
Alaska Permanent Fund Corporation 1976 Oil 46.8
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Financial development is considered a critical factor for long-term economic
development (see Goldsmith, 1969 and Levine, 1997 among others).
Sophisticated financial systems support economic growth, reduces income
inequality and poverty rates, provide access to financial source for industries
(Beck, Demirguc-Kunt and Maksimovic 2005) (Panicos O Demetriades 1996).
Deepened financial systems also help countries better allocate resources rather
than inefficient concentration of capital. While it is of no surprise that majority
of highly developed countries have sophisticated and well established financial
sectors, as pointed out by (T. Beck 2010), most resource rich developing countries
have poor financial sectors.
Summarizing the relationships between several socio-economic factors
involving resource rich countries, employment of resource funds and financial
development, the following crude association can be produced as a ―big picture‖
based on the existing literature:
Resource endowment
Financial Sector Development Long Term Economic Growth
Existence of Resource Funds Institutional Quality,
Governance and Rule of Law
( + )*
( - )**
( + )
( + )
[?]
( - )
Figure 1: The Research Question
( + )
( - )
*positive association **negative association
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As indicated in Figure 1, there is limited literary evidence exploring the
direct relationship between resource funds and financial development. Given the
growing footprint of resource funds in the global financial sector and the
subsequent socio-political implications, investigating the relationship between
resource funds and financial sector development may lead to important policy
measures and further studies on the uses and effectiveness of resource funds.
This paper is organized in a top down approach. Part 2 lays the conceptual
framework and literary review on the various concepts and theories related to
resource curse, resource funds and financial development. Part 3 explains the
methodology and data used in analysing the relationship between resource funds
and financial development using Pooled OLS and Quantile Regression methods
as well as the relationship between financial development and economic growth
using Barro-style growth regression. Part 4 presents the empirical results
derived from these analysis, and Part 5 concludes the paper and provides
potential policy implications for countries with resource funds.
II. LITERATURE REVIEW
1. Resource Curse
Poor economic development in resource rich countries is a well-studied
phenomenon with numerous sources explaining its cause and effect. It is often
referred to as the ―Dutch disease‖ relating to Netherlands’ discovery of large gas
fields in the North Sea in the 1960s resulting in positive revenue shock, but an
appreciation of the Dutch guilder and the subsequent suppression of the local
manufacturing sector. Now the term Dutch disease may refer to any adverse
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situation resulting from sudden excess income, foreign direct investment and
foreign aid. Earlier studies including (Prebisch 1950) indicates that resource
rich countries face sluggish economic growth due to declining trade and low
elasticities of demand for resource based goods, and a general tendency for slow
growth compared to resource poor countries. (Corden and J.P.Neary 1982) with
further elaboration in (Corden and Max 1984) state that a booming-sector (i.e.
sudden discovery of natural resources, or sudden increase in price of a particular
commodity) supresses the economics of other traded goods by directly taking
resources away while imposing upward pressure on the exchange rate.
Additional study by (Richard M Auty 1998) finds that resource rich countries
have relatively high rate of trade volatility which could result in inconsistent
growth rate, but the causal relationship between trade-volatility and growth
were not significant. Another influential study on the same topic is by (Sachs
and Warner 1995), in that an empirical study on the association between GDP
growth and resource abundance is conducted while controlling for other variables
affecting economic growth, and statistically significant, negative association
between resource intensity and growth were found. They explained the negative
association within the framework of endogenous growth favouring tradable
manufacturing to natural resources sector. Other explanations include resource
industries having lower linkage effects than manufacturing sector, or the
resource sector attracts capital, skilled labour and investment from the
manufacturing sector, which in turn makes the economy dependent on boom-
bust cycle of commodity sector.
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Literature to date suggest several ways to overcome or to cure the ―resource
curse‖ including classic approaches such as ―Hotelling rule‖ which proposes
optimal pricing of non-renewable resources in order to maintain equal capital
gain between resources and non-resources sector (Hotelling 1931). Another
proposed solution is to impose special tax on natural resources sector which is a
common policy in almost all resource exporting countries through mineral
royalty taxes. The special taxation is imposed to prevent economic imbalance
caused by booming resource sector. (Dixit and Newbery 1985). Recurring theme
in the topic of curing for resource curse are institutional quality, rule of law and
corruption. Figure 2 produced by (Mehlum, Moene and Torvik 2006) provides a
visual representation for the association between resource abundance and
institutional quality showing that bad institutions result in negative correlation
between resource dependence and GDP growth with the potential implication
that improved institutional quality may as well be the cure for resource curse.
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Figure 2: Institutional Quality and Resource abundance (Mehlum, Moene and Trovik 2006)
2. Resource Funds
Relatively recent approach in addressing resource curse is the establishment
of special purpose wealth funds that are based on windfall revenues from natural
resource exports, and almost all of these funds are owned and managed by the
governments1. According to (Bacon and Tordo 2006) resource funds are
established with wide range of objectives including managing of government
expenditures, saving of natural resource revenues for future generations,
maintain budgetary stabilization and precautionary saving against price
1 It is important to distinguish between sovereign wealth funds that are strictly based on natural resource revenues from funds that are based on foreign currency reserves, pension reserves or any other financial resource. There are notable differences in terms of transparency, governance and financial performances among the various funds with natural resource based funds mostly lacking in most of these indicators which in itself is another symptom of the resource curse.
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volatility. Some funds are established with the sole purpose of generating
additional income through investment in financial and non-financial assets.
Regardless of their purpose, resource funds became a common policy tool among
resource rich countries with the presence of at least one resource fund in every
country with natural resources comprising 40% or more of total merchandise
export. Certain literary evidence finds that resource funds may help achieve
optimal allocation of resource rents, or employed as fiscal tools to combat budget
deficits and volatility for achieving macroeconomic stabilization (S. Tsani 2012).
However, the literature to date draws mixed results on the usefulness of
resource funds. For instance, the case of Botswana and its overall management
of its resource revenues is a successful implementation of resource funds in a
developing economy. Despite high earnings associated with equally high price
volatility, Botswana maintained strict expenditure controls while increasing its
foreign exchange reserves from US$75 million to US$5 billion from 1976 to 1996
through its state run Pula Fund (Sarraf and Jiwanji 2001).
3. Financial Development
Positive relationship between financial development and long-term economic
growth is a well evidenced subject. Related studies go as far back as (Bagehot
1873) explaining the benefits of the financial system to (Goldsmith 1969) and
(Levine 2004) all pointing out that financial systems’ development is an
inseparable part of long term economic development despite short term issues
such as recessions. Also, as (Levine 1997) concluded, there are number of factors
shaping the financial industry such technological advancements, monetary and
fiscal policies, and political changes and national institutes. Following this
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relationship, there is notable difference in financial development across countries
with economically advanced countries having more sophisticated banking and
capital markets (Monshin S Khan 2000). In contrast, (Panicos O Demetriades
1996) concludes that there is little evidence in supporting the view that financial
development is a leading factor of economic development.
Number of authors investigated if poorly developed financial system is a
result of natural resource endowment, which in turn relates to an entire array
literature on the popular topic of resource curse. The general conclusion is that
resource rich countries tend to have poorer financial systems. For instance, (T.
Beck 2010) argue that due to supply constraints in the financial sector in
resource based countries there is an overall lower level of financial development.
(Hassan 2013) concluded that more levels of resource endowment results in
lower level of insufficient credit for the private sector due to lack of motivation in
developing non-resource sectors such as the financial sector. Another set of
literary evidence defines the ―resource curse‖ as deterioration of governance and
institutional quality (Mehlum, Moene and Torvik 2006), (James A Robinson
2006). An overarching conclusion is that concentrated ownership in natural
resource lead to rent-seeking behaviours within the small group of political elite
that tend to over-extract natural resources. Using this power, they influence the
shape of the political system degrading the quality of proper institutional
controls and transparency. This phenomenon is evident in many of the resource
rich countries around the world with significant portion of the nation’s mineral
resources are in the hands of few state owned enterprises. For instance, the
―Minerals Law of Mongolia‖ the main legislation of the its mining sector defines
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some 15 large mineral deposits as ―Deposits of Strategic Importance‖ all of which
are to be owned by the state owned ―Erdenes-Mongol LLC‖. On a similar note
(Menzue D Chinn 2006) and (Siong Hook Law 2008) conclude that institutional
quality significantly enhance financial development, especially banking sector
development. Therefore, one may conclude that resource endowment may trap
countries in a limbo state of poor institutional quality and less developed
financial system that result in slow economic growth. Interestingly, (S. Tsani
2012) finds that establishment of resource funds is positively associated with
governance and institutional quality.
III. METHODOLOGY AND DATA
This section describes the empirical models and the data set used to analyse
the relationship between 1) resource funds and financial development and 2)
financial development and economic growth. However, the emphasis is on the
effects of resource funds on financial development. According to existing
literature, there are mixed views on the uses and effectiveness of resource funds
in general, and investigating its impact on the financial sector might lead to
important policy implications. In order achieve this with certain degree of
robustness, I use two methods – Pooled Ordinary Least Squares and Quantile
Regression both of which largely expands the model used in (S. Tsani 2012). In
order to justify the use of financial development as measurement for
investigating the effectiveness of resource funds, and as measurement for
growth, I analyse how financial development affects GDP growth, while
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controlling for resource endowment and resource dependence. Another reason is
to confirm the wide array of literary evidence on the positive correlation between
financial development and economic growth. It is done by using Barro-style
growth regression. I largely expanded on (T. Beck 2010) which investigates
symptoms of ―Dutch disease‖ in financial development for resource rich
countries.
a. Rationale and Criterion
According to (N. Beck 2001) there are notable differences between time-
series cross-sectional data and panel data. Especially for studies in economics
and political science discipline, time-series cross-sectional data concerns limited
number of rather fixed samples (i.e. countries) rather than random chosen
samples in panel data. Panel data also ignores individuality of the samples, but
for time-series cross-sectional data it is the opposite (i.e. we are in fact interested
in country specific effects. Also panel data is assumed to have small number of
observations (small T) while having many subjects (large N). Considering these
factors, time-series cross-sectional data is used in this paper that covers longer
periods of time (large T) spanning 1981 to 2013 with fixed number of subjects
(i.e. countries) which is consistent with most political economy studies. Due to
these characteristics certain literature recommends the use of OLS with panel-
corrected standard errors (see N. Beck, 2001 and Agung, 2014 for more details).
Another common issue with using both panel and time-series cross-section data,
despite the benefits, is the problem of unobserved heterogeneity and the related
challenges in controlling for it. The reason I am explaining all this is to validate
the using of Quantile Regression in addition to Ordinary least squares in this
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paper. The reason I use Pooled OLS is that I am largely following (S. Tsani
2012). However, when Pooled OLS regression model is used on panel data there
is no regard for the individual effects of each subject / country, which gives rise to
independent, not identically distributed (i.i.n.d) observations (Woolbridge 2010).
Pooled OLS assumes homogeneity among all subjects, which is clearly not an
accurate assumption to apply for different countries around the world. Several
methods exist for resolving unobserved heterogeneity including hierarchical
linear models such as Fixed and Random effect models, but since our most
important variable is a dummy variable (ofd_dum), employing Fixed Effects
model would remove the effects of the dummy variable. As for Random Effects
model, (Western 1998) argues that random effects model is important if we are
interested in each individual coefficients βi that is more relevant for comparative
political studies, which is not the case for this paper. The reason for using
Quantile Regression becomes clear after the following section.
b. Testing and Robustness
Common issue with using Pooled OLS regression is that its errors may
contain time or cross-sectional specific effects. Although, residual diagnostics on
all of the models I use return favourable results on fulfilling the
homoscedasticity and independence assumptions, it does not guarantee that
errors are in fact heteroskedastic and serially correlated. The statistics software
I use (EViews) do not have a built-in Heteroscedasticity testing for panel
data. Therefore, in addition to using ordinary least squared method, in order to
improve the robustness of the regressions I employ few alternatives of coefficient
covariance methods to see if there are any substantial differences.
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First, in order to attain "robustness" I run the regressions with White cross-
sections, which handles clustering by cross-section (White 1980). White cross-
section method assumes that errors are contemporaneously (cross-sectional)
correlated (period clustered) and the method treats the pool regression as a
multivariate regression (with an equation for each cross-section), and computes
robust standard errors for the system of equations (Startz 2015).
However, according to (S. Tsani 2012) "robust" errors may show poor results
in the presence of large serial correlation and small sample size, and White
cross-section technique does not consider cross-sectional correlation. Therefore,
in order to control for contemptuous correlation of errors, panel corrected
standard error (PCSE) technique is also employed for the regressions. Beck and
Katz (1995) used Monte Carlo simulation to find that the PCSE method produces
accurate standard errors compared to Feasible General Least Squared
(FLGS) method. However, employment of these alternative methods does not
result in significant differences in the coefficients of the models. Thus, the
results sections only deals with the results from the Pooled OLS method only.
Even so, I am still not convinced with the results of the Pooled OLS method and
concerned with the robustness of this model. Given the limitations posed by
Pooled OLS and the presence of a dummy variable, Quantile Regression method
might improve the robustness of the analysis. The main idea of Pooled OLS is
that it gives summary of the averages of distributions of the independent
variables, but gives an incomplete picture just as a mean gives incomplete
picture of any distribution (Tukey 1977). For this reason, as introduced by
(Koenker and Basett 1978), quantile regression estimates the linear relationship
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between the regressors to certain quantile of the dependent variable. The main
benefit of employing quantile regression is that it gives broader set of
explanations on the conditional distribution compared to conditional mean
analysis which is Pooled OLS. Also quantile regression ―does not require strong
distribution assumptions, which offers a robust method of modelling
relationships‖ (Startz 2015).
c. Pooled OLS
Expanding on (S. Tsani 2012), the following slightly modified Pooled OLS
model is used to investigate the association between resource funds and financial
development:
∑
where FDi,t is a measure of private credit and liquid liability as financial
development indicators for country i at year t. is the intercept term, and is
a set of j explanatory variables of financial development and economic growth.
is the dummy variable for existence of a resource fund taking value of 1
if it exists in country i in year k = t - 10 and if not the value is 0. The usual error
term captures all other factors that are not explained by this model. The
results mainly focus on the values of and as coefficients for the explanatory
variables and the resource fund dummy variable respectively. Even though
Pooled OLS model assumes homogeneity among the countries, the value of at
least shows the general association between resource funds and financial
development which is adequate for the purposes of this analysis.
(2)
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d. Quantile Regression
The Quantile regression model to extend the results of the Pooled OLS
regression is as follows:
∑
|
where represents a set of financial development indicators for country i in
year t,
represents set of j explanatory variables including the dummy
variable ofd_dum with as an unknown vector of coefficients that is associated
with the θth quantile and is the error term. In terms of calculating the
standard errors, EViews provides several options within its quantile regression
option including Ordinary (IID) covariance, Huber-Sandwich method and
Bootstrap resampling. (S. Tsani 2015) recommends the use of Bootstrap method
as it provides better results with small samples and it is valid in most forms of
heteroscedasticity. Among the four different approaches of Bootstrap available in
EViews include residual bootstrap, XY pair, and two versions of Markov Chain
Marginal Bootstrap, I use XY-pair as most natural form of bootstrap resampling
(Startz 2015). The model works to increase the value of quantile value from 0
to 1, and the distribution of GIQi,t conditional upon xj,i,t can be estimated for each
quantile. I use 0.1, 0.3, 0.5, 0.7 and 0.9 custom quantile values. In this manner
the quantile regression enables the observation of the effects of the variables at
different locations in the conditional distribution.
Literature to date provide mixed views on capturing the timing effects of
resource funds to socio-political factors. For instance, (S. Tsani 2012) argues that
(3)
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resource funds may create ―shocks‖ on the host country’s governance and
institutional qualities. But since these factors are usually resistant to changes,
longer period of time should be used to capture the real effects and as a result,
a10 year lag is used as in k = t – 10. Similarly, (Sugawara 2014) uses t – 5 in
order to capture the effects of stabilization funds on government spending. In our
case of capturing the effects of resource funds on financial development, I use k =
t – 10. There are several reasons to choose relatively longer lag periods.
Financial sectors around the world are heavily regulated and monitored,
resulting in regulatory changes taking longer time take effect. Resource rich
countries are heavily affected by the boom-bust cycles of the commodities
industry, which required longer periods for recovery. Also according to (S. Tsani
2012), 10-year period is considered adequate for political and constitutional
changes to be recorded. The longer period also gives room for the resource funds
to build-up notable financial position and capital adequacy.
Given the existence of resource fund as a dummy variable with no time
varying component Fixed Effect Regression model do not produce favourable
results. In fact, any statistical program would drop the effects of the dummy
variable from the equation. According to (Woolbridge 2010), I can resort to
Random Effects model in order to capture the effects of the resource fund
dummy. However, Hausman Fixed/Random Effects testing reveals that it is not
appropriate to use the Random Effects Model. Therefore, the results are based on
the Pooled OLS Regression in Equation (1).
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e. Supplementary Barro-regression
For analysing the relationship between financial development and economic
growth in resource rich countries, I run the following pooled OLS regression
model spanning years 1980 – 2011 for the Global data of 82 countries. It is a
Barro-type regression model that explains long term economic growth in the
context of ―conditional convergence‖ such that if certain parameters are held
fixed all countries converges to similar rate of labour performance, and standard
of living (R. J. Barro 1991). The model is as follows:
where Gi,t is growth of Gross Domestic Product per capita as dependent variable
for country i in year t covering the period of 1980 - 2011. On the right hand side,
β1 captures the long-term relationship between economic growth and financial
development represented by prvcrdt or Private Credit to GDP. and are
control variables measuring the differential effects of a country relying on
natural resources by using orexp which is value of ores and minerals as
percentage of merchandise export. Xi is a set of explanatory variables for
economic development including log of initial real GDP per capita to control for
convergence, inflation rate, log of government consumption to GDP, and log of
trade to GDP following (R. J. Barro 1991). The above equation largely follows (T.
Beck 2010) which uses the average of all explanatory variables for the purposes
of avoiding heterogeneity. Although the results in (T. Beck 2010) show
significant correlation between economic growth and natural resources export as
percentage of merchandise export, I find that there is no significant correlation
(1)
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between the two despite using all control variables. Even doing stepwise
regression does not help finding the correlation. Therefore, I transformed the
equation into Pooled OLS with log transformation on all explanatory variables
except for the initial GDP, which results in Equation (1)2.
f. Data and Variables
This paper uses cross-sectional time-series data. Besides becoming more
accessible, cross-sectional time-series data provides number of advantages
including more accurate prediction on model parameters with higher degrees of
freedom and sample variability (Hsiao 2014). Data used in this paper is divided
into three main categories. First category involves data for explaining financial
sector development, second category includes indicators for resource dependence
and mineral rent, and the last category includes explanatory variables that are
related to institutional qualities that might affect financial development in
resource rich countries.
Table 2: Description of variables
DEPENDENT VARIABLES: Unit Code
Financial sector:
Private Credit to GDP % prv_crdt
Liquid Liability to GDP % liq_liab
Market Capitalization to GDP % mkt_cap
INDEPEDENT VARIABLES:
2 Cross country, finance and growth regression within the framework of (R. J. Barro 1991) has
the general form of:
( )
Barro-regression explains growth as a function of an initial variable (i.e. constant initial GDP) that results in global convergence when controlled for variables such as human capital, government spending and inflation.
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23
Economic growth:
Inflation % inflation
GDP per capita US$ gdp_pc
Government expenditure to GDP % govt_cons
Foreign direct investment (net inflow) US$ fdi
Trade to GDP % trade_gdp
Population density per sq.km pop_dens
Years of schooling years yrs_sch
Natural Resources:
Ore to export to GDP % ore_to_exp
Fuel to export to GDP % fuel_to_ex
p
Mineral rent to GDP % min_rent
Resource Fund
Dummy variable binary ofd_dum
Financial Sector Variables
While GDP per growth is a widely used gauge for measuring economic
development, choice of measurements for financial development varies. Majority
of existing literature conforms to four categorical measurements to assess
financial sector development including 1) depth 2) access 3) efficiency and
stability (A. D.-K. Thorsten Beck 1999). All financial sector variables are
obtained from the Global Financial Development Databased compiled by the
World Bank (The World Bank 2016). Based on existing literature the following
measurements are most commonly used all of which are used for the purposes of
this paper as well.
Private Credit to GDP: Formally defined as Private credit by deposit money
banks and other financial institutions to GDP (%), this is a value of available of
financial resources to private sector by local banks as percentage of GDP. Local
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24
banks include banks and other banking institutions that accept transferrable
deposits and demand deposits (GlobalBanking.org 2016). In other words, it
measures the strength and coverage of the financial intermediaries connecting
those who possess excess capital and those in need of capital. PRCD is probably
the most important and most commonly used statistics for financial
development.
Liquid Liability to GDP: Liquid Liabilities to GDP, also known as broad moneys
(M3) to GDP, are the measure of sum of currency and deposits in the central
bank (M0) represented by percentage of GDP which is a widely used
measurement for financial depth.
Market Capitalization to GDP: Local stock market capitalization to GDP (%) is
the measure of total value of all listed shares in a stock market as a percentage
of GDP. Besides the banking sector, capital markets development is an
important indicator for mature economy. For instance, countries such as
Mongolia has a stock exchange valued at a mere US$2.3 billion while the
Toronto Stock Exchange total market capitalization is over US$ 1 trillion.
Although direct comparison of these numbers may be irrelevant, when
controlling for GDP, market capitalization is an important gauge for capital
market development.
Resource endowment variables
It is important to distinguish countries that are rich with natural resources
as opposed to countries that are dependent on natural resources. Some of the
most economically advanced economies in the world such as United States,
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25
Canada and Australia are also rich with natural resources but do not necessarily
depend on natural resources. Therefore, focusing on countries that are resource
dependent (only) might result in endogeneity issues. In order to avoid this, two
sets of samples will be used which is explained in detail in Sampling. Also not all
resource rich countries have resource funds, but majority of countries that have
resource funds tend to be resource dependent. Therefore, following existing
literature, following variables are used for natural resource endowment.
Mineral Rent: Measuring country’s natural resource wealth by directly valuing
its mineral deposits underground can be very ambiguous and misleading given
extraction costs, frequent volatilities in commodity prices as well as geological
and geopolitical factors. According to (The World Bank 2016) natural resources
rent is calculated by the differences between the averages of extraction cost and
selling cost of natural resource products. This gives an accurate view on exactly
how much a country is entitled to resource rents for the given year.
Ore export to GDP: Based on existing literature the most commonly used
criteria for resource richness is the share of sum of exports of ores, minerals and
metal divided by total value of merchandise export. This is different from fuel
and petroleum based products to export. It is important to consider and control
for the distinction between mainly oil exporting countries mostly based in Middle
East and North Africa in comparison to countries that mainly export minerals
and ores such as iron ore, copper, etc.
Fuel export to GDP: Majority of resource funds around the world are based on
excess incomes originated from exporting what is officially defined as Mineral
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26
fuels, lubricants and related materials. This is an important measure of not only
resource endowments but also resource dependence as the average fuel to
percentage of merchandise export is at 55% for the resource rich countries.
Resource Fund Dummy variable: The effects of resource fund are observed by a
dummy variable with a value of 1 if resource funds exists in country i at
time k. Given majority of resource funds were established in late 1990s, studying
the relationship between resource funds and any other factor is constrained by
the short timing. Although it is better to use variables that are more descriptive
on resource funds instead of a dummy variable, quantitative variable on resource
funds is due to lack of information on resource funds in general. However, this is
a common approach used by many of the existing literature on resource funds (
(Sugawara 2014). In order to obtain reliable information, I first referred to the
official websites or the particular fund’s own information source. However, many
of these resource funds tend not to publicly disclose information. I used
information obtained from the (The Natural Resource Governance Institute
2016), and (Sovereign Wealth Center 2016), although most of the more useful
data are not free of charge from these sources. Number of explanatory variables
for explaining financial and economic development used to control for their
effects as detailed in Table 2.
Table 3: Sources of explanatory variables
Potential determinant of
financial development Definition / Notes Source
GDP per capita growth Average real GDP per capita growth for the period
1980 – 2011
(The World Bank
2016)
Inflation Inflation for the period 1980 - 2011 (The World Bank
2016)
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27
General government final
consumption expenditure
Value of purchases of goods and services by the
government divided by GDP in percentage for the
period 1980 – 2011
(The World Bank
2016)
Foreign Direct Investment Net foreign direct investment in US dollars for the
period 1980 - 2011
(The World Bank
2016)
Trade to GDP Sum of exports and imports divided by GDP for the
period 1980-2011
(The World Bank
2016)
Population density Number of population per square kilometre as an
indicator for economic development
(The World Bank
2016)
Years of schooling Barro-Lee Average years of total schooling, age
15+ (The World Bank)
All models used in this paper are applied to two sets of samples. The reason
to employ two separate samples is to avoid sampling bias and control for the
effects of natural resource endowment. First sample consists of 27 countries that
are considered 1) resource rich3 and 2) employ some type of resource fund4 . The
second set of sample consists of 83 countries which includes Sample 1 (S. Tsani
2012). The second set not only includes countries such as Singapore and South
Korea that have low levels natural resources but also countries such as USA,
China, Russia and Canada that are economically advanced and resource rich.
g. Sample Data Analysis
This section provides general analysis and comparison between the two sets
of data samples used in this paper. Sample 1 consists of 27 resource rich
countries that are specifically chosen for observing the effects of resource
endowment and resource dependence. Sample 2 consists of 83 countries (called
the global sample) that include countries such as South Korea and Hong Kong to
3 Countries with natural resource based exports comprising 40% or more of total merchandise export 4 A natural resource fund—a type of sovereign wealth fund—is a special-purpose investment vehicle owned by a government whose principal source of financing is revenue derived from oil, gas or mineral sales (Natural Resource Governance Institute)
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28
compare and contrast with resource rich countries. The global sample also
includes advanced economies that are also resource rich including USA, China
and Canada.
As for financial development indicators, resource rich countries have liquid
liabilities to GDP rate at 0.38 compared to o.54 for the Global Data (sample of 82
countries), also private credit to GDP rate at only 0.27 in sample countries
compared to 0.52 in the wider group. Stock market capitalization and stock
market turnover are both lower in resource rich countries at 0.32 and 0.30
respectively compared to 0.48 and 0.53 in Global data sample. This is consistent
with the findings of (Frederick van der Ploeg 2007) in their findings of well-
developed financial sectors result in less pronounced resource curse.
In terms of economic development indicators, considering the 1980 – 2011-
time frame, both samples show similar rates of initial GDP per capita within the
US$ 5000 range with US$500 difference. However, current GDP per capita is
almost US$ 3000 less at US$ 6759 for resource rich countries that is US$ 9785
for the Global data. A striking difference is observable in Foreign Direct
Investment (FDI), as resource rich countries have current average FDI of US$1.9
billion, which is 4 times, less than the Global data average of US$ 7.7 billion.
This is consistent with the conclusions of even though subsoil assets boost
resource FBD, it supresses non-resource FDI which results in reduced aggregate
FDI in resource rich countries (Steven Poelhekke 2010). In terms of government
consumption to GDP, resource rich countries’ governments tend to spend much
more than that of the Global data average at 116.2% of GDP for resource rich
countries compared to 47.0% respectively.
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29
In terms resource endowment and resource dependence statistics, resource
rich countries are notably different from the Global data average. Fuel export as
percentage of merchandise export is at 55% on average for resource rich
countries compared to 20.21% for the Global data average. This is an indication
that Mineral fuels, lubricants and related materials comprise large portion of
global trade volume. As for natural resources, rent resource rich countries stand
at 25% compared to 13.25% of the Global average. Even for non-fuel based ores
and metals export to percentage of merchandise exports, resource rich countries
show higher rate of 14.88% compared to 7.52% for Global average.
Regarding resource funds, 29 out of the 35 resource funds are established
using excess income from oil and gas industries. Over half of these funds are
established after year 2000, and 8 funds or 21% are established before year 1990.
It consistent with the conclusions by (S. Tsani 2012) that resource funds are a
relatively new phenomenon with high concentration on the oil and gas sector.
Summary statistics for the both sample of countries are in Appendix I and II.
IV. RESULTS
a. Pooled OLS and Quantile Regression
Pooled OLS and Quantile Regression methods are used for the purposes of
analysing the relationship between resource funds and financial development. In
order to avoid sampling bias and bring out the effects of controlling for resource
rich countries, the models are applied to two sets of samples with one comprising
only resource rich countries with resource funds, and another a global set of 82
countries. Both models are estimated on three financial development indicators
including private credit to GDP, liquid liability to GDP and market capitalization
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30
of domestic companies to GDP as independent variables with set of dependent
variables and a resource fund dummy variable. In order to attain robustness and
avoid contemporaneous correlation, White cross-section and Panel corrected
standard error techniques are applied to the Pooled OLS model. The differences
in employing these techniques were not significant from the ordinary coefficient
covariance method. In order to verify the results of the Pooled OLS model and to
obtain more detailed explanation, a Quantile Regression analysis is employed for
the same data set. Tables 3 and 4 show the results of the Pooled OLS regression
model for the global and resource rich samples respectively.
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31
Table 4: Resource fund and financial development using Pooled OLS (global sample)
Explanatory
variables
Private Credit Liquid Liabilities Market capitalization
1 2 3 4 5 6 7 8 9 10 11 12
Inflation -0.110*** -0.108*** -0.086*** -0.075* -0.124*** -0.121*** -0.101*** -0.110*** -0.355*** -0.354*** -0.372*** -0.368***
GDP per
capita 0.284*** 0.295*** 0.247*** 0.282*** 0.150*** 0.156*** 0.107*** 0.158*** 0.143 0.101 0.176* 0.102
Govt.
consumption 0.731*** 0.704*** 0.673*** 0.880*** 0.405*** 0.383*** 0.410*** 0.406*** 0.049 0.074 0.067 0.078
Foreign Direct
Investment 0.054*** 0.064*** 0.088*** 0.084*** 0.028* 0.040*** 0.053*** 0.036*** 0.161*** 0.171*** 0.129*** 0.153***
Trade to GDP 0.119*** 0.140*** 0.164*** 0.167*** 0.038 0.058** 0.064*** 0.057** 0.160** 0.139* 0.098 0.095
Population
density 0.082*** 0.062* 0.024 0.090** 0.115*** 0.090*** 0.078*** 0.114*** 0.179*** 0.121** 0.183*** 0.159***
Years of
schooling -0.070 -0.123 -0.250* -0.040 -0.068 -0.105 -0.125 -0.081 0.099 0.277 0.197 0.182
Ore to export -0.003 0.013 0.163***
Fuel to export -0.040** -0.038*** -0.017
Mineral rents -0.110*** -0.071*** 0.072*
Resource fund -0.362*** -0.288*** 0.469**
Constant -2.324*** -2.400*** -2.120*** -3.759*** 0.619 0.551 0.624 0.347 -2.830 -2.573 -2.242* -2.016
Observations 331 331 346 254 332 332 347 351 200 200 206 206
R-squared 0.52 0.53 0.56 0.56 0.48 0.49 0.51 0.50 0.46 0.43 0.45 0.45
Cross sections 76 76 75 76 75 75 74 75 67 67 67 67
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32
Table 5: Resource fund and financial development using Pooled OLS (resource rich sample)
Explanatory
variables
Private Credit Liquid Liabilities Market Capitalization
1 2 3 4 5 6 7 8 9 10 11 12
Inflation -0.031 -0.040 -0.030 -0.039 -0.074*** -0.062*** -0.069*** -0.063*** -0.230*** -0.432*** -0.430*** -0.406***
GDP per capita 0.316*** 0.283*** 0.304*** 0.329*** 0.107*** 0.095*** 0.097*** 0.179*** 0.264*** 0.245*** 0.230*** 0.259***
Govt.
consumption 0.919*** 0.999*** 0.682*** 0.884*** 0.684*** 0.717*** 0.615*** 0.523*** 0.369** 0.009 0.067 0.024
Foreign Direct
Investment 0.029** 0.044** 0.041*** 0.051** -0.003 0.017 0.007 -0.003 0.195*** 0.184*** 0.180*** 0.191***
Trade to GDP -0.177*** -0.205*** -0.098** -0.141*** -0.234*** -0.222*** -0.206*** -0.162*** 0.089 0.171** 0.151** 0.184
Population
density 0.100*** 0.125*** 0.103*** 0.101*** 0.164*** 0.173*** 0.160*** 0.131*** 0.287*** 0.272*** 0.253*** 0.271***
Ore to export 0.003 0.018** 0.018
Fuel to export -0.015 0.008 -0.000
Mineral rents -0.173*** -0.052 0.149**
Resource fund -0.174** -0.331*** -0.037
Constant -2.043*** -2.143*** -1.270*** -2.597 1.491*** 0.920*** 1.616 1.228*** -5.101*** -3.659*** -3.995 -4.116***
Observations 427 385 463 375 422 382 455 370 223 219 249 244
R-squared 0.44 0.43 0.40 0.45 0.41 0.40 0.40 0.41 0.67 0.62 0.62 0.61
Cross-sections 26 26 25 26 25 25 24 25 18 18 18 18
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33
First row of Table 3 shows that there is significant negative correlation
between inflation and financial development for both sample sets. This is
consistent with findings of (John H Boyd 2001) in which there is empirical
evidence of significant, economically important, and negative relationship
between inflation and both banking and equity market activity. The negative
impacts of inflation on the equity markets is evidenced by Columns 9 - 12 in
Table 3. The negative relationship is further evidenced by the results of the
Quantile regression model for the global sample. As for the resource rich sample,
inflation enters negatively but insignificantly for both Pooled OLS and Quantile
Regression models.
There is positive and significant relationship between GDP per capita and
financial development across all three measurements. This is consistent with
numerous literary evidences on positive relationship between the two indicators
(see for instance Levine, 2004 and Goldsmith, 1969). Quantile regression results
also show the same significant and positive relationship, but with more
pronounced results from 30th to 70th quantiles in both global and resource rich
samples. These results also confirm that financial development positively affects
economic development regardless of resource endowment.
The results indicate significant and positive relationship between financial
development and government consumption as observed in third row of Table 3
for global set of samples. However, there are mixed views on the association
between government spending and financial development in existing literature.
For instance, (R. J. Barro 1988) concluded that there are constant returns to
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34
scale between economic growth and government spending. However, (Pär
Hansson 1994) on the other hand concludes that there is negative correlation
between government spending and financial development, except majority of
government spending is focused on sectors such education. Size of equity
markets are not explained by government spending as it enters insignificantly
albeit being positive. However, quantile regressions provide clearer picture on
this relationship. For the global sample, 70th – 90th quantiles show significant
results (
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35
Total monetary value of imports and exports as percentage of GDP is also an
important control variable for economic development and a measurement for
financial sector capacity. There is statistically significant and positive
relationship between financial deepening and trade for the global data of 82
countries. This is consistent with the findings of (T. Beck n.d.) in which he
concludes countries with better-developed financial system have a higher export
share and trade balance in manufacture goods. Interestingly enough there is
negative but statistically significant relationship between financial development
and trade in the resource rich sample as opposed to the positive relationship in
the global sample. This confirms to the findings of (Quy-Toan Do 2004) in which
trade openness results in faster financial development in wealthier countries,
but associated with slower financial development in poorer countries. These
results are confirmed by Quantile Regressions as trade value in resource rich
countries have significant and negative relationship between private credit and
liquid liability. This is also a symptom of the Dutch disease as natural resource
export dominates international trade for all 27 resource rich sample, which in
turn supresses the economy as a whole including the financial sector.
Population density is statistically and, positively correlated to financial
development in all three measurements. This relationship is consistent with both
samples. Quantile regressions generally conforms to these findings but with
mixed results among some of the quantiles.
There is mixed results in terms of relationship between percentage of
minerals and ore to merchandise export to financial development. It is mostly
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36
insignificant for both samples, and negatively correlated to financial
development for the global sample but positive for resource rich countries. Fuel
exports enter significantly and negatively for global sample but insignificant in
the resource rich sample which I find to be odd given the high rate of dependence
on oil exports among the resource rich countries. However, I assume this is
probably due to sampling bias, as average fuel to export rate for the resource rich
sample is at 55.08% with standard error 33.9 while 20% for the global sample
with standard error of 29.5. Therefore, based on the global sample there is
significant and negative relationship between financial development and fuel to
export. This is consistent with existing literary evidence on poorly developed
financial systems in oil rich countries. The reason for looking at both ore to
export and fuel to export is control for countries that rely on resources that are
not oil or gas. For instance, the main exporting product of Zambia, Chile and
Peru is copper which comprise over 40% of the entire merchandise export.
However, a more accurate measurement for natural resource dependence is total
natural resources rent as percentage of GDP given its consideration to a broader
economic indicator instead of only looking at merchandise exports. Accordingly,
natural resource rent enters both samples significantly in the regressions. It also
confirms the literary evidence of negative relationship between financial
development and resource dependence. Yet, there is contrasting evidence in
terms of relationship between market capitalization of listed domestic companies
to GDP and natural resource indicators. Private credit and liquid liability both
have negative and significant relationship mineral rents. However, market
capitalization enters positively and significantly for resource rich countries. This
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37
is consistent with (T. Beck 2010) in that countries rely on natural resources do
not necessarily have small stock exchanges but are significantly less liquid.
Columns 4, 8 and 12 of Table 4 and 5 show the estimation results for the
dummy variable for resource funds (i.e. the association between resource funds
and financial development). The results for both global and resource rich
samples indicate that an existence of resource fund significantly but negatively
affects private credit and liquid liability. Association between market
capitalization and resource fund enters significantly and positively in the global
sample but negatively and insignificantly in the resource rich sample. These
results are robust in terms of alternative cross sections of White cross-sections
and panel-corrected standard error (PCSE), as the models were run with these
additional specifications in both sets of sample. The White-cross section and
PCSE versions did not produce significantly different results, thus the ordinary
cross-section Pooled OLS results are presented here.
The coefficient for the dummy variable indicates that countries with resource
funds have negative values for private credit at -0.36, liquid liability at -0.28 but
positive for market capitalization at 0.46. Results are similar for the resource
rich sample at -0.17 for private credit, -0.33 for liquid liability, negative but
statistically insignificant for market capitalization as opposed to positive and
significant value in the global sample. In all instances of the model, the resource
fund dummy remains to be significant. Simple data inspection also suggests that
countries that established resource funds show significantly less developed
financial sector when compared to countries that do not have resource funds.
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38
Results from the Quantile regressions supplements the findings from the Pooled
OLS model in that there is negative correlation between private credit and
resource fund within all five quantiles of the spectrum showing significant and
negative relationship for the global sample, but insignificant results for the
resource rich sample which is the same in the Pooled OLS model. Liquid liability
to GDP and resource funds have negative and significant results in all five
quantiles in both samples confirming the findings of the Pooled OLS regression.
There results for market capitalization and resource fund are rather mixed as it
is insignificant with a mix of positive and negative values for the resource rich
sample. However, it enters positively and significantly for the 10th and 20th
quantile for the global sample but given the limited range, these results do not
hold much value.
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39
Table 6: Private Credit and Resource Fund using Quantile Regression (global sample)
10th quant. 30th quant. 50th quant. 70th quant. 90th quant. OLS
Inflation -0.073*** -0.108*** -0.103*** -0.111*** -0.126*** -0.089***
(0.021) (0.025) (0.018) (0.018) (0.024) (0.016)
GDP per capita 0.274*** 0.323*** 0.322*** 0.247*** 0.168*** 0.246***
(0.047) (0.029) (0.022) (0.019) (0.022) (0.019)
Govt. consumption 0.219 0.071 0.133** 0.342*** 0.443*** 0.364***
(0.155) (0.096) (0.061) (0.071) (0.112) (0.059)
Foreign Direct Investment 0.127*** 0.071*** 0.062*** 0.072*** 0.084*** 0.098***
(0.021) (0.017) (0.012) (0.014) (0.012) (0.010)
Trade to GDP 0.259*** 0.174*** 0.152*** 0.129*** 0.099*** 0.181***
(0.018) (0.018) (0.012) (0.014) (0.014) (0.014)
Population density -0.060* 0.011 -0.005 -0.013 0.052** -0.006
(0.036) (0.019) (0.015) (0.013) (0.023) (0.015)
Mineral rents -0.179*** -0.099*** -0.079*** -0.076*** -0.071*** -0.109***
(0.023) (0.016) (0.014) (0.013) (0.018) (0.011)
Resource fund 0.330** -0.178* -0.277*** -0.340*** -0.485*** -0.175**
(0.144) (0.104) (0.087) (0.080) (0.102) (0.079)
Constant -3.311*** -1.557*** -1.091*** -0.788*** -0.300 -1.979
(0.543) (0.451) (0.251) (0.319) (0.386) (0.256)
Pseudo R-squared 0.42 0.43 0.43 0.42 0.37 0.61
Observations 1439 1439 1439 1439 1439 1439
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40
Table 7: Private Credit and Resource Fund using Quantile Regression (resource rich sample)
10th quant. 30th quant. 50th quant. 70th quant. 90th quant. OLS
Inflation -0.038 0.014 0.03 -0.022 -0.094* -0.007
(0.038) (0.03) (0.041) (0.041) (0.052) (0.03)
GDP per capita 0.381*** 0.409*** 0.357*** 0.32*** 0.276*** 0.316***
(0.056) (0.04) (0.031) (0.046) (0.053) (0.035)
Govt. consumption 0.67** 0.76*** 0.646*** 0.378*** 0.346** 0.821***
(0.269) (0.177) (0.174) (0.143) (0.171) (0.13)
Foreign Direct Investment 0.08* 0.062** 0.055** 0.032 0.05** 0.067***
(0.043) (0.026) (0.026) (0.02) (0.021) (0.021)
Trade to GDP 0.000*** -0.059*** -0.06*** -0.018 -0.029 -0.112**
(0.081) (0.071) (0.066) (0.048) (0.055) (0.047)
Population density 0.217*** 0.121*** 0.148*** 0.027 0.032 0.123***
(0.047) (0.039) (0.036) (0.034) (0.032) (0.026)
Mineral rents -0.349*** -0.271*** -0.186** -0.18* -0.261*** -0.227***
(0.046) (0.042) (0.077) (0.104) (0.071) (0.054)
Resource fund 0.122 0.033 -0.02 -0.235* -0.36** -0.113
(0.095) (0.085) (0.138) (0.142) (0.147) (0.097)
Constant -0.691*** 0.98*** 1.637*** 2.256 0.821 -2.17***
(0.362) (0.256) (0.24) (0.203) (0.384) (0.539)
Pseudo R-squared 0.29 0.34 0.32 0.28 0.29 0.44
Observations 354 354 354 354 354 354
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41
Table 8: Liquid Liability and Resource Fund using Quantile Regression (global sample)
10th quant. 30th quant. 50th quant. 70th quant. 90th quant. OLS
Inflation -0.055*** -0.071*** -0.117*** -0.159*** -0.127*** -0.100***
(0.017) (0.018) (0.022) (0.022) (0.019) (0.009)
GDP per capita 0.116*** 0.172*** 0.141*** 0.116*** 0.09*** 0.116***
(0.025) (0.013) (0.022) (0.019) (0.017) (0.009)
Govt. consumption 0.434*** 0.173*** 0.12*** 0.122*** 0.387*** 0.364***
(0.069) (0.044) (0.067) (0.052) (0.078) (0.026)
Foreign Direct Investment 0.06*** 0.022*** 0.023*** 0.023*** 0.058*** 0.037***
(0.017) (0.009) (0.01) (0.009) (0.011) (0.006)
Trade to GDP 0.11*** 0.058*** 0.034*** 0.017*** 0.081*** 0.057***
(0.015) (0.011) (0.012) (0.01) (0.049) (0.008)
Population density 0.055*** 0.046*** 0.075*** 0.059*** 0.125*** 0.064***
(0.027) (0.016) (0.012) (0.009) (0.016) (0.006)
Mineral rents -0.099*** -0.066*** -0.056*** -0.057*** -0.036*** -0.052***
(0.011) (0.009) (0.012) (0.01) (0.012) (0.004)
Resource fund 0.12*** -0.198*** -0.253*** -0.293*** -0.438*** -0.222***
(0.06) (0.053) (0.073) (0.062) (0.07) (0.024)
Constant -0.691*** 0.98*** 1.637*** 2.256*** 0.821*** 0.825***
(0.362) (0.256) (0.24) (0.203) (0.384) (0.133)
Pseudo R-squared 0.31 0.35 0.33 0.31 0.28 0.49
Observations 1456 1456 1456 1456 1456 1949
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42
Table 9: Liquid Liability and Resource Fund using Quantile Regression (resource rich sample)
10th quant. 30th quant. 50th quant. 70th quant. 90th quant. OLS
Inflation -0.044** -0.028 -0.059 -0.139*** -0.007 -0.06***
(0.022) (0.035) (0.038) (0.047) (0.053) (0.019)
GDP per capita 0.243*** 0.189*** 0.165*** 0.101*** -0.016 0.155***
(0.022) (0.023) (0.024) (0.032) (0.108) (0.023)
Govt. consumption 0.719*** 0.731*** 0.494*** 0.377** 0.218 0.528***
(0.133) (0.116) (0.105) (0.152) (0.176) (0.088)
Foreign Direct Investment -0.008 -0.006 -0.013 -0.001 0.026 0.004***
(0.019) (0.016) (0.015) (0.021) (0.023) (0.013)
Trade to GDP -0.207*** -0.224*** -0.166*** -0.149*** -0.105** -0.166***
(0.048) (0.044) (0.032) (0.041) (0.053) (0.031)
Population density 0.168*** 0.169*** 0.098*** 0.089** 0.182*** 0.136***
(0.025) (0.028) (0.025) (0.035) (0.032) (0.017)
Mineral rents -0.113*** -0.05 0.038 -0.001 0.003 -0.053***
(0.034) (0.04) (0.043) (0.072) (0.067) (0.034)
Resource fund -0.049 -0.226*** -0.367*** -0.377*** -0.069 -0.323***
(0.062) (0.08) (0.088) (0.103) (0.264) (0.062)
Constant 0.129 0.633 1.629*** 2.682*** 3.022*** 1.418***
(0.525) (0.5) (0.431) (0.719) (0.9) (0.349)
Pseudo R-squared 0.32 0.32 0.29 0.21 0.20 0.40
Observations 349 349 349 349 349 349
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Table 10: Market Capitalization and Resource Fund using Quantile Regression (global sample)
10th quant. 30th quant. 50th quant. 70th quant. 90th quant. OLS
Inflation -0.371*** -0.295*** -0.254*** -0.273*** -0.215*** 0.046
(0.058) (0.034) (0.028) (0.031) (0.039) (0.032)
GDP per capita 0.281*** 0.281*** 0.27*** 0.303*** 0.191*** 0.165***
(0.067) (0.038) (0.047) (0.029) (0.042) (0.039)
Govt. consumption -0.453* -0.18 -0.125 -0.056 -0.043 0.58***
(0.245) (0.144) (0.122) (0.109) (0.197) (0.124)
Foreign Direct Investment 0.233*** 0.151*** 0.151*** 0.099*** 0.078*** 0.293***
(0.044) (0.024) (0.025) (0.015) (0.029) (0.02)
Trade to GDP 0.284*** 0.173*** 0.159*** 0.101*** 0.202*** 0.266***
(0.048) (0.032) (0.028) (0.015) (0.108) (0.028)
Population density 0.179*** 0.097*** 0.106*** 0.128*** 0.04*** 0.057**
(0.061) (0.03) (0.028) (0.015) (0.038) (0.028)
Mineral rents 0.028 0.027 0.051*** 0.082*** 0.059** -0.068***
(0.066) (0.021) (0.016) (0.013) (0.026) (0.023)
Resource fund 0.578*** 0.219** 0.065 -0.16** -0.231* 0.001
(0.201) (0.105) (0.111) (0.076) (0.141) (0.127)
Constant -5.503*** -3.039*** -2.613*** -1.418*** 0.389 -7.531***
(1.073) (0.711) (0.586) (0.427) (1.07) (0.573)
Pseudo R-squared 0.33 0.29 0.27 0.25 0.15 0.38
Observations 1219 1219 1219 1219 1219 1200
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Table 11: Market Capitalization and Resource Fund using Quantile Regression (resource rich sample)
10th quant. 30th quant. 50th quant. 70th quant. 90th quant. OLS
Inflation -0.389** -0.237*** -0.208*** -0.232*** -0.203*** -0.418***
(0.171) (0.077) (0.051) (0.045) (0.05) (0.108)
GDP per capita 0.243** 0.276*** 0.314*** 0.313*** 0.268** 0.432***
(0.102) (0.045) (0.057) (0.091) (0.126) (0.166)
Govt. consumption 0.217 0.254 0.442*** 0.312* 0.343** -0.027
(0.397) (0.167) (0.168) (0.19) (0.134) (0.28)
Foreign Direct Investment 0.229*** 0.162*** 0.199*** 0.161*** 0.195*** 0.168**
(0.074) (0.054) (0.041) (0.031) (0.027) (0.068)
Trade to GDP 0.197 0.11 0.075 0.067 0.071 -0.069
(0.139) (0.08) (0.063) (0.064) (0.053) (0.287)
Population density 0.29*** 0.236*** 0.242*** 0.223*** 0.332*** 0.529
(0.052) (0.062) (0.033) (0.041) (0.069) (0.569)
Mineral rents 0.249 0.144 -0.053 0.025 0.09* 0.452***
(0.194) (0.115) (0.098) (0.059) (0.055) (0.174)
Resource fund 0.182 0.086 -0.062 -0.092 0.004 -0.2*
(0.328) (0.136) (0.132) (0.146) (0.237) (0.114)
Constant -7.089*** -4.876*** -5.301*** -4.002*** -4.69*** -5.763**
(1.695) (1.281) (0.984) (0.787) (0.78) (2.363)
Pseudo R-squared 0.43 0.45 0.46 0.48 0.47 0.38
Observations 244 244 244 244 244 1200
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In summary, results from the Pooled OLS and Quantile Regression models
suggest that establishment of resource funds do not help resource rich countries
improve their financial system. These results are somewhat contradictory with
(S. Tsani 2012) in that resource funds may be associated with better institutional
quality, which in turn promotes financial development. These results might
associate with the common characteristic that many of the countries with
resource funds are all struggling with poor economic development and low
financial sector development. On the other hand, I suspect that the results could
be a potential ―correlation rather than causation‖ issue, but for this exact reason
two sets of samples are used for all models while controlling for three separate
resource endowment variables and several financial sector development
measures based on existing literature. Results from the Pooled OLS model are
further checked by an additional Quantile regression model as an alternative.
Despite all these measures, the results are consistent across the board. From a
methodological point of view, one potential explanation for negative relationship
between resource funds and financial development might be using of dummy
variable for resource funds. This is a common issue for all literary work on
resource funds as there is simply lack of reliable data for resource funds.
b. Barro-regression
In order to validate the use of financial development as an indicator for
growth, the supplementary Barro-regression is employed to answer the question
of ―Does financial development positively influence long term economic growth in
resource rich countries?‖ Table 12 Columns 1 – 4 show results of the Barro-
regression on the global sample of 83 countries in which how financial
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46
development and economic growth interact when controlled for resource
dependence. The global sample helps us to distinguish the characteristics of
resource rich countries compared to their global mean.
There is strong and positive relationship between financial development and
long term economic growth as indicated by the positive and significant
association between Private Credit to GDP and GDP growth. Interesting
observation is made Column 2 when the control variable for resource dependence
(Private credit * Mineral exports) enters the equation with significant but
considerably low value of -0.004. This confirms the findings of (T. Beck 2010)
that natural resource endowment does not affect the positive correlation between
financial development and economic growth.
Table 12: Financial Development and Economic Growth controlling for resource
dependence
Explanatory
variables
GDP per capita growth
(Global data)
GDP per capita growth
(Resource rich countries)
1 2 3 4 5 6 7 8
Initial GDP -0.403*** -0.401*** -0.365*** -0.373*** -0.568** -0.721** -0.569** -0.569***
Private Credit 0.305*** 0.468*** -0.172 -0.195 -0.103***
Inflation -0.001*** -0.001*** -0.001*** -0.001*** -0.111*** -0.634** -0.103** -1.535*
Gov
Consumption -0.978*** -0.986*** -1.062*** -1.034*** -1.742** -2.013*** -1.535* 1.821***
Trade 1.303*** 1.305*** 1.319*** 1.323*** 1.913*** 1.939 1.821*** -0.439
Years of
schooling 3.278*** 2.849*** 3.021*** 2.968*** 4.691*** 4.076*** 4.302*** 4.721***
Mineral exports 0.110* 0.257*** 0.114* 0.467 0.269** 0.275 -0.439
Private credit *
Mineral exports -0.004*** -0.023
Liquid Liability 0.416*** 0.507*** -0.240 -0.490
Liquid Liability
* Mineral
exports
-0.099 0.204
Constant 1.203 0.710 0.562 0.218 3.073* 6.196** 4.161* 4.161
Observations 2031 2031 2031 2053 506 475 501 501
R-squared 0.32 0.32 0.32 0.32 0.53 0.48 0.53 0.53
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Column 3, further supplements the argument using an alternative
measurement for financial development - Liquid Liabilities to GDP which also
shows significant and positive relationship between financial development and
economic growth. In Column 4, when controlling for resource endowment with
Liquid Liabilities to GDP * Ore to export the control variable enters the equation
insignificantly. This further confirms that financial development is important for
long-term growth regardless of natural resource dependence. As for the other
variables, inflation is negatively correlated with less significance although with
high p-value. This is consistent with literary findings on the negative
relationship between inflation and long term economic growth, despite the effect
being seemingly insignificant (R. Barro 2013). Total volume of exports and
imports to GDP (%) has the highest correlation to GDP per capita growth. This
association supplements numerous literary evidence between the two factors
including a recent study by (Matthias Busse 2012) as it concludes that trade to
GDP has positive and highly significant impact on economic growth. Another
important observation is the insignificant relationship between growth and
resource dependence. In comparison to (T. Beck 2010), results presented here are
particularly weaker which is probably due to the different set of samples used in
this paper.
Table 12 Columns from 4 to 8 show the same analysis for the resource rich
sample of the 27 countries. The results from the resource rich sample shows
significantly different results from the global sample. For instance, financial
development and economic development do not have significant relationship as
both private credit and liquid liability do not enter the equation significantly
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with negative signs. Given the exact same attributes for the regression but the
only difference being in the samples, these results are rather odd as financial
development and economic growth showed positive association regardless of
resource endowment in the previous model. However, the results are somewhat
consistent with (Frederick van der Ploeg 2007) in their findings of negative
correlation between financial development and resource curse. Although I am
not necessarily assuming all 27 countries in the resource rich sample are
―resource cursed‖, majority of them are low economic performers despite having
large resource endowments. In addition, these findings contradict with the
conclusions of (T. Beck 2010) that financial development and economic growth
are positively correlated regardless of resource wealth. Given the regression is a
Pooled OLS, there could be potential heterogeneity issue within the model, but
given Barro-regression explains growth as a function of initial income (i.e.
constant initial GDP) it is not possible to run regression with fixed effects.
V. CONCLUSION AND POLICY IMPLICATIONS
This paper supplements the existing literary evidences on the implications of
establishing natural resource based sovereign wealth funds for the purposes of
resolving issues related to ―resource curse‖ – a common economic paradox. It is
done so by exploring the association between resource funds and financial
development by employing Pooled OLS and Quantile Regression methods. The
additional quantile regression model is aimed at validating and further
explaining the results of the Pooled OLS model as well as studying the
interactions of the independent and dependent variables at various
measurement levels. Another motivation to use Quantile Regression is due to
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49
limitations of the dummy variable used for resource funds, such as restrictions
for using hierarchical models (e.g. Fixed-Effect model) for individual effects. For
robustness check, White cross-section and Panel-corrected standard error
techniques are applied for the Pooled OLS model. In order to validate the use of
financial development as measurement factor for economic growth, a separate
analysis on the association between financial development and economic growth
is conducted by employing Barro-style growth regressions. All models are applied
to two sets of samples of time-series cross-sectional data with one consisting of
27 resource rich countries and the other 83 countries for the purposes of avoiding
sampling bias and to observe the implications of resource endowment.
Two main conclusions can be drawn from the results of the models employed
in this paper. First, the Barro-regression results show that financial sector
development is an important factor for long term economic growth in any
economy regardless of natural resource endowment and natural resource
dependence. Second, and the main findings of this paper is that the Pooled OLS
and Quantile Regression results indicate significant negative association
between natural resource funds and financial development. Several explanations
for this relationship can be observed from literature do date. For instance,
existence of resource funds may lead to high concentration of resource revenues
under government possession that results in exacerbate rent-seeking behaviours
among the political elite. It is almost as if the resource fund may function as a
platform for such rent seeking behaviours as strengthened by lack of
transparency and corruption. However, from a methodological point of view,
these results should be treated with caution. Given the apparent lack of quality
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50
data on resource funds around the world, especially on their investment
management practices, it is challenging to conduct reliable analysis. Based on
the observations made in this paper, following policy measures might benefit
resources funds and governments that employ them:
a) For certain countries that have relatively low governance and
institutional quality, it may be effective to channel resource revenues for
direct policy measures for sustainable development instead of
concentrating in one location. This may prevent resource funds to become
a potential platform for rent-seeking behaviours among policy makers.
b) Major obst
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