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

2

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

3

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

5

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

6

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

8

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

9

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.

10

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.

11

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.

12

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

13

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

14

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

15

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

16

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.

17

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

18

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)

19

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)

20

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).

21

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)

22

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.

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

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,

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

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)

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)

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.

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

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.

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

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

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

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 (

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

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

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.

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.

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

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

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

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

43

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

44

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

45

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

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

47

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

48

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

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

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