financial development and economic growth: cross-country...

Financial development and economic

growth: cross-country comparisons.

Paper within MASTER THESIS IN ECONOMICS

Author: KRASULINA NATALIA

Tutor: AGOSTINO MANDUCHI

VIROJ JIENWATCHARAMONGKHOL

Jönköping 05/2012

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ABSRACT

This study attempts to investigate the relationship between financial development and eco-

nomic growth and also the empirical analysis examines Granger causality of this relationship.

Time series models are applied for six countries with emerging markets and different types of

financial system (Saudi Arabia, Kuwait, Tunisia, Morocco, Israel and Egypt). For the pair-

wise combinations of financial development indicators and economic growth which do not

have cointegrating relationships, Granger causality is applied within the vector autoregressive

(VAR) model. When the variables have cointegrating relationship, Granger causality test is

applied using the vector error correction model (VECM). The empirical results in the study

case suggest that financial structure in some degree can explain economic growth indicator.

Moreover the test results show weak dependence between financial development and econom-

ic growth. The Granger causality test indicates unidirectional Granger causality running from

financial development and economic growth, reverse relationship and bidirectional Granger

causality.

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Table of Contents

1 INTRODUCTION ......................................................................... 1

2 BACKGROUND .......................................................................... 3 2.1 Theoretical framework ........................................................................... 3 2.2 Empirical evidence ................................................................................. 4 2.2.1 The role of financial structure ................................................................. 4 2.2.2 Financial development and economic growth ........................................ 5

3 DATA SPECIFICATION AND METHODOLOGICAL ISSUES ... 7 3.1 Country Selection ................................................................................... 7 3.2 Indicators of financial development and economic growth ..................... 8 3.3 Methodology ........................................................................................ 11

4 EMPIRICAL RESULTS ............................................................. 14 4.1 The results of the preliminary steps ..................................................... 14 4.2 Granger causality test for non-cointegrated variables .......................... 15 4.3 Granger causality test for cointegrated variables ................................. 18

5 CONCLUDING REMARKS ....................................................... 21

REFERENCES .............................................................................. 23

APPENDIX ..................................................................................... 26

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1 INTRODUCTION

Economic growth is a positive change in the level of production of goods and services of a

country over a certain time period. Generally accepted economics suggest that growth in the

quantities available of factors of production such as labor, capital and land are the main de-

terminants of growth. In the course of time some economists include also the financial devel-

opment as a factor of economic growth.

The relationship between financial development and economic growth has been the subject of

increasing attention over the 20th

century. There are still old disputes concerning the direction

of causality between financial development and economic growth, the power of influence and

the way of financial factors’ impact.

Another issue of this topic is: does the type of financial system matter for economic growth?

In the economic literature there is no single answer to the question of what is better for eco-

nomic growth: the banks, the stock market or neither. As it is well known, all the financial

systems of all countries depend on the predominant mechanism of mobilizing resources for

investment. Due to this the financial system can be divided into two main categories: 1) the

bank-based financial system and 2) the market-based financial system.

In the bank-based financial system banks play a significant role in firms’ financing, allocating

resources, mobilizing savings. The process of investment and allocation of resources occurs

through the bank loans, which are a major share of external financing of firms. According to

the market-based financial system, financial markets such as securities market, stock market

and etc., play an important role in providing financial services. In this case firms rely primari-

ly on the stock markets by issuing shares and bonds in free circulation.

Recently the study of the financial development as an engine of economic growth and investi-

gation of importance of financial systems has been considered by a number of articles. Even

though there are not plenty of studies that examine the financial structure by using time series

technique. Moreover, countries that are considered in this work have not been tested from this

point of view.

The countries I choose are Saudi Arabia, Kuwait, Tunisia, Morocco, Israel and Egypt. The se-

lection of these countries was based on an analysis that has been made concerning two indi-

ces. Furthermore, in this study case Saudi Arabia and Kuwait are taken as countries with a

market-based financial system, Tunisia and Morocco – a bank-based financial system, Israel

and Egypt – a diversified financial system.

The purpose of this work is to investigate the relationship between economic growth and fi-

nancial development and if there is any relationship, it is relevant to check for the existence of

Granger causality.

So the problems that will be examined are the following:

Is there any existent relationship between financial development and economic growth in the chosen countries?

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Is there any Granger causal relationship between indicators of financial devel-opment and economic growth?

Do different financial systems have a different impact on economic growth?

The rest of the work is organized as follows. The background of the topic is presented in sec-

tion 2, both theoretical and empirical evidences. Section 3 defines the proxies of financial de-

velopment and economic growth, discusses the econometric specification and indicates the

country selection for the time series analysis. The main empirical results of the research are

presented in section 4. Finally, section 5 describes the conclusion that can be drawn.

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2 BACKGROUND

This section provides some literature reviews of two main issues: 1) analysing different forms

of financial systems; 2) investigations concerning the relationship between financial devel-

opment and economic growth. There is a set of theoretical and empirical study cases of the

relative merits of market-based and bank-based financial systems. Allen and Gale (2000) pro-

vide a broad spectrum of literature review pertinent to this topic. In regard to relationship be-

tween financial development and economic growth plenty of studies both theoretical and em-

pirical have been done starting by Schumpeter (1911), Gurley and Shaw (1955, 1960).

2.1 Theoretical framework

Some economists argue that the banking system finances industrial development more

effectively than market system (Gerschenkron (1962), Rajan and Zingales (1998), Stulz

(2000)). On the contrary, Levine and Zervos (1998) argue that stock markets have a positive

role in allocating resources, corporate governance, strengthening risk management and etc.

They assumed that more market-based financial system provides basic financial services that

stimulate long-run economic growth.

Another conventional and basic issue of this topic is the causal relationship between financial

development and economic growth. Earlier study by Bagehot (1873) argues the financial

system played an important role in the conception of industrialization in England by

promoting capital formation for the “immense work”. Many others studies indicate that

financial development is correlated with current and future economic growth, physical capital

accumulation and economic efficiency. In this case the financial development may cause

economic growth.

On the contrary, authors such as Robinson (1952) and Kuznets (1955) argue that economic

growth causes financial development and financial development simply follows economic

growth.

Patrick (1966) introduced the idea of the bi-directional relationship between financial

development and economic growth, suggested “supply-leading” and “demand-following”

patterns. In the “supply-leading” role, the financial development causes economic growth.

Patrick describes the functions of this phenomenon as follows: “to transfer resources from

traditional (non-growth) sector to modern sectors, and to promote and stimulate an

entrepreneurial response in these modern sectors”. In other words, financial intermediation

allocates resources to more productive sectors.

In the “demand following” role economic growth causes financial development. According to

Patrick, “the creation of modern financial institutions, their financial assets and liabilities, and

related financial services is in response to the demand for these services by investors and

savers in the real economy”. The increasing demand for financial services might lead to the

expansion of the financial system as the real sector of the economy.

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2.2 Empirical evidence

The main approaches to testing the correlation between financial development and economic

growth are:

1) cross-section or panel data techniques for testing the group of countries;

2) industry-level or firm-level evidence;

3) time series techniques for testing the hypothesis for a particular country.

We will highlight just more relevant studies according to stated questions

2.2.1 The role of financial structure

In this subsection the studies which investigate the relationship between different financial

structures and economic growth are described. Some works conclude that there is no connec-

tion between the bank-based or market-based financial systems with economic growth.

For example, Levine (2002) examines which financial systems are better in supporting eco-

nomic growth by using cross-country technique. The empirical findings present that “although

overall financial development is robustly linked with economic growth, there is no support for

either the bank-based or market-based view”.

The work by Beck and Levine (2002) can be presented as another example. They analyze

whether market-based or bank-based financial systems have impact on improving the effi-

ciency of capital allocation across industries and influence industries’ expansion. As opposed

to the previous study the industry level analysis is applied for 42 countries and 36 industries.

The empirical results show that neither the market-based nor the bank-based financial system

can explain industrial growth or the efficiency of capital allocation.

On the contrary, the studies based on the time series technique indicate that different types of

financial structure promote economic growth. Arestis, Luintel D., and Luintel B. (2005) ana-

lyze whether financial structure influences economic growth. In the work three views of fi-

nancial system is highlighted: the bank-based, the market-based and the financial services

view. The empirical issue is tested by time series data and Dynamic Heterogeneous Panel ap-

proach on developing countries. The results indicate “significant cross-country heterogeneity

in the dynamics of financial structure and economic growth”. The time series results present

that financial structure significantly explains economic growth.

Lee (2012) reexamines the relationship between financial structure and economic growth. By

testing the different countries that were not tested in previous analyzed work he found that

different financial systems promote long run economic growth. Except of one country, all

others show that the financial development Granger causes economic growth.

The preliminary study about different types of financial systems can help with understanding

this issue, the work by Demitguc-Kunt and Levine (1999) can be highlighted. They analyze

advantages and disadvantages of bank-based and market-based financial systems. They com-

pare German and Japan as bank-based countries and England and the United States as market-

based financial systems. They found that “bank, nonbanks, and stock markets are larger, more

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active, and more efficient in richer countries. In higher income countries, stock markets be-

come more active and efficient relative to banks”.

2.2.2 Financial development and economic growth

Most studies conclude that there is a positive relationship between financial development in-

dicators and economic growth. For example, Goldsmith (1969) and King and Levine (1993)

empirically examine the relationship between financial development and economic growth for

a set of countries. The difference between these two works is that different proxies of finan-

cial development and different sets of countries are used. Nevertheless their empirical results

indicate a strong positive relationship between financial development indicators and economic

growth indicators.

Levine and Zervos (1998) examine whether stock market and banking development correlated

with current and future rates of economic growth. The empirical findings suggest that the lev-

el of stock market liquidity and banking development are positively and significantly correlat-

ed with rates of economic growth, capital accumulation, and productivity growth. Lately Lev-

ine, Loyaza and Beck (2000) investigate whether the exogenous component of financial in-

termediary development influences economic growth.

At the industry and firm levels studies investigate the performance of financial sector and in-

dustrial or firm growth. The positive unidirectional causality running from financial develop-

ment to industrial growth has been found. For example, Rajan and Zingales (1998) examines

whether financial-sector development has an influence on industrial growth. Demirguc-Kunt

and Maksimovic (1998) examine whether the financial development influences firms’ deci-

sion of investing in potentially profitable growth opportunities. In this study they also “focus

on the use of long-term debt or external equity to fund growth”.

Within time series technique, Acaravci at al (2007) test the causal relationship between two

proxies of financial development and economic growth. The empirical findings indicate that

there is no long-run relationship between financial indicators and economic growth. Moreo-

ver, it should be indicated that the results show a one-way causal relationship from the finan-

cial development to economic growth. The same methodology will be used in this paper.

On the contrary, some studies indicate not only causality running from financial development

to economic growth, but also the reverse and bidirectional causality. The main prove of this

fact is the work by Demetriades and Hussein (1996). They examine 16 countries by using

time series technique.

Abu-Bader and Abu-Qarn (2008) investigate the causal relationship between financial devel-

opment and economic growth for six Middle Eastern and North African countries (Algeria,

Egypt, Morocco, Israel, Tunisia and Syria). They applied quadvariate vector autoregressive

framework and Granger causality test. The empirical findings show strong causal relationship

from financial development and economic growth. But in case of Israel the results imply

“weak support for causality running from economic growth to financial development but no

causality in the other direction”.

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Within the time series technique some studies show only Granger causality running from eco-

nomic growth to the development of financial intermediaries. For example Guryay, Safakli

and Tuzel (2007) in their work made the same conclusion by examining this relationship in

Northern Cyprus.

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3 DATA SPECIFICATION AND METHODOLOGICAL ISSUES

3.1 Country Selection

Financial systems vary across different countries. Most countries have banking system and fi-

nancial markets, but in different countries these financial institutions play different roles.

Some countries have the market based financial system; others have the financial system that

is oriented to the banking institutions. The country selection in this research will be based on

different forms of financial system.

There are no generally adopted rules for defining the bank based and the market based finan-

cial system. In this case it is necessary to provide measures, which can partly show the form

of the financial system. Based on Levine (2002) we can compute new rankings by providing

“structure-activity” and “structure-size” indices for 50 countries over the 1995-2009 period

(15 years changes). The country choice is based on data availability.

“Structure-activity” shows “the activity of stock markets relative to that of banks”. For meas-

uring the activity of stock markets the ratio of total value stock traded are used which equals

the total value of shares traded during the period divided by GDP. This ratio indicates market

liquidity by the reason of measuring trading in the market relative to economic activity. As

indicator of banks activity, the bank credit ratio can be used. This measure equals the value of

domestic credit provided by banking sector as a share of GDP. To calculate “structure-

activity” index it is necessary to take the natural logarithm of the total value stocks traded to

GDP divided by domestic credit provided by banking sector to GDP.

“Structure-activity” index = ln ( 𝑠𝑡𝑜𝑐𝑘 𝑡𝑟𝑎𝑑𝑒𝑑 ,𝑡𝑜𝑡𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 (𝑎𝑠 𝑠𝑎𝑟𝑒 𝑜𝑓 𝐺𝐷𝑃)

𝐵𝑎𝑛𝑘 𝑐𝑟𝑒𝑑𝑖𝑡 (𝑎𝑠 𝑠𝑎𝑟𝑒 𝑜𝑓 𝐺𝐷𝑃))

Higher values of this index imply a more market based financial system. The values are

ranked and presented in table 1 (see Appendix).

“Structure-size” index indicates the size of performance of stock markets relative to that of

banks. The market capitalization ratio indicates the size of domestic stock market. As in the

previous case to measure the size of banking system the bank credit ratio is used. “Structure-

size” index equals the natural logarithm of the market capitalization to GDP divided by do-

mestic credit provided by banking sector to GDP.

“Strucure-size” index = ln( 𝑀𝑎𝑟𝑘𝑒𝑡 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 (𝑎𝑠 % 𝑜𝑓 𝐺𝐷𝑃)

𝐵𝑎𝑛𝑘 𝑐𝑟𝑒𝑑𝑖𝑡 ( 𝑎𝑠 𝑠𝑎𝑟𝑒 𝑜𝑓 𝐺𝐷𝑃))

The same logic can be applied for this index, the greater value the more market based system.

These indices should be very carefully interpreted by the reason of some abnormal results.

The values are ranked and presented in table 1 (see Appendix).

According to calculated rankings, it can be assumed that Saudi Arabia, Singapore, Finland,

Switzerland, Kuwait have more market based financial system. These countries are on top of

both rankings. On the contrary, Tunisia, Cyprus, Sri Lanka, Slovenia and Morocco are situat-

ed below the mean of rankings. By this reason, we can argue that these countries have more

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bank-based financial systems. Moreover, countries as Israel, Egypt, Indonesia, and Poland in

the ranking are close to the mean, so these countries have more diversified financial systems.

Based on conclusions above we can choose countries according to the different financial sys-

tems and data availability. The six countries with emerging markets are applied in current

analysis: 1) market-oriented financial system (Saudi Arabia, Kuwait); 2) bank-based financial

system (Tunisia, Morocco); 3) diversified financial system (Israel, Egypt). To sum up, the

Table 2 below observes the list of chosen countries with corresponding time period and num-

ber of observations.

According chosen countries we can observe some similarities and differencies. It should be

mentioned that all countries except Israel have Islamic Banking system. This type of banking

is based on Islamic Law (Sharia ) that prohibits a set of banking operation such as: the fixed

of floating payment, acceptance of interest of futures and forwards contracts. Another princi-

ple of following Islamic Banking is that it is not allowed to invest in businesses that are di-

verged according Islamic law. Such areas can be alcohol or drug production and etc. 1

Table 2 The list of countries

Country Period Observations

Saudi Arabia 1969-2010 42

Kuwait 1963-2009 47

Tunisia 1962-2010 49

Morocco 1961-2010 50

Israel 1961-2009 49

Egypt 1961-2010 50

Source: Author’s calculations

3.2 Indicators of financial development and economic growth

One of the most important issues in evaluating the relationship between financial develop-

ment and economic growth is how to obtain the proper measure of financial development. Re-

searchers and economists select different proxies for financial development. For example,

King and Levine (1993) described four proxies of financial development: 1) liquid liabilities

of financial system to GDP, 2) the ratio of deposit money bank domestic assets to deposit

money bank domestic assets plus central bank domestic assets, 3) the ratio of claims on the

nonfinancial private sector to total domestic credit and 4) the ratio of claims on the nonfinan-

cial private sector to GDP.

1 Rammal, H. G. and Zurbruegg, R. (2007). Awareness of Islamic Banking Products Among Muslims: The Case of

Australia. Journal of Financial Services Marketing, 12(1), 65-74.

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Moreover, Levine, Loayza and Beck (2000) used as indicators of financial development three

measurements: 1) the same as King and Levine (1993) - liquid liabilities of the financial sys-

tem; 2) the ratio of commercial bank assets divided by commercial bank plus central bank as-

sets; 3) the ratio of credits by financial intermediaries to the private sector as a share of GDP.

On the contrary, Fink, Haiss and Vuksic (2004) used not only the set of proxies of financial

development but also control variables, such as real growth of capital stock per capita, change

of labor participation rate, educational attainment. As financial intermediation variables they

used domestic credit, private credit, stock market capitalization, bonds outstanding and also

two aggregate indicators. Thereby they describe not only the banking sector, but also the

stock and bond markets.

The first proxy of financial development that is used in the analysis is a ratio of broad meas-

ure of money stock, usually M2, to the level of nominal income. ( King and Levine (1993),

Levine and Zervos (1998), Unalmis (2002), Par and Pentecost (2000)). The formula of this

indicator is the following:

Proxy 1 = 𝐵𝑟𝑜𝑎𝑑 𝑚𝑜𝑛𝑒𝑦 𝑠𝑢𝑝𝑝𝑙𝑦 (𝑀2)

𝐺𝐷𝑃∗100

The World Bank defines M2 as “the sum of currency outside banks, demand deposits other

than those of the central government, and the time, savings, and foreign currency deposits of

resident sectors other than the central government”. This indicator reflects the “financial

depth” and shows the degree of monetization. The advantage of this measure is that you can

evaluate the size of the financial sector relative to the economic activity in which money pro-

vides payment and saving services. As noticed by Levine and Zervos (1998), “this type of fi-

nancial depth indicator does not measure whether the liabilities are those of banks, the central

bank, or other financial intermediaries, nor does this financial depth measure indentify where

the financial system allocates capital”.

The next variable that will be used in this study is the ratio of banking sector credit as a share

of GDP. The formula of this indicator is the following

Proxy 2 = 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑐𝑟𝑒𝑑𝑖𝑡 𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑑 𝑏𝑦 𝑏𝑎𝑛𝑘𝑖𝑛𝑔 𝑠𝑒𝑐𝑡𝑜𝑟

𝐺𝐷𝑃∗ 100

This variable reflects all credits to various sectors on a gross basic. It also includes the credit

of monetary authorities, deposit money banks and also other banking institutions, such as loan

and building associations and also savings and mortgage loan institutions2. I can conclude that

this measure constitutes most part of the total domestic credit. By using this measure we can

estimate the banking sector activity, size and performance.

The private sector credit ratio can be applied as another proxy of financial intermediation.

This indicator can be measured with the help of the following formula:

Proxy 3 = 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑐𝑟𝑒𝑑𝑖𝑡 𝑡𝑜 𝑝𝑟𝑖𝑣𝑎𝑡𝑒 𝑠𝑒𝑐𝑡𝑜𝑟

𝐺𝐷𝑃∗ 100

2 The World Bank definitions

10

This ratio reflects the financial resources provided to private sector such as loans, trade credits

and etc. It is assumed that this ratio generates increases in investment to much larger extent

than credits to public sector. Also I can conclude that loans to the private sector are improving

the quality of investment as soon as financial intermediaries’ more stringently evaluation of

project viability.

Another proxy of financial development that can be used is the ratio of private sector credit to

domestic credit. The formula of this indicator is the following:

Proxy 4 = 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑐𝑟𝑒𝑑𝑖𝑡 𝑡𝑜 𝑝𝑟𝑖𝑣𝑎𝑡𝑒 𝑠𝑒𝑐𝑡𝑜𝑟

𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑐𝑟𝑒𝑑𝑖𝑡 𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑑 𝑏𝑦 𝑏𝑎𝑛𝑘𝑖𝑛𝑔 𝑠𝑒𝑐𝑡𝑜𝑟∗ 100

This indicator reflects the domestic assets distribution of an economy and also it computes the

proportion of credit allocated to private enterprises by the financial system. By using this ratio

it can be concluded if the financial intermediations can satisfied the private sector’s claims or

not.

The ratio of private sector credit to domestic credit and the private sector credit ratio still have

some problems. Both indicators do not indicate the degree of public sector borrowing; they

just reflect the private sector’s demand. In spite of the criticism we can assume that this num-

ber of financial indicators can be used to maximize the information of financial development.

In the case of the indicator for economic growth, there are some common proxies that have

been used, For example, King and Levine (1993) apply four indicators for economic growth:

“real per capita GDP, the rate of physical capital accumulation, the ratio of domestic invest-

ment to GDP, a residual measure of improvements in the efficiency of physical capital alloca-

tion”. Demetriades and Hussein (1996) use real GDP per capita as an indicator of economic

development, but they measure this variable in domestic currency. The analyses of Kar and

Pentecost (2000) and Unalmis (2002) were based on Gross National Product (GNP) at current

prices as proxy for economic growth.

In our study I will use real GDP per capita on U.S. dollars. The definition of The World Data

is: “GDP is the sum of gross value added by all resident producers in the economy plus any

product taxes and minus any subsidies not included in the value of the products divided by

midyear population”. This measure evaluates the activity of an economy, and by using this

indicator for all the chosen countries on the same currency (U.S. dollars) we can properly

compare the results. Another advantage of this proxy is that the population differences are al-

so included in this indicator, so the correct estimations can be computed. But GDP per capita

does not reflect the distribution of the resources of the economy.

All the data is taken from the World DataBank, World Development Indicators & Global De-

velopment Finance. Table 3 indicates the summarizing information about the data and also

provides the notation for the present analysis.

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Table 3 Variables’ notations.

Indicator Notation

GDP per capita Y

Broad money supply ratio M2

Banking credit ratio BC

Private credit ratio PC

Private credit to banking credit ratio PC/BC

3.3 Methodology

In order to empirically test the causal relationship between financial development and eco-

nomic growth it is common to apply Granger causality test (Granger (1969), Sims (1972)).

This test provides a “useful way of describing the relationship between two (or more) varia-

bles when one is causing the other(s)”3. Moreover, the cointegration technique (Engle and

Granger (1987)) provides us with more informative results about the causal relations. Accord-

ing to this technique, Engle and Granger (1987) argue that if two (or more) variables are

found to be cointegrated, there is a corresponding error-correction representation.

The basic concept of the empirical investigation is to estimate a simple bivariate model (pair-

wise combination between economic growth (Y) and the four proxies of financial develop-

ment (FD)). The first step in this study is to test the variables for unit root. For this purpose

the Augmented Dickey Fuller test will be used.

The testing procedure for this test is applied to the following regression:

ΔYt = β1+β2t+δYt-1+α1ΔYt-1+…+ αp-1ΔYt-p+1+εt

where β1 is a constant, β2 the coefficient on a time trend, p the lag of order of the autoregres-

sive process, εt – is a pure white noise error term.

The Augmented Dickey Fuller is estimated in three different forms:

1) β1 and β2 equal 0 corresponds to modeling a random walk (ΔYt = δYt-1+εt)

2) β2=0 corresponds to modeling a random walk with a drift (ΔYt = β1+δYt-1+εt)

3) ΔYt = β1+β2t+δYt-1+α1ΔYt-1+εt - Yt is a random walk with drift around a stochastic trend.

3 Granger, C.W.J. (1969). "Investigating Causal Relations by Econometric Models and Cross- Spectral Methods,' Econometrica, 37 (3), p. 428

12

The null hypothesis is that δ=0, so there is a unit root and the time series is non stationary.

The alternative hypothesis is that δ less than zero, so the time series dataset is stationary. If

the test statistic is less that the critical value, then the null hypothesis can be rejected. It means

that there is no unit root and the time series is stationary.

If all the variables turn out to be integrated of the same order, it is necessary to check for

cointegrating relationship between these variables. For this purpose we will apply Johansen

cointegration test.

If two time-series are non stationary, but their linear combination is stationary, it is called as

the cointegrating equation and can be interpreted as a long run equilibrium relationship among

two chosen time series. The purpose of Johansen cointegration test is to determine whether a

group of non-stationary series is cointegrated or not. This methodology is based on the VAR

model of order p:

yt = A1yt-1 +…+ ApYt-p + Bxt + εt

where yt is a k-vector of non-stationary I(1) variables, xt is a d-vector of deterministic varia-

bles, and ε is a vector of innovations.

Johansen offers two different likelihood ratio test of the significance: the trace test and maxi-

mum eigenvalue test. The null hypothesis for the trace statistics is to test that there are r

13

If there is a cointegration relationship between non-stationary variables, we will deal with

vector error correction model (VECM). The VECM in this paper is:

Yt = π1+µ11.1ΔFDt-1+ µ 12.1ΔFDt-2+…+ µ 1p-1.1ΔFDt-(p-1)+µ11.2ΔYt-1+

+ µ12.2ΔYt-2+…+ µ1p-1.2ΔYt-(p-1)+δ1ECt-1 + γt1

FDt= π2+ µ 21.1ΔFDt-1+ µ 22.1ΔFDt-2+…+ µ 2p-1.1ΔFDt-(p-1)+µ21.2ΔYt-1+

+ µ22.2ΔYt-2+…+ µ2p-1.2ΔYt-(p-1)+ δ2ECt-1 + γt2

where EC is the error correction term, p is the order of the VAR, π is the constant term, γ is an

error term, FD denotes proxy of financial development and Y denotes economic growth.

As a final step, the models will be tested for non-causality. First, we test for the non-causality

between the non-stationary and non-cointegrated variables. By working with the first differ-

ence we test for the joint significance of the coefficients of the lagged variables using a Like-

lihood Ratio test.

Next we will test for the non-causality between non-stationary and cointegrated variables.

Firstly t-test will be used for determining the significance of the error correction term, second-

ly, we test for joint significance of the lagged variables and finally joint significance of the

lagged variables and the error correction term is examined.

In this study unidirectional Granger causality suggests that financial development Granger

causes economic growth. On the contrary, reverse Granger causality means that indicator of

economic growth influences financial development. And finally, when financial development

and economic growth cause each other we can assume that there is bidirectional Granger cau-

sality.

The calculations are made in Excel, and all tests are applied in Eviews 6.

14

4 EMPIRICAL RESULTS

4.1 The results of the preliminary steps

The empirical results of Augmented Dickey Fuller test indicate that for Saudi Arabia and

Egypt the cointegration test will not be applied because the variables are not integrated of the

same order. The pairwise combinations of financial development and economic growth indi-

cators of the four other countries (Kuwait, Tunisia, Morocco and Israel) can be tested for ex-

istence of cointegrating relationship.

Johansen cointegration test indicates overall four cointegrating relationship over four coun-

tries. In case of Tunisia none of this relationship is observed after applying Johansen

cointegration test. For Kuwait and Israel only one cointegration relation is indicated. And fi-

nally in case of Morocco two long-run pairwise relationships can be presented. For the de-

scribed above cointegrating variables VECM will be applied. On the contrary, for the rest of

pairwise combinations VAR model will be used. The empirical outputs of Augmented Dickey

Fuller and Johansen cointegration tests will be considered below in more details.

Table 4 observes the result of Augmented Dickey Fuller test for Saudi Arabia. All the varia-

bles except of GDP per capita do not fluctuate over the time. In this case we applied third

form of the test, where there is a random walk with drift around stochastic trend. GDP per

capita indicates the stable growth over time, thereby the second form was applied. The empir-

ical findings show that all the variables except of Y integrated of order 0. To sum up, it can be

conclude that in case of Saudi Arabia we will work with VAR model, preliminarily taking the

first difference of Y (DY).

Table 5 indicates the empirical results of the unit root test for Kuwait. All the variables except

of PC/BC show the growth over the given period. In the case of Y, we can observe more sig-

nificant increase. Moreover, all the variables are integrated of order 1, so Johansen

cointegration test can be applied in this case. According all above, in case of Kuwait (Table

10) we can conclude that only broad money supply ratio (M2) and Y have long-run relation-

ship. For other pairwise combination of financial proxies and economic growth indicators the

cointegration test does not indicate any cointegrating vectors. In latter case VAR model will

be implied.

The next analyzed countries are Tunisia and Morocco (Table 6 and Table 7). All the variables

for each country show the stable growth over the time. Moreover, all the variables integrated

of the same order, so as in the previous case the cointegration test can be computed in these

cases. Although we can observe different results of Johansen cointegration test.

For Tunisia (Table 11) Johansen cointegration test does not indicate any long run relationship

between financial development measures and economic growth indicator. As in the previous

situation VAR model will be used for determining whether there is any Granger causal rela-

tionship or not.

In case of Morocco (Table12) this test concludes that there are two combinations of Y and

proxies of financial development that indicate cointegrating relationship: 1) Y and banking

credit ratio (BC); 2) Y and broad money supply ratio (M2). For these pairwise combinations

15

VECM will be implied. For others variables bivariate VAR model will estimated for deter-

mining Granger causal relationships.

In case of Israel ( Table 8) it can be observed that all the t-statistic value of the first difference

are significant at 5 % level, all the variables are integrated of order 1. Johansen cointegration

test for Israel (Table 13) indicates only one cointegrating relationship between PC/BC and Y.

So, for this set of variables VECM will be applied.

And finally in case of Egypt (Table 9) only one variable is integrated of order 1, others are in-

tegrated of order two. By the reason that Y is integrated of different order from other variables

we cannot apply Johansen cointegration test.

4.2 Granger causality test for non-cointegrated variables

If the combination of non-stationary variables has no cointegrating relationship or if depend-

ent variable and independent variables are integrated of different order, VAR model should be

applied. Before estimating this model two preliminary steps should be taken:

1) make the variables stationary by taking first or second difference, depending on the result

of unit root test;

2) determine an optimal lag length by using information criterion like Akaike information cri-

terion.

The estimation of bivariate VAR models is not relevant in this study, but we are interested in

the direction of causality. After determining the order of VAR model, we can proceed to the

Granger causality test. According to Augmented Dickey Fuller and Johansen cointegration

tests, which are discussed above, we can conclude that for all pairwise combination of the var-

iables of Saudi Arabia and Egypt, Granger causality test can be applied. For other countries

this test will not be used for all combinations of variables. More details will be provided fur-

ther.

Granger causality test is applied for the following directions:

Direction 1 - from financial development to economic growth;

Direction 2 – from economic growth to financial development.

The null hypothesis for both directions is: Ho – the first variable does not Granger cause an-

other variable. On the contrary, the alternative hypothesis means, that the first variable do

Granger cause other variable.

After applying Granger Causality test, I can assume that there is a weak pattern between fi-

nancial structure and economic growth. It can be observed that in two cases economic growth

variable has impact on Private credit ratio (PC). In the three cases out of six, broad money

supply (M2) Granger causes economic growth. Banking credit ratio (BC) has causal relation-

ship with economic growth in two countries (Tunisia and Israel). This result can be expected

by the reason that Tunisia has more bank based financial system, so the banking sector has an

16

impact of economic development in country, and in case of Israel, I can argue that the bank-

ing system is quite developed, so it can influence economic performance. More detailed anal-

ysis is presented below.

Table 14 below presents the empirical results of Granger causality test for Saudi Arabia. As

you can observe, this test indicates only two Granger causal relationships from financial de-

velopment variables to economic growth: Broad money supply ratio (M2) and Private credit

to Banking credit ratio (PC/BC) Granger cause GDP per capita (Y). The others combinations

of variables do not show any Granger causality.

Table 14 Granger causality results for Saudi Arabia

Saudi Arabia Variables Order Direction 1

(p-value)

Direction 2

(p-value)

Results

DY-M2 VAR(1) 0.0194* 0.8586 Financial development Granger causes economic growth

DY-BC VAR(1) 0.8413 0.6956 There is no Granger causality

DY-PC VAR(1) 0.7668 0.6092 There is no Granger causality

DY-PC/BC VAR(1) 0.0469* 0.4513 Financial development Granger causes economic growth

Notes: * - significance level of 5% D indicates first difference of the variable Source: Author’s calculations

The next analyzed country is Kuwait (Table 15). Granger causality test represents different

results from the findings for Saudi Arabia. As you can see, in this case the indicator of eco-

nomic growth Granger causes Private Credit ratio (PC). The correlation between these two

variables is equals 0.50, so we can admit that there is a positive correlation. Moreover we can

conclude that if GDP per capita is increasing, private credit ratio (PC) will also grow.

Table 15 Granger causality results for Kuwait

Kuwait Variables Order Direction 1

(p-value)

Direction 2

(p-value)

Results

DY-DBC VAR(2) 0.8218 0.2152 There is no Granger causality

DY-DPC VAR(1) 0.3805 0.0443* Economic growth Granger causes financial development

DY-D(PC/BC) VAR(2) 0.7066 0.8635 There is no Granger causality Notes: * - significance level of 5% D indicates first difference of the variable Source: Author’s calculations

17

In case of Tunisia (Table 16) we can observe the only Granger causality between Banking

Credit ratio (BC) and economic growth indicator. If financial development indicator changes

over the time, GDP per capita will also change. The correlation indicates positive relationship

(0. 65) between these variables.

Table 16 Granger causality results for Tunisia

Tunisia Variables Order Direction 1

(p-value)

Direction 2

(p-value)

Results

DY-DM2 VAR(1) 0.4997 0.7237 There is no Granger causality

DY-DBC VAR(3) 0.0179* 0.7150 Financial development Granger causes economic growth

DY-DPC VAR(1) 0.3406 0.3207 There is no Granger causality

DY-D(PC/BC)

VAR(1) 0.5549 0.1958 There is no Granger causality

Notes: * - significance level of 5% D indicates first difference of the variable Source: Author’s calculations

Table 17 below shows the result of Granger causality test for Morocco. In this case we can

observe interesting result – there is bidirectional Granger causality between private credit ra-

tio (PC) and GDP per capita. It means that changes of one variable have an impact of perfor-

mance of the other variable, but also if latter indicator changes by some external reasons, the

first one will change as well.

Table 17 Granger causality results for Morocco

Morocco Variables Order Direction 1

(p-value)

Direction 2

(p-value)

Results

DY-DPC VAR(2) 0.0001* 0.0289* There is a bidirectional Granger causality

DY-D(PC/BC) VAR(1) 0.9577 0.3050 There is no Granger causality Notes: * - significance level of 5% D indicates first difference of the variable Source: Author’s calculations

Granger causality result indicates the Granger causal relations between two combinations of

variables in the case of Israel (Table 18). Moreover it can be admit that only financial devel-

opment proxies have impact on economic growth.

Table 18 Granger causality results for Israel

Israel Variables Order Direction 1

(p-value)

Direction 2

(p-value)

Results

DY-DM2 VAR(1) 0.0477* 0.3781 Financial development Granger causes economic growth

DY-DBC VAR(1) 0.0309* 0.9145 Financial development Granger

18

causes economic growth

DY-DPC VAR(1) 0.4509 0.7755 There is no Granger causality Notes: * - significance level of 5% D indicates first difference of the variable Source: Author’s calculations

Granger causality test for Egypt (Table 19) indicates the similar results as in the case of Saudi

Arabia. Broad money supply (M2) and Private Credit to Banking Credit ratio (PC/BC)

Granger cause the economic growth indicator. There are no other Granger causal relations be-

tween economic growth and financial development indicators.

Table 19 Granger causality results for Egypt

Egypt Variables Order Direction 1

(p-value)

Direction 2

(p-value)

Results

DY-DDM2 VAR(1) 0.0274* 0.3667 Financial development Granger causes economic growth

DY-DDBC VAR(4) 0.7530 0.2166 There is no Granger causality

DY-DDPC VAR(1) 0.3026 0.9880 There is no Granger causality

DY-DD(PC/BC) VAR(4) 0.0495* 0.1273 Financial development Granger causes economic growth

Notes: * - significance level of 5% D indicates first difference of the variable DD indicates second difference of the variable Source: Author’s calculations

4.3 Granger causality test for cointegrated variables

If the combination of non-stationary variables has cointegration relationship, for testing the

Granger causality VECM should be applied. In this case, the variables that are used can be at

level, so it is only necessary to determine the optimal lag length. For that, we are using the

same approach as in case of VAR model: the order of VECM is selecting by using such in-

formation criterion as Akaike information criterion.

As in the previous case we are not so much interested in VECM estimation as in Granger cau-

sality test that we can apply after computing the model. By this reason only Granger causality

outputs will be presented. Granger causality test is applied for the following directions, the

same as in the case of VAR model:

Direction 1 - from financial development to economic growth;

Direction 2 – from economic growth to financial development.

19

The null hypothesis for both directions is: Ho – the first variable does not Granger cause an-

other variable. On the contrary, the alternative hypothesis means, that the first variable do

Granger cause other variable.

The difference from Granger causality test of VAR model is that in this case we can test for

different type of causality. While applying t-test of the error correction term, we can observe

the results about long run causality. The second test for joint significance of the lagged varia-

bles indicates the short run causality. And finally the t-test for joint significance of both the

lagged variables and the error correction term can show us if this causality is strong or not.

According to the previous result, it should be mentioned that only for three countries we will

apply this test, because only its combination of variables indicates the cointegrating relation-

ship. The results that we received indicate in all tested cases long run causal relationship from

economic growth to financial development. Moreover we can conclude that there are causal

relations between financial development and economic growth in two countries in the short

run term. In only case of Morocco we can observe long run bidirectional causality between fi-

nancial development and economic growth.

More detailed analysis will started from Kuwait. Table 20 indicates the result of Granger cau-

sality test. We can observe that there is Granger causal relationship running from economic

growth indicator (Y) to broad money supply ratio (M2) in the long run term. On the contrary

in the short run we can conclude that there is causality from broad money supply ratio (M2) to

economic growth. Both these Granger causal relationship are strong.

Table 20 Granger causality test for Kuwait

Notes: * - significance level of 5% Source: Author’s calculations

The next analyzed country is Morocco (Table 21). In both pairwise combinations we can ob-

serve two way causality in the long run term. In the short run it can be concluded that proxy

of financial development Granger causes economic growth.

Table 21 Granger causality test for Morocco

Combina-tion of vari-

ables

T-ratio of the error correction term

(p-value)

Joint significance of lagged coefficients

(p-value)

Joint significance of both the error correc-

tion term and the lagged coefficients

(p-value)

Direction 1

Direction 2

Direction 1

Direction 2

Direction 1

Direction 2

Y – M2 0.6567 0.0178* 0.0175* 0.0979 0.0316* 0.0331*

Combina-tion of vari-

ables

T-ratio of error correc-tion term (p-value)

Joint significance of the lagged coefficients

(p-value)


tion term and the

20


And finally in the case of Israel (Table 22) we can observe the only causality from economic

growth indicator to private credit to banking credit sector (PC/BC) in the long run term. The

test for joint significance of the lagged variables and the error correction term indicates the

strong causality.

Table 22 Granger causality test for Israel


lagged coefficients (p-value)

Direction 1

Direction 2

Direction 1

Direction 2

Direction 1

Direction 2

Y – M2 0.0060* 0.0434* 0.0005* 0.3948 0.0000* 0.0089*

Y – BC 0.0357* 0.0011* 0.0014* 0.1899 0.0005* 0.0001*

Combination of variables

T-ratio of error correc-tion term (p-value)

Joint significance of the lagged coefficients

(p-value)


tion term and the lagged coefficients

(p-value)

Direction 1

Direction 2

Direction 1

Direction 2

Direction 1

Direction 2

Y – PC/BC 0.9161 0.0104* 0.1204 0.3099 0.1749 0.0458*

21

5 CONCLUDING REMARKS

The purpose of this paper was to investigate if there is any kind of Granger causal relationship

between the financial development and economic growth in a set of countries with emerging

markets. If such dependence exists we were interested in which direction this relationship

works. It has been shown that the indicators of financial development influence in some de-

gree the economic growth. The results show weak dependence between financial development

and economic growth. Moreover in some cases it can be observed either a unidirectional

Granger causality from financial development to economic growth or bidirectional Granger

causality.

Another stated question in this study case was whether different financial structures different-

ly influence economic growth. The summarizing table 14 of results you can observe in Ap-

pendix. In the case of countries with market-based financial system it can be observed some

expected pattern - banking credit ratio does not Granger cause economic growth, so we can

conclude that in Saudi Arabia and Kuwait banking sector have no impact on economic

growth. This may indicates that the banking system compare with the stock markets is not

strong enough to influence economic growth. We should take into account that Saudi Arabia

is one of the biggest exporter of petroleum, so most impact on economic growth has export

volume. On the contrary Kuwait stock exchange is one of the largest stock exchange within

Arabic world. But in case of Kuwait the results indicate that the Granger causal relationship

from economic growth to two proxies of economic growth, but the same finding cannot be

applied for Saudi Arabia.

The empirical results indicate for the countries with bank oriented financial system some

similarity. Banking credit ratio (BC) Granger causes economic growth in both countries, but

in Morocco we also can observe causality running from economic growth indicator to BC ra-

tio in the long run term. Moreover, the findings strongly support the hypothesis that economic

growth indicator has Granger causal relations with three out of four proxies of financial de-

velopment.

Comparing the empirical results for Israel and Egypt, it should be mentioned that in the case

of the former country there is a stronger Granger causality running from financial develop-

ment to economic growth. Moreover for private credit to banking credit ratio the different di-

rections of Granger causality are indicated.

To sum up it is necessary to analyze and compare the findings that have been computed in this

study case with the results of empirical work that was mentioned in background section. We

can assume that our conclusion is similar with the empirical findings of Lee (2012) and

Arestis, Luintel D., and Luintel D. (2005). Different financial systems cause economic

growth. But a strong pattern cannot be observed in the present analysis. On the contrary, this

work provides an opposite results that have been concluded by Levine (2002).

In regard to Granger causality, in the case of Saudi Arabia, Egypt and Tunisia there is only

unidirectional Granger causality running from financial development to economic growth. On

the contrary in the case of Kuwait, Morocco and Israel we can indicate bidirectional Granger

causality. These findings contradict the results of the empirical study computed by Abu-Bader

and Abu-Qarn (2008).

22

The difference of these results may be due to the different data and methodology. The indica-

tors of financial development differ and also Abu-Bader and Abu-Qarn (2008) use the set of

control variables. In this study case I estimated bivariate vector autoregressive model, while

quadvariate vector autoregressive model is used in the other work.

The empirical findings of this study case indicate that countries that are used are different

with their own historical, economical and geographical aspects. I can say in some degree the

financial structure have different impact on economic growth. Some financial institutions are

stronger, more stable and developed, so these give opportunity for economic development.

23

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26

APPENDIX

Table 1 Countries' ranking

Structure activity Structure size

1 Saudi Arabia 0,7988 South Korea 0,24761691

2 Singapore 0,794566566 Saudi Arabia 0,196963971

3 Peru 0,5147 Singapore 0,194634183

4 Finland 0,337926332 Finland 0,151489368

5 Switzerland 0,233476211 Switzerland 0,138462444

6 Chile 0,179588493 United States -0,063963785

7 Jordan 0,175424852 Sweden -0,090449811

8 South Africa 0,124516322 India -0,137257032

9 Kuwait 0,102154716 Turkey -0,160428513

10 Malaysia 0,079546214 United Kingdom -0,211675338

11 Australia -0,041673944 Spain -0,288742972

12 Sweden -0,077984423 Pakistan -0,372720654

13 United Kingdom -0,084424399 Kuwait -0,391230033

14 Philippines -0,1669 Australia -0,46925042

15 Argentina -0,171896983 France -0,60532062

16 India -0,194100399 CANADA -0,882249767

17 Israel -0,27285614 Malaysia -0,932683308

18 Mexico -0,322317037 South Africa -1,007449411

19 France -0,429311304 Germany -1,007768659

20 CANADA -0,446751736 Italy -1,068847443

21 South Korea -0,489174783 Denmark -1,115180431

22 United States -0,494496553 Israel -1,135153472

23 Turkey -0,549587252 Thailand -1,341834874

24 Colombia -0,607820418 Jordan -1,385194149

25 Spain -0,648276388 Greece -1,39523591

26 Brazil -0,649052641 Brazil -1,40916159

27 Indonesia -0,664879955 Indonesia -1,413590189

28 Morocco -0,67181023 Hungary -1,521741786

29 Belgium -0,689313013 Mexico -1,569766726

30 Greece -0,737111064 Ireland -1,626972564

31 Denmark -0,773621202 Japan -1,684507632

32 Egypt -0,825627288 Philippines -1,698051254

33 Iceland -0,859248069 Peru -1,70882188

34 Poland -0,8604 Poland -1,759266441

35 Ireland -0,905341301 Portugal -1,800970327

27


Table 4 Unit root test for Saudi Arabia

Saudi Arabia

Variables T-statistic Critical values P-value Integrated

of order 1% 5% 10%

Y Level -1.879 -3.606 -2.937 -2.607 0.3383 I(1)

First

difference

-4.988* -4.205 -3.527 -3.195 0.0012

M2 -6.752* -4.199 -3.527 -3.1929 0.0000 I(0)

BC -6.053* -4.199 -3.527 -3.1929 0.0001 I(0)

PC -6.514* -4.199 -3.527 -3.1929 0.0000 I(0)

PC/BC -33.171* -4.199 -3.527 -3.1929 0.0000 I(0)

Notes: *-significance level of 1% ** - significance level of 5% Source: Author’s calculations

36 Iran -0,943587528 Belgium -1,813585142

37 Pakistan -0,9551 Chile -1,937278887

38 Italy -1,032493252 Iceland -1,957839192

39 Thailand -1,063996345 New Zealand -1,992981373

40 New Zealand -1,0712 Egypt -2,23467797

41 Hungary -1,085681251 Argentina -2,469919147

42 Sri Lanka -1,105189449 Austria -2,688877171

43 Germany -1,1407635 Morocco -2,691968418

44 Portugal -1,2420 Iran -2,835917119

45 Slovenia -1,267632533 Bangladesh -2,882842572

46 Japan -1,419674721 Slovenia -2,897185336

47 Cyprus -1,607787335 Colombia -2,969742673

48 Tunisia -1,659296534 Sri Lanka -2,987553667

49 Austria -1,806623777 Cyprus -3,244026205

50 Bangladesh -2,306748993 Tunisia -3,728105557

28

Table 5 Unit root test for Kuwait

Kuwait

Variables T-statistic Critical values P-value Integrated

of order 1% 5% 10%

Y Level -1.584 -4.170 -3.510 -3.185 0.7839 I(1)

First difference -5.094* -4.175 -3.513 -3.186 0.0008

M2 Level -1.614 -4.170 -3.510 -3.185 0.7719 I(1)


BC Level -1.283 -4.180 -3.515 -3.188 0.8790 I(1)


PC Level -1.573 -3.584 -2.928 -2.602 0.4880 I(1)


PC/B

C

Level -2.032 -4.180 -3.515 -3.188 0.5679 I(1)



Table 6 Unit root test for Tunisia

Tunisia

Variables T-

statistic

Critical values P-value Integrated

of order 1% 5% 10%

Y Level -0.719 -4.161 -3.506 -3.183 0.9657 I(1)


M2 Level 0.361 -3.574 -2.923 -2.599 0.9791 I(1)


BC Level -2.151 -3.574 -2.923 -2.599 0.2262 I(1)


PC Level -1.702 -3.592 -2.931 -2.603 0.4230 I(1)


PC/BC Level -1.567 -3.574 -2.923 -2.599 0.4913 I(1)

First difference -3.688** -4.186 -3.518 -3.189 0.0340

Notes: *-significance level of 1% ** - significance level of 5%

29


Table 7 Unit root test for Morocco

Morocco

Variables T-

statistic


of order 1% 5% 10%

Y Level 2.209 -3.571 -2.922 -2.599 0.9999 I(1)


M2 Level 3.556 -3.574 -2.923 -2.599 1.0000 I(1)


BC Level 1.294 -3.596 -2.933 -2.604 0.9982 I(1)


PC Level 1.666 -3.588 -2.929 -2.603 0.9994 I(1)


PC/BC Level -1.586 -4.165 -3.508 -3.184 0.7833 I(1)



Table 8 Unit root test for Israel

Israel

Variables T-

statistic


of order 1% 5% 10%

Y Level 1.335 -3.574 -2.923 -2.599 0.9985 I(1)


M2 Level -1.150 -3.577 -2.925 -2.600 0.6881 I(1)


BC Level -1.778 -4.165 -3.508 -3.184 0.6993 I(1)


PC Level -1.144 -3.574 -2.923 -2.599 0.6907 I(1)


PC/BC Level -2.92*** -3.615 -2.941 -2.609 0.0522 I(1)


Notes: *-significance level of 1% ** - significance level of 5%

30

***-significance level of 10% Source: Author’s calculations

Table 9 - Unit root test for Egypt

Egypt

Variables T-

statistic


of order 1% 5% 10%

Y Level 4.499 -3.605 -2.936 -2.606 1.0000 I(1)


M2 Level -1.519 -3.596 -2.933 -2.604 0.5140 I(2)

First difference -2.228 -4.192 -3.520 -3.191 0.4623

Second difference -4.198* -4.192 -3.520 -3.191 0.0098

BC Level -1.839 -4.170 -3.510 -3.185 0.6690 I(2)



PC Level -3.308 -4.175 -3.513 -3.186 0.0779 I(2)



PC/BC Level -1.484 -3.577 -2.925 -2.600 0.5327 I(2)




Table 10 Johansen cointegration test for Kuwait

Kuwait – per capita GDP

Variables Null

Hypothesis

P-value

trace statistic

p-value maximum Eigen-

value statistic

Results

BC r=0 0.5713 0.4865 Not cointegrated

r=1 0.8589 0.8589

PC r=0 0.3923 0.3746 Not cointegrated

r=1 0.4114 0.4114

M2 r=0 0.0454** 0.0287** Cointegrated

r=1 0.9523 0.9523

PC/BC r=0 0.3687 0.3203 Not cointegrated

r=1 0.5503 0.5503

Notes: ** - significance level of 5%

31

r indicates the number of cointegrating vectors Source: Author’s calculations

Table 11 Johansen cointegration test for Tunisia

Tunisia - per capita GDP (Y)

Variables Null

Hypothesis

P-value

trace statistic

p-value maximum Ei-

genvalue statistic

Results


r=1 0.1847 0.1847


r=1 0.1794 0.1794

M2 r=0 0.1191 0.2926 Not cointegrated

r=1 0.0465 0.0465


r=1 0.4074 0.4074

Notes: *-significance level of 1% ** - significance level of 5% r indicates the number of cointegrating vectors Source: Author’s calculations

Table 12 Johansen cointegration test for Morocco

Morocco – per capita GDP (Y)

Variables Null

Hypothesis

P-value

trace statistic

p-value maximum Ei-

genvalue statistic

Results

BC r=0 0.0170** 0.0425** Cointegrated

r=1 0.0511 0.0511


r=1 0.0180 0.0180

M2 r=0 0.0036* 0.0105** Cointegrated

r=1 0.0513 0.0513


r=1 0.1199 0.1199


32

Table 13 Johansen cointegration test for Israel

Israel – per capita GDP (Y)

Variables Null

Hypothesis

P-value

trace statistic

p-value maximum Ei-

genvalue statistic

Results


r=1 0.6909 0.6909


r=1 0.5299 0.5299

M2 r=0 0.2074 0.2470 Not cointegrated

r=1 0.2105 0.2105

PC/BC r=0 0.0307** 0.0203** Cointegrated

r=1 0.6491 0.6491


Table 14 Summarizing table of results

Economic Growth across countries

Variables Saudi

Arabia

Kuwait Tunisia Morocco Israel Egypt

M2 1 2 - long run

1 – short run

3 – long run

1 – short run

1 1

BC 1 3 – long run

1 – short run

1

2 – long run

PC 2 3

PC/BC 1 1

1 – Unidirectional Granger causality

2 - Reverse Granger causality

3 – Bidirectional Granger causality

Long run/short run indicate Granger causality for cointegrated pairwise combinations.

financial development and economic growth: cross-country...

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