financial development and economic growth: cross-country...
TRANSCRIPT
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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
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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
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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
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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.
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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
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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
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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
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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.
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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)
Joint significance of both the error correc-
tion term and the
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20
Notes: * - significance level of 5% Source: Author’s calculations
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
Notes: * - significance level of 5% Source: Author’s calculations
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)
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 – PC/BC 0.9161 0.0104* 0.1204 0.3099 0.1749 0.0458*
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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).
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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.
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23
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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
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27
Source: Author’s calculations
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
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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)
First difference -6.729* -4.185 -3.513 -3.186 0.0000
BC Level -1.283 -4.180 -3.515 -3.188 0.8790 I(1)
First difference -7.581* -4.180 -3.515 -3.188 0.0000
PC Level -1.573 -3.584 -2.928 -2.602 0.4880 I(1)
First difference -4.889* -4.175 -3.513 -3.186 0.0014
PC/B
C
Level -2.032 -4.180 -3.515 -3.188 0.5679 I(1)
First difference -9.134* -4.180 -3.515 -3.188 0.0000
Notes: *-significance level of 1% ** - significance level of 5% Source: Author’s calculations
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)
First difference -5.660* -4.165 -3.508 -3.184 0.0001
M2 Level 0.361 -3.574 -2.923 -2.599 0.9791 I(1)
First difference -6.242* -4.166 -3.508 -3.184 0.0000
BC Level -2.151 -3.574 -2.923 -2.599 0.2262 I(1)
First difference -4.633* -4.186 -3.518 -3.189 0.0030
PC Level -1.702 -3.592 -2.931 -2.603 0.4230 I(1)
First difference -4.333* -4.198 -3.523 -3.192 0.0072
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%
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29
Source: Author’s calculations
Table 7 Unit root test for Morocco
Morocco
Variables T-
statistic
Critical values P-value Integrated
of order 1% 5% 10%
Y Level 2.209 -3.571 -2.922 -2.599 0.9999 I(1)
First difference -5.423* -4.161 -3.506 -3.183 0.0003
M2 Level 3.556 -3.574 -2.923 -2.599 1.0000 I(1)
First difference -9.325* -4.161 -3.506 -3.183 0.0000
BC Level 1.294 -3.596 -2.933 -2.604 0.9982 I(1)
First difference -4.365* -4.192 -3.520 -3.191 0.0064
PC Level 1.666 -3.588 -2.929 -2.603 0.9994 I(1)
First difference -4.387* -4.180 -3.515 -3.188 0.0058
PC/BC Level -1.586 -4.165 -3.508 -3.184 0.7833 I(1)
First difference -6.002* -4.161 -3.506 -3.183 0.0000
Notes: *-significance level of 1% ** - significance level of 5% Source: Author’s calculations
Table 8 Unit root test for Israel
Israel
Variables T-
statistic
Critical values P-value Integrated
of order 1% 5% 10%
Y Level 1.335 -3.574 -2.923 -2.599 0.9985 I(1)
First difference -5.396* -4.170 -3.510 -3.185 0.0003
M2 Level -1.150 -3.577 -2.925 -2.600 0.6881 I(1)
First difference -4.556* -4.165 -3.508 -3.184 0.0035
BC Level -1.778 -4.165 -3.508 -3.184 0.6993 I(1)
First difference -5.191* -4.165 -3.508 -3.184 0.0005
PC Level -1.144 -3.574 -2.923 -2.599 0.6907 I(1)
First difference -5.682* -4.170 -3.510 -3.185 0.0001
PC/BC Level -2.92*** -3.615 -2.941 -2.609 0.0522 I(1)
First difference -8.566* -4.165 -3.508 -3.184 0.0000
Notes: *-significance level of 1% ** - significance level of 5%
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30
***-significance level of 10% Source: Author’s calculations
Table 9 - Unit root test for Egypt
Egypt
Variables T-
statistic
Critical values P-value Integrated
of order 1% 5% 10%
Y Level 4.499 -3.605 -2.936 -2.606 1.0000 I(1)
First difference -4.656* -4.205 -3.526 -3.194 0.0031
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)
First difference -2.677 -4.186 -3.518 -3.189 0.2507
Second difference -10.720* -4.170 -3.510 -3.185 0.0000
PC Level -3.308 -4.175 -3.513 -3.186 0.0779 I(2)
First difference -2.570 -4.165 -3.508 -3.184 0.2949
Second difference -7.566* -4.170 -3.510 -3.185 0.0000
PC/BC Level -1.484 -3.577 -2.925 -2.600 0.5327 I(2)
First difference -2.907 -4.165 -3.508 -3.184 0.1694
Second difference -5.263* -4.180 -3.515 -3.188 0.0005
Notes: *-significance level of 1% ** - significance level of 5% Source: Author’s calculations
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%
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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
BC r=0 0.1511 0.1891 Not cointegrated
r=1 0.1847 0.1847
PC r=0 0.2398 0.3066 Not cointegrated
r=1 0.1794 0.1794
M2 r=0 0.1191 0.2926 Not cointegrated
r=1 0.0465 0.0465
PC/BC r=0 0.1151 0.0996 Not cointegrated
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
PC r=0 0.0717 0.2993 Not cointegrated
r=1 0.0180 0.0180
M2 r=0 0.0036* 0.0105** Cointegrated
r=1 0.0513 0.0513
PC/BC r=0 0.5204 0.7316 Not cointegrated
r=1 0.1199 0.1199
Notes: *-significance level of 1% ** - significance level of 5% r indicates the number of cointegrating vectors Source: Author’s calculations
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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
BC r=0 0.7272 0.6686 Not cointegrated
r=1 0.6909 0.6909
PC r=0 0.6976 0.6665 Not cointegrated
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
Notes: *-significance level of 1% ** - significance level of 5% r indicates the number of cointegrating vectors Source: Author’s calculations
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.