chapter iv stock market development and economic growth...
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CHAPTER IV
STOCK MARKET DEVELOPMENT AND ECONOMIC
GROWTH IN INDIA
4.1 Introduction
The main objective behind promoting the development of stock markets in India
was to contribute to raising capital and assisting its allocation process in order to
strengthen the Indian economy. Consequently, in order to investigate whether the Indian
Stock Markets achieves its objective in enhancing the economic growth of the country,
this chapter proposes a simple plausible framework for studying some elements of
growth that relate to the main aspects of the functions of financial markets. The chapter
has four parts. The first part examines the growth trajectory of India with emphasis
during the liberalised era. The second part reviews the theoretical literature with regard
to stock market and economic growth. The thread of the theoretical argument is that the
degree to which financial markets, particularly stock markets, influence real economic
growth depends on how effectively they improve capital accumulation, facilitate capital
mobilization and increase the productivity of capital investment. The stock market
development indicators are dealt in the third part. In the fourth part, an empirical attempt
has been made to examine the link between stock market development and economic
growth of India. Thus the particular questions that we are trying to answer in this chapter
are the following: does the development of the stock market have any influence on
India's real economic growth? If it does, have the level of stock market development
influenced India's economic growth?
4.2 Growth experience of Indian Economy- A Brief Review
Economic Growth in India is often biased by the beliefs in fashion or distortions
of perceptions which shape conventional wisdom. The second half of the 20th century
witnessed major swings in perception about economic development in India. The turning
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point in economic growth was approximately around 1950s. During that period, the
objective of India’s development strategy had been to establish a socialistic pattern of
society through economic growth with self-reliance, social justice and alleviation of
poverty. These objectives were to be achieved within a democratic political framework
using the mechanism of a mixed economy where both public and private sectors co-
exist. India initiated planning for national economic development with the establishment
of the Planning Commission.(Kochhar et al 2006).
While the reasons for adopting a centrally directed strategy of development were
understandable against the background of colonial rule, it, however soon became clear
that the actual results of this strategy were far below expectations. Instead of showing
high growth, high public savings and a high degree of self-reliance, India was actually
showing one of the lowest rates of growth in the developing world with a rising public
deficit and a periodic balance of payment crises. Between 1950 and 1990, India’s growth
rate averaged less than 4 per cent per annum. The strategy of industrialization, which
protected domestic industries from foreign competition, was also responsible for high
cost and low growth in the economy.(Kochhar et al 2006, Virmani 2004).
During the late 80’s and early 90’s it was imperative for the country to correct its
clearly faulty developmental process. There have been several reasons put forward for
the failure of the developmental path which necessitated the reforms in 1991. (Ahluwalia
2002) Considering the growth of the country since independence, the turning point or
structural break in economic growth is during the period from 1980-81. The trends in the
GDP( at constant prices) and percapita GDP during the period from 1950’s till 2011 is
shown in Figure 4.1 and Figure 4.2.
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FIGURE:- 4.1
GDP at Factor Cost (At Current Prices- Rs Crore)
Source: Economic Survey, Various Issues.
FIGURE:- 4.2
GDP Percapita (At Constant Prices- 1999 to 2000) in US Dollars
Source: Economic Survey, Various Issues.
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
1950-51 1960-61 1970-71 1980-81 1990-91 2000-01 2007-08 2008-09 2009-10 2010-11
GDP at factor cost current price Rscrore
0
100
200
300
400
500
600
700
800
900
1960-61 1970-71 1980-81 1990-91 2000-01 2007-08 2008-09 2009-10 2010-11
GDP Percapita(Constant Price 1999-00) US dollars
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The above charts reveal that the GDP(at constant prices) was low since 1950’s
and have risen steadily after 1980’s and the trend has continued till 2010-11. Similarly,
GDP percapita (in US Dollars) have increasing steadily since 1990’s.
The annual growth rate of Real GDP at factor cost(at 2004-05 prices) is shown in
Figure 4.3.
FIGURE:-4.3
Annual Growth Rate in GDP at Factor Cost(At 2004-05 prices)
Source: Economic Survey, Various Issues.
The growth of GDP over the period from 1950 to 2011 indicates the overall
improvement of the economy. The rate of growth of GDP vis-à-vis population growth
and inflation rate is indicative of the additional resources being made available in the
country. It also facilitates capital formation as higher FII and FDI is attracted. This, in
turn, is likely to propel further growth and add impetus to the buoyancy of the economy.
A clear picture of the macro-economic environment of the Indian Economy can
be seen by looking at few key indicators for the period from 1950 to 2011.Table 4.1 lists
the trends in a select list of key indicators of Indian economy for the period from 1950 to
2011
0123456789
10
1950-51 1960-61 1970-71 1980-81 1990-91 2000-01 2007-08 2008-09 2009-10 2010-11
Annual Growth rate of GDP at factor cost( 2004-2005 prices)
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TABLE:- 4.1
THE KEY INDICATORS OF THE INDIAN ECONOMY
YEAR
GDP at
factor cost
current
price Rs
crore
GDP at
factor cost
constant
price Rs
crore
Percapita
NNP at
factor cost at
constant
price Rs
crore
Gross
Domestic
Capital
Formation
as a % of
GDP at
current
market price
Gross
Domestic
Savings
as a % of
GDP at
current
market
price
Wholesale
Price
Index
Average
1950-51 9719 224786 5708 8.4 8.6 6.8
1960-61 16512 329825 7121 14 11.2 7.9
1970-71 42981 474135 8091 15.1 14.2 14.3
1980-81 132520 641921 8594 19.9 18.5 36.8
1990-91 515032 1083572 11535 26 22.8 73.7
2000-01 1925017 1864300 16172 24.3 23.7 155.7
2007-08 4582086 3896636 30332 38.1 36.8 116.6
2008-09 5303567 4158676 31754 34.3 32 126
2009-10 6091485 4507637 33843 36.6 38.3 132.8
2010-11 7157412 4885954 35993 35.1 132.8 143.3
Source: Economic Survey, Various Issues.
TABLE:-4.2 ANNUAL GROWTH RATE OF REAL GDP AT FACTOR COST BY INDUSTRY OF ORIGIN
(At 2004-05 prices)
Year Agriculture,
Forestry &
Fishing,
Mining and
Quarrying
Manufacturing,
Construction,
electricity, gas
and water
supply
Trade,
hotels,
transport &
Communic
ation
Financing,
Insurance,
real estate
and
business
services
Public
administrati
on &
defence and
other
services
Gross
Domestic
Product
at factor
cost
1951-52 1.9 4.6 2.6 2.6 3.0 2.3
1961-62 0.3 6.9 6.5 4.3 4.7 3.1
1971-72 -1.7 2.5 2.3 5.2 4.5 1.0
1981-82 5.2 7.4 6.1 8.1 2.1 5.6
1991-92 -1.4 -0.1 2.3 10.8 2.6 1.4
2001-02 5.5 2.7 8.6 7.1 4.1 5.5
2008-09 0.4 4.7 7.5 12 12.5 6.7
2009-10 1.7 8.6 10.3 9.4 12.0 8.4
2010-11 6.8 7.4 11.1 10.4 4.5 8.4
2011-12 1.9 4.5 11.2 9.1 5.9 6.9
Source: Economic Survey, Various Issues.
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TABLE:-4.3
SECTOR-WISE COMPOSITION OF GDP AT FACTOR COST(AT CONSTANT PRICES
(At 2004-05)
Year
Agriculture,
Forestry,
Fishing,
Mining and
Quarrying
Manufacturing,
Construction,
Electricity, Gas
and Water
Supply
Trade, Hotels,
Transport &
Communication
Financing,
Insurance,
Real Estate
& Business
Services
Public
Administration
& Defence and
other Services
GDP at
factor Cost
1950-51 55.03 14.71 11.28 8.55 10.43 100.00
1960-61 50.86 18.31 12.91 7.78 10.14 100.00
1970-71 44.51 21.74 14.49 7.53 11.74 100.00
1980-81 38.70 23.27 16.94 8.23 12.86 100.00
1990-91 33.11 24.22 17.69 11.55 13.44 100.00
2000-01 25.28 24.35 21.63 14.05 14.68 100.00
2007-08 19.28 26.28 25.91 16.12 12.42 100.00
2008-09 18.12 25.77 26.09 16.92 13.09 100.00
2009-10 17.01 25.82 26.56 17.08 13.53 100.00
2010-11 16.75 25.57 27.23 17.40 13.05 100.00
2011-12 15.97 25.00 28.34 17.76 12.93 100.00
Notes : Data for 2009-10 are Provisional Estimates, 2010-11 are Quick Estimates and 2011-12 are Revised
Estimates.
Source: Economic Survey, Various Issues
From the above facts and figures, it is very ev ident that India is
developing into an open-market economy, yet traces of its past autarkic
policies remain. It is clearly evident that the largest contributor to the GDP
is the service sector. Gross Domestic Savings as a percentage of GDP has
risen from 8.6% to 32.3% during the period from 1950 to 2011. Also, Gross
Domestic Capital Formation as a percentage of GDP had increased from
8.4% to 35.1% during the period from 1950 to 2011. Economic liberalization,
including industrial deregulation, privatization of state-owned enterprises,
and reduced controls on foreign trade and investment, began in the early
1990’s and has served to accelerate the country's growth, which has averaged
more than 7% per year since 1997. Also, it is observed from Table 4.3 that
the contribution of Agriculture and allied sector’s to GDP has reduced from
55.05% in 1950-51 to 15.97 % in 2011-12.The contribution of Finance,
Insurance and Real Estate to GDP has increased from 8.55% in 1950 -51 to
17.76% in 2011-12. India's diverse economy encompasses traditional village
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farming, modern agriculture, handicrafts, a wide range of modern industries,
and a multitude of services. Slightly more than half of the work force is in
agriculture, but services are the major source of economic growth,
accounting for more than half of India's output, with only one-third of its
labor force. India has capitalized on its large educated English -speaking
population to become a major exporter of information technology services
and software workers. India’s import bill increased over the years on account
of both crude oil as well non-oil imports. However, the trade deficit has
narrowed because of a rebound in exports. In 2010, the Indian economy
rebounded robustly from the global financial crisis - in large part because of
strong domestic demand - and growth exceeded 8% year-on-year in real
terms. However, India's economic growth in 2011 slowed because of
persistently high inflation and interest rates and little progress on economic
reforms. High international crude prices have exacerbated the government's
fuel subsidy expenditures contributing to a higher fiscal deficit, and a
worsening current account deficit. India's medium-term growth outlook is
positive due to a young population and corresponding low dependency ratio,
healthy savings and investment rates, and increasing integration into the
global economy. India has many long-term challenges that it has not yet fully
addressed, including widespread poverty, inadequate physical and social
infrastructure, limited non-agricultural employment opportunities, scarce
access to quality basic and higher education, and accommodating rural -to-
urban migration.
4.3 Review of literature on causal link between stock market development
and economic growth
There are many studies that emphasise the links between the state of
development of a country's financial sector and the level and rate of economic growth.
The argument essentially is that the functions the financial sector provides are an
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essential catalyst of economic growth. This type of empirical study started with
Goldsmith (1969), and McKinnon (1973), and more recently, Ghani (1992), King and
Levine (1993a, b), Degregorio and Giudotti (1995), Rousseau and Wachtel (1998), Beck
et al., (2000), Levine et al., (2000), Levine (2000) and others. While all these studies
utilize bank measures of financial development, with the exception of a very few recent
empirical works (Atji and Jovanovic (1993), Hargis (1997), and Levine and Zervos
(1996, 1998)), the role of stock markets in the economic development process has been
completely ignored. A part of the problem may stem from the absence of indicators that
can accurately measure the extent of stock market development.
In the present review of literature, the researcher presents firstly the most
important theoretical literature that directly models the role of financial markets in
economic development.
Greenwood and Jovanovic (1990) emphasise in their model both the
informational and risk sharing roles of financial markets in improving capital
mobilisation to the optimal use and hence in increasing growth. They develop a model
with two assets: safe, low-yield technology, and a risky high-yield one, where the return
on the latter is affected by an aggregate and a project specific shock. Financial markets
are able to offer agents a higher return than they invested individually because they
collect information that enables them to decipher the aggregate productivity shock and
they can better diversify project-specific risk due to the large portfolios they hold.
Therefore, financial markets allocate capital more efficiently and the resulting higher
productivity of capital increases growth. It is worth noting that in this model higher
growth stimulates increased participation in financial markets, which leads to the
expansion of financial institutions. Thus, a two-way causality between financial
development and growth emerges in their model.
Greenwald and Stiglitz (1989) propose a theoretical model to examine the impact
of financial market imperfections on the long-term productivity growth of firms. Their
model focuses on failures of firms in selling equity securities, which help firms by
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diversifying the risk of real investment. Particularly, they argue that failures in stock
markets limit the abilities of firms to diversify the risks of their operations and hence
lead to a reduction in the level of such operations as an alternative means of risk
management. They show that since the curtailment of firms' operations will limit the
extent of "on-the-job training" and other learning effects, as well as direct investment in
productivity improvements, the stock market imperfection will adversely affect the rate
of productivity growth.
Levine (1991) constructs an endogenous growth model in which the stock market
emerges to allocate risk, and explores how the markets alter investment incentives in
ways that change steady-state growth rates. He demonstrates that stock markets
accelerate growth by facilitating the ability to trade ownership of firms without
disrupting the productive process occurring within firms and by allowing agents to
diversify portfolios. In the absence of the stock markets, lenders facing liquidity
constraints which would force firms to pay back loans, thus forcing firms to liquidate
(fully or partially) those assets which they own. Since such assets include capital assets,
which embody a firm's technology, this will lower the firm's productivity. He further
explains the effect of tax policies on growth both directly by altering investment
incentives and indirectly by changing the incentives underlying financial contracts.
Levine's model uses the Diamond and Dybvig (1983) structure of preference to create
liquidity risk and also to include productivity shocks that create production risk.
Liquidity risk and productivity risk create incentives for the formation of stock markets.
Productivity risk lowers welfare and discourages agents from investing in firms. The
stock market allows investors to invest in a large number of firms and to diversify away
from idiosyncratic productivity shocks. This raises welfare, the fraction of resources
invested in firms, and the economy's steady-state growth rate. In Levine's model, the
stock market raises the growth rate by increasing the productivity of firms or by
improving the allocation of resources. Thus, the emergence of stock markets to manage
productivity and liquidity risk accelerates growth by attracting resources to socially
productive firms.
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King and Levine (1994) proposed a model in which innovation activities serve as
an engine of growth. A higher rate of successful innovations results in a high growth rate
of productivity. Financial markets appear in two different forms in the model. The first
is where the intermediaries’ acts like venture capital firms. They evaluate, finance and
monitor the risky and costly innovations. The second form is like the stock market. The
present value of the innovation is revealed in the stock market and selling the equity
shares on the market can diversify the risk associated with innovation. Therefore,
according to King and Levine, better development of the financial market can improve
the possibility of successful innovations. They point out that financial institutions play
an active role in evaluating, managing, and funding the entrepreneurial activity that
leads to productivity growth.
Bencivenga and Smith (1991) construct a model that by pooling the economy's
resources eliminates liquidity risk and invests more efficiently. In their model, a bank
enables individuals to pool liquidity risks and can promote higher growth by shifting the
composition of savings towards more capital accumulation and by reducing unnecessary
capital liquidation. Banks channel funds from risk-averse savers to entrepreneurs who
invest in productive capital and hence provide liquidity to the former group by enabling
them to hold bank deposits instead of other liquid and unproductive assets. These funds
are then available for investment in capital accumulation and thus reduce the need for
the self-financing of investment.
The role and impact of stock markets on the economic development process have
not received as much attention as other elements of the financial sector. Historically, the
economists have focused on banks. In the last decade the availability of more
appropriate data has increased the number of empirical researches in this field. Debate
exists, however, over the signs of the effects of stock markets on economic growth:
many theoretical studies suggest that stock market development slows economic growth.
With regard to this debate on the relationships between stock market development and
economic growth, a detailed discussion of the stock market functions is mentioned
above, and how these functions affect economic growth.
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Mohtadi and Agarwal(2004) attempts to evaluate the empirical relationship
between stock market development and economic growth for 21 developing countries
for the period 1977 to 1997 using dynamic panel method. Stock market development is
measured using Market Capitalization Ratio, Total Value of Shares Traded Ratio and
Turnover ratio. The other control variables used are Growth, FDI, Investment and
Secondary School Enrollment. Two alternative models for estimating the long-run
effects of stock market on economic growth are used. The first model is a two stage test
of hypothesis of whether the stock markets effect economic growth and the second
model examines the relationship between Stock market development and economic
growth directly rather than through investment behavior. The empirical relation between
stock market and long run growth is found to be strong even after controlling for lagged
growth, initial level of GDP, FDI and Secondary School Enrollment and Domestic
Investment. The paper suggests that stock market development contributes to economic
growth both directly and indirectly. The results of the study suggests that market size
affects investment which affects growth, market liquidity (Turnover Ratio) has a
positive impact on growth while Values of Shares Traded Ratio is found to be not an
effective measure of stock market liquidity.
Aboudou (2010) examines the impact of stock market development on growth in
West African Monetary Union for the period from 1995 to 2006. Stock Market
Development is measured using Stock Market Size (Market Capitalisation to GDP) and
Liquidity (Volume of shares traded over GDP). Economic Growth is measured using
real per capita GDP. The two controlling variables having impact on economic growth
are FDI and Human Capital (Secondary School enrollment ratio). The two Step
procedure of Engle and Granger approach are adopted. The findings show that both
measures of Stock market development demonstrate the importance of stock market
development to growth. The results show that Liquidity has greater impact on Growth
than size. Also, the two controlling variable, namely FDI and Human Capital are found
to be crucial determinants of economic growth in West African Monetary Union.
Aboudou(2009) examines the causal relationship between stock market
development and economic growth for the West African Monetary Union economy.
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Economic growth is proxied by GDP and stock market capitalization is proxied by
market capitalization and total value traded ratio. By applying the techniques of unit–
root tests and the long–run Granger non causality test proposed by Toda and Yamamoto
(1995), the causal relationships between the real GDP growth rate and two stock market
development proxies are tested. The results are in line with the supply leading
hypothesis in the sense that there is strong causal flow from the stock market
development to economic growth. A unidirectional causal relationship is also observed
between real market capitalization ratio and economic growth.
Odhiambo(2010)examines the dynamic causal relationship between stock market
development and economic growth in South Africa for 1971 to 2007 using ARDL-
Bound Testing Procedure. Stock Market Development was measured by stock market
capitalization ratio, value traded ratio and turnover ratio. The economic growth variable
is measured by real per capita GDP. The results show that causal relationship between
stock market development and economic growth is sensitive to the proxy used for
measuring stock market development. When the stock market capitalisation is used as a
proxy for stock market development, the economic growth is found to Granger-cause
stock market development. However, when the stock market traded value and the stock
market turnover are used, the stock market development seems to Granger-cause
economic growth. Overall, the study finds the causal flow from stock market
development to economic growth to predominate. The findings of this study are
consistent with the conventional supply-leading response in which the financial sector is
expected to precede and induce the real sector development. The results apply
irrespective of whether the causality is estimated in the short-run or in the long-run
Gursoy and Muslumov (1998) examines the causality relationship between stock
markets and economic growth in 20 countries based on time series data from 1981 to
1994 using Sims Test based on Granger causality test. Economic growth indicator used
in this study is real per capita GDP. To develop a stock market development indicator,
the study has created an index comprising of volume and liquidity indicators, namely,
total capitalization/GDP, Volume Transactions/GDP and Volumes of transactions/total
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capitalization. The simple arithmetic average of the relative values of the three indictors
has been computed and this average is used as the stock market development index. The
analysis was based on panel data covering all countries with a time lag of three years
and two years was used to detect the direction of causality. The analysis has shown two-
way causation between stock market development and economic growth. With a 3 year
lag, a feedback phenomenon between stock market development and economic growth
was seen at 5 % level and with a 2 year time lag, causation ran from economic growth
to stock market development at 1 % level. However, time series analysis, for individual
countries does not show conclusive results but suggested a stronger link between stock
market development and economic growth in developing countries.
Suliman and Hala(2011) examines the causal relationship between stock market
development and economic growth for Sudan for the period from 1995 to 2009 using
Granger causality approach. The two indicators for stock market development variables
used in this study are market capitalization ratio and real value traded ratio. Real GDP
growth is used as a proxy for economic development. It is found that causal relationship
between stock market development and economic growth is sensitive to the proxy used
for describing stock market development. When stock market capitalization is used, the
results indicate a bivariate causal relationship between stock market development and
economic growth. When stock market liquidity is used, the results show unidirectional
causal relationship from economic growth to stock market development. Hence, Granger
causality test results suggest that stock market development in Sudan leads to economic
growth for the period under study.
Hossain and Kamal(2010) examines the causal relationship between stock market
development and economic growth in Bangladesh for the period from 1976 to 2008
using Unit Root Test, Cointegration test, Granger causality test and Lagrange Multiplier
test. Economic development is measured by the growth rate of real GDP at a constant
market price and real percapita GDP. Stock Market Development is measured by real
market capitalization ratio. The results suggest a long-run equilibrium relationship
between stock market development and economic growth in Bangladesh and
unidirectional causality from stock market development and economic growth.
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Nikolaos and Antonios(2004)examines empirically the direction of causality
between financial development, economic growth and the degree of openness in Greece
for the period from 1960 to 2000 using multivariate auto regressive VAR model,
Johansen cointegration test and Granger causality test. GDP is used as a proxy for
economic growth, the ratio of money supply(M2) to the level of GDP is used as proxy
for financial development and the actual trade flows, exports plus imports is used as a
proxy for degree of openness. The results of the cointegration analysis suggest the
existence of cointegration between the three variables indicating the presence of
common trend or long-run relationship among these variables. The results of the
causality analysis suggest that there exists a strong bilateral relationship between
financial development and economic growth and between degree of economic growth
and degree of openness.
Pradhan(2011) examines the causality and cointegration relationship between
financial development, economic growth and stock market development in India for the
period from 1994 to 2010 using the unit root test, cointegration test and Error Correction
Model. The findings of the analysis confirm that the time series variables are stationary
at the first differences and there is presence of one cointegrating vectors between
financial development, economic growth and stock market development indicating the
presence of long run equilibrium relationship between financial development, economic
growth and stock market development. The findings suggest that stock market
development is an integral part of economic growth which in turn is associated with the
financial development in the economy.
Arestis, Luintel and Luintel (2005) examines the relationship between economic
growth and stock market development controlling for the effects of commercial banking
sector and stock market volatility for five developed countries viz., Germany, United
States, Japan, United Kingdom and France for the period from 1968 to 1998. Output is
measured by the logarithm of real GDP, stock market development by the logarithm of
stock market capitalisation ratio, banking system by the logarithm of the ratio of
domestic bank credit to nominal GDP and Stock market volatility is measured by an
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eight quarter moving standard deviation of the end of quarter change of stock market
prices. The results suggest that banks and stock markets are promoting economic growth
but the influence of banks is more powerful.
Brasoveanu et al(2008) examines the correlation between capital market
development and economic growth in Romania for the period from 2000 to 2006 using
linear regression function and VAR models. Capital market development is measured by
market capitalization ratio(size Variable), turnover ratio and value traded ratio(liquidity
variable) and eight-quarter moving standard deviation of the end of quarter change of
stock market prices(Volatility ratio). Economic growth is measured by logarithm of real
GDP, GDP growth rate and GDP per capita growth rate. The results suggest that capital
market development is positively correlated with economic growth with feed back
effect, but the strongest link is from economic growth to capital market suggesting that
financial development follows economic growth, economic growth determining
financial institutions to change and develop.
Singh (1997) concentrates in the role of stock markets in the liberalization
process in the developing countries in the 1980's and 1990's. He argues that stock market
development is unlikely to help in achieving quicker industrialization and faster long-
term economic growth in most developing countries. He had cited three reasons for the
same. First, the inherent volatility and arbitrariness of the stock market pricing process
under developing country conditions make a poor guide to efficient investment
allocation. Second, the interactions between the stock and currency markets in the wake
of unfavourable economic shocks may exacerbate macroeconomic instability and reduce
long-term growth. Third, stock market development is likely to undermine the existing
group-banking systems in developing countries, which, despite their many difficulties,
have not been without merit in several countries, not least in the highly successful East
Asian economies.
Levine and Zervos (1996) uses pooled cross-country time series regressions
considering the data on 41 countries over the period 1976-1993. The paper uses an
aggregate index of overall stock market development constructed by Demirguc-Kunt
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and Levine (1996b) which combines information on stock market size, liquidity and
integration with world capital markets. While assessing the relationship between stock
market development and economic growth the paper includes a large number of control
variables namely, the logarithm of initial per capita GDP, the logarithm of initial
secondary school enrollment rate, the number of revolutions and coups, the ratio of
government consumption expenditures to GDP, the inflation rate and the black market
exchange rate premium. Using the instrumental variable method of estimation the study
observes that the stock market development is positively correlated with economic
growth even after controlling for other factors associated with long-run growth.
Nazir et al(2010) examines the relationship between stock market development
and economic growth in Pakistan for the period from 1986 to 2008 using Augmented
Dicky-fuller test. The relationship between stock market and economic growth was
analyzed using two major measures of stock market development, Size(market
capitalization divided by GDP) and liquidity(total value of traded shares divided GDP)
as independent variables along with FDI and HDI of Pakistan. The impact of these
variables is empirically tested on GDP per capita as dependent variable for economic
growth. The results revealed that economic growth can be attained by increasing the size
of the stock markets of a country as well as the market capitalization in an emerging
market like Pakistan
Alajekwu and Achugbu(2012) investigate the role of stock market development
on economic growth of Nigeria using a 15-year time series data from 1994 - 2008. The
method of analysis used Ordinary Least Square (OLS) techniques. The stock market
capitalization ratio was used as a proxy for market size while value traded ratio and
turnover ratio were used as proxy for market liquidity. The results show that market
capitalization and value traded ratios have a very weak negative correlation with
economic growth while turnover ratio has a very strong positive correlation with
economic growth. Also, stock market capitalization has a strong positive correlation
with stock turnover ratio. This result implies that liquidity has propensity to spur
economic growth in Nigeria and that market capitalization influences market liquidity.
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Nieuweburgh, Buelens and Cuyvers(2006) attempt to investigate the role of
finance for economic growth in Belgium during the period from 1830 to 2005 using
Granger Causality test and Cointegration Analysis. Stock market development is
measured using 4 indicators, namely, total market capitalization, total number of listed
shares, international character of BXS and the financial depth (degree of concentration).
Bank Development is measured using saving in commercial banks and bank note
circulation and Economic Growth is measured using annual percentage increase in real
percapita GDP. Stock market development and bank development, independently predict
economic growth. Granger Causality analysis finds that both stock market development
and bank development independently predicts economic growth. The cointegration
analysis finds the strongest evidence for growth promoting role of stock markets and
finds that stock market development was a better forecaster of economic growth than
bank based development.
Caporale, et al(2004) examines the causal linkage between stock market
development, financial development/bank development and economic growth for 7
countries, namely, Argentina, Chile, Greece, Korea, Malaysia, Philippines and Portugal
for the period from 1977 to 1998. The econometric methodology used in the study is
Toda and Yamamoto(1995) approach to test for causality in VARs and emphasizes the
possibility of omitted variable bias. Stock Market development is measured using
market capitalization ratio and value traded ratio. Bank deposit liabilities to nominal
GDP and the ratio of bank claims on the private sector to nominal GDP are used as
proxy for bank development. Economic Development is measured using GDP. The
results show that very little evidence of causality was found between bank development
and economic growth. However, causality between financial development, stock market
development and economic growth has been found in 5 countries out of the seven but
the measure of financial development which produced this result was stock market
development.
Seetanah et al(2010) examines the complex linkages between banking sector
development, stock market development and economic growth in a unified framework
for 27 developing countries for the period from 1991 to 2007 using Panel VAR
113
framework. The indicators of Stock Market development used in this study are ratio of
stock market capitalization to GDP, ratio of total value of shares traded to GDP and ratio
of total value if shares traded to market capitalization. Banking sector development is
measured using value of credits by financial intermediaries to private sector divided by
GDP. Growth is measured using real per capita GDP, country’s investment divided by
GDP and secondary school enrollment rate and total of export and import divided by
GDP. The findings show that stock market development is an important ingredient of
growth and stock market development and banking development complement each
other.
Levine and Zervos (1998) investigates whether measures of stock market
liquidity, size, volatility and integration with world capital markets are robustly
correlated with current and future rates of economic growth, capital accumulation,
productivity improvements and saving rates using data on 47 countries from 1976 to
1993. Stock market development indicators used in this study are market capitalization
ratio, turnover ratio and value traded ratio. Integration with world markets is measured
using International Capital Asset Pricing Model and International Arbitrage Pricing
Theory. Banking sector development is measured using the ratio of value of loans made
by commercial banks to private enterprises to GDP. The growth indicators used in the
study are output growth, capital stock growth, productivity growth and savings. The
results suggest a strong and statistically significant relationship between stock market
development and economic growth after controlling for initial income, initial investment
in education, political stability, fiscal policy, openness to trade and macroeconomic
stability. The level of banking development also turns out to be significant in explaining
growth.
Beck and Levine (2004) use panel econometric techniques to assess the
relationship between stock markets, banks and economic growth over the period 1976-
1998 in a panel of 40 countries. They specifically examine whether both measures of
stock market and bank development, have a positive relationship with economic growth
after (i) controlling for simultaneity bias, omitted variable bias and the routine inclusion
of lagged dependent variables in growth regressions (ii) moving to data averaged over
114
five-years instead of quarterly or annual data (iii) assessing the robustness of the results
using several variants of the system estimator and (iv) controlling for many other growth
determinants. Their study shows that the turnover ratio and bank credit both enter
significantly and positively in the growth regressions using the two-step estimator. The
one-step estimator, however, indicates that bank credit does not always enter with a p-
value below 0.10. Specifically, bank credit does not enter significantly when either trade
openness or inflation is controlled for. However, even with the one-step estimator the
financial indicators always enter jointly significantly. Using the alternative system
estimator, it is found that both the stock market liquidity and bank development enter the
growth regressions significantly except when controlling for trade openness. In the
regression controlling for trade openness, bank credit enters with a p-value below 0.05
but turnover is insignificant. Even in this regression, however, they enter jointly
significantly.
Kirankabes and Basarir(2012) examines the causality relationship between the
economic growth and stock market development of Turkey and for the period from 1998
to 2010 using Unit Root Test, cointegration test, Granger Causality Test and VAR
model. Economic Growth is measured using GDP and stock market development is
measured using Istanbul Stock Exchange (ISE) 100 index. The findings show that there
is a long-term relationship between economic growth and the ISE 100 Index, and a one-
way causality relationship with the ISE 100 towards Economic Growth.
Arestis, Luintel and Luintel (2005) examines whether financial structure
influences economic growth in six developing countries namely., Greece, India, South
Korea, Philippines, South Africa and Taiwan over a period of 30(minimum) to
39(maximum) years using multivariate vector auto-regression(VAR), time series and
dynamic heterogeneous panel methods . Financial Structure Ratio is defined as the ratio
of market capitalization to bank lending and economic growth is measured using real
Gross Domestic Product and real gross Fixed Investment. Thus higher Financial
Structure Ratio means a system that is more of the market-based variety while a lower
Financial Structure Ratio means more of a bank-based type. Based on a Cobb-Douglas
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production function specification relating output-labour ratio to capital-labour ratio and
financial structure, their time-series results show that for the majority of the sample
countries financial structure significantly explains economic growth. The results from
the dynamic heterogeneous panels also confirm the significance of the financial
structure.
Demetriades and Khaled (1996) examine the causal relationship between
financial development and economic growth from a time-series perspective considering
data from 16 countries over 27 years and demonstrate that the relationship is country-
specific. Financial development has been measured by two ratios viz., ratio of bank
deposit liabilities to nominal GDP and ratio of bank claims on the private sector to
nominal GDP. They find from the Engle-Granger results that at least one of the financial
indicators is cointegrated with real GDP per capita in five countries viz., Honduras,
South Africa, Sri Lanka, Turkey and Venezuela. On the other hand, based on the
Johansen cointegration test, cointegration was determined between at least one indicator
of financial development and real GDP per capita in 13 out of 16 countries. The
evidence seems stronger in the case of the first financial indicator as this indicator is
observed to be cointegrated with real GDP per capita in 13 countries. Countries which
show no evidence of cointegration between financial development and economic growth
according to Johansen results are Pakistan, Spain and Sri Lanka. Causality tests show
that a bi-directional causal relationship exists in six countries viz., Honduras, India,
Korea, Mauritius, Thailand and Venezuela. There are only three countries viz.,
Honduras, Spain and Sri Lanka in which financial indicator causes economic growth.
The study also finds clear evidence of reverse causation in six countries viz., El
Salvador, Greece, Pakistan, Portugal, South Africa, and Turkey, which refutes the
hypothesis that finance is a leading sector in these countries.
Calderon and Liu (2003) examines the direction of causality between financial
development and economic growth using Geweke decomposition test on pooled data of
109 developing and industrialized countries from 1960 to 1994. The study divides the
countries into two sub-samples viz., developing countries and industrial countries, and
uses two measures of financial development viz., the ratio of broad money (M2) to GDP
116
and the ratio of credit provided by financial intermediaries to the private sector to GDP.
The study also includes a set of controlling variables namely initial human capital, initial
income level, a measure of government size, black market exchange rate premium and
regional dummies for Latin America, East Asia and Africa. The results show that
financial development generally leads to economic growth, the Granger causality from
financial development to economic growth and the Granger causality from economic
growth to financial development coexist, financial deepening contributes more to the
causal relationships in the developing countries than in the industrial countries, the
longer the sampling interval, the larger the effect of financial development on economic
growth and financial deepening propels economic growth through both a more rapid
capital accumulation and productivity growth, with the latter channel being the
strongest.
Beck and Levine(2004) attempts to analyze the link between stock market
development, bank development and economic growth for 40 countries for the period
1975-1998. Stock market development is measured using turnover ratio, banking sector
development is measured by using ‘bank credit’ which is deposit-taking bank claims on
the private sector divided by GDP. Economic Growth is measured using real per capita
GDP growth rate. The result suggest evidence for robust statistical relationship between
banks, stock markets and economic growth and cross-country growth regressions show
the importance of the overall level of financial development, rather than the
composition of the financial system.
Boubakari and Jin(2010) explores the causality relationship between stock
market development and economic growth for 5 Euronext countries, namely, Belgium,
France, Portugal, Netherlands and United Kingdom) for the period from 1995 to 2008
using Granger causality test. Stock market development is measured using market
capitalization, total trade value, turnover ratio and Economic growth is measured using
GDP in USD and Foreign Direct Investment Causal relations were investigated for each
country. The results of the study suggest a positive links between the stock market and
economic growth for some countries for which the stock market is liquid and highly
117
active. However, the causality relationship is rejected for the countries in which the
stock market is small and less liquid
Paramati and Gupta(2011) investigates whether the stock market performance
leads to economic growth or vice versa and examines the short-run and long-run
dynamics of the stock market in India using monthly Index of Industrial Production (IIP)
and GDP data for the period from 1996 to 2009. The study uses Unit root (ADF, PP and
KPSS) tests, Granger Causality test, Engle-Granger Cointegration test and Error
Correction Model. The monthly results of Granger causality test show that there is a
bidirectional relationship between IIP and Stock prices (BSE and NSE) and quarterly
results reveal that there is no relationship between GDP and BSE but in the case of NSE
and GDP there is a unidirectional relationship and that runs from GDP to NSE. The
Engle-Granger residual based cointegration test suggests that there is a long-run
relationship between the stock market performance and economic growth. Similarly, the
results of error correction model reveal that when the long-run equilibrium deviates then
the economic growth adjusts to restore equilibrium by rectifying the disequilibrium. This
study provides evidence in favor of ‘demand following’ hypothesis in the short-run.
Antonios(2010) examines the relationship between financial development and
economic growth for Ireland for the period 1965-2007 using a vector error correction
model (VECM). Financial market development is estimated by the effect of credit
market development and stock market development on economic growth. The objective
of this study was to examine the long-run relationship between these variables applying
the Johansen cointegration analysis taking into account the maximum eigenvalues and
trace statistics tests. The results of the Granger causality tests indicated that economic
growth causes credit market development, while there is a bilateral causal relationship
between stock market development and economic growth. Therefore, it can be inferred
that economic growth has a positive effect on stock market development and credit
market development taking into account the positive effect of industrial production
growth on economic growth for Ireland.
118
Acaravci et al(2007)examines the causal relationship between financial
development and economic growth in Turkey for the period from 1986 to 2004 using
unit root tests, cointegration tests, VECM and VAR framework. Economic growth is
proxied using GDP and financial development is proxied using domestic credit provided
by banking sector. The results show one-way causality from financial development to
economic growth in Turkey.
Azarmi et al(2005) examines the empirical relation between stock market
development and economic growth for a period from 1981 to 2001. Growth is proxied
by using real per capita GDP while stock market development is proxied using market
capitalization ratio, total turnover ratio and turnover ratio. The study finds a negative
correlation between stock market development and economic growth for the post-
liberalization period. The results are consistent with the suggestion that the Indian Stock
market is a casino for the sub-period of post liberalization and for the entire ten-year
event study period.
In order to evaluate the relationship between stock market development and
national growth rates, capital accumulations, rates of technological change, and savings
rates, in the two important recent papers, Levine and Zervos (1996,1998) build on Atji
and Jovanovic's study using various measures of stock market development. They argue
that well-developed stock markets may be able to offer different kinds of impetus to
investment and growth from the development of the banking system. In particular, they
show that increased stock market capitalisation measured by the ratio of the stock
market value to GDP, may improve an economy's ability to mobilise capital and
diversify risk. Liquidity is another important indicator of stock market development in
that it may be inversely related to transaction costs, which impede the efficient
functioning of stock markets. Liquidity is measured by total value of shares traded
relative to either GDP or total market capitalisation. The latter is known as the turnover
ratio and may be an indicator of the level of transaction costs. Other stock market
developments indicators in which Levine and Zervos used are the volatility of market
119
returns and the ability of markets to diversify risk internationally- the degree of stock
market integration with world markets.
Using data from 47 countries over the period 1976-93 Levine and Zervos run
cross-country regressions and find that stock market liquidity is positively and
significantly correlated with current and future rates of economic growth, capital
accumulation, and productivity growth. They also find after including both stock market
and bank indicators in the same regressions, that both banking development and stock
market liquidity are good predictors of economic growth, capital accumulation and
productivity growth. They conclude that stock markets provide different services from
those provided by banks.
In this chapter we attempt to address the gap in this field by providing an
empirical analysis of the effect of stock market development on economic growth in an
individual country- India- by proposing a simple plausible framework that suggests that
the stock market may influence economic growth.
4.4 Stock Market Development Indicators
As mentioned above, well-functioning stock markets can play an important role
in economic development processes by performing the following functions: aggregate
and mobilise capital, enhance liquidity, provide risk pooling and sharing services,
monitor managers and exert corporate control. It is difficult, however, to construct
accurate measures of these functions. Consequently, this study use indicators to suit the
purpose of the concept of stock market development, by constructing proxies for stock
market development that are most commonly used by academics and practitioners (see
Demirguc-Kunt and Levine, 1996a; Levine and Zervos, 1998a, b; and Beck et al.,
1999a). These indicators are associated with the size, and liquidity of the stock market.
While these indicators may be still imperfect measures of how well a stock market
performs the above functions, these measures or indicators together may provide a more
120
accurate picture than if we use only a single indicator. It is useful to provide a brief and
schematic description of such indicators:
(i) Stock Market Size
The study uses Market Capitalisation Ratio (MCR) as indicator to measure the
stock market size. The assumption behind this measure is that overall market size is
positively correlated with the ability to mobilize capital and diversify risk on an
economy-wide basis. The market capitalisation refers to the total value of listed shares
on the stock exchange. Capitalisation of a company is calculated by multiplying the
number of shares outstanding of that company by its share price. To calculate the market
capitalisation, this information is aggregated for all the companies listed in the stock
market. The assumption underlying the use of this variable as an indicator for stock
market development is that the size of the stock market is a measure of the availability
of finance (Rajan and Zingales, 1996; Demirguc-Kunt and Maksimovic, 1998; and
Subrahamanyam and Titman, 1999) and the ability to mobilise capital, diversify the risk
and resources allocation processes. Bekaert and Harvey (1995b, 1997) also argue that
the ratio of equity capitalization to GDP is a useful tool in characterizing the time-series
of market integration. A large market size (market capitalisation relative to economic
activity) suggests that the country is more likely to be integrated into world capital
markets. Furthermore, in an important empirical study, Demirguc-Kunt and Levine
(1996a) find that large stock markets measured by equity capitalisation to GDP are more
liquid, less volatile, more internationally integrated, stronger with regard to information
disclosure laws and international accounting standards, and have unrestricted capital
flows than smaller markets.
Table:- 4.4 and Figure:- 4.4 shows the market capitalisation in BSE and NSE after
1990. It is seen that the market capitalisation has increased over the period of years
indicating that the size of the stock market has been growing over the period of time.
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FIGURE:- 4.4
Trends of market capitalisation in BSE and NSE(Rs Crore)
(ii) Stock Market Liquidity
Liquidity is an important attribute of stock market development because
theoretically more liquid stock markets improve the allocation of capital to their optimal
use, influence investment in the long term and facilitate technological innovation,
thereby enhancing long term growth. Greater liquidity also has a direct impact on the
effectiveness of the governance function of the stock market. First, increased market
activity encourages information acquisition, which in turn increases the information
content of share prices. Second, the effective use of the stock market for corporate
control activities requires that the market be liquid. Takeovers require a liquid capital
market where bidders access a vast amount of capital at short notice. Thus, measures of
market liquidity may reflect the function of the market for corporate control as well.
Therefore, a measure of market liquidity may be a good proxy for information
production as well as the monitoring control function of capital markets. Increased stock
market liquidity can also reduce the cost of equity capital through a reduction in the
expected return that investors require when investing in equity to compensate them for
0
10,00,000
20,00,000
30,00,000
40,00,000
50,00,000
60,00,000
70,00,000
80,00,000
Market Capitalisation BSE Market Capitalisation NSE
122
the risks i. e., risk premium (Ahimud and Mendelson, 1986; Ahimud et al., 1997; Henry,
2000a, b).
A comprehensive measure of liquidity would quantify all the costs associated
with trading, including the time costs and the uncertainty of finding a counterpart and
settling the trade. To measure liquidity, we will use two measures: turnover ratio and
total value of shares traded ratio.
The total value of shares traded ratio equals total value of shares traded on the
stock market exchange divided by GDP. The total value traded ratio measures the
organized trading of firm equity as a share of national output and therefore should
positively reflect liquidity on an economy-wide basis. The total value traded ratio
complements the market capitalization ratio: although a market may be large, there may
be little trading. A higher value traded corresponds to greater liquidity in the market and
greater attractiveness for investors. If trading in the market represents the actions of
investors buying and selling to attain their desired position, then trading activity
measures the speed at which new information is incorporated into prices. The ratio of
organized equity trading as a share of GDP positively reflects liquidity on an economy-
wide base. This ratio also complements the market capitalisation ratio since the market
size measured by market capitalisation be large, but relatively inactive as measured by
trading activity.
The second measure of market liquidity is the turnover ratio. This ratio is equal to
the value traded divided by market capitalisation. It measures the size of equity
transaction relative to the size of the stock market. High turnover ratio is often used as
an indicator of low transaction costs. A higher turnover ratio may represent greater
liquidity and market efficiency. Brennan and Subrahmanyam (1996) find that the
number of analysts following a stock is strongly positively related to the liquidity of the
stocks and that low turnover stocks are followed by fewer analysts and thus are slower to
react to information than high turnover stocks. Thus, illiquid stocks react to market
information more slowly than do liquid stocks. However, an excessively high turnover
123
ratio may represent inefficiency or excessive speculative trading. The higher turnover
ratio in many Asian markets has been attributed to the speculative trading in those
markets, which may not represent useful economic activity. Bencivenga et al., (1996)
give a model in which excessive liquidity and turnover lower the economic growth rates.
Since this indicator is the ratio of a stock and a flow variable, we apply a similar
deflating procedure as for the market capitalisation indicator.
It is worth noting here that the turnover ratio complements the earlier cited
measure of liquidity, since although markets may be small compared to the size of the
economy (as measured by the value traded as a percentage of GDP) they may be liquid.
Thus, while an absolute measure of liquidity (such as the value traded as a percentage of
GDP) may be indicative of liquidity in the economy as a whole, it may be misleading as
a measure of market liquidity if the size of the economy is very large. A classic example
is Brazil. In this country there is not much equity trading relative to the size of the
economy (which is large), however, it has a higher turnover ratio reflecting a small but
active stock market (Demirguc-Kunt and Levine, 1996a). Consequently, incorporating
market size measures by market capitalisation, total value traded as a percentage of
GDP, and turnover ratio, provides a more comprehensive picture of stock market
development than any single indicator can provide.
Another advantage of using value-traded ratio and turnover ratios is that the main
purpose of this study is to evaluate whether the liquidity services provided by the stock
market are robustly correlated with economic growth. Unlike much of the literature on
liquidity that focuses on evaluating whether a stock's liquidity affects its price and rate
of return we do not want to measure the degree of liquidity. We want to measure the
degree to which the stock market provides liquidity to the Indian economy. The stock
market may be highly liquid with correspondingly high turnover ratios, but it may not be
providing significant liquidity to the economy as a whole. Thus, turnover ratio may not
satisfy our objectives. However, value-traded ratio measures trading relative to the size
of the whole economy. Therefore, the value-traded and trading-volatility ratios may
provide more information about the provision of liquidity than turnover ratios.
124
As shown from the above discussion, it is apparent that the value-traded ratio is
more closely associated with this study, because, unlike other liquidity indicators, it
focuses on economy wide bases. However, using the value-traded ratio has a potential
disadvantage. If the market anticipates large corporate profits, stock prices will
invariably rise. This price rise would increase the value of transactions and therefore
raise the value-traded ratio. In this case, this liquidity indication would rise without a
rise in the number of transactions or a fall in the transaction costs (Levine and Zervos,
1998a). This price also affects the market capitalization ratio. To avoid the influence of
the price effect we need to look at the stock market capitalisation and the value-traded
ratio together. If we include both indicators together in the regression and the value
traded remains significantly correlated with growth after controlling for the market
capitalisation ratio, then this implies that the price effect is not dominating the
relationship between the value-traded ratio and growth.
Table 4.4 and Figure 4.5 shows the total value traded in BSE and NSE after
1992. It is seen that the total value traded has increased over the period of years
indicating that liquidity of the Indian stock markets has been increasing over the period
of time.
FIGURE 4.5
Trends of total value added in BSE and NSE
0
5,00,000
10,00,000
15,00,000
20,00,000
25,00,000
30,00,000
35,00,000
40,00,000
45,00,000
Total Value Traded BSE Total Value Traded NSE
125
TABLE:-4.4
Market capitalisation, value traded and turnover ratio of BSE and NSE
Year
Market Capitalisation BSE
Market Capitalisation NSE
Total Value Traded BSE
Total Value Traded NSE
Turnover ratio in BSE
Turnover ratio in NSE
1990-91 90,836 NA
1991-92 3,23,363 NA
1992-93 1,88,146 NA 45,696
0.243
1993-94 3,68,071 NA 84,536
0.230
1994-95 4,35,481 3,63,350 67,749 1,805
0.156 0.005
1995-96 5,26,476 4,01,459 50,064 67,287
0.095 0.168
1996-97 4,63,915 4,19,367 1,24,190 2,95,403
0.268 0.704
1997-98 5,60,325 4,81,503 2,07,113 3,70,193
0.370 0.769
1998-99 5,45,361 4,91,175 3,10,750 4,14,474
0.570 0.844
1999-00 9,12,842 10,20,426 6,86,428 8,39,052
0.752 0.822
2000-01 5,71,553 6,57,847 10,00,032 13,39,510
1.750 2.036
2001-02 6,12,224 6,36,861 3,07,292 5,13,167
0.502 0.806
2002-03 5,72,198 5,37,133 3,14,073 6,17,989
0.549 1.151
2003-04 12,01,207 11,20,976 5,03,053 10,99,534
0.419 0.981
2004-05 16,98,428 15,85,585 5,18,715 11,40,072
0.305 0.719
2005-06 30,22,191 28,13,201 8,16,074 15,69,558
0.270 0.558
2006-07 35,45,041 33,67,350 9,56,185 19,45,287
0.270 0.578
2007-08 51,38,014 48,58,122 15,78,857 35,51,038
0.307 0.731
2008-09 30,86,075 28,96,194 11,00,074 27,52,023
0.356 0.950
2009-10 61,65,619 60,09,173 13,78,809 41,38,023
0.224 0.689
2010-11 6,839,084 6,009,173 1,105,027 3,577,410
0.162 0.595
2011-12 6,214,941 6,702,616 667,498 2,810,893
0.107 0.419
126
4.5 Methodology and Data Analysis
The present study uses monthly data of BSE for the time span of November 1994
to March 2012. The collected data are converted into logarithmic form. The variables
selected are used for understanding the causal relationship between stock market
development and economic growth. One of the important indicators of economic growth
is the GDP. However, the GDP data are available only annual basis. Hence, Index of
Industrial Production (IIP) is taken as the proxy variable. The selection of IIP as a proxy
variable is supported by various studies pertaining to stock market and economic growth
(Paramati and Gupta 2005, Nair 2008).
For measuring the Market Capitalisation Ratio(MCR) and Value Traded
Ratio(VTR), the GDP figure is needed. However, monthly GDP figure is not available
in India during the analysis period. As IIP is used as a proxy for GDP, MCR and VTR
cannot be estimated and only Turnover ratio (TR) is used as an index of stock market
development.
4.5.1 Empirical model
The present study undertakes a comprehensive set of econometric tests for the
empirical analysis such as; Unit root (ADF, PP and KPSS) tests, Granger Causality test,
Engle-Granger Cointegration method and finally; Error Correction Model (ECM).
The study starts with the conventional unit root tests, to find out the order of
integration. The important unit root tests used here are the Augmented Dickey-Fuller
(Dickey and Fuller, 1979) test, Phillips-Perron (Phillips and Perron, 1988) test and the
KPSS (Kwiatkowski et al. 1992) test. All of these unit root tests are used to test whether
the data contains unit root (non-stationary) or is a stationary process. A series is said to
be stationary if the mean and auto co variances of the series do not depend on the time
factor. Any series that is not stationary then it is said to be non-stationary. A series is
127
said to be integrated of order ‘d’ which can be denoted by I (d), means that it has to be
differenced ‘d’ times before it becomes stationary. Otherwise, if a series by itself, let say
stationary at levels, without having to be differenced, then that is said to be I(0). It is
very essential to apply unit root tests for individual series to come up with some idea
that whether the variables are integrated with same order or not. If the order of
integration is same for the entire variables then it is quite possible that study can find out
the long run and short run dynamic behavior of the variables by employing Engle-
Granger cointegration test and error correction model. (Details of the test are given in
Chapter I).
Cointegration exists for variables means despite variables are individually non-
stationary, a linear combination of two or more time series can be stationary and there is
a long-run equilibrium relationship between these variables. If the error term in (1) or (2)
is stationary while the regressors are individually trending, there may be some transitory
correlation between the individual regressors and error term. However, in the long run,
the correlation must be zero because of the fact that the variables must eventually
diverge from stationary ones. Thus the regression on the level of the variables is
meaningful and not spurious.
There are two most widely used cointegration tests namely Engle-Granger (1987)
two model approaches and the Johansen (1998) and Johansen and Juselius (JJ) (1990)
maximum likelihood estimator. Gonzalo (1994) provide empirical evidence to support
the Johansen’s method is superior over other methods (ordinary least squares, nonlinear
least squares, principal components and canonical correlations) for testing the number of
so integrating relationship. Therefore, we employ the maximum likelihood method of
Johansen (1988 and Johansen (1988) and Johansen and Juselius (1990) to test the
cointegration. The JJ test is based on vector autoregressive model. (Details are given in
Chapter I).
The causality between stock market development and economic growth are tested
by using ordinary Granger Causality bi-variate test and Granger test based on Error
Correction Model(ECM) methodology. From the ordinary Granger bi-variate test, we
128
will be able to test the existence of short run relationship between variables. However,
the ECM methodology provides the evidence of short run as well as long run dynamic
relationship between variables. (Details are given in Chapter I).
Variance decomposition and impulse response analysis are also done in this
context. Impulse responses trace out the responsiveness of the dependent variables in the
VAR to shocks to each of the variables. So, for each variable from each equation
separately, a unit shock is applied to the error, and the effects upon the VAR system
over time are noted. Variance decompositions offer a slightly different method for
examining VAR system dynamics. They give the proportion of the movements in the
dependent variables that are due to their ‘own’ shocks, versus shocks to the other
variables. A shock to the ith variable will directly affect that variable of course, but it
will also be transmitted to all of the other variables in the system through the dynamic
structure of the VAR. (Details are given in Chapter I).
4.5.2 Analysis and results
(i) Descriptive statistics for BSE index
Table:- 4.5 displays general information about the Index for industrial production
(IIP), Market Capitalisation (MC), Turnover Ratio (TR) and Value Traded (VT) of
BSE. The all stock market indices are positively skewed. It indicates there is more
number of occurance of all these indices. During the selected period of the analysis ie,
since 1991 all selected indices such as market size and liquidity are showing very high
fluctuations and that too at a higher level. The values of Kurtosis show that except MCR,
all indices are showing a leptokurtic shapes. The leptokurtic distribution means that the
concerned distributions are more “peaked” and have “fatter tails; and hence greater
possibility of extreme outcomes, than is the case in the normal distribution. Consistent
with the skewness and kurtosis findings, the Jarque-Bera statistic is highly significant
(P=0.000) thereby rejecting the hypothesis that the series in BSE are normally
distributed.
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Table:-4.5
Stock Market Indices of BSE
BSE
IIP
Market
Capitalisation
(MC)
Value Traded
(VT)
Turnover Ratio
(TR)
Mean 208.9801 23012.41 557.1474 3.586400
Median 184.7000 9273.830 466.3906 2.870490
Maximum 402.9000 72945.70 1990.887 16.52077
Minimum 109.6000 3485.160 20.24000 0.444527
Std. Dev. 71.44913 22542.91 404.1543 2.784921
Skewness 0.594829 0.915977 0.803357 2.132559
Kurtosis 2.184393 2.270862 3.275433 8.487960
Jarque-Bera 18.11771 33.85538 23.14149 420.6903
Probability 0.000116 0.000000 0.000009 0.000000
Sum 43676.84 4809593. 116443.8 749.5576
Sum Sq. Dev. 1061836. 1.06E+11 33974871 1613.203
Observations 209 209 209 209
The following charts represent the trends in IIP with stock market indices of
MCR, TR and VTR in log forms. The trends in movements of MCR and TR are more
close to IIP movements. Compared to other stock market indices, the VTR witnessed
more fluctuations.
130
Figure:- 4.6
Trends in IIP and stock market variables of BSE
(ii) Granger causality test results
The Granger causality test shows the short run relation between various entities.
Here the test result shows relationship between economic growths as indicated by the
proxy variable IIP with stock market index Turnover Ratio (TR). Analysis is done on the
log values of the variables. The results of this test are shown in Table:-3. The results
indicates that there is an unidirectional relationship exists between IIP and TR. Turnover
ratio does not Granger cause the economic growth while the economic growth as
represented by IIP does Granger cause the stock market development which is
represented by TR.
-2
0
2
4
6
8
10
12
25 50 75 100 125 150 175 200
LOGIIP LOGMCLOGVT LOGTR
Trends in IIP and stock market variables of BSE
logTR
logMC
logVT
logIIP
131
Table:-4.6
Granger Causality Results for Economic growth
Index Vs. selected Stock Market Indices of BSE
Variables Causality F-statistics P-value
Turnover ratio (TR) TR IIP 0.29875 0.7421
IIP TR 3.10637* 0.0469
‘*’ shows the test statistics at 5% level of significance and ‘**’ shows the test statistics at 10% level of significance
(iii) Unit root and cointegration analysis
If there is any causal connection exists between economic growth and stock
market development, the next immediate concern would be to understand the long-run
relationship between these variables. The existence of long run relationship can be
understood through the integration test. If the two variables are co-integrated, then it
could be presumed that these variables have a long run relationship. For the
cointegration analysis, stationarity is first verified with estimates in Table :-4.7 The
initial hypothesis is that the variables contain a unit root. As discussed before, the unit
root test are conducted by using Augmented Dickey-Fuller (ADF) test, Phillips-Perron
(PP) test and the Kwiatkowski, Phillips, Schmidt and Shin (KPSS) test. The tests are
conducted for BSE. The tests are conducted at variable levels and first difference levels.
The results show that the hypothesis of unit root is not rejected at levels for BSE and the
same null hypothesis of unit root is rejected at first difference levels. Hence all variables
are integrated of order 1, I(1) for BSE. The cointegration analysis and long relationship
of economic growth and stock market development are pursued only in the case of BSE.
132
Table:- 4.7
Unit root result (log values) of IIP and Turnover ratio of BSE
(iv) The Engle Granger Method
The cointegration and long run relationship between stock market development
and economic growth are evaluated based on the Engle Granger method. According to
this method, suppose we expect that there exists a single long-run relationship between
the two I(1) variables Y and X of the form : Y = 0 + 1X . Two steps involved in this
procedure. As a first step, estimate by OLS the long run relationship using the
cointegrating regression:
Y = 0 + 1X +u ----------------------------(4.1)
^ ^ ^
It is estimated as: Y = 0 + 1X ----------------------------(4.2)
Defining and saving the disequilibrium errors as:
Level Variables
ADF PP KPSS
t-value P-value t-value P-value LM stat Critical
value at
5%
IIP -0.74613 0.8312 -0.71154 0.8403 1.824631 0.463
TR -1.46464 0.5497 -1.7249 0.4172 0.477925 0.463
First Difference variables
ADF PP KPSS ADF PP KPSS
t-value P-value t-value t-value P-value t-value
IIP -2.88064 0.0495 -37.3113 0.0001 0.07763 0.463
TR -14.0392 0 -19.6921 0 0.301596 0.463
133
^ ^ ^ ^
u = Y – Y = Y – ( 0 + 1X) -----------------------------(4.3)
If Y and X are cointegrated, u should be stationary I(0). If ‘u’ is stationary, there
exists a long run relationship between these variables and in the long run the market
disequilibrium will be corrected to certain extent by the movement of these variables.
The Engel Granger method is known as residual method for evaluating the co-
integration. Once it is cointegrated, then the Residual based Error Correction model will
be the next step, which is given as below.
From the basic model of Y = 0 + 1X +u, the Error Correction model (ECM)
specification is as follows.
^ ^ ^
Y= 0 + 1X + 2 (Yt-1- 0- 1 Xt-1) +v ------------------------------(4.4)
The interpretation is that Y is purported to change between t-1 and t as a result of change
in X between t-1 and t and in part to correct for any disequilibrium that existed in
previous period. The Cointegrating vector is [ 0 1]. 1 measures the LR relation
between Y and X, 1 measures the SR relation between X and Y and 2 speed
adjustment back to equilibrium.
(v) Relationship between economic growth and stock market development
The relationship between economic growth and stock market development are
assessed based on IIP as the dependent variable (variable for economic growth) and
turnover ratio(TR) as the independent variable (variable for stock market development).
LogIIP = 0 + 1logTR+u ------------------------------(4.5)
The result of the above model is given in Table 4.8.
The result shows that there is a negative relationship between TR and IIP in the
long run and this relationship is significant.
134
Table:-4.8
Regression result of turnover ratio on IIP index
Variable Coefficient Std. Error t-Statistic Prob.
LOGTR -0.139306 0.031596 -4.408918 0.0000
C 5.429788 0.039463 137.5901 0.0000
R-squared 0.085845 Akaike info criterion 0.573196
Adjusted R-
squared 0.081429 Schwarz criterion 0.605180
F-statistic 19.43856 Hannan-Quinn criter. 0.586127
Prob(F-
statistic) 0.000017 Durbin-Watson stat 0.047133
To understand whether these two variables are integrated, the residuals terms of
the first equation is regressed to test the stationarity. The result of the stationarity result
of the ‘u’ term is given in the Table:- 4.9..
Table:-4.9
Unit root result if residual term of regression of TR on IIP index
Null Hypothesis: RESIDTRLEVEL has a unit root
Exogenous: Constant
Lag Length: 12 (Automatic based on SIC, MAXLAG=14)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.959301 0.0406
Test critical values: 1% level -3.463749
5% level -2.876123
10% level -2.574622
The test result indicates that null hypothesis of existence of unit root in residual
term is rejected. It implies that ‘u’ is stationary, there exists a long run relationship
between these variables and in the long run the market disequilibrium will be corrected
to certain extent by the movement of these variables. The error correction mode is
applied to understand the long run relationship between IIP and TR.
Y= 0 + 1X + 2 (Yt-1- 0- 1 Xt-1) +v ---------------------------(4.6)
135
logIIP= 0.006+0.046logTR-0.013(logIIPt-1-5.42+-0.14logTRt-1) -----(4.7)
The above error correction (ECM) shows that in the short run there exists
positive relationship between IIP and TR. It shows that the stock market development
positively affect the economic growth of the nation. However, in the long run, the
relationship is negative. These two variables are cointegrated in the long-run. The
deviation between IIP and TR are corrected in the long-run by a desirable change in TR
by 1.3 percent per period.
TABLE:-4.10
The Result of Error Correction model showing the causal relation
between IIP and Turnover ratio (TR)
Dependent Variable: IIP
Variable Coefficient Coefficient values Std. Error t-Statistic Prob.
Constant of IIP 0 0.005568 0.003795 1.467159 0.1439
TR 1 0.045976 0.016821 2.733187 0.0068
U(-1) 2 -0.012752 0.012023 -1.060585 0.2901
Constant of IIP 0 5.429788 0.039463 137.5901 0.0000
TR 1 -0.139306 0.031596 -4.408918 0.0000
R-squared 0.044593 Akaike info criterion -2.958305
Adjusted R-squared 0.035272 Schwarz criterion -2.910168
F-statistic 4.784127 Hannan-Quinn criter. -2.938841
Prob(F-statistic) 0.009318 Durbin-Watson stat 2.916552
(vi) Vector Autoregression (VAR) model between IIP and TR
Various literature survey results indicate that economic growth and stock market
development are mutually interrelated and these relationships may be time lagged also.
VAR model helps us to decipher the mutual interconnection between variables with time
lag.
The following Table shows the VAR model result between IIP and TR, which
represent economic growth and stock market development respectively. The result is
given in the following Table. It shows that economic growth has a trend always. Current
136
values of economic growth are influenced by its lagged values. Compared to IIP, the TR
has more fluctuating trend.
TABLE:-4.11
VAR between IIP and TR
LOGIIP LOGTR
LOGIIP(-1)
0.496784
[ 8.04727]
-0.541057
[-1.93772]
LOGIIP(-2)
0.495842
[ 8.02308]
0.458225
[ 1.63924]
LOGTR(-1)
-0.011572
[-0.75702]
0.745799
[ 10.7867]
LOGTR(-2)
0.011644
[ 0.76630]
0.207177
[ 3.01443]
C
0.046708
[ 0.80239]
0.486249
[ 1.84681]
R-squared 0.979282 0.906019
Adj. R-squared 0.978871 0.904158
Sum sq. resids 0.469876 9.612812
S.E. equation 0.048230 0.218147
F-statistic 2386.945 486.8448
Log likelihood 336.3883 23.98560
Akaike AIC -3.201819 -0.183436
Schwarz SC -3.121319 -0.102935
Mean dependent 5.291224 1.038675
S.D. dependent 0.331803 0.704648
Determinant resid covariance (dof adj.) 0.000109
Determinant resid covariance 0.000104
Log likelihood 361.7235
Akaike information criterion -3.398295
Schwarz criterion -3.237294
137
IIP has positive relationship with its lagged values. IIP has negative relationship with
one month lagged values of TR. However, IIP has positive relationship with two month
lagged values of TR. The result obtained is similar to the error correction model
explained earlier.
(vii) Variance Decomposition and Impulse Response Results
The variance decompositions which show the proportion of the movements in the
dependent variables that are due to their ‘own shocks, versus shocks to the other
variable. The result of variance decomposition is given in Table :- 4.12. The variance
decomposition has done for twenty months. The result indicates that the economic
growth behaves exogenously. In the initial period, the variation in changes in economic
growth is caused by the economic growth itself. As time passes, the change in economic
growth is contributed by the selected stock market variables. However, the impact
exerted by the stock market development variables on economic growth is very low.
Only less than 1% of variation in economic growth is attributed by stock market
development even after twenty months. Similar to economic growth, the stock market
development represented by TR behaves exogenously. The contribution of economic
growth in creating variation in TR is very minimum in the Indian stock market.
Table:-4.12
Forecast Variance Decomposition Analysis of Stock Market
Development and Economic Growth
Horizon IIP TR
IIP
1 100.00 0.00
5 99.84 0.16
10 99.86 0.14
15 99.89 0.11
20 99.90 0.10
TR
1 1.30 98.70
5 0.51 99.49
10 0.46 99.54
15 0.79 99.21
20 1.41 98.59
138
The impulse response estimates is given in the Table 4.13. It provides normalized
responses for the economic growth (IIP) for a typical shock to and from the economic
growth variable. These responses represent unit shocks measured standard deviations.
As can be seen from the results, the shocks in stock market development variables have
less impact on economic growth variable, IIP. The shock emanated from economic
growth has some impact on the stock market development and the impact decrease over
the time.
TABLE:-4.13
Result of impulse response function between
stock market development and economic growth
Horizon IIP TR IIP TR
To IIP From IIP
1 0.048 0.000 0.000 0.217
5 0.033 -0.001 -0.001 0.152
10 0.031 -0.001 -0.001 0.125
15 0.030 -0.001 -0.001 0.103
20 0.029 0.000 0.000 0.084
The impulse response function is shown graphically as given below. The first set
of figures show the response of IIP and TR due to one unit shock impulse of IIP. It has
fluctuating impact on TR. After initial periods, the effect creates negative impacts which
sustain over a long period. The second set of figures show the impact of changes in TR
on IIP and TR. It shows that impact is negative on IIP and it become steady after the
initial seven time periods.
139
Figure 4.7
Impulse response functions diagramatically
4.6 Conclusion
The foregoing analysis made an attempt to explore the relationship between stock
market development and economic growth in the Indian economy during the period
from 1994 to 2012. The study primarily revolved around two major questions: first
whether at all any relationship exists between stock market development and economic
growth and secondly, what could be the nature and direction of the causal relationship, if
any i.e. does development of stock market promote economic growth or vice versa? The
nature of the causal link between these is evaluated by using IIP (Index of Industrial
-.02
.00
.02
.04
.06
2 4 6 8 10 12 14 16 18 20
Response of LOGIIP to LOGIIP
-.02
.00
.02
.04
.06
2 4 6 8 10 12 14 16 18 20
Response of LOGIIP to LOGTR
-.1
.0
.1
.2
.3
2 4 6 8 10 12 14 16 18 20
Response of LOGTR to LOGIIP
-.1
.0
.1
.2
.3
2 4 6 8 10 12 14 16 18 20
Response of LOGTR to LOGTR
Response to Cholesky One S.D. Innovations ± 2 S.E.
140
Production)as proxy for economic growth and Turnover ratio (TR) as proxy for stock
market development. The result of the Granger causality test shows that there is
unidirectional relationship between IIP and Turnover Ratio. Turnover ratio does not
Granger causes the economic growth while the economic growth as represented by IIP
does Granger cause the stock market development which is represented by TR. The test
results suggest that stock market development in India leads to economic growth at least
for the period under study, which is in line with the ‘supply leading’ hypotheses. In
addition, to that the causal relationship between stock market development and
economic growth is sensitive to the proxy used for describing the stock market
development.
REFERENCES
1) Aboudou, MamanTachiwou.( (2009), ‘Causality Test Between Stock Market
Development and Economic Growth in Western African Monetary Union,’
Economia Seria Management, Vol 12(2), pp 14-27.
2) Aboudou, MamanTachiwou,(2010), ‘ Stock Market Development and Economic
Growth: The case of West African Monetary Union,’, International Journal of
Economics and Finance, Vol 2(3), pp 97-103.
3) Acaravi, Al., Ozturk Ilhan. and Acaravci, Songul Kalilli(2007), ‘ Finance-Growth
Nexus: Evidence from Turkey’, http://dx.doi.org/10.2139/ssrn.1104693.
4) Ahluwalia, Montek S. (2002), ‘Economic Reforms in India since 1991: Has
Gradualism Worked?,’ Journal of Economic Perspectives, Vol 16 (3), pp 67-
88.
5) Alajekwu, U. B. and Achugbu, A. A. (2012), ‘The Role of Stock Market
Development on Economic Growth in Nigeria: A Time Series Analysis,’
African Review Research: Multidisciplinary Journal, Vol6(1), pp 51-70.
141
6) Antonios A. (2010), ‘Stock Market and Economic Growth: An Empirical
Analysis for Germany,’ Business and Economics Journal, Volume 2010: BEJ-
1.
7) Arestis, Philip., Ambika D. Luintel and Kul B. Luintel(2005), ‘Financial
Structure and Economic Growth,’ Center for Economic and Public Policy,
Working Paper 06/05.
8) Atje, R. and Jovanovic, B. (1993), ‘Stock Markets and Development,’ European
Economic Review, Vol 37, pp 632-640.
9) Azarmi, T., Lazar, D. And Jeyapaul, J.(2005), ‘ Is the Indian stock market a
casino?,’ Journal of Business and Economic Research, Vol 3(4), pp 40-86.
10) Beck, T, Levine R and Norman Loayza(2000), ‘Finance and the Sources of
Growth,’ Journal of Financial Economics, Vol 58, pp 261-300.
11) Beck T. and Levine R.( 2004), ‘Stock Markets, Banks, and Growth: Panel
Evidence,’ Journal of Banking & Finance, Vol 28, pp 423–442.
12) Bencivenga, V.R and Smith, B.D. (1991), ‘Financial Intermediation and
Endogenous Growth,’ Review of Economic Studies, Vol 52(2), pp195 – 209.
13) Boubakari, Ake and Jin, Dehuan.(2010), ‘The Role of Stock Market
Development in Economic Growth: Evidence from Some Euronext Countries,’
International Journal of Financial Research, Vol 1(1), pp 14-20.
14) Brasoveanu, L. O., Dragota, V., Catarama, D. and Semenescu, A. (2008),
‘Correlations between Capital Market Development and Economic Growth: The
Case of Romania,’ Journal of Applied Quantitative Methods, Vol 3, pp 64-75.
142
15) Calderon, C. and Liu, L. (2002), ‘The Direction of Causality between Financial
Development and Economic Growth,’ Working Paper, Central Bank of Chile.
16) Caporale, G., Howells, P. and Soliman, A. (2004), ‘Stock Market Development
and Economic Growth: The Causal Linkages,’ Journal of Economic
Development, Vol 29, pp 33-50.
17) De Gregorio, J. and Guidotti, P.E. (1995), ‘Financial Development and Economic
Growth,’ World Development, Vol 23, pp 433-48.
18) Demirguc-Kunt, A. and Levine, R. (1996), ‘Stock Markets, Corporate Finance
and Economic Growth: An Overview,’ World Bank Economic Review, 10(2),
pp 223-239.
19) Demetriades, P. and Khaled H. (1996), ‘Does Financial Development Cause
Economic Growth? Time-series Evidence from 16 Countries,’ Journal of
Development Economics, Vol 51, pp. 387-411.
20) Devereux, M.B. and Smith, G. W. (1994), ‘International Risk Sharing and
Economic Growth,’ International Economic Review, Vol. 35(4), pp. 535-550.
21) Diamond,D.W. and Dybvig, P.H.(1983), ‘Bank Runs, Deposit Insurance and
Liquidity,’ Journal of Political Economy, Vol 85, pp 191-206.
22) Dickey, D.A. and W.A. Fuller (1979). ‘Distributions of the Estimators for
Autoregressive Time Series with Unit Root,’ Journal of the American
Statistical Association, Vol 74, pp 427-431.
23) Engle, R. and C. Granger. (1987), ‘Cointegration and Error Correction:
Representation, Estimation and Testing,’ Econometrica, Vol 55, 251-276.
143
24) Ghani, E. (1992), ‘How Financial Markets Affects Long-run Growth: A Cross-
Country Study,’ Washington DC, World Bank, PPR Paper Series WPS 843, pp 1-
29.
25) Goldsmith, R.W. (1969), ‘Financial Structure and Development,’ New Haven,
CT: Yale University Press.
26) Gonzalo, Jesus.(1994), ‘ Five alternative methods of estimating long-run
equilibrium relationships,’ Journal of Econometrics, Vol 60(1-2), pp 203-233.
27) Greenwald, B. and Stiglitz, J. E.(1989), ‘Financial Market Imperfections and
Productivity Growth,’ NBER Working Paper No. 2365.
28) Greenwood, J. and Jovanovic, B. (1990), ‘Financial Development, Growth and
the Distribution of Income,’ Journal of Political Economy, Vol. 98(5), pp
1076-1107.
29) Gursoy, T. and Muslumov, A (1998) ‘Stock Markets and Economic Growth: A
Causality Test,’ MBA thesis, Institute of Social Sciences, Istanbul.
30) Hargis, K. (1997), ‘Forms of Foreign Investment Liberalization and Domestic
Stock Market Development in Emerging Economies,’ University of North
Carolina, Working Paper.
31) Hossain, Sharif and Kamal, Mostafa(2010), ‘Does Stock Market Development
Cause Economic Growth? A Time Series Analysis for Bangladesh Economy,’
International Conference on Applied Economics- ICOAE 2010.
32) Johansen, S. (1988), ‘Statistical Analysis of Cointegration Vectors,’ Journal of
Economic Dynamics and Control, Vol. 12, pp. 231-54.
144
33) Johansen, S. and K. Juselius (1990), ‘Maximum Likelihood Estimation and
Inference on Cointegration - with Application to the Demand for Money,’
Oxford Bulletin of Economics and Statistics, Vol 52, pp 211-244.
34) King, G. and Levine, R. (1994), ‘Capital Fundamentalism, Economic
Development, and Economic Growth,’ World Bank Policy Research Working
Paper WPS 1285, pp1-48.
35) King, G. and Levine, R. (1993a), ‘Finance and Growth: Schumpeter Might Be
Right,’ Quarterly Journal of Economics, Vol 108(3), pp1351-1353.
36) Kirankabes, Mustafa Cem; Basarir, Agatay(2012), ‘Stock Market Development
and Economic Growth in Developing Countries: An Empirical Analysis for
Turkey,’ International Research Journal of Finance & Economics, Vol 87, pp
134-146.
37) Kochhar, Kalpana, Raghuram Rajan, Arvind Subramanian and Ioannis
Tokatlidis. (2006), ‘India’s Pattern of Development: What happened, What
Follows,’ NBER Working Paper No. 12023, Cambridge, Massachusetts.
38) Kwiatkowski D., P.C.B. Phillips, P. Schmidt and Y. Shin (1992), ‘Testing the
Null Hypothesis of Stationary against the Alternative of a Unit Root,’ Journal
of Econometrics, Vol 54, pp 159-178.
39) Levine, R. (1991), ‘Stock Markets, Growth and Tax Policy,’ Journal of Finance,
Vol. 46(4), pp. 1445-1465.
40) Levine, R(2000), ‘ Are bank-based or market based financial systems better?,’
Journal Economia Chilena(The Chilean Economy), Vol 3(1), pp 25-55.
145
41) Levine, R.(1997), ‘Financial Development and Economic Growth: Views and
Agenda,’ Journal of Economic Literature,Vol 35 (1), pp 688–726.
42) Levine, R. and S. Zervos, (1996), ‘Stock Market Development and Long-Run
Growth,’ World Bank Economic Review, Vol 10(2), pp 323-339.
43) Levine, R. and Zevros, S. (1998), ‘Stock Markets, Banks and Economic Growth,’
American Economic Review, Vol 88(3), pp 537-558.
44) Levine, R., Loayza, N., Beck, T.(2000), ‘Financial intermediation and growth:
causality and cause,’ Journal of Monetary Economics, Vol 46(1), pp 31-77.
45) McKinnon, R.I. (1973), ‘Money and Capital in Economic Development,’
Washington, DC: Brookings Institution.
46) Mohtadi, H and Agarwal, S (2004), ‘Stock Market Development and Economic
Growth: Evidence from Developing Countries,’ http//www.edu/mo.htadi/PAN–
4– 01. pdf, pp 1 –18.
47) Nair, Lekshmi R.(2008), ‘ Macroeconomic Determinants of Stock Market
Development in India,’ NSB Management Review, Vol 1(1), pp 1-9.
48) Nazir M. S., Nawaz M. M. and Gilani U. J. 2010, ‘Relationship between
Economic Growth and Stock Market Development,’ African Journal of
Business Management, Vol. 4(16), pp. 3473-3479.
49) Nieuwerburgh, S.T; Buelens, F. and Cuyvers, L. (2006), ‘Stock Market
Development and Economic Growth in Belgium,’ Explorations in Economic
History, Vol 43, pp 13-38.
146
50) Nikolaos, Dritsakis and Antonios Adamopoulos(2004) ‘Financial development
and economic growth in Greece: an empirical investigation with Granger
causality analysis,’ International Economic Journal, Vol 18(4), pp 547-559.
51) Odhiambo (2010), ‘Stock Market Development And Economic Growth In South
Africa: An ARDL-Bounds Testing Approach,’ A paper presented at the Annual
American Business Research Conference, Las Vegas, Nevada, USA
52) Paramati, Sudharshan R. and Rakesh Gupta (2011), ‘An Empirical Analysis of
Stock Market Performance and Economic Growth: Evidence from India,’
International Research Journal of Finance and Economics, Vol 73, pp 133-
149.
53) Phillips, P.C.B. and P. Perron (1988), ‘Testing for a Unit Root in Time Series
Regression,’ Biometrika, Vol 75, pp 335-346.
54) Pradhan, R.(2011), ‘Finance Development, Growth and Stock Market
Development: The Trilateral Analysis in India’ Journal of Quantitative
Economics, Vol 9(1), pp 134-145.
55) Rousseau, L. and Wachtel, P. (2000), ‘ Equity market and growth: Cross country
evidence on timing and outcomes, 19980-1995,’ Journal of Banking and
Finance, Vol 24, pp 1933-57.
56) Seetanah, Boopen.,RojidSawkut and Vinesh Sannasee, ‘Stock Market
Development and Economic Growth in Developing Countries: Evidence from
Panel VAR framework,’ http://www.csae.ox.ac.uk/conferences/2010-
EDiA/papers/041-Seetanah.pdf.
57) Singh, A. (1997), ‘Financial Liberalization, Stock Markets and Economic
Development,’ The Economic Journal, Vol 107, May, pp. 771-782.
147
58) Suliman, Zakaria Suliman Abdalla and Hala, Ahmed Dafaalla. (2011), ‘Stock
Market Development and Economic Growth in Sudan(1995-2009): Evidence
from Granger Causality Test,’ Journal of Business Studies Quarterly, Vol 3(2),
pp 93-105.
59) Virmani, Arvind (2004), ‘Sources of India’s Economic Growth: Trends in Total
Factor Productivity,’ ICRIER Working Paper No. 131, New Delhi.