Stock Market Development and Economic Growth in Developing countries:
Evidence from Panel VAR framework
Boopen Seetanah, V Sannassee, M Lamport
aUniversity
of Mauritius,Reduit, Mauritius
* Corresponding author. Tel.:+230 4541041;
E-mail address:b.seetanah@uom.ac.mu (Boopen Seetanah)
Introduction
‘World stock markets are booming and stock markets in developing countries
account for a disproportionately large share of this boom. Investors are
venturing into the world’s newest markets and some are seeing handsome
returns. But are developing countries themselves reaping any benefits from
their stock markets?’ (Levine 1996)
Economists have traditionally concentrated on the role of financial
development to the economic growth of countries Overall there exists an
overwhelming consensus that well-functioning financial intermediaries have
played a significant role in economic growth (King and Levine, 1993 (a, b);
Levine and Zervos, 1998; Levine et al, 2000 (a, b); Wachtel, 2003, Seetanah
2008 among others). More recently, the emphasis has been increasingly
shifted to stock market indicators and the effect of stock markets on economic
development1 and the latter has been the subject of recent theoretical interest
(see Demirguc-Kunt and Levine, 1996, Levine and Zervos,1993, 1995, 1998
among others). Although some analysts view stock markets in developing
countries as “casinos” that have little positive impact on economic growth,
recent evidence suggests that stock markets may give a big boost to economic
development. In fact, the focus on stock markets as an engine of economic
growth is a new opening in financial literature. Going further, its benefits had
been largely ignored in the past, but now there is consensus concerning the
positive effects brought about by stock markets (see Pagano 1993, Levine and
Zervos, 1998).
In principle a well-developed stock market should theoretically increase saving
(by enhancing the set of financial instruments available to savers to diversify
their portfolios) and efficiently allocate capital to productive investments,
which leads to an increase in the rate of economic growth. In doing so they
provide an important source of investment capital at relatively low cost
(Dailami and Aktin, 1990; Greenwood and Smith, 1992). A more developed
equity market also provides liquidity that lowers the cost of the foreign capital
that is essential for development. As such the presence of stock markets would
mitigate the principal agent problem and reduce assymetryinformation, thus
promoting efficient resource allocation and growth (Adjasi and Biekpe 2006).
Perotti and van Oijen (1999) stress on the fact that diverse equity ownership
creates a constituency for political stability, which, in turn, promotes growth.
However it has also been argued that stock markets may be counter
productive, for instance more liquid stock markets may put companies at risk
of counter-productive takeovers or even with the high level of integration and
with the development of technological progress, if left uncontrolled, a stock
1
Stock market development involves the deepening of the stock market by increasing market
capitalisation, as compared to banking sector development where monetisation increases
financial intermediary development.
2
market can lead to economic collapse. Moreover, the effect of uncertainty on
savings rate and economic growth as markets become more liquid is
ambiguous. (Bencivenga and Smith 1991).
Empirical evidence shows the existence of a strong positive correlation
between stock market development and economic growth (Atje and
Jovanovich, 1993, Demirgüç-Kunt and Levine, 1996a, b, Korajczyk, 1996, Levine
and Zervos, 1996, 1998). However there exists some authors who could not
established any significant link between stock market development and growth
as well, for instance Bencivenga and Smith (1991), Naceur and Ghazouani
(2007) and Adjasi and Biekpe (2006) for developing countries cases.
In fact, previous empirical research has suggested a connection between stock
market development and economic growth, but is far from definitive. Although
the relationship postulated is a causal one, most empirical studies have
addressed causality, dynamics and endogeneity obliquely, if at all. Moreover
the existing literature tend to focus overwhelmingly on developed countries
sample and moreover have failed (Levine and Zervos (1993), Atje and
Jovanovic (1993), Levine and Zervos (1998)) to decouple the relative
contribution of banking and stock market development on economic growth in
a single framework.
Our work is believed to depart from and contribute to the existing literature in
various ways. In the first instance it focuses on a panel set of developing
countries
and
moreover
simultaneously
examines
banking
sector
development, stock market development, and economic growth in a unified
framework. It deals with issues of unmeasured cross country heterogeneity,
causality, dynamics and endogeneity, elements relatively ignored in the
literature of growth modelling, by innovatively employing rigorous panel VAR
procedures to examine the complex linkages between stock market
3
development, bank development and economic growth. Such a framework will
enable us not only to detect any bi causal relationships but also the presence
of indirect effects banking and stock market development on growth. The
complementarity and subsitutabilty element of bank and stock market
development will eventually be assessed. The data set comprises of 27
developing countries studies over a period of 15 years (1991-2007).
The rest of the paper is as follows: Section II reviews existing literature on the
link between financial development and economic growth; Section III describes
the methodology applied in this research as well as sources of data; section IV
deals with the empirical analysis and section V concludes the study.
II Literature review
Financial Development and Economic Growth
The theoretical underpinnings of the relationship between financial depth and
growth can be traced back to the work of Schumpeter (1912) and, more
recently to McKinnon (1973) and King and Levine (1993). These authors
claimed that financial development may lead to growth in that a welldeveloped financial system performs several critical functions to enhance the
efficiency of intermediation namely by reducing information, transaction, and
monitoring costs. Creane, Goyal, Mushfiq, and Sab (2003) argued that a
modern and efficient financial system mobilizes savings, promotes investment
by identifying and funding good business opportunities, monitors the
performance of managers, enables the trading, hedging, and diversification of
risk, and facilitates the exchange of goods and services. These functions
ultimately result in a more efficient allocation of resources, a more rapid
accumulation of human and physical capital, and in faster technological
progress, which in turn feed economic growth. Tsuru (2000) also explained the
finance-growth link by arguing that financial development can promote
economic growth via its positive impact on capital productivity or the
4
efficiency of financial systems in converting financial resources into real
investment. However, its effect on the saving rate is ambiguous and could
affect the growth rate negatively. ‘In net terms, the impact on welfare is likely
to be positive, since increased efficiency of investment in the long term can
offset any reduction in the propensity to save’ Tsuru (2000).
The relationship between financial development and economic growth was in
fact extensively analysed more than two decades ago by Goldsmith (1969),
McKinnon (1973), Shaw (1973) and others. They found strong and positive
correlations between the degree of financial market development and the rate
of economic growth. More comprehensive empirical research was undertaken
by King and Levine (1993) who confirmed a very strong relationship between
each of their four financial development indicators. Subsequent empirical work
by Jayaratne and Strahan (1996), Levine and Zervos (1998) and more recently
Roisseau and Sylla (2001) and Seetanah (2008) also confirmed the above. At
the micro-economic, Demirguc-Kunt and Maksimovic (1998) and Rajan and
Zingales (1998) reported that financial institutions have been crucial for firm
and industrial expansion. However it is worth mentioning that there have been
also some studies which could not confirm the beneficial economic effect of
financial development, for instance Jappelli and Pagano (1994) and Ram (1999)
among others. It should be pointed out that is only recently that scholars have
been incorporating the
issue of causality and endogeneity in the debate.
Among the very few studies is that of Levine et al (2000,a,b) who used dynamic
panel estimators to overcome the issue of dynamic in the system. Their results
were seen to reconcile with the fact that financial development is a good
predictor of economic growth. Similar results were obtained by Beck, Levine
and Loayza (1999), Xu (2000). Among the recent few studies focusing
exclusively on developing countries feature Christopoulos and Tsionas (2004),
Seetanah (2007, 2008) which confirmed earlier work of Luintel and Khan
(1999) and that of Demetriades and Hussein (1996). Odedokun (1996) however
found mixed results.
5
Stock Market Development and Economic Growth
Literature on the topic of economic growth has taken a new stance, given the
significance of the effect of stock markets on economic growth. Indeed, past
literature considered financial intermediaries as the only causative channel to
economic growth, and this new phase of developmental economics has
achieved much in helping us understand this unexplored channel of causation
since Bagehot (1873). In fact, stock markets and banks provide services that
could either be complements or substitutes for each other depending on the
industrialisation extent of the economy. Several possible avenues whereby
stock market development have been advanced and discussed among them
are the following.
Stock markets provide an alternative channel for savings mobilisation and
better resource allocation (N’Zué 2006). They enable savings mobilisation for
financing “immense works” (Bagehot 1906, Hicks (1969), Greenwood and
Smith 1996). More efficiently mobilised savings cause capital accumulation,
which firms tap to finance large projects via equity issues. This, undoubtedly,
spurs economic growth (Levine and Zervos 1998a, 1998b; Adjasi and Biekpe
2006). Focusing on liquidity, Bencivenga, et. al. (1996) and Levine (1991) argue
that stock market liquidity plays a key role in economic growth. Without a
liquid stock market, many profitable long-term investments would not be
undertaken because savers would be reluctant to tie up their investments for
long periods of time. In contrast, a liquid equity market allows savers to sell
their shares easily, thereby permitting firms to raise equity capital on favorable
terms. By facilitating longer-term, more profitable investments, a liquid market
improves the allocation of capital and enhances prospects for long-term
economic growth. A more developed equity market may also provide liquidity
that lowers the cost of the foreign capital that is essential for development.
Bencivenga et. al. (1996), and Neusser and Kugler (1998)). Liquidity has also
6
been argued to increase investor incentive to acquire information on firms and
improve corporate governance (Kyle, 1984; Holmstrom and Tirole, 1993),
thereby facilitating growth. Levine further argued that a liquid stock market
complements a strong banking system, suggesting that banks and stock
markets provide different bundles of financial services to the economy.
However Demirguc-Kunt and Levine (1996) point out that increased liquidity
can deter growth through at least three channels2.
Indeed, for the case of the African subcontinent, liquidity has been a significant
factor in hamper stock market development (Adjasi and Biekpe 2006) and
consequently retards economic growth. Naceur and Ghazouani (2007) also
posits that the beneficial effect of liquidity is only found after a threshold level.
Sarkar (2006) also discussed that stock market development may have no
effect on fixed capital formation due to the high transaction and information
costs in least developed countries.
A second link of stock market development on the economy is based on the
premise that the presence of stock markets would mitigate the principal agent
problem, thus promoting efficient resource allocation and growth (Adjasi and
Biekpe 2006). Firstly, given that the stock price at any time is mirror of firm
performance, weakening corporate governance would be reflected as a fall in
share price. Management would have a disincentive to work in their personal
interests if their compensation is tied to stock performance (Jensen and
Murphy 1990). Thus the emphasis is on the role of equity markets in providing
proper incentives for managers to make investment decisions. Dow and
2
Demirguc-Kunt and Levine (1996) point out that increased liquidity can deter growth through
at least three channels. First, by increasing returns to investments, greater stock market
liquidity may reduce saving rates through income and substitution effects. If savings rates fall
enough and if there is an externality attached to capital accumulation, greater stock market
liquidity may slow economic growth. Second, by reducing the uncertainty associated with
investment, greater stock market liquidity may reduce saving rates because of the ambiguous
effects of uncertainty on savings. Third, stock market liquidity encourages investor myopia,
adversely affecting corporate governance and thereby reduces economic growth.
7
Gorton (1997) argued that such investment decisions affect firm value over a
longer time period than the managers’ employment horizons through equitybased compensation schemes. Binswanger (1999) and Yartey 2007, Bhide
(1999) however argued that the above might not be true in a situation of
investor myopia.
The role of equity markets in providing portfolio diversification, enabling
individual firms to engage in specialized production is bound to result in
efficiency gains ( Acemoglu and Zilibotti, 1997, Capasso 2008). Indeed, in the
presence of stock markets which provide for various vehicles for transferring
risk through which investors can confidently invest. What follows from this is
that investors now have the opportunity of switching from low-risk to high risk
investments. Obstfeld (1994) shows that international risk-sharing through
internationally integrated stock markets improves resource allocation and can
accelerate growth.
Indeed, stock markets have more information than do financial intermediaries
and usually translate in more efficient allocation and better growth (Caporale
et al 2004, Adjasi and Biekpe 2006, Atje and Jovanovic 1993). King and Levine
(1993b) also stressed on the ability of equity markets to generate information
about the innovative activity of entrepreneurs or the aggregate state of
technology. Perotti and van Oijen (1999) also argued that that diverse equity
ownership creates a constituency for political stability, which, in turn,
promotes growth.
Empirical review
Pioneering work from Spears (1991), Pardy (1992) Atje and Jovanovic (1993),
show that stock market development is strongly correlated with growth rates
of real GDP per capita. More importantly, they found that stock market
liquidity predict the future growth rate of economy. Levine and Zervos (1998),
Filer et al. (1999), Rousseau and Wachtel (2000) and Tuncer and Alovsat
8
(2001) examined stock market-growth nexus and exhibited positive casual
correlation between stock market development and economic activity.
Nevertheless, most of earlier studies suffered from various statistical
weaknesses namely with respect to endogeneity and causality issues together
with unmeasured cross country heterogeneity. Subsequent research, with
larger panel sets and longer time series and attempted to attend to the earlier
criticisms. Beck, and Levine (2003) for instance investigated the impact of stock
markets and banks on economic growth using a panel data set dynamic panels
(GMM), that stock markets and banks positively influence economic growth.
Chen et al (2004) , Paudel (2005) and Love and Zicchino
(2006) also
acknowledged that stock markets, due to their liquidity, enable firms to attain
much needed capital quickly, hence facilitating capital allocation, investment
and growth. Alam and Hasan (2003) for the case of the US also found a
significant positive impact of stock market development on economic growth.
Bahadur and Neupane (2006) concluded that stock markets fluctuations
predicted the future growth of an economy and causality is found only in real
variables.
More recent studies have employed vector models but has been restricted to
country case studies mainly and among them features Enisa and Olufisayo
(2009), N’Zué (2006) for the case of Ghana , Shahbaz, Ahmed and Ali (2008) for
Pakistan and Agrawalla and Tuteja (2007) for the Indian case.
There exists however few studies which could not establish any significant link
in the stock market-economic growth nexus (refer to theoretical and empirical
works of Bencivenga and Smith 1991; Demirguc-Kunt and Levine, 1996; Adjasi
and Biekpe 2006; Ghazouani, 2007 and Sarkar 2006 among others). It is
noteworthy that Rousseau and Xiao (2007) for the case of China although
found that banking sector development was central to the Chinese success,
however could not establish any significant relationship for the case of stock
market development.
9
In summary, previous empirical research has suggested a connection between
stock market development and economic growth, but is far from definitive.
Although the relationship postulated is a causal one, most empirical studies,
until lately have addressed causality obliquely, and this even more pronounced
in the case of panel data analysis. Moreover very few of them have adopted a
unified economic mode where both banking and stock development are
simultaneously considered in a framework that allows for dynamics, feedbacks
and endogeneity issues. To resolve them this paper uses VAR procedures in
panel analysis to examine the linkage between stock market development,
bank development and economic growth.
III Methodology and Data Analysis
The model that has been used in this study is based on the principles of some
earlier studies (e.g. King and Levine, 1993; Levine and Zervos, 1998; Levine et
al., 2000, Wachtel, 1998, 2001, Christopoulos and Tsionas, 2004 and Seetanah,
2008). The model takes the following functional form3:
Y  f ( IVTGDP, XMGDP, SER, INDEX , PRIGDP )
(1)
The dependent variable output, Y was proxied by the real per capita gross
domestic product (GDP)4. IVTGDP is the country’s investment divided by its
Gross Domestic Product (investment ratio), XMGDP is total of export and an
import divided by the GDP of the country and is a measure of openness, EMP is
the employment level and SER is the secondary enrolment ratio and proxies for
3
Refer to Seetanah(2008) for a comprehensive justification of the explanatory variables.
This measure has been widely used in the literature for instance King and Levine, 1993,
Odedokun ,1996; Levine and Zervos, 1998; Ram, 1999 , Seetanah, 2007)
4
10
the quality of human capital. To measure banking development, we follow
(Levine et al., 2000) and used PRIGDP, that is the value of credits by financial
intermediaries to the private sector divided by GDP and. PRIGDP has been
used extensively as an indicator because it improves on other measures of
financial development. Higher levels of PRIGDP are interpreted as higher levels
of
financing
services
and
therefore
greater
financial
intermediary
development. This measure of financial development is also more than a
simple measure of banking sector size. PRIGDP isolates credit issued to the
private sector, as opposed to credit issued to governments, government
agencies, and public enterprises. Furthermore, it excludes credits issued by the
central bank.
Particularly in the context of our study, we consider individual indicators of
stock market liquidity. The rationale behind adopting disaggregated indicators
is to capture the different effects between stock market development and
economic growth more feasibly rather than adopting a single indicator that
would be focussed on a single aspect ((Levine and Zervos, 1998).
Our indicators of stock market development have been used in previous
studies (for example, Pagano 1993 and Demirgiic-Kunt and Levine 1996,
Rousseau and Wachtel, 2000, Beck and Levine, 2003). The ratio of stock
market capitalization to GDP (INDEX1) is a measure of both the stock market's
ability to allocate capital to investment projects and its ability to provide
significant opportunities for risk diversification for investors. The ratio of total
value of shares traded to GDP (INDEX2) and the ratio of total value of shares
traded to market capitalization (INDEX3) are indicators of market liquidity. The
former measures the ability to trade economically significant positions on the
stock market, and the latter is an indicator of liquidity of assets traded on the
market, not adjusted for the size of the market relative to the economy. We
also combine the three indicators in an equally weighted index of market
development, INDEX. For the sake of discussion in this study we will focus on
11
the result using the composite proxy INDEX and only report the results of the
other three indices.
The specification used in this study is of a log linear one. Recall model from
above and taking logs on the right hand side of the equation results in the
following:
yit   0  1ivtgdpit   2 xmgdpit   3 serit   4 index   5 prigdp it   it
(2)
where i denotes the different countries in the sample and t denotes the time
dimension. The small letters denotes the natural logarithm of the variables.
The main sources of our independent variables are from the World Bank’s
‘World Development Indicators’ CD RPM and the IMF’s ‘International Financial
Statistics’ (IFS) CD ROM except for the case of SER, where the World
Development Reports and individuals country’s Central Statistical Office’s has
been consulted.
Using the Im, Pesaran, and Shin (1995) panel unit root tests, the test was
applied on the dependent and independent variables. Im, Pesaran, and Shin
(1995) developed a panel unit root test for the joint null hypothesis that every
time series in the panel is non-stationary. This approach is based on the
average of individual series ADF test and has a standard normal distribution
once adjusted in a particular manner. Results of this test applied on our time
series in levels reject a unit root in favor of stationarity (the results were also
confirmed by the Fisher-ADF and Fisher-PP panel unit root tests, refer to
appendix) at the 5 percent significance level for each variable. It was judged
safe to continue with the panel data estimates of the above econometric
specifications.
12
Analysis and discussion
Endogeniety issues and the Panel Vector Autoregressive Model
There might still be the possibility of the loss of dynamic information even in
panel data framework as the dependent variable may have something to do in
explaining itself as well (Levine et al, 2000). It is likely that there exists dynamic
feedbacks and indirect effects among the variables in a growth function,
particularly with respect to our financial development variables. Including
these feedbacks are essential to the modeling of our hypotheses since as
Pereira and De Fructos (1999) argued ‘if feedback exists, an elasticity of zero
with respect to some determinants in a static framework would have been
neither necessary nor sufficient for public capital to have no effect on output.’
Indeed both banking and stock market development can directly affect output
directly but they may also have indirect positive impacts5 on a country’s output
as they may affect private capital and other inputs in the growth functions.
Furthermore one should realise that the output level of a country may also
translate into the more development in the financial system, that this resulting
in reverse causation (see Seetanah 2008 among others).
Given the possibility of endogeneity and causality issues we use vector
autoregressions (VAR) on panel data to enable us to consider the complex
relationship that might exist between banking and stock market development
economic growth6. This framework also allows us to test the substitutability
and complementarity issue between banking and stock market development
rigourously. Moreover, Panel VAR also allows for a firm-specific unobserved
5
It has also been argued though that higher availability of the public input could also reduce
the demand for private inputs and crowding out effects (Lighthart, 2000).
6
Powell, Ratha, and Mohapatra (2002) and Love and Zicchino, (2006) used similar approach in
their respective study. The former studied the interrelationships between inflows and outflows
of capital and other macro variables and the later that of financial development and dynamic
investment behaviour.
13
heterogeneity in the levels of the variables. Panel-data vector autoregression
combines the traditional VAR approach, which treats all the variables in the
system as endogenous, with the panel-data approach, which allows for
unobserved individual heterogeneity. We specify a first order VAR model as
follows:
 it  0  1  it 1   i   t
(3)
where zt is a six-variable vector (gdp, ivtgdp, xmgdp, ser,index, prigdp) and the
variables are as defined previously. We use i to index countries and t to index
time,  are the parameters and  is the error term. For the econometric
analysis, the system of equations as above is expressed as a log-linear
regression for ease of interpretation. The lowercase variables are the natural
log of the respective uppercase variables
In applying the VAR procedure to panel data, we need to impose the
restriction that the underlying structure is the same for each cross-sectional
unit. Since this constraint is likely to be violated in practice, one way to
overcome the restriction on parameters is to allow for “individual
heterogeneity” in the levels of the variables by introducing fixed effects,
denoted by i in the model (Love and Zicchino, 2006). Since the fixed effects
are correlated with the regressors due to lags of the dependent variables, the
mean-differencing procedure commonly used to eliminate fixed effects would
create biased coefficients. To avoid this problem we use forward meandifferencing, also referred to as the ‘Helmert procedure’ (see Arellano and
Bover, 1995). This procedure removes only the forward mean, i.e. the mean of
all the future observations available for each firm-year. This transformation
14
preserves the orthogonality between transformed variables and lagged
regressors, so we can use lagged regressors as instruments and estimate the
coefficients by system GMM7.
Estimation and Analysis
We estimate the coefficients of the system given in (3) after the fixed effects
have been removed and Table 1 report the results of the model.
Table 1:Results from the VAR model (1991-2007)
Response to 
Respons
Constant gdp t-1
e of
ivtgdpt- xmgdpt- sert-1
index- prigdpt-1
1
1
1

Gdp
ivtgdp
xmgdp
ser
-0.43
0.25
0.44
0.38
0.21
0.13
(1.64)
(2.34)*
(2.15)*
(2.19)*
(1.99)* (1.87) (1.98)*
0.36
0.26
0.64
0.15
0.17
(1.87)*
(2.15*)
(2.21)*
(2.14*)
(1.81)* (2.22) (1.82)
0.88
0.21
0.17
0.57
0.09
0.11
(1.75)*
(2.21)*
(2.13)*
(1.97)*
(1.58)
(1.51) (1.02)
0.11
0.26
0.07
0.08
0.54
0.03
(1.44)
(2.24)*
(1.33)
(1.65)*
(1.98)* (0.45) (1.52)
0.12
0.17
0.19
0.05
0.08
7
In our case the model is “just identified”, i.e. the number of regressors equals the number of
instruments, therefore system GMM is numerically equivalent to equation-by-equation 2SLS.
15
index
prigdp
0.43
0.12
0.11
0.19
0.19
(1.34)
(1.88)*
(1.72)*
(1.87)*
(2.11)* (1.96) (1.77)
0.32
0.16
0.12
0.05
0.15
(2.23)** (1.96)*
(1.44)
(1.95)* (1.22) (2.22)***
405
405
405
405
405
405
25
25
25
25
25
25
(1.54)
No.
of
0.57
0.13
0.15
0.56
obs
No
of
countries
The VAR model is estimated by GMM and fixed effects are removed prior to
estimation. Reported numbers show the coefficients of regressing the row
variables on lags of the column variables. Heteroskedasticity adjusted tstatistics are in parentheses. *** indicates significance at 1% level, ** at 5%
and *** at 10% respectively. The small letters denotes variables in natural
logarithmic and t values are in parentheses
Table 1 is a composite table where each column can be viewed and analysed as
an independent function. For instance, of interest to us primaririly is column 1
which is in fact our output equation. It is observed that the coefficient of index
is positive and significant (+0.13) and this suggest that stock market
development has had a positive and significant effect on economic growth for
our sample of developing countries over the year of studies. In fact, the
coefficient of 0.17, a measure of output elasticity, denotes that a one percent
increase in stock market development contributed to 0.17 percent increase in
16
the GDP of our selected economies and this is the direct effect. These positive
elasticities results takes into account the often ignored issues of panel
stationarity, dynamics, and endogeinty. The results are consistent to those of
Levine and Zervos (1998) and Rousseau and Wachtel (2000) but do not validate
those of Adjasi and Biekpe (2006). It is noteworthy that the three indices of
stock market development namely index1, index2 and index3 all also yielded
positive and significant coefficients of 0.15, 0.09 and 0.11 respectively.
Interestingly the banking development proxy also exhibits positive and
significant effects on growth and suggest that banks and stock markets play
somewhat different roles in economic progress (similar to Levine and Zervos
1998) and that put together financial development is confirmed to be an
ingredient of growth. Interestingly the higher coefficient of PRIGDP tends to
show that in, developing countries at least, the banking sector’s contribution
to economic performance is relatively higher than stock market development.
This can be explained by the fact that developing countries’ stock markets have
not yet reached those maturity levels (from both investors and firms
perspectives) as the case of developed or newly industrialised countries.
Moreover financial intermediaries remain till now the major source of
investment credits in most of the economies under study. It should be stressed
that our results oppose those found by Atje and Jovanovic (1993) and
Caporale, Howells and Soliman (2004).
The rest of the growth explanatory variables turned out to be also significant
and have the expected signs. It should be however noted that the magnitude
of the stock market development (and even the banking development)
coefficient remains relatively smaller than for instance private investment and
17
openness which remains the major growth drivers in developing according to
this study.
The VAR framework, as discussed before, enables us to gauge more
interesting insights on endogeneity issues and indirect effects as well. While it
has been shown that stock market development induces growth (stock market
led growth), referring to the ‘index’ equation, it is observed that a reverse
causation exists as well as output appears to be also a determinant of stock
market development thus supporting a bi-causal and reinforcing relationship.
In other words, output level which proxies economic well being and level of
development, may play an important role in the development of stock markets
as well. These results are consistent with those obtained from Rousseau and
Wachtel (2000) and Seetanah (2008). The ‘index’ equation can also be viewed
as a stock market development equation with, in addition to income level,
education attainment investment level and banking development being other
determinants. The latter is particularly interesting as it reveals some sort of
complementarity between these the apparently competing source of bank
based and market based finance. This is yet another rejoiner to Levine and
Zervos (1998) argument that banks and stock markets play different roles in
economic progress. One could also argue that banking development levels acts
as some signalling effect on the financial system stability, lead to optimistic
outlook and thus encouraging financial investment and stock development8. In
general, thus, stock market development is bound to have better growth
effects in the presence of the above three variables.
Referring to the ‘investment equation’, positive indirect effects of stock
market development on private investment are noted. This confirms the view
that stock market development encourages private investment (including
foreign investment). An elasticity value of 0.12 denotes that a percentage
8
Referring to the banking development equation, there is not significant evidence that stock
market development encourages banking sector development directly
18
increase in index would lead to 0.12 increase in private investment. Given that
the direct effect of investment is to the order of 0.44 percent increase in the
GDP for a 1 percentage increase in private investment, put together this leads
to a 0.12 x 0.44 percentage increase in the output after two years. This is an
estimate of the indirect effect of stock market development on output via the
private capital channel. Similar findings can be found for banking sector
development.
Finally, the presence of bi-causality between private
investment, education and income level is found.
Orthogonalised
impulse-response
functions
analysis9
and
variance
decompositions10 overall produced equivalent results. Details are available
from the corresponding author upon request.
Conclusion and policy implications
This paper focused on a panel set of developing countries to simultaneously
examined banking sector development, stock market development, and
economic growth in a unified framework. It employed rigorous panel VAR
procedures to examine the complex linkages between stock market
development, bank development and economic growth for the case of 27
developing countries studies over a period of 15 years (1991-2007). Results
from the analysis showed that stock market development is an important
ingredient of growth, but with a relative lower magnitude as compared to the
other determinants of growth, particularly with banking development.
Interesting stock market development and banking development are seen to
9
The impulse-response functions describe the reaction of one variable to the innovations in
another variable in the system, while holding all other shocks equal to zero. Impulse response
are orthogonalised since the actual variance–covariance matrix of the errors is unlikely to be
diagonal.
10
This shows the percent of the variation in one variable that is explained by the shock to
another variable, accumulated over time. The variance decompositions show the magnitude of
the total effect
19
be complement to each other and moreover there are important indirect
effects through ‘investment channel’ to growth. It is noteworthy to emphasise
on the dynamic process of the stock market development and growth nexus.
Given the above findings, the policy implications for government are
numerous. They should attempt to develop the financial sector even more and
one of the first steps is to have less state involvement in the system. This
includes cutting back on public ownership of financial institutions and
minimising monetary financing of budget deficits. Government should ensure
clear and concise rules for investment, and to attract capital on equity markets
from the international monetary system.The deregulation and liberalization
process should continue in a cautious way and more competition within the
financial sector should be encouraged. This may mean a more opened
approach to multinational banks and other institutions which would also
benefit the industry in terms of financial innovation. As such stronger, more
transparent institutional and legal framework should be present to consolidate
the sector. Investment in human resources remains also a major factor to
accompany the sector’s development process.
Moreover, governments should promote stock market liquidity by for instance
propagating knowledge to the public of the benefits of investing in stock
markets (N’Zué 2006) and moreover, create state-run mutual funds so as to
ensure higher liquidity on stock markets11. These incentives would promote
both domestic and foreign investments to penetrate the domestic economies,
and thus help draw immense benefits from these sources of capital.
11
An example is the case of Argentina where the state runs its mutual fund to favour its stock
market.
20
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Appendix: List of developing countries
Algeria, Angola, Brazil, Cameroon, China, Ghana, Egypt, Ethopia, India,
Indonesia, Israel, Kenya, Mauritius, Malaysia, Malawi, Morroco, Mexico,
Nigeria, Pakistan, South Africa, South Korea, Taiwan, Thailand, Tunisia, Uganda
24