Equity markets and economic development:
Does the primary market matter?
Andriansyaha,b,*and George Messinisa
aCentre
for Strategic Economic Studies, Victoria University, Melbourne, Australia
bMinistry
of Finance of the Republic of Indonesia, Jakarta, Indonesia
This paper examines the role played by primary and secondary equity markets in economic
growth. In contrast to standard literature consideringsecondary market indicators, this study
integrates both types of market while explicitly acknowledging the role of primary markets.
By employinga variety of dynamic panel estimators for 54 countries over the period 19952010, we show that the primary equity market is not an important determinant of economic
growth, despite facilitating development of the secondary market. This study also confirms
the importance of secondary market activity, such as trading liquidity,as a determinant of
economic growth. These results call for a further investigation into the capital-raising
function of equity markets in relation to their liquidity function.
Subject Keywords:Equity Markets, Primary Markets, Secondary Markets, Development
JEL Codes:E44, G23, 016
*
Corresponding author. Postal Address: Centre for Strategic Economic Studies, Victoria University, PO Box
14428, Melbourne, Victoria, Australia 8001, Street address: Level 13, 300 Flinders St, Melbourne Victoria
Australia 3000. E-mail: andriansyah.andriansyah@live.vu.edu.au
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1.
Introduction
While studies in the finance-growth nexus initially focused on relationships between
bank-based measures of financial development and economic growth, the impact of marketbased measures on growth has assumed importance. This has been particularly so since the
World Bankconference on this issue in 1995 and the publishing of papers presented in a
special edition of the bank’s economic journal a year later. In summarizing these papers,
Demirguc-Kunt and Levine (1996b)stressed that equity market development is an important
determinant of corporate financing choices and long-run economic growth. They argue that
the main channelmoving the equity market to achieve economic growth is liquidity that leads
to capital accumulation and allocation. This paperargues that the capital-raising function of
primary equity markets is in fact the main driver of, and at least as important as, the liquidity
function of secondary equity markets for the stimulation of economic growth. In this context,
this paper examines the importance of both types of equity markets in an integrated
framework.
The primary market can be defined as the marketplace where new shares, either from
unissued or issued shares, are offered to public investors, either at the initial public offering
(IPO) or seasoned equity offering (SEO) market. The new shares may then be traded among
investors on a stock exchange(s) where they are listed. This trading marketplace is referred
to as the secondary market. An economic consequence of these two markets is on the cash or
capital inflows to offering firms. New capital can be raised and then used by the listed firms,
but no additional money can flow into the firms from the secondary market transactions. In
addition, from a macroeconomics perspectivea transaction on a stock exchange is not
considered as an investment, while the selling of new shares at the IPO and SEO markets is
(Mankiw 2010).
Despite these different market characteristics, economists and researchers tend to only
usesecondary market indicators such as market liquidity, market capitalization, composite
index returns and volatility as measures of stock market development. They overlook
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primary market indicators such as the total capital raised and number of listed companies
(see for example,Demirguc-Kunt and Levine (1996a),Levine and Zervos (1998), Beck and
Levine (2004a),and most recently Lee (2012)). This oversightmay be due to the common
understanding of a stock market being mainly a secondary market – as stated in standard
textbooks of finance such as Pilbeam (2010, pp. 214-5).
Theoretically, functions of the equity market are similar to those of the capital market.
Thus, theoretical frameworks that explain why equity market development is related to
economic development are mainly the same as those used in the finance-growth nexus. The
main functions of the equity market are also summarized byLevine (2005)as similar to those
of the financial system, namely: providing capital and efficiently allocating this capital into
productive investments; utilizing domestic savings; improving information; effectively
monitoring mechanisms of good corporate governance practices; providing risk-reduction
mechanisms; and facilitating exchange of financial instruments that representthe ownership
of capital.By ensuring that these five functions run well without any friction, Ang (2008)
furthermore highlighted that equity markets will lead to long-term growth through two main
transmission channels: the capital accumulation channel; and the total factor productivity
(TFP) channel. The first channel reflects and refers to the most important function of capital
accumulation and allocation, while the second channel mainly refers to the qualitative effects
of these functions.
Some endogenous growth models have examined stock market developmentas one of
the important factors explaining economic growth. For example, by developing the model of
Greenwood and Jovanovic (1990), researchers including Atje and Jovanovic (1993)stress that
the stock market is an important determinant of economic development, measured either at
the actual level or growth rate. Furthermore, they explain that funds will move to profitable
investments when better information regarding investment projects is provided. In another
study, Bencivenga, Smith and Starr (1996) explain that efficient allocation of resources can
only be achieved by reducing the transaction costs of saving mobilization. This is because the
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liquidity created by efficiency in trading will secure permanent access to the capital invested
by initial investors for the financing of long-term and high-return projects. However
Bencivenga, Smith and Starr (1996) show that the relationship between the equity market
and economic development may be imperfect and therefore unclear. For example, they
showed that when there is a decrease in transaction costs, an efficiency or liquidity
improvement in secondary equity markets will stimulate an increase in market activity
volume.However, when economic agents are only active in the secondary markets and
reluctant to invest in a new project (i.e. pure speculative trading), this increasemay have no
impact on the level of real activity. Furthermore, Deidda and Fattouh (2002) found that the
relationshipmay be non-linear and non-monotonic withthe positive impact of financial
development depending on the maturity stage of financial markets.
These theoretical frameworks have been supported by many empirical studies that
mainly found positive relationships between stock market development and economic
growth. Having used a stock market development index developed by Demirguc-Kunt and
Levine (1996b)that combined indicators of market size, liquidity, and risk diversification as
well as controlling initial condition and other economic and political factors,Levine and
Zervos (1996)find evidence of a strong relationship between the two variables of interest. A
recent study byLee (2012)shows that causal relationshipis mainly from financial markets to
economic growth in cases of the U.S., the U.K., Japan, Germany and France. In fact, the
assessment of direction of causality between finance and growth based on the notion of
‘supply-leading’ and ‘demand-following’ of Patrick (1966) has dominated most of the
empirical studies. Rather than being ‘caused by’ (in Granger causality sense) as implied in the
supply-leading, the demand-following postulates that economic growth will stimulate the
demand for financial services and instruments. A recent study of Halkos and Trigoni (2010),
for example, shows that there is a long-term relationship between these two notions of
causality in 15 European countries. Another study of Rachdi and Mbarek (2011) also
provides similar results for some OECD (Organisation for Economic Cooperation
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andDevelopment) and MENA (Middle East and North Africa) countries. However, the
direction of causality is different; bi-directional causality exists for the OECD economies while
demand-following exists for the MENA ones.1
There have also been studies, however, that show opposite results, or at least find no
significant relationship between stock market development and economic growth.Singh, A
(1997) and Harris (1997), for instance, argue that stock markets may be more beneficial for
developed countries, not for developing ones. Singh, A (1997) argues that equity market
development in developing economies – that mainly evolve as the result of the financial
liberalization – creates more negative effects on their economic development than positive
ones. He contends that the TFP channel is more dominant than its capital accumulation
counterpart, but this channel, especially through the functions of trading and corporate
controls, does not work efficiently so the markets tend to produce speculative market prices
and financial-engineered-based (non-organic) growth. He also criticizes the type of
investments undertaken by the listed firms that are mostly not in the form of firm-specific
human capital. Supporting the argument of Bhide (1993), Singh, A (1997) argues that
liquidity has a devastating effect on financial stability because it make the markets more
volatile, and eventually affecting the volatility of the exchange rates. Bhide (1993)mentions
that the U.S. regulations aiming at increasing market liquidity have negative effects on the
governance of listed firms, especially by reducing the role of active stockholding in
monitoring managers.Nagaishi (1999) and Chakraborty (2010) principally support the
conclusions of Singh, A (1997),arguing that the relationship between stock markets and
economic growth in India is not clear and tends to be negative. Nagaishi (1999) finds that
stock markets do not contribute to gross domestic savings and the share of financial assets of
the household sector, and, in fact, foreign capital inflows potentially create more volatility in
1
Other cross-country studies supporting the notion that stock market development also plays a significantly positive role
in economic growth are Demirgüç-Kunt and Levine (1996b), Fry (1997), and Levine and Zervos (1998). Meanwhile,
single-country studies among others, are: Gan et al. (2006) for New Zealand; Kaplan (2008) for Turkey; Maysami, Howe
and Hamzah(2004) for Singapore; Shahbaz, Ahmed and Ali (2008) for Pakistan; Siliverstovs and Duong (2006) for
European countries; and Chaudhuri and Smiles (2004) for Australia.
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stock prices and the balance of payments. Furthermore, he argues that the substitutive
function of stock markets is more dominant in the role of financing private investments, so an
increase in public offering in the primary markets does not necessarily boost economic
growth.
To summarize, the debate about the benefits of the equity markets on economic
development has been still inconclusive. While the theoretical studies and empirical findings
support the importance of liquidity in secondary markets, the impact of such trading
activities on capital allocation in primary markets needs further examination.Therefore, there
is still a need to explicitly differentiate the role of the primary market and the secondary
market in their relation to economic growth, as well as to empirically test if the latter has
positive impacts to the former. This paper attempts to address thesequestions.
In terms of differentiating equity markets into primary and secondary markets, to the
best of our knowledge there has not been any study examining the different roles of the
markets on economic growth. The closest one to our study may be a study of Singh, T (2008).
Using the financial interrelation ratio (FIR) and new issue ratio (NIR) as measures of financial
development developed by Goldsmith (1969), Singh (2008, p. 1616) claims that “the ratios
show the activities of both primary and secondary financial markets…” However, by
understanding that FIR measures the activities of both financial markets and intermediaries,
while NIR just measures financial market activities, the author’simpression that FIR and NIR
relates with primary and secondary markets cannot be supported. This is our first main
contribution to the debate on the finance-growth nexus.
The structure of this paper is as follows. Section 2 describes the theoretical framework
and the methodology developed is explained in Section 3. Next, the data and sample selection
process are presented in Section 4, followed by discussions of the empirical results in Section
5. Section 6 concludes this paper.
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2.
The Conceptual Framework and Methodology
In examining the relationship between equity markets and growth, it is important to
compare the role of equity markets and that of banks, or in a general term of the financial
structure of an economy, because banks may have important roles on economic development
that outweighs the role of the markets. In the case of developing countries or financially lessdeveloped countries, banking sectors are often more advanced compared to their equity
market counterpart, especially in terms of their assets and long-term presence in the
countries. Rioja and Valev (2004) and Lee (2012), for example, conjecture that the banking
sectors is a more important determinant of economic development than equity markets in
the early years of development. Therefore, if the role of the banking sector (or, equivalently,
equity markets) is ignored, there may be a spurious positive relationship between equity
markets (the banking sector) and economic development. However, the inclusion of the
banking sector in the analysis of equity markets and economic growth in general still
confirms the important role of equity markets as shown by Lee (2012), Antonios (2010),
Rousseau and Wachtel (2000), Caporale, Howells and Soliman (2004), and Levine and Zervos
(1996, 1998).
We follow aBeck and Levine (2004b) and Rioja and Valev (2004) baseline dynamic
panel growth regression to assess the relationship between an equity market and growth as
follows:
[1]
𝐺𝑟𝑜𝑤𝑡ℎ𝑖𝑡 = 𝛼1 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑃𝐺𝐷𝑃𝑖𝑡 −1 + 𝛽11 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽12 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 + 𝛽13 𝐵𝑎𝑛𝑘𝑖𝑡 + 𝛾1′ 𝑋𝑡 + 𝜂1𝑖 +
𝜀1𝑖𝑡
where the subscript i and tdenote country and time period, respectively. Growth and Initial
PGDP denotethe growth and initial value of real per capita GDP, respectively. Primary and
Secondarydenote our variables of interest, i.e. the primary market measure and the secondary
marketmeasure, respectively. X is a set of control variables consisting of average years of
schooling and time dummies,  is an unobservable country effect, and  is the error termthat
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is assumed to be homoscedastic and mutually uncorrelated over time and across countries
either individually or collectively.
Based on the public offering process and its corresponding functions as shown in
Figure 1, we develop a conceptual framework for differentiating the primary market and the
secondary market. The five functions summarized by Levine (2005)can practicallybe
classified by type of market: the primary market adds to capital mobilization and allocation,
while the secondary market plays other functions.
INSERT FIGURE 1 ABOUT HERE
We develop the relationship between the two markets and economic growth by
considering the existence of simultaneity between them. We treat our variables of interest
and economic growth as endogenous variables and set them in an equilibrium mechanism.
The explanation of this conceptual framework is based on the fact that the variables are
determined simultaneously. For instance, an increasing trend in IPO volume and value are
jointly determined by increasing economic growth (Draho 2004). Subrahmanyam and Titman
(1999)argue that the interrelation between the primary market and the secondary market is
essentially a “snow ball” effect. They postulate that the more new firms listing on a stock
exchange, the more liquid and efficient the secondary market will be, and then the more able
the market is to grow with more firms to list. In addition, we also argue that the primary
market is also a supplier of the shares traded on the secondary market. Therefore, we use a
simultaneous equation model to examine the interrelationship between the primary equity
market, the secondary equity market, the banking sector and economic output, by adding
three additional equations which has ceteris paribus interpretation as follows:
[2]
𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 = 𝛼2 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 −1 + 𝛽21 𝑃𝐺𝐷𝑃𝑖𝑡 + 𝛽22 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 + 𝛽23 𝐵𝑎𝑛𝑘𝑖𝑡 + 𝛾2′ 𝑋𝑡 + 𝜂2𝑖 + 𝜀2𝑖𝑡
[3]
𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 = 𝛼3 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 −1 + 𝛽31 𝑃𝐺𝐷𝑃𝑖𝑡 + 𝛽32 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽33 𝐵𝑎𝑛𝑘𝑖𝑡 + 𝛾3′ 𝑋𝑡 + 𝜂3𝑖 + 𝜀3𝑖𝑡
[4]
𝐵𝑎𝑛𝑘𝑖𝑡 = 𝛼4 𝐵𝑎𝑛𝑘𝑖𝑡 −1 + 𝛽41 𝑃𝐺𝐷𝑃𝑖𝑡 + 𝛽42 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽43 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 + 𝛾4′ 𝑋𝑖𝑡 + 𝜂4𝑖 + 𝜀4𝑖𝑡
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Most of the abovementioned studies have been conducted by using 5, 8, or 10-year
averagevalues. The main reason of the averaging is to accommodate business cycles and
identify long-term relationship between the variables of interest (see for instance Harris &
Tzavalis 1999, p. 427). However, Aretis and Demetriades (1997)argue that this approach
imposes an average effect limitation making it impossible to capture each country’s
individual idiosyncrasy. Attending to this, we argue that the use of annual time series data,
instead of average data over a period of study, could possibly minimize the shortcoming of
pure cross section regressions. This hasalso been tried by Beck and Levine (1999)who choose
initial GDP as the only control variable. However, we accommodate here the two approaches:
(1)estimate equation [1] by using the 5-year average data, and (2) estimate all four equations
by using annual data.
To estimate the unbiased and inconsistent growth regression parameters, we employ
the two-steps system GMM developed by Arellano and Bond (1991), Arellano and Bover
(1995), and Blundell and Bond (1998). The population moment orthogonality condition used
for [1] is as follows:
[5]
E ∆𝑃𝐺𝐷𝑃it −1 𝜀it = 0
for t=3,…,T
The GMM estimators are claimed as suitable for small T and large N (Roodman 2006,
2009). While this does not create a problem for the average data, the implementation of the
estimators to annual data is rather problematic. However, simulations conducted by Soto
(2009) and Mitze (2012) show that the estimators could still be used in a finite sample. In
fact, for a small sample (N=25, 35, or 50 and T=12, or 15), the level GMM estimators are
relatively better than the system GMM estimators. Therefore, due to our small sample
properties, for the annual data we avoid using the system GMM estimators used in Beck and
Levine (2004b) and Rioja and Valev (2004).We also follow Roodman(2009) to employ the
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collapsing technique and only use one lag to reduce the number of instruments used. The
corresponding moment orthogonality condition used for [1] is therefore as follows:2
[6]
E 𝑃𝐺𝐷𝑃it −1 ∆𝜀it = 0
for t=3
We do not rule out the possibility of cross error correlationsbetween equations, therefore we
estimate the equations separately. In addition, the separated estimation is beneficial because
it might avoid the misspecification of the sensitivity of the individual equation that can occur
with a joint estimation.
The external exogenous explanatory variables we use here are those variables that are
mainly found to be statistically significant to growth in existing studies such as in Levine and
Zervos (1998), Rousseau and Wachtel (2000), Beck and Levine (1999), Rioja and Valev
(2004), and Naceur and Ghazouani(1999).They aretrade openness (Trade), inflation rate
(Inflation), government spending (Gov) and foreign direct investments (FDI).
The main concern about estimating a simultaneous equation is the selection of
exogenous variables that must be excluded in each equation to satisfy the order of condition
for identification, i.e. the number of excluded exogenous variables in the equation is at least
as large as the number of endogenous variables included in the same equation. This condition
is important to make sure that there are the necessary numbers of potential instrument
variables for the included endogenous variables for identification. The GMM models
constructed in this article guarantee that this condition is satisfied. The choice of variables to
include and exclude in the equations is also based on both theoretical and practical
considerations.
3.
Data
The primary market series (Primary) that we use here is the dollar value of capital
raised through equity public offerings (either through initial public offerings or seasonal
equity offerings) and expressed as a percentage of GDP. We utilizedannual data of the total
2
The moment conditions for equation [2], [3], and [4] are set accordingly.
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new capital raised by shares or investment flow over the period 1995-2011 that is publicly
available on the World Exchange Federation’s(WFE) website.3In the case of a country that has
more than one stock exchange listed as the member of WFE, we added up the observed
values and treated the total as a representative primary market indicator for that country.
The sample has 54 countries.4 Table 1 presents list of the countries and their corresponding
data period coverage.
INSERT TABLE 1 ABOUT HERE
Market liquidity is used as a proxy for the secondary market series (Secondary). Here
we used two measures of liquidity: the ratio of the value of shares traded to GDP (value
traded) and the ratio of the value of shares traded to market capitalization. The latter is also
known as the turnover ratio that measures not only market liquidity but also the transaction
cost. These measures empirically have been used in many studies such as Levine and Zervos
(1996, 1998), Rousseau and Wachtel (2000), Beck and Levine (2004a), Rioja and Valev
(2004), and Yu, Hassan and Sanchez (2012). To measure the banking sector development
(Bank), we experimented with two different indicators: private credit by deposit money bank
and liquid liabilities. Both are as percentage of GDP and have been used in Levine and Zervos
(1998) and Beck and Levine (2004a). Other bank development indicators such as total
savings and bank assets, the private credit and the liquid liabilities consistently have a
significant relationship with the growth indicator. Value traded, turnover ratio, private credit,
and liquid liabilities data are collected from an updated September 2012 database on financial
development and structure (Beck, Demirguc-Kunt & Levine 2000). As explained in Beck,
Demirguc-Kunt and Levine (2001), turnover ratio, private credit, and liquid liabilitiesare
calculated by deflating both the stock variable by the end-of-period customer price index and
the flow variable by the average annual customer price index. This method is employed to
make an adjustment for inflation. Similar adjustment is not needed for capital raised and
3
4
http://www.world-exchanges.org/
We excluded Taiwan due to its data unavailability in the World Bank’s database.
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value traded because both are stock variables. Neither is needed for exogenous control
variables.
The data source is the World Development Indicators that is publicly available on the
World Bank’s website. From the same source, GDP per capitais used as an indicator of
economic development. All variables and their definitions are presented in Table 2.
INSERT TABLE 2 ABOUT HERE
In our estimation we transform the data by taking a natural logarithm of the value of
variable (log(X)), except for Capital and Inflationwhere we taking a natural logarithm of the
value of the variable plus one (log (1+X)).
4.
Results
4.1. Descriptive statistics
Table 3 presents the descriptive statistics of our sample. The secondary market is more
dominant than the primary market as shown by the value of total shares traded, either in
terms of percentage of GDP or market capitalization. Over the sample period, the average of
capital raised through the public equity offering markets is only 3 per cent of GDP, far below
the percentage of shares traded on the stock exchanges with a value of 54 per cent of the
national output. There are three cases where no firm in a country conducted theofferings in a
particular year. This occurred in Germany and Switzerland in 2003 and in Mauritius in 2006.
Hong Kong has been the only country which was able to raise new capital of more than a
quarter of its GDP since 2003. In fact, it raised the highest record in 2010 with a value of 49
per cent. Hong Kong also has the highest total value traded in the last three years. In 2010, its
value traded reached almost eight times the national product, which was much higher than
the US that only reached four times its national product. The US equity market, however, is
still the biggest market around the world in terms of market capitalization. Equity financing is
also still not as important as bank credits. Banks provided about 38 times more capital than
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the value of equity market. In terms of sources of financing, Domowitz, Glen and Madhavan
(2001) show that on average over the period 1980-1997, in thirty countries equity financing
is responsible only for 1.93 per cent of external financing. This may due to the pecking order
paradigm of finance conjecturing firms that tend to use debt financing instead of equity
financing (Froot, Scharfstein & Stein 1994).In terms of size, the secondary equity market does
not relatively lag behind than the financial intermediaries. The average liquid liabilities or
broad money (M3) is 85 per cent of GDP, which is 8 per cent higher than the average market
capitalization (not shown in Table 3).
INSERT TABLE 3 ABOUT HERE
The relationship between economic development measured by GDP per capita and the
primary market is illustrated in Figure 2. The scatter plot shows a positive correlation
between the two variables for the year of 2010. The strength of the relationship is, however,
rather weak, which is confirmed by the correlation coefficient between Capital raised and
PGDP. A higher positive relationship between PGDP and other variables of the stock market
and banks are visible. In general, however, simple correlation coefficients show there are
positive associations between per capita GDP and all financial sector indicators. Economic
output relatively has a positive high correlation to equity trading turnover and banking
credit, while it has small, but still positive, correlation with capital raised through equity
public offerings.
INSERT FIGURE 2 ABOUT HERE
4.2. The 5-Year Average Data
The estimates of the traditional growth model using 5-year average data for the full
sample are presented in Table 4. There are three 5-year average periods: 1995-1999, 20002004, and 2005-2010. The last period consists of six years. Initial PGDPs are the value of
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PGDP in year 1995, 2000 and 2005. We also deleted any samples that had only a one-year
period. We experimented with two measurements of Capital: Initial Capital which is the
average of two years of capital raised in the beginning of each period and AverageCapitalis
just the 5-year average. With two alternative measurements for each Secondary and Banking,
we therefore had eight (2x2x2) models of equation [1]. Their estimates are summarized in
Table 4.
INSERT TABLE 4 ABOUT HERE
We find thatCapital, either measured by a 2- or 5-year average, is not statistically
significant on Growth. Vtradedand Turnover on the other hand, are statistically significant,
and confirmed the existing findings that liquidity of the secondary equity market is an
important determinant of economic growth.
4.3. The Annual Data
To explore the time dimension of our data, we also utilized our annual data. However
due to our unbalanced data with variations in the length of time period, we decide to only use
samples that had a minimum of 15 years data. This is important to accommodate panel timeseries methods. There were 28 countries that fulfilledthese requirements.
Many studies have shown that many macroeconomic variables are often integrated.
Here, we employ two panel unit root tests: the Im-Pesaran-Shin(IPS) test (Im, Pesaran & Shin
2003)and the CSD test(Pesaran, M. Hashem 2007). The two different tests actually have
different results, depending on the inclusion of deterministic trends (see Table 5). However,
the results in general reveal that all variables contain a unit root, except Turnover. The first
test for non-stationarity (IPS W-t-bar) strongly confirms the existence of unit roots for PGDP,
Credit and Liquid Liabilities, while the other variables are stationary. The second test (CADF
Z-t-bar) creates a slightly different result depending on the choice of whether a trend is
included. Only Turnoverand Capital consistently show that they are stationary and non-
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stationary, respectively. In the case of inclusion of a trend, the statistics show thatPGDP,
Capital and Vtraded contain unit roots. Despite these different empirical results, by also
considering theoretical expectations, we finally decide to consider that all variables are nonstationary or integrated at order one, e.g. I(1).This has been confirmed by the results of the
panel unit root tests for the first difference of the series. The results of the unit root tests
bring us to transform all variables to their first difference. The transformed variables are
therefore changes in the variables in focus.
INSERT TABLE 5 ABOUT HERE
INSERT TABLE 6 ABOUT HERE
To avoid a spurious regression, we conducted a panel cointegration test of Pedroni and
its results are presented in Table 6. We employ the tests with the inclusion of a deterministic
intercept and trend, and one lag length due to our finite sample properties. It is safe to say
that the test shows that in general there are no cointegration relationships between the
economic development indicator and financial sector indicators. However, there is one
exception to note;here might be a cointegration relationship between Capital and PGDP,
Secondaryand Bank. Three of the six statistics indicate that those variables might be cointegrated.
Formal econometric investigations to assess the relationship between growth and the
financial sectors are conducted by one-step level GMM regressions using ARDL model
specificationsand the results are presented in Table 7 and Table 8. Two specification tests are
used to examine the appropriateness of the model and its assumptions used: a test for AR(2)
in difference;Hansen (1982)test for joint validity of instruments and Pesaran, M. Hashem
(2004)cross-sectional dependence test. All of them provide evidence that our model
generally could be methodologically accepted.
INSERT TABLE 7 ABOUT HERE
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INSERT TABLE 8 ABOUT HERE
Table 7 using Vtradedas the proxy for Secondary, in general, shows that the notion of
supply-leading is confirmed; that the causality is from the secondary equity market to
economic growth, in particular for the contemporaneous variables. Capital is not statistically
significant on Growth, but it is statistically significant on Vtraded.This indicates that primary
equity market development does not affect economic growth. It only affects the secondary
equity market. The secondary market in fact also affects the primary markets. Meanwhile, the
results show that there is bi-directional relationship between banking sectors and economic
development.
Table 8 shows the results when Turnover instead of Vtradedis used as the proxy for
liquidity. The findings in Table 8are relatively similar to those found in Table 7. Turnover
positively affects PGDP and Capital. However, both equity market indicators are not affected
by PGDP. Capital is only statistically significant on Turnover when we use Liquid Liabilities as
the proxy for Bank rather than Credit.
4.4. Further Analysis using the Annual Data
As for robustness and sensitivity tests, we also experiment with a panel VAR as in Love
and Zicchino (2006). In contrast with the models in [1]-[4] with contemporaneous
independent variables and one lagged dependent variable, we here employ a first order panel
VAR by only using one lag of all endogenous variables and present the estimation results in
Table 9.
INSERT TABLE 9 ABOUT HERE
In the first part of Table 9, we find that Growth is not statistically affected by the lag
value of Vtraded,instead the current value of Vtraded is affected by the lag value of PGDP. The
current value of Vtraded is also affected by the lag value of Capital. The second part of Table
- 16 -
9 shows that Turnover indeed affects Growth, but it has no statistical relationship with
Capital. In general, the findings confirm that the primary market is not an important
determinant of economic development;however, it does have an impact on the development
of the secondary market.
We also consider an alternative model in order to overcome the possibility of slope
heterogeneity in the previous model specification. The slope heterogeneity is also important
to accommodate the critique of Aretis and Demetriades (1997)on the individual country
idiosyncrasy. Here we re-estimate our models by using the pooled mean group estimation
(PMG) of Pesaran, M. Hashem, Shin and Smith (1999). This estimator allows for slope
heterogeneity in the short run, but still assumes slope homogeneity in the long run. Even
though the PMS estimator is robust to the existence of cointegration, Pesaran, M. Hashem,
Shin and Smith (1999)still suggests conducting a test for a long-run relationship between
variables before estimating. Table 6 indicates the possibility of a cointegration relationship
between Capital, PGDP, Vtraded, and Credit. Based on this, we focus on the following model
specification:
[8]
∆𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 = 𝜃1𝑖 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 −1 − 𝜎11𝑖 𝑃𝐺𝐷𝑃𝑖𝑡 − 𝜎12𝑖 𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 − 𝜎13𝑖 𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛼11 ∆𝑃𝐺𝐷𝑃𝑖𝑡 +
𝛼12 ∆𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 + 𝛼13 ∆𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛿1𝑖 + 𝜀1𝑖𝑡
[9]
∆𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 = 𝜃2𝑖 𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 −1 − 𝜎21𝑖 𝑃𝐺𝐷𝑃𝑖𝑡 − 𝜎22𝑖 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 − 𝜎23𝑖 𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛼21 ∆𝑃𝐺𝐷𝑃𝑖𝑡 +
𝛼22 ∆𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 + 𝛼23 ∆𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛿2𝑖 + 𝜀2𝑖𝑡
The estimates after time-demeaning (subtracting cross-sectional mean) to mitigate the
impact of cross-sectional dependence are provided in Table 10.
INSERT TABLE 10 ABOUT HERE
All models indicate the possibility of a long-run relationship between Capital and
Vtraded. In the long run, the causality relationship tends to go only from Capital to Vtraded. In
the short run, however, the direction is reversed, i.e. it goes from Vtradedto Capital. These
results support the majority of findings in the studies discussed in Section 1.
- 17 -
4.5. Discussion
All these results raise an important question on the importance of primary markets to
economic development. If the primary markets only function as a supply for secondary
markets (while in fact that the capital raised in the primary markets are more important to
listed companies to do investment), we need to reconsider the capital accumulation channel
to economic growth. Firstly, what is the main motivation of private firms going forpublic
investment financing? Whatare the IPO proceeds used for? Private firms go public because
they expect to get the following benefits and opportunities: future growth financing;
improvements offinancial condition; incrementalmarket value and shareholder value; future
external source of financing opportunities; merger and acquisition possibilities; stock
exchange listing; increase in corporate image and reputation due to public awareness; and
increase in founders’ wealth incremental (Draho 2004; Kleeburg 2005; Sherman 2005).
However, a survey conducted by Brau and Fawcett (2006)shows that managers’ motives for
going public is mainly for the purpose of future acquisitions. Investment financing as an
alternative of debt, in fact, is the least motivation, behind the motives of market valuation,
reputation, cost of capital, and ownership distribution. Secondly, is there disconnection
between financial markets and the real sector? As indicated by Bencivenga, Smith and Starr
(1996), speculative trading boosts investors’ reluctance to invest in a real rector investment
project. Capitalists tend to invest their capital in financial markets, in particular the
secondary markets. In this case, an increase in trading liquidity may lead to less long-term
and productive investments because there will be less creation of new capital investments.
Capital in equity markets is just transferred between investors through trading on stock
exchanges. Savings are only utilizedfor capital formation and accumulation, but not for capital
allocation to productive investments; therefore they may have no impact on the level of real
activity. Singh, A (1997)also argues that the expected functions of trading and corporate
controls from the secondary markets do not work efficiently. The primary markets
- 18 -
themselves are not a preferred way to undertake investment in firm-specific human capital.
Thirdly, does financial liberalization exclude long-term commitment? The main assumption of
Bencivenga, Smith and Starr (1996) model is that a more productive investment needs a longterm fund commitment through the creation of new capital. Financial liberalisation allows
foreign investors to invest in a country, allowing them to withdraw their money at any time
without restrictions. They may prefer to just buy and sell existing shares on stock exchanges.
The functions of trading and corporate controlcannot work if there is no long-term
commitment from investors, as indicated by Bhide (1993) that liquidity makes investors
reluctant to monitor managers.
5.
Conclusion
This paper examines the different role played by primary and secondary equity
markets in relation to economic growth. While most studies on thefinance-growth nexus only
consider secondary market indicators and tend to underestimate the primary market
counterpart when examining the relationship between stock market development and
economic growth, this study separates and integrates both markets at the same time by using
a simultaneous framework.From a microeconomic perspective, listed firms could raise
money through primary markets by offering equity to publics, with no additional cash inflow
for firms when their stocks traded on a stock exchange(s). Furthermore, the transactions on
the exchanges are not classified as an investment from macroeconomic point of view, while
selling new shares is.
We investigate capital accumulation function of equity markets here by employing the
dynamicpanel regressions of Blundell and Bond (1998) and other alternative model
specifications for small sample of 54 countries over the period 1995-2010. We show that
capital raised through primary equity markets are not an important determinant of economic
growth. The primary markets are only significant as a supplier of new shares to secondary
- 19 -
market activities on the stock exchanges. We also confirm the existing findings on the
importance of secondary market activities on the stock exchange and that trading liquidity is
an important determinant of the economic growth.
- 20 -
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Figure 1.Public equity offering process and the functions of the equity markets
9.00
CHE
8.00
IND
Capital raised to GDP (%)
7.00
6.00
BRA
5.00
MYS
4.00
IRL
SGPCAN
ESP GBR
POL
3.00
NOR
CHN
PHL
2.00
EGY
IDN
MAR
CYP
GRC
ISR
CHL
THA
JOR
LKA
1.00
MEX
PER
ZAF
MUS
COL
TUR
RUS HUN SAU
ARG
KOR
MLT
SVN
USA
JPN
AUT
DEU
0.00
3.00
3.50
4.00
4.50
Per capita GDP (in log)
5.00
5.50
Figure 2.Scatter plot capital raised (as percentage of GDP) and GDP per capital (current USD, in log), 2010
Note: Hong Kong is excluded because it is an outlier. Its ratio of capital raised to GDP is 48.76%. Australia and Iran are not displayed due to incomplete data.
Table 1
List of selected countries, their corresponding exchanges and data period coverage
Country
Stock exchange
Data period
Argentina
Buenos Aires SE
1995-2010
Australia
Australian SE
1995-2010
Austria
Wiener Börse
1995-2010
Belgium
Euronext Brussels
1995-1999
Bermuda1
Bermuda SE
1998-2006
Brazil
BM&FBOVESPA
1995-2010
Canada2
TMX Group
1995-2010
Chile
Santiago SE
1995-2010
China3
Combined
2001-2010
Colombia
Colombia SE
2003-2010
Cyprus
Cyprus SE
2004-2010
Denmark4
Copenhagen SE
1995-2003
Egypt
Egyptian Exchange
2004-2010
Finland4
OMX Helsinki SE
1995-2003
France
Euronext Paris
1995-1999
Germany
Deutsche Börse
1995-2010
Greece
Athens Exchange
1995-2010
Hong Kong
Hong Kong Exchanges
1995-2010
Hungary
Budapest SE
2000-2010
India5
Combined
2001-2010
Indonesia
Indonesia SE
1995-2010
Iran
Tehran SE
1995-2010
Ireland
Irish SE
1995-2010
Israel
Tel Aviv SE
1995-2010
Italy
BorsaItaliana
1995-2008
Japan6
Combined
1995-2010
Jordan
Amman SE
2006-2010
South Korea
Korea Exchange
1995-2010
Luxembourg
Luxembourg SE
1995-2006
Malaysia
Bursa Malaysia
1995-2010
Malta
Malta SE
1998-2010
Mauritius
Mauritius SE
2006-2010
Mexico7
Mexican Exchange
1995-2010
Morocco
Casablanca SE
2009-2010
Netherland
Euronext Amsterdam
1995-1999
New Zealand
New Zealand SE
1995-2008
Norway
Oslo Børs
1995-2010
Peru
Lima SE
1995-2010
Philippines
Philippine SE
1995-2010
Poland
Warsaw SE
1995-2010
Portugal
Lisbon SE
1995-2000
Russia8
Combined
2009-2010
Saudi
Saudi Stock Market - Tadawul
2008-2010
Singapore
Singapore SE
1999-2010
Slovenia
Ljubljana SE
1996-2010
South Africa
Johannesburg SE
1995-2010
Spain
BME Spanish Exchanges
1998-2010
Sri Lanka
Colombo SE
1997-2010
Sweden4
OMX Stockholm SE
1995-2003
Switzerland9
SIX Swiss Exchange
1995-2010
Taiwan10
Combined
1995-2010
Thailand
Thailand SE
1995-2010
Turkey
Istanbul SE
1995-2010
U.K.
London SE Group
1995-2010
U.S.A11
Combined
1995-2010
Note:(1) 1999-2000 is not available. (2) Before 2001, combined Canadian Venture Exchange and Toronto Stock
Exchange. 2008 data is not available. (3) Combined Shanghai Stock Exchange and Shenzhen Stock Exchange. (4)
Merged into NASDAQ OMX Nordic Exchange in 2004. After that data for each country is not available. (5)
Combined Bombay Stock Exchange and National Stock Exchange India. 2011 data is only from National Stock
Exchange India. (6) Combined Tokyo Stock Exchange Group and Osaka Securities Exchange. Before 2009, JASDAQ
also included. (7) 2001 data is not available. (8) Since 2010, combined MICEX and RTS Stock Exchange. (9) 2008
data is not available. (10) Taiwan is excluded. Since 2010, combined Taiwan Stock Exchange Corp and Gretai
Securities Market. Before that, data is only from Taiwan Stock Exchange Corp. (11) Combined NASDAQ and NYSE.
Before 2005, combined NASDAQ, NYSE and American Stock Exchange.
Table 2
List of variables and their definitions
Variable
Definition
Source
PGDP
WDI
Capital raised
Value traded
Turnover
Private credit
Liquid liabilities
Schooling
FDI
Trade
Gov
Inflation
Gross domestic product divided by the number
of population (in current USD)
Total amount of capital raised through primary
equity markets either by an initial public offering
or seasonal equity offering (as a percentage of
GDP)
Value of total shares traded on the stock market
exchange (as a percentage of GDP)
Deflated value of total shares traded on the stock
market exchange (as a percentage of market
capitalization)
Deflated private credit by deposit money banks
(as percentage of GDP)
Deflated liquid liabilities (as percentage of GDP)
Gross rate of secondary enrollment (as
percentage of GDP)
Net inflow of foreign direct investment (as
percentage of GDP)
Total value of export and import of goods and
services (as percentage of GDP)
Government expense (as percentage of GDP)
Annual percentage change in the consumer price
index
WFE and WDI (for GDP)
Beck, Demirguc-Kunt and
Levine(2000)
Beck, Demirguc-Kunt and
Levine(2000)
Beck, Demirguc-Kunt and
Levine(2000)
Beck, Demirguc-Kunt and
Levine(2000)
WDI
WDI
WDI
WDI
WDI
The deflation formula for calculating turnover is
𝑉𝑡
𝐶𝑃𝐼𝑡
𝑀
𝑀
0.5 × ( 𝑡 𝐶𝑃𝐼 + 𝑡−1 𝐶𝑃𝐼 )
𝑡
𝑡−1
The deflation formula for calculating private credit is
𝐶
𝐶
0.5 × ( 𝑡 𝐶𝑃𝐼 + 𝑡−1 𝐶𝑃𝐼 )
𝑡
𝑡−1
𝐺𝐷𝑃𝑡
𝐶𝑃𝐼𝑡
The deflation formula for calculating liquid liabilities is
𝐿𝐿
𝐿𝐿
0.5 × ( 𝑡 𝐶𝑃𝐼 + 𝑡−1 𝐶𝑃𝐼 )
𝑡
𝑡−1
𝐺𝐷𝑃𝑡
𝐶𝑃𝐼𝑡
Where
Vt
Mt
Ct
LLt
CPIt
𝐶𝑃𝐼 t
GDPt
: the value of total shares traded on the stock market exchange at year t
: the value of total stock market capitalization of the stock market exchange at year t
: the value of private credit by deposit money banks at year t
: the value of liquid liabilities at year t
: End-of-period customer price index at year t
: Average annual customer price index at year t
:the value of GDP at year t
-1-
Table 3
Descriptive statistics
Statistic
PGDP
Capital
Vtraded
Turnover
Credit
Liquid
liabilities
664
19,335.596
17,154.697
459.230
93,366.810
660
2.482
4.636
0.000
48.763
655
53.904
75.364
0.271
740.057
655
64.007
58.0187
0.360
435.561
626
76.600
47.828
9.617
270.840
612
85.431
58.620
17.319
361.885
Correlation (No. observations = 604)
PGDP
1.000
Capital
0.162
Vtraded
0.385
Turnover
0.298
Credit
0.587
Liquid liabilities
0.529
1.000
0.512
0.073
0.280
0.486
1.000
0.639
0.402
0.408
1.000
0.233
0.129
1.000
0.733
1.000
Pesaran CD Cross-sectional Independence test
Average coefficient
0.775
0.100
CD-statistic
57.76***
7.46***
0.329
24.57***
0.370
27.62***
0.148
10.46***
0.327
23.51***
Full Sample
No. observations
Mean
Std. dev
Min
Max
Table 4
Two-step system GMM estimates regressions of the relationship between the primary market, the secondary market (Vtradedor
Turnover), banks (Credit or Liquid Liabilities) and growth, 5-year average data
Variables
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Initial PGDP
Schooling
Initial Capital
-0.0161
(0.0118)
0.0373
(0.0450)
0.0150
(0.0308)
-0.0254*
(0.0129)
0.0956
(0.0594)
-0.0388
(0.0519)
-0.0208**
(0.0102)
0.0720*
(0.0367)
-0.0026
(0.0364)
-0.0286**
(0.0138)
0.1251**
(0.0584)
-0.0530
(0.0497)
Average Capital
Vtraded
-0.0095
(0.0068)
-0.0107***
(0.0024)
Turnover
Credit
Liquid Liabilities
-0.0115**
(0.0054)
-0.0002
(0.0178)
0.0044
(0.0194)
0.0219
(0.0176)
-0.0159
(0.0120)
0.0415
(0.0415)
-0.0262*
(0.0144)
0.0968*
(0.0576)
-0.0204*
(0.0106)
0.0752**
(0.0291)
-0.0290*
(0.0144)
0.1262**
(0.0511)
0.0113
(0.0273)
-0.0094
(0.0070)
-0.0399
(0.0505)
-0.0104***
(0.0030)
-0.0058
(0.0308)
-0.0544
(0.0446)
-0.0114**
(0.0055)
-0.0010
(0.0178)
-0.0113***
(0.0024)
-0.0115***
(0.0026)
0.0036
(0.0188)
0.0151
(0.0185)
0.0236
(0.0203)
0.0162
(0.0203)
Observations
112
110
112
110
112
110
112
110
Number of instruments
22
22
22
22
22
22
22
22
AR(1)
0.267
0.234
0.256
0.235
0.263
0.234
0.253
0.234
Hansen test
0.146
0.220
0.146
0.273
0.152
0.219
0.151
0.291
Notes: Standard errors are in parentheses. The standard errors are robust to heteroskedasticity and autocorrelation with Windmeijer
correction. Covariance matrix estimate is based on small sample correction. The number of instruments used is reduced both by using only one
lag (i.e. lag 1) and collapsing the instrument matrix. The instrument variable set is one-period lag exogenous variables. Year dummies are
included. ***, **, and * indicate p<0.01, p<0.05, and p<0.1, respectively.
Table 5
Panel unit root tests
Variable
In Level
PGDP
Capital
Vtraded
Turnover
Credit
Liquid liabilities
IPS Test [W-t-bar]
No trend
-0.738
-2.456***
-4.045***
-3.792***
0.999
1.637
trend
0.637
-3.125***
-1.360*
-1.626*
-0.677
0.645
CADF Test [Z-t-bar]
No trend
-2.026**
2.354
-1.950*
-3.499***
4.024
-0.091
trend
-1.165
-0.257
0.013
-3.414***
-1.470*
-3.065***
In First Difference
PGDP
-5.813***
-5.188***
-3.605***
-1.159
Capital
-11.116***
-7.443***
-7.376***
-5.846***
Vtraded
-6.766***
-6.939***
-3.852***
-6.568***
Turnover
-8.261***
-6.523***
-5.925***
-4.863***
Credit
-5.439***
-4.153***
-3.908***
-2.661***
Liquid liabilities
-4.454***
-2.823***
-3.225***
-2.594***
Notes: The statistics are computed in all the unit root tests by making cross-sectional removal. Null
hypothesis is panels contain unit roots. We set the number of lag for ADF regression in the tests to one
due to our limited sample size. All calculations are using Stata’s routine xtunitrootandpescadfdeveloped
by Lewansowski.***, ** and * indicate significance at 1%, 5% and 10% level, respectively.
-1-
Table 6
Pedroni residual panel cointegration tests
Statistics
PGDP
Dependent variable
Capital
Turnover
Liquid
liabilities
Within-dimension
Panel v-Statistics
Panel rho-Statistic
Panel PP-Statistic
Panel ADF-Statistic
-0.3095
5.1407
3.6578
3.8165
-4.0629
3.1375
-15.3214***
-6.7970***
-0.0695
2.8094
-2.5992***
0.4843
0.3148
5.9795
6.3946
3.0789
Between-dimension
Group rho-Statistic
Group PP-Statistic
Group ADF-Statistic
6.7750
3.0757
1.4087
3.4879
-20.6458***
-2.3154**
5.4367
-3.1871***
-0.1628
6.7514
2.6298
1.2453
PGDP
Capital
Turnover
Credit
Within-dimension
Panel v-Statistics
Panel rho-Statistic
Panel PP-Statistic
Panel ADF-Statistic
0.5547
4.2111
0.7440
1.9496
-3.0941
3.3241
16.3283***
-6.2837***
0.7530
3.3727
-3.3996***
-0.8694
1.2756
4.2328
1.2856
1.8964
Between-dimension
Group rho-Statistic
Group PP-Statistic
Group ADF-Statistic
6.5306
3.0942
3.8731
3.5539
-20.0094***
-3.1546***
5.4137
-3.8543***
-0.0667
6.4128
2.9675
2.6784
PGDP
Capital
Vtraded
Credit
Within-dimension
Panel v-Statistics
Panel rho-Statistic
Panel PP-Statistic
Panel ADF-Statistic
-0.8486
5.4232
3.7271
2.9966
-4.1360
2.1834
-29.0333***
-10.4401***
0.3010
4.3589
1.3954
-1.7631***
0.2929
4.7979
2.3868
1.4703
Between-dimension
Group rho-Statistic
Group PP-Statistic
Group ADF-Statistic
6.9818
4.2852
3.7312
3.3216
-28.1544***
-3.4975***
6.8511
2.7530
-3.4995***
6.2594
1.4635
-0.6597
PGDP
Capital
Vtraded
Liquid
liabilities
Within-dimension
Panel v-Statistics
Panel rho-Statistic
Panel PP-Statistic
Panel ADF-Statistic
-0.2637
5.5224
4.7159
3.2335
-4.5704
2.1365
-24.6427***
-10.6660***
0.2883
4.5885
2.3315
0.9370
0.5717
5.9360
6.3645
3.4997
Between-dimension
Group rho-Statistic
Group PP-Statistic
Group ADF-Statistic
6.8536
3.3992
0.3264
3.4475
-25.1713***
-2.3615***
7.2366
3.4434
-1.8763***
6.5868
1.6325
0.8027
Notes: The statistics are computed by using the assumptions of deterministic trend and trend, degree of
freedom corrected Dickey-Fuller residual variances, one lag length and Newey-West automatic bandwith
selection and Bartlett kernel. All calculations are using Eviews.***, ** and * indicate significance at 1%,
5% and 10% level, respectively
Table 7
One-step level GMM estimates regressions of the relationship between the primary market, the secondary market (VTraded), banks
(Credit or Liquid Liabilities) and growth, selective sample, annual data
Variables
PGDP
Capital
Vtraded
Credit
PGDP
Capital
Vtraded
Liquid
liabilities
PGDPt-1
0.8494***
0.0371
-0.3587
0.2327***
0.3825**
0.0030
0.1644
0.1337**
(0.1449)
(0.0393)
(0.4342)
(0.0538)
(0.1442)
(0.0167)
(0.3436)
(0.0637)
Capitalt-1
-0.6810
-0.3659
6.1435
-0.6322
1.3451
-0.3063
5.9331**
-0.7379
(2.1880)
(0.3885)
(3.9578)
(0.9528)
(1.5183)
(0.4173)
(2.7759)
(0.7273)
Vtradedt-1
0.0159
0.0029
0.3154*
0.0518
-0.1073
-0.0064
0.4417***
0.0684***
(0.0923)
(0.0174)
(0.1763)
(0.0343)
(0.0653)
(0.0098)
(0.1493)
(0.0223)
Creditt-1
1.0379**
0.2038*
-1.6515*
0.5568***
(0.4875)
(0.1109)
(0.8203)
(0.1253)
Liquid liabilitiest-1
0.2234
0.1368**
-0.5268
0.5481***
(0.3448)
(0.0643)
(0.8166)
(0.1115)
PGDPt
-0.0391
0.7703**
-0.1479***
-0.0102
0.5442**
-0.0134
(0.0241)
(0.2934)
(0.0426)
(0.0112)
(0.2225)
(0.0657)
Capitalt
-2.9643
6.2154*
-1.2713
-0.8460
2.8149
-0.7194
(2.1230)
(3.5323)
(0.9195)
(0.7433)
(2.2745)
(0.7198)
Vtradedt
0.3319***
0.0354**
0.0524
0.3281***
0.0205**
-0.0327
(0.1096)
(0.0154)
(0.0542)
(0.0915)
(0.0099)
(0.0393)
Creditt
-2.1315***
-0.2419
1.7536
(0.6559)
(0.1480)
(1.2111)
Liquid liabilitiest
-0.1355
-0.0878
-0.5475
(0.4027)
(0.0550)
(0.9121)
Observations
214
214
232
214
212
212
232
212
Number of instruments
26
26
26
26
26
26
26
26
AR(2)
0.847
0.183
0.0720
0.325
0.896
0.148
0.0287
0.273
Hansen test
0.134
0.788
0.236
0.301
0.269
0.691
0.233
0.415
Notes: Standard errors are in parentheses. The standard errors are robust to heteroskedasticity and autocorrelation with Windmeijer correction.
Covariance matrix estimate is based on small sample correction. The number of instruments used is reduced both by using only one lag (i.e. lag 1)
and collapsing the instrument matrix. The instrument variable set is one-period lag external exogenous variables. ***, **, and * indicate p<0.01,
p<0.05, and p<0.1, respectively.
-1-
Table 8
One-step level GMM estimates regressions of the relationship between the primary market, the secondary market (Turnover), banks
(Credit or Liquid Liabilities) and growth, selective sample, annual data
Variables
PGDP
Capital
Turnover
Credit
PGDP
Capital Turnover
Liquid
liabilities
PGDPt-1
0.8184***
0.0242
0.1597 0.2588***
0.3843***
0.0071
0.0335
0.1097*
(0.1693)
(0.0509)
(0.5092)
(0.0685)
(0.1023)
(0.0224)
(0.4746)
(0.0639)
Capitalt-1
0.6567
-0.0329
-4.9634
-0.8915
3.3917
0.1490
-6.6674
-1.9437*
(1.6326)
(0.2815)
(4.4413)
(0.7660)
(2.1209)
(0.4587)
(5.3178)
(1.0106)
Turnovert-1
0.2481
-0.0059
0.4945*
0.1277**
0.0522
-0.0289* 0.5701**
0.1572**
(0.1821)
(0.0198)
(0.2467)
(0.0511)
(0.1408)
(0.0141)
(0.2225)
(0.0632)
Creditt-1
0.7336
0.1382
0.0769 0.4940***
(0.4662)
(0.1110)
(1.2944)
(0.1410)
Liquid liabilitiest-1
0.0370
0.0813
0.2752 0.4527***
(0.2795)
(0.0764)
(1.0662)
(0.1243)
PGDPt
-0.0318
0.5138 -0.1451**
-0.0174
0.5585
-0.0397
(0.0374)
(0.4575)
(0.0675)
(0.0244)
(0.4527)
(0.0824)
Capitalt
-1.9387
5.4022
-0.6430
-1.5140
5.6965*
-0.1909
(1.3434)
(3.2198)
(0.5688)
(0.9548)
(2.9555)
(0.5925)
Turnovert
0.2013*
0.0347**
-0.0199
0.3126***
0.0366**
-0.0413
(0.1057)
(0.0141)
(0.0467)
(0.0966)
(0.0156)
(0.0542)
Creditt
-1.9773**
-0.1436
-0.6942
(0.8463)
(0.1700)
(1.7830)
Liquid liabilitiest
-0.4645
-0.0256
-0.8631
(0.4141)
(0.0925)
(1.3182)
Observations
212
212
212
214
214
214
214
212
Number of instruments
23
23
23
23
23
23
23
23
AR(2)
0.734
0.560
0.889
0.286
0.982
0.642
0.939
0.132
Hansen test
0.398
0.624
0.735
0.821
0.395
0.655
0.536
0.783
Notes: Standard errors are in parentheses. The standard errors are robust to heteroskedasticity and autocorrelation with Windmeijer correction.
Covariance matrix estimate is based on small sample correction. The number of instruments used is reduced both by using only one lag (i.e. lag 1)
and collapsing the instrument matrix. The instrument variable set is one-period lag external exogenous variables. ***, **, and * indicate p<0.01,
p<0.05, and p<0.1, respectively.
-2-
Table 9
Panel 4-variable VAR estimates
Response to
PGDPt-1
Capitalt-1
Vtradedt-1
Creditt-1/Liquid liabilitiest-1
PGDP
Capital
Vtraded
0.9918***
(0.0396)
-0.0440
(0.1853)
0.0214
(0.0153)
-0.0826
(0.0637)
-0.0106
(0.0082)
0.1856
(0.2539)
0.0044
(0.0037)
0.0111
(0.0087)
0.1587**
(0.0761)
1.6927**
(0.6929)
0.8476***
(0.0320)
-0.2693***
(0.0.990)
No. Observations
Capitalt-1
Turnovert-1
Creditt-1/Liquid liabilitiest-1
0.0977***
(0.0294)
-0.0569
(0.2522)
0.0404***
(0.0104)
0.8196***
(0.3778)
0.9727***
(0.0374)
0.0077
(0.1817)
0.0187
(0.0167)
0.0012
(0.0553)
354
Capital
Vtraded
Liquid
liabilities
-0.0095
(0.0075)
0.1615
(0.2564)
0.0043
(0.0034)
0.0179
(0.0144)
0.1205
(0.0752)
2.0685**
(0.7530)
0.8494***
(0.0318)
-0.3742***
(0.1220)
0.0163
(0.0186)
0.2085*
(0.1153)
0.0259***
(0.0077)
0.8294***
(0.0314)
Capital
Turnover
Liquid
liabilities
-0.0064
(0.0067)
0.1665
(0.2541)
0.0013
(0.0027)
0.0224
(0.0157)
0.0984
(0.1002)
-0.9499
(0.8474)
0.7582***
(0.0494)
-0.1184
(0.1452)
0.0268
(0.0178)
0.2340**
(0.1176)
0.0156*
(0.0088)
0.8512***
(0.0308)
351
Response to
PGDPt-1
Response of
Credit
PGDP
PGDP
Capital
Turnover
0.9714***
(0.0434)
-0.0414
(0.1887)
0.0379*
(0.0229)
-0.0654
(0.0607)
-0.0092
(0.0082)
0.1967
(0.2483)
0.0025
(0.0029)
0.0138
(0.0093)
0.1335
(0.0945)
-1.214
(0.7996)
0.7458***
(0.0471)
-0.1153
(0.1076)
Response of
Credit
PGDP
0.1147***
(0.0262)
0.0519
(0.2437)
0.0194
(0.0132)
0.8445***
(0.0352)
0.9464***
(0.0416)
0.0073
(0.1859)
0.0439*
(0.0249)
-0.0034
(0.0562)
No.Observations
354
352
Notes: Time-demeaned removal and helmet transformation are employed before the system GMM estimation. Statapropertiaryroutine pvar by Love
and Zicchino (2006).***, ** and * indicate significance at 1%, 5% and 10% level, respectively.
Table 10
Estimates of heterogeneous panels
Variables
CAPITAL
Long-run relationship
PGDP
-0.0020**
(0.0009)
Vtrade
0.0001
(0.0004)
Credit
-0.0002
(0.0007)
Speed of adjustment
Short-run relationship
PGDP
-0.8780***
(0.0833)
VTRADED
Long -un relationship
PGDP
Capital
Credit
Speed of adjustment
4.9401***
(0.8080)
1.0873***
(0.0841)
-0.0309
(0.1401)
-0.3026***
(0.0497)
Short-run relationship
0.0174
PGDP
0.2412
(0.0294)
(0.1852)
Vtraded
0.0094*
Capital
2.9015
(0.0052)
(2.3985)
Credit
-0.0226
Credit
1.0976***
(0.0232)
(0.2892)
Notes: Estimates and standard errors (in parentheses) are calculated by using Stata’s routine xtpmg
developed by Blackburne and Frank (2007). ***, ** and * indicate significance at 1%, 5% and 10%
level, respectively.