equity markets and economic development: does the primary ... · pdf fileequity markets and...

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 1995-

2010, 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: [email protected]

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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 market-

based 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

1Other 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 less-

developed 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 = 𝛼1𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑃𝐺𝐷𝑃𝑖𝑡−1 + 𝛽11𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽12𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 + 𝛽13𝐵𝑎𝑛𝑘𝑖𝑡 + 𝛾1′ 𝑋𝑡 + 𝜂1𝑖 +

휀1𝑖𝑡

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 = 𝛼2𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡−1 + 𝛽21𝑃𝐺𝐷𝑃𝑖𝑡 + 𝛽22𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 + 𝛽23𝐵𝑎𝑛𝑘𝑖𝑡 + 𝛾2′ 𝑋𝑡 + 𝜂2𝑖 + 휀2𝑖𝑡

[3] 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 = 𝛼3𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡−1 + 𝛽31𝑃𝐺𝐷𝑃𝑖𝑡 + 𝛽32𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽33𝐵𝑎𝑛𝑘𝑖𝑡 + 𝛾3

′ 𝑋𝑡 + 𝜂3𝑖 + 휀3𝑖𝑡 [4] 𝐵𝑎𝑛𝑘𝑖𝑡 = 𝛼4𝐵𝑎𝑛𝑘𝑖𝑡−1 + 𝛽41𝑃𝐺𝐷𝑃𝑖𝑡 + 𝛽42𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽43𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑖𝑡 + 𝛾4

′𝑋𝑖𝑡 + 𝜂4𝑖 + 휀4𝑖𝑡

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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−1휀it = 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∆휀it = 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

2The 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 http://www.world-exchanges.org/ 4We 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.


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).


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, 2000-

2004, 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.


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 time-

series 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 non-

stationary 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.



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 co-

integrated.

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.


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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.


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 − 𝜎11𝑖𝑃𝐺𝐷𝑃𝑖𝑡− 𝜎12𝑖𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 − 𝜎13𝑖𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛼11∆𝑃𝐺𝐷𝑃𝑖𝑡 +

𝛼12∆𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 + 𝛼13∆𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛿1𝑖 + 휀1𝑖𝑡

[9] ∆𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡 = 𝜃2𝑖 𝑉𝑡𝑟𝑎𝑑𝑒𝑑𝑖𝑡−1 − 𝜎21𝑖𝑃𝐺𝐷𝑃𝑖𝑡− 𝜎22𝑖𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 − 𝜎23𝑖𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛼21∆𝑃𝐺𝐷𝑃𝑖𝑡 +

𝛼22∆𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 + 𝛼23∆𝐶𝑟𝑒𝑑𝑖𝑡𝑖𝑡 + 𝛿2𝑖 + 휀2𝑖𝑡

The estimates after time-demeaning (subtracting cross-sectional mean) to mitigate the

impact of cross-sectional dependence are provided in Table 10.


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 long-

term 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

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.

ARGAUT

BRA

CAN

CHL

CHN

COL

CYP

EGY

DEU

GRC

HUN

IND

IDN

IRL

ISR

JPNJOR

KOR

MYS

MLTMUS

MEX

MAR

NOR

PER

PHL

POL

RUS SAU

SGP

SVN

ZAF

ESP

LKA

CHE

THATUR

GBR

USA

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

3.00 3.50 4.00 4.50 5.00 5.50

Cap

ital

rai

sed

to

GD

P (

%)

Per capita GDP (in log)

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 Gross domestic product divided by the number of population (in current USD)

WDI

Capital raised Total amount of capital raised through primary equity markets either by an initial public offering or seasonal equity offering (as a percentage of GDP)

WFE and WDI (for GDP)

Value traded Value of total shares traded on the stock market exchange (as a percentage of GDP)

Beck, Demirguc-Kunt and Levine(2000)

Turnover Deflated value of total shares traded on the stock market exchange (as a percentage of market capitalization)


Private credit Deflated private credit by deposit money banks (as percentage of GDP)


Liquid liabilities Deflated liquid liabilities (as percentage of GDP) Beck, Demirguc-Kunt and Levine(2000)

Schooling Gross rate of secondary enrollment (as percentage of GDP)

WDI

FDI Net inflow of foreign direct investment (as percentage of GDP)

WDI

Trade Total value of export and import of goods and services (as percentage of GDP)

WDI

Gov Government expense (as percentage of GDP) WDI

Inflation Annual percentage change in the consumer price index

WDI

The deflation formula for calculating turnover is

𝑉𝑡

𝐶𝑃𝐼 𝑡

0.5 × (𝑀𝑡

𝐶𝑃𝐼𝑡 +

𝑀𝑡−1𝐶𝑃𝐼𝑡−1

)

The deflation formula for calculating private credit is

0.5 × (𝐶𝑡

𝐶𝑃𝐼𝑡 +

𝐶𝑡−1𝐶𝑃𝐼𝑡−1

)

𝐺𝐷𝑃𝑡

𝐶𝑃𝐼 𝑡

The deflation formula for calculating liquid liabilities is

0.5 × (𝐿𝐿𝑡

𝐶𝑃𝐼𝑡 +

𝐿𝐿𝑡−1𝐶𝑃𝐼𝑡−1

)

𝐺𝐷𝑃𝑡

𝐶𝑃𝐼 𝑡

Where Vt : the value of total shares traded on the stock market exchange at year t Mt : the value of total stock market capitalization of the stock market exchange at year t Ct : the value of private credit by deposit money banks at year t LLt : the value of liquid liabilities at year t CPIt : End-of-period customer price index at year t 𝐶𝑃𝐼 t : Average annual customer price index at year t GDPt :the value of GDP at year t

- 1 -

Table 3 Descriptive statistics Statistic PGDP Capital Vtraded Turnover Credit Liquid

liabilities

Full Sample No. observations 664 660 655 655 626 612 Mean 19,335.596 2.482 53.904 64.007 76.600 85.431 Std. dev 17,154.697 4.636 75.364 58.0187 47.828 58.620 Min 459.230 0.000 0.271 0.360 9.617 17.319 Max 93,366.810 48.763 740.057 435.561 270.840 361.885 Correlation (No. observations = 604) PGDP 1.000 Capital 0.162 1.000 Vtraded 0.385 0.512 1.000 Turnover 0.298 0.073 0.639 1.000 Credit 0.587 0.280 0.402 0.233 1.000 Liquid liabilities 0.529 0.486 0.408 0.129 0.733 1.000 Pesaran CD Cross-sectional Independence test Average coefficient 0.775 0.100 0.329 0.370 0.148 0.327 CD-statistic 57.76*** 7.46*** 24.57*** 27.62*** 10.46*** 23.51***

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 -0.0161 -0.0254* -0.0208** -0.0286** -0.0159 -0.0262* -0.0204* -0.0290*

(0.0118) (0.0129) (0.0102) (0.0138) (0.0120) (0.0144) (0.0106) (0.0144)

Schooling 0.0373 0.0956 0.0720* 0.1251** 0.0415 0.0968* 0.0752** 0.1262**

(0.0450) (0.0594) (0.0367) (0.0584) (0.0415) (0.0576) (0.0291) (0.0511)

Initial Capital 0.0150 -0.0388 -0.0026 -0.0530

(0.0308) (0.0519) (0.0364) (0.0497)

Average Capital

0.0113 -0.0399 -0.0058 -0.0544

(0.0273) (0.0505) (0.0308) (0.0446)

Vtraded -0.0095 -0.0107***

-0.0094 -0.0104***

(0.0068) (0.0024)

(0.0070) (0.0030)

Turnover

-0.0115** -0.0115***

-0.0114** -0.0113***

(0.0054) (0.0026)

(0.0055) (0.0024)

Credit 0.0044

-0.0002 0.0036

-0.0010

(0.0194)

(0.0178) (0.0188)

(0.0178)

Liquid Liabilities

0.0219

0.0151

0.0236 0.0162

(0.0176)

(0.0185)

(0.0203) (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 IPS Test [W-t-bar] CADF Test [Z-t-bar] No trend trend No trend trend

In Level PGDP -0.738 0.637 -2.026** -1.165 Capital -2.456*** -3.125*** 2.354 -0.257 Vtraded -4.045*** -1.360* -1.950* 0.013 Turnover -3.792*** -1.626* -3.499*** -3.414*** Credit 0.999 -0.677 4.024 -1.470* Liquid liabilities 1.637 0.645 -0.091 -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.

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Table 6 Pedroni residual panel cointegration tests Statistics Dependent variable

PGDP Capital Turnover Liquid liabilities

Within-dimension Panel v-Statistics -0.3095 -4.0629 -0.0695 0.3148 Panel rho-Statistic 5.1407 3.1375 2.8094 5.9795 Panel PP-Statistic 3.6578 -15.3214*** -2.5992*** 6.3946 Panel ADF-Statistic 3.8165 -6.7970*** 0.4843 3.0789 Between-dimension Group rho-Statistic 6.7750 3.4879 5.4367 6.7514 Group PP-Statistic 3.0757 -20.6458*** -3.1871*** 2.6298 Group ADF-Statistic 1.4087 -2.3154** -0.1628 1.2453 PGDP Capital Turnover Credit Within-dimension Panel v-Statistics 0.5547 -3.0941 0.7530 1.2756 Panel rho-Statistic 4.2111 3.3241 3.3727 4.2328 Panel PP-Statistic 0.7440 16.3283*** -3.3996*** 1.2856 Panel ADF-Statistic 1.9496 -6.2837*** -0.8694 1.8964 Between-dimension Group rho-Statistic 6.5306 3.5539 5.4137 6.4128 Group PP-Statistic 3.0942 -20.0094*** -3.8543*** 2.9675 Group ADF-Statistic 3.8731 -3.1546*** -0.0667 2.6784 PGDP Capital Vtraded Credit Within-dimension Panel v-Statistics -0.8486 -4.1360 0.3010 0.2929 Panel rho-Statistic 5.4232 2.1834 4.3589 4.7979 Panel PP-Statistic 3.7271 -29.0333*** 1.3954 2.3868 Panel ADF-Statistic 2.9966 -10.4401*** -1.7631*** 1.4703 Between-dimension Group rho-Statistic 6.9818 3.3216 6.8511 6.2594 Group PP-Statistic 4.2852 -28.1544*** 2.7530 1.4635 Group ADF-Statistic 3.7312 -3.4975*** -3.4995*** -0.6597 PGDP Capital Vtraded Liquid

liabilities Within-dimension Panel v-Statistics -0.2637 -4.5704 0.2883 0.5717 Panel rho-Statistic 5.5224 2.1365 4.5885 5.9360 Panel PP-Statistic 4.7159 -24.6427*** 2.3315 6.3645 Panel ADF-Statistic 3.2335 -10.6660*** 0.9370 3.4997 Between-dimension Group rho-Statistic 6.8536 3.4475 7.2366 6.5868 Group PP-Statistic 3.3992 -25.1713*** 3.4434 1.6325 Group ADF-Statistic 0.3264 -2.3615*** -1.8763*** 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


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.

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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


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.

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Table 9 Panel 4-variable VAR estimates

Response to Response of

PGDP Capital Vtraded Credit PGDP Capital Vtraded Liquid liabilities

PGDPt-1 0.9918*** -0.0106 0.1587** 0.0977*** 0.9727*** -0.0095 0.1205 0.0163

(0.0396) (0.0082) (0.0761) (0.0294) (0.0374) (0.0075) (0.0752) (0.0186)

Capitalt-1 -0.0440 0.1856 1.6927** -0.0569 0.0077 0.1615 2.0685** 0.2085*

(0.1853) (0.2539) (0.6929) (0.2522) (0.1817) (0.2564) (0.7530) (0.1153)

Vtradedt-1 0.0214 0.0044 0.8476*** 0.0404*** 0.0187 0.0043 0.8494*** 0.0259***

(0.0153) (0.0037) (0.0320) (0.0104) (0.0167) (0.0034) (0.0318) (0.0077)

Creditt-1/Liquid liabilitiest-1 -0.0826 0.0111 -0.2693*** 0.8196*** 0.0012 0.0179 -0.3742*** 0.8294***

(0.0637) (0.0087) (0.0.990) (0.3778) (0.0553) (0.0144) (0.1220) (0.0314)

No. Observations 354 351

Response to Response of

PGDP Capital Turnover Credit PGDP Capital Turnover Liquid liabilities

PGDPt-1 0.9714*** -0.0092 0.1335 0.1147*** 0.9464*** -0.0064 0.0984 0.0268

(0.0434) (0.0082) (0.0945) (0.0262) (0.0416) (0.0067) (0.1002) (0.0178)

Capitalt-1 -0.0414 0.1967 -1.214 0.0519 0.0073 0.1665 -0.9499 0.2340**

(0.1887) (0.2483) (0.7996) (0.2437) (0.1859) (0.2541) (0.8474) (0.1176)

Turnovert-1 0.0379* 0.0025 0.7458*** 0.0194 0.0439* 0.0013 0.7582*** 0.0156*

(0.0229) (0.0029) (0.0471) (0.0132) (0.0249) (0.0027) (0.0494) (0.0088)

Creditt-1/Liquid liabilitiest-1 -0.0654 0.0138 -0.1153 0.8445*** -0.0034 0.0224 -0.1184 0.8512***

(0.0607) (0.0093) (0.1076) (0.0352) (0.0562) (0.0157) (0.1452) (0.0308)

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 VTRADED

Long-run relationship Long -un relationship PGDP -0.0020**

(0.0009) PGDP 4.9401***

(0.8080) Vtrade 0.0001

(0.0004) Capital 1.0873***

(0.0841) Credit -0.0002

(0.0007) Credit -0.0309

(0.1401)

Speed of adjustment -0.8780*** (0.0833)

Speed of adjustment -0.3026*** (0.0497)

Short-run relationship Short-run relationship

PGDP 0.0174 (0.0294)

PGDP 0.2412 (0.1852)

Vtraded 0.0094* (0.0052)

Capital 2.9015 (2.3985)

Credit -0.0226 (0.0232)

Credit 1.0976*** (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.

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