The Stock Markets, Banks and Growth Nexus: Asian Islamic Countries

The Stock Markets, Banks and GrowthNexus: Asian Islamic Countries

EHSAN RAJABI� – JUNAINA MUHAMMAD†

This paper is primarily concerned with the empirical relationship betweenthe stock market and banking developments and economic growth in apanel of 10 Asian Islamic countries over the period 1990–2009. In order toexplore whether there is a positive relationship between the financialdevelopment and growth, this study employs the dynamic panel pooledmean‐group techniques. Having controlled the simultaneity bias, theobtained results prove that the development of both banks and stockmarkets exerts a significant effect on the economic growth. Furthermore,the results also indicated that in the long run, the bank credit, the turnoverratio, as well as the government consumption variables would besignificant while positively contributing to the economic growth.Nonetheless, it was observed that not always the sign of the relationshipwas identical among diverse specifications and proxies. More specificallywhen controlling the Asian financial crisis, the stock market developmentexerts a negative effect on the growth, although it is negative for thebanking development. It needs to be asserted that the findings obtained inthis research are in accordance with the theories which lay emphasis onthe significant and positive role of the financial development in the processof economic growth.

(J.E.L.: G1,O1,O4).

1. Introduction

To address the foremost development challenges confronted by variousIslamic countries, Islamic financing has emerged as an alternate source offinance. In 2012, the global market has tangibly grown for the Islamicfinancial services by 20 per cent ($1460 bn), as estimated by the Sharia

The authors appreciate the anonymous referees who provided invaluable comments andrecommendations for further enhancing the earlier version of this paper. We appreciativelyacknowledge the generous financial support of the international graduate research fellowship (IGRF)sponsored by Universiti Putra Malaysia.

�Corresponding Author: Department of Economics, Faculty of Economics and Manage-ment, Universiti Putra Malaysia, Selangor, Malaysia, Tel.: þ60 10549 2531. E‐mail: [email protected]

†Department of Accounting and Finance, Faculty of Economics andManagement, UniversitiPutra Malaysia, Selangor, Malaysia, Tel.: þ60 38946 7745. E‐mail: [email protected]

Economic Notes by Banca Monte dei Paschi di Siena SpA,

vol. 43, no. 2-2014: pp. 137–165

© 2014 Banca Monte dei Paschi di Siena SpA. Published by John Wiley & Sons Ltd, 9600Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

compliant assets from the City UK (2012). The industry goal is to seesubstantial development in the following years, while it is underscored that,by the end of 2014, the market would elevate $2 trillion in assets, havingconsidered the current growth rate. Indeed, major centres have concerted inMalaysia as well as the Middle Eastern countries such as Iran, Saudi Arabia,UAE and Kuwait. The Organization of Islamic Cooperation (OIC) plays aleading role in the growth of the industry, having a collective share of 98 percent of these assets. Although the global economy has decelerated and inWestern countries the conventional banking has been besieged, the Islamicfinance has experienced resilience. Moreover, it is confirmed that, in themeantime, the economic downturn has emerged and the global assets for theIslamic finance have decreased twofold.

Recently, diverse issues have resulted from the global financial andeconomic crises that occurred during the period 2008–2009 regarding thesteadiness and reliability of the conventional financial system. Seemingly, acomprehensive global reconsideration has been impelled by the internation-al community on the satisfactoriness of the current international economicand financial architecture as well as the pursuit for a more durable solution.In line with this, with the aim of granting services which augment the valueof the real economy, there was a common agreement while seeking newarchitecture regarding the necessity of reinstating the financial transactionsfor their basic functions. In fact, this embodies the quintessence of theIslamic finance that is traceable to the Shariah principles. Yet, thecompatibility of Islamic financial principles with the conventionalperformance metrics is of great concern. The challenge is to recognize ifthe socioeconomic goals, such as sustainability and poverty alleviation, canwork in accordance with the profitability goals and the market share(Series, 2012).

In addition, the pursuit of identity was accomplished by theconstructivist endeavours with modernist understanding in order todetermine the Islamic equivalent of the modern institutions, includingeconomics and finance. This was because of the economic developmentfailure experienced until the 1970s, especially in the MuslimWorld and as awhole in the developing world. It is reported that, as a part of a biggerparadigm of Islamic moral economy (IME), the Islamic Banking andFinance (IBF) came into existence in the 1970s. In effect, IME is a moderndefinition for the divinely ordered rules and principles associated with theeconomic and financial activities, instruments, contracts and choices.Moreover, as part of the identity politics, the term ‘Islamic financial system’

is established on the outright prohibition of paying or receiving anyprearranged assured rate of return. Having evaded the concept of interest,this system impedes the use of the debt‐based instruments while inspiringrisk‐sharing, encouraging entrepreneurship, dispiriting the speculativebehaviour and placing emphasis on the sanctity of contracts (Iqbal, 1997).

138 Economic Notes 2-2014: Review of Banking, Finance and Monetary Economics

© 2014 Banca Monte dei Paschi di Siena SpA.

Similar to the conventional bank, the Islamic bank is known as aninstitution whose chief task is to mobilize the funds from the savers and offersuch funds to the agents who have a deficit (companies, businessmen) whilecasting surveillance on the entire banking activities to be performed withoutusing the interest rate. It is well agreed that the Islamic banks mostly grow intwo directions. At the outset, the Islamic banks provide capital lendingmajorly to the production processes with the aim of contributing to thecompanies’ capital via its instruments. The core fact is that the impact of thefinancial resources in keeping with the production requirements seems to bemore efficient when compared to allotting such resources in keeping withpure lending. The anticipation is that the mentioned effects on the economicdevelopment would be evenmore vital. Secondly, it is also guaranteed by theIslamic banks to assure the Muslims that their contracts will exclude theelements of interest that are formally prohibited in Islam.

Measured by total assets, it is declared that the value of Shariah‐compliant assets was worth in excess of $1130 bn at the end of 2010, whichshowed a 21 per cent rise from $933 bn in 2009. It saw even further growth of14 per cent in 2011, recording an amount of $1289 bn, which indicates anincrease of around 150 per cent from $509 bn in 5 years since 2006. Assetsthat could be apportioned to the individual countries from the Banker’ssurvey of 500 organizations disclose that the primary countries for Sharia‐compliant assets include Iran with $388 bn, Saudi Arabia $151 bn andMalaysia $133 bn, ensued by other Persian Gulf countries such as UAE,Kuwait, Bahrain and Qatar as well as Turkey. Those countries with over 345firms responding to the Banker’s survey consist of Kuwait and Malaysia,each having 39 firms, and Bahrain with 33 firms. Moreover, each countrysuch as Indonesia, Iran, Saudi Arabia, Pakistan, UAE and the UK hasbetween 20 and 27 firms which offer Islamic finance (Table 1). In addition,there was a 22 per cent increase in the balance sheet assets of Sharia‐

Table 1: Islamic Finance by Country in 2010 (US$ Billion)

Countries Shariah‐compliant assets Share of Banks Number of firms

Iran 388.0 383.5 27Saudi Arabia 151.0 147.8 26Malaysia 133.4 120.4 39Kuwait 79.7 68.9 39Bahrain 57.9 56.2 33Indonesia 10.5 10.0 26Jordan 5.9 5.7 10Pakistan 5.7 5.6 23Other countries 254.4 249.6 122Total 1086.5 1047.7 345

Source: The Banker (2010).


E. Rajabi and J. Muhammad: The Stock Markets, Banks & Growth Nexus 139

compliant banks from $863 bn in 2009 to $1048 bn in 2010. Commercialbanks constitute most of the assets, while the rest is made up by theinvestment banks.

It is necessary to note that the Islamic financial institutions had veryconspicuous performance as their profits and rates of return stood far abovethat of the Iranian state‐owned Islamic banks. According to Table 2, it isobserved that the Al Rajhi Bank has been undoubtedly the most profitableIslamic financial institution while somewhat revealing the high marginsbetween its minimal funding costs and the rates which have been charged forits financing. Yet, six banks among the world’s 10 largest Islamic banks havebeen reported to be Iranian. It is well admitted that these Iranian banksdemonstrate startling potential if some restraints are overcome, such as thesanctions imposed by the United States and the years of economicmismanagement in the Islamic Republic. Owing to its population ratingabove 75 million, Iran has a huge customer base as opposed to the otherIslamic countries, yet it has been largely detached from the internationaldevelopments in Islamic finance on account of its economic and financialisolation (Wilson, 2009).

Also, we discuss the state of the stock market in the Islamic world tobetter contextualize the topic and the economic rationale behind the mainresults yielded by the research. A stock market located in Islamic countriesneed not only have Shariah‐complaint stocks, but also a large portion of thelisted stocks and components ought to be Shariah compliant.

Aside from the small market capitalization, these markets are plaguedby the problem of liquidity and high volatility. Table 3 and Figure 1 show therelative annual returns and volatility of the return related to nine largestMuslim stock exchange and S&P 500 and MSCI World Index. We examinethe 10‐year period of 1999 to 2009. The MSCI World Index is a market

Table 2: Leading Islamic Banks by Asset Values

Rank Institution CountriesAssets

(US$ billion)Profits

(US$ million)ROA(%)

1 Bank Melli Iran 48.5 542.1 1.22 Al Rajhi Bank Saudi Arabia 44.0 801.1 1.83 Kuwait Finance House Kuwait 38.2 633.1 1.74 Bank Saderat Iran 32.6 228.0 0.85 Bank Mellat Iran 32.5 162.2 0.66 Bank Tejarat Iran 26.3 0.0 0.07 Bank Sepah Iran 24.1 28.8 0.18 Dubai Islamic Bank UAE 23.1 471.0 2.09 Bank Keshavarzi Iran 16.3 0.0 0.010 HSBC Amanah UK 15.2 n/a n/a

Source: The Banker, Supplement on the Top 500 Islamic Financial Institutions, October 2008, p. 34. Thedata refer toDecember 2007, except in the case of Al Rajhi Bank, Kuwait Finance House andDubai IslamicBank, where December 2008 data were available.



Table3:

Com

parisonof

Islamic

Stock

MarketCapitalization2000

–2010

(US$Billions)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Egypt

28.74

24.34

26.09

27.07

38.52

79.67

93.48

139.29

85.95

89.95

82.49

Indonesia

26.83

23.01

29.99

54.66

73.25

81.43

138.89

211.69

98.76

178.19

360.39

Iran

7.35

9.70

14.34

34.44

47.00

38.72

37.94

45.57

49.004

63.30

86.62

Kuw

ait

20.77

23.19

30.70

59.41

69.37

130.08

128.94

188.05

107.17

95.94

119.62

Malaysia

116.93

120.01

123.87

168.38

190.01

181.24

235.36

325.66

187.07

255.95

410.53

Oman

3.46

2.61

4.00

5.01

6.33

15.26

16.16

23.06

14.91

17.30

20.27

Pakistan

6.55

4.94

10.20

16.58

29.00

45.94

45.52

70.26

23.49

33.24

38.17

Saudi

Arabia

67.17

73.20

74.86

157.30

308.25

646.10

326.87

515.11

246.34

318.77

353.41

Turkey

69.66

47.15

33.96

68.38

98.30

161.54

162.40

286.57

117.93

225.74

306.66

UnitedStates

15,104

13,855

11,098

14,266

16,324

16,971

19,426

19,947

11,738

15,077

17,139

UnitedKingdom

2577

2165

1864

2460

2816

3058

3794

3859

1852

2796

3107

Source:World

HankDatabase.



capitalization‐weighted index of 21 developed markets. The S&P 500 is anindex of the U.S. stocks.

It is clear from Figure 1 that the Islamic stock exchanges demonstratemuchmore volatility of returns compared to the S&P 500 or theMSCIWorldIndex over a 10‐year period. This visual evidence is confirmed in thestatistics shown in Table 3. The huge range in annual return between years isobvious. Turkey, for instance, has returns ranging from 525.53 per cent in1999 to negative 45 per cent in the following year. The stock markets ofEgypt and Saudi Arabia also portray significant volatility in returns.Interestingly, the Islamic stock exchange has a mean return for the 10‐yearperiod that is much higher than that of the S&P 500 or the MSCI World.Unfortunately, their volatilities as measured by the standard deviation arealso several‐fold higher. Equity financing, which resembles mudarabah‐type financing, is congruent to the Shariah requirement that risk and returnsbe shared. Even so, not all listed stocks can play as important a role inMuslim countries as they do in developed ones. The banking sector probablyplays a predominant role within financial sectors of these Muslim countries.The immature nature of the equity market in most Islamic countries issymptomatic of the overall underdevelopment of the capital market inMuslim countries (Bacha and Mirakhor, 2013).

By presenting any information about economic and financialbackground of the Muslim countries in the case of Islamic finance(Shariah‐compliant assets), the Islamic bank (asset values, profit and ROA)and the stock market (Islamic stock market capitalization), we try firstly toinvestigate empirically the relationship between the financial developmentand growth in some Islamic countries to explain the Islamic architecture offinance.

Figure 1: Relative Market Cap to GDP in 2010



The relationship between the financial development and growth hasbeen so far established according to the existing literature as reported bySchumpeter (1912), Gurley and Shaw (1955), McKinnon (1973) and Shaw(1973). Consistent with the theory, any development in the banking industrypositively affects economic growth, as the banks’ activity would give rise tothe mobilization of the savings in addition to boosting the efficiencyassociated with the resource allowance as well as encouraging thetechnological innovation. Yet, there have been several policies that focussedon the financial liberalization which have been seemingly incapable ofpromoting the financial development and economic growth. Indeed, thementioned downside can be regarded as a disadvantage of the studies,underlining the strong relationship existing between the financial develop-ment and economic growth. In practice, there will still be doubts cast onseveral applied studies (including the most recent one) in which a positiverelation has been underscored between financial development and growth, incompliance with the theoretical predictions (see Levine et al., 2000). Inaddition, the relationship within the financial development is proposed byseveral studies, such as De Gregorio and Guidotti (1995), Fernandez andGaletovic (1994), Ram (1999), Naceur andGhazouani (2007), Andersen andTarp (2003) and Favarra (2003) to be possibly less general in comparisonwith the traditional literature. These studies also emphasize that there is anotable variation in the results of the econometric studies with respect to thesample and the considered period.

Stock market development has been also omitted from the majority ofthe surveys regarding the relationship between the financial systemdevelopment and economic growth. We had to await the publication of astudy conducted by Levine and Zervos (1998), who empirically evaluatedthe association between the stock markets as well as the banks’ developmentand economic growth. Nevertheless, this study faces the disadvantage ofnumerous econometric problems.

Apparently, statistical and conceptual issues would still persist despitethe efforts of some contemporary surveys to resolve the statisticaldeficiencies observed in this approach. A noteworthy contribution wasadded by Rousseau and Wachtel (2000) to the growth literature, as theyemployed some panel data techniques. In effect, they exploited thedifference panel estimator method, which had been proposed by Arellanoand Bond (1991), with the aim of exploring the relationship among the stockmarkets, banks and economic growth. They indicated that the developmentof both stock markets and banks can promote economic growth.

The prominence of the Islamic financial development has beenunderlined in the process of economic development by several economists,although such a relationship has not been tested empirically because of theunavailability of the aggregate data on the Islamic sectors (Naceur andGhazouani, 2007). The current study is an attempt to empirically examine



the relationship between the financial development and growth in someIslamic countries. In addition, it is targeted at scrutinizing some gaps thatmight shed light on the vague results obtained through the preceding studies.In detail, new econometric techniques have been employed in the currentstudy in the panel data context in order to solve the statistical downsides withthe accessible data of 10 Muslim countries collected over the period 1990–2009. Indeed, it was aimed at exploring the relationship between the stockmarkets, banks and economic growth. The reasons the Muslim countrieswere selected to accomplish our empirical investigations were firstly that asmall number of surveys have been done about the region and because of theresemblance in the size and the structure of the financial systems in theselected countries. Moreover, the obtained results focussing on the Islamiccountries might be attractive for the other developing countries at a similarstage of financial development; such countries include African, EasternEuropean and Latin American nations which face the enormous restructur-ing of their financial systems. In particular, this is test in the current study ofwhether the development of stock markets and banks separately imposes apositive effect on economic growth by controlling the simultaneity bias,omitting the variables and routinely including the lagged dependentvariables in growth regressions as well as controlling other growthdeterminants. In this study, furthermore, it was investigated whether thevariables related to banks and stock markets jointly enter the growthregression, significantly exploiting the findings in the recent growth surveyswhich underline the significance of employing the panel data analysis whilescrutinizing the cross‐country growth dynamics.

The remainder of this paper is outlined as follows: Section 2 elaborateson the role of the stockmarkets and banks in the process of economic growthand summarizes the present empirical literature, while Section 3 deals withthe data and econometric methodology. In Section 4, our findings arepresented alongside the deliberation on their implications for the debate onfinancial systems. Section 5 provides a summary and concludes the paper.

2. Literature Review

Financial development consists of establishing and expanding theinstitutions, instruments and markets which advocate for such an investmentand growth process. The process of enhancing the quantity, quality andefficiency of the financial intermediary services is typically referred to as thefinancial development which encompasses the interaction of variousactivities and institutions while it might be concomitant with economicgrowth. Concerning the ways through which the financial systemdevelopment promotes the economic growth, Pagano (1993) and Levine(1997, 2002) proposed that the financial intermediaries might primarily



decrease the costs involved in gathering and processing the information,while this might per se enhance the allocation of resources (e.g. Boyd andPrescott, 1986). The economic growth can be practically enhanced by suchinformation improvement about all of the economic agents. Also, the banksmight promote the rate of the technological innovation by choosing theentrepreneurs which possibly stand the utmost chance for initiation ofeffective ventures (e.g. King and Levine, 1993). Another method wasintroduced by Bencivenga and Smith (1993), affirming that the banks thatmitigate the problems associated with the corporate governance throughreducing the monitoring costs would possibly lower the credit rationing,which is then followed by promoting the growth. Thirdly, financialintermediaries and security markets are able to supply vehicles for trading,pooling and varying the risk.

Consequently, those financial systems which enable the agents tomaintain a diversified portfolio of the risky projects would encourage societyto be inclined towards the projects with greater predictable returns, havingpositive incidence of economic growth (e.g. Greenwood andJovanovic, 1990; Gurley and Shaw, 1955). Moreover, the financial systemsinducing the mobilization of savings through supplying attractive instru-ments and saving vehicles might be able to overpoweringly influence theeconomic development. In sum, the theory of finance and growthemphasizes certain functions exerted by the financial system—generatinginformation, monitoring investment, applying the corporate governance,enabling the trading, diversification and risk management and pooling thesavings—and how such an effect influences economic growth throughresource allocation decisions.

The theory also brings contradictory predictions on whether the stockmarkets and banks are substitutes or complements or whether one of themcan be more favourable to growth than the other. Yet, specific theoriesemphasize that it is not the banks or stock markets, but the banks and stockmarkets; these diverse components of the financial system upgrade variousinformation and transaction costs (Beck and Levine, 2004).

Even if the ever‐increasing body of empirical literature advocates thatwell‐functioning banks promote economic growth, by and large such studiesdo no concurrently scrutinize the stock market development. King andLevine (1993) demonstrated that the bank development which can beassessed by the total liquid liabilities of the financial intermediaries (e.g.M3)divided by gross domestic product (GDP) might predict the economicgrowth in a sample of more than 80 countries. Although Levine and Zervos(1998), Levine et al. (2000) and Beck et al. (2000) later endorsed thesementioned findings, they even enhanced the finding by King and Levine(1993) through exploiting the measures of bank development, which onlyinvolved the credit to private firms while excluding the credit to the publicsector. They also did so through employing the instrumental variable



procedures in order to control the simultaneity bias. The measures of stockmarket development were omitted in the mentioned studies, since suchmeasures for a 20‐year period were only accessible for around 40 countries.Examining whether there is a positive relationship between the bankdevelopment and growth would be then daunting once the stock marketdevelopment is omitted. Similarly, every bank and stock market imposes anindependent effect on economic growth. Although the overall financialdevelopment is important for growth, identifying the separate effect of thestock markets and banks on the economic success would be far too difficult(Rajabi and Muhammad, 2012).

The relationship between the growth and both stock markets and bankswas empirically explored by Levine and Zervos (1998), but this survey wasdisadvantaged by an assortment of econometric weaknesses. They reportedthat the initial measures of the stock market liquidity and bankingdevelopment could be strong predictors of economic growth over the next18 years. In detail, they applied the bank credit to the private sector as a shareof GDP with the purpose of determining the bank development.Furthermore, they exploited an assortment of stock market developmentmeasures which entailed the overall size of the market (market capitalizationrelative to GDP), the stock market activity (the value of trades relative toGDP) and the market liquidity (the value of trades relative to marketcapitalization). However, the Ordinary Least Squares (OLS) approach,which had been adopted by Levine and Zervos (1998), did not formallyexplain the potential simultaneity bias while it was incapable of explicitlycontrolling the country‐fixed effects or the routine use of lagged dependentvariables in the growth regressions. Additionally, the theory underscored thepotential relationship between the economic growth and the contemporane-ous level of the financial development, while Levine and Zervos (1998)exploited the initial values of the stock market and bank development. Thisindeed indicates an informational loss in relation to utilizing the averagevalues as well as implying a potential consistency loss.

Still, statistical and conceptual problems persist, although variouscontemporary surveys have endeavoured to mitigate the existing statisticalweaknesses in the study of Levine and Zervos (1998). Arestis et al. (2001),for example, employed the quarterly data while adopting the time seriesmethods to five developed economies in search of indication of the fact thatthe effect of banking development is significantly greater than the effect ofthe stock market development, while both banking and stock marketdevelopment could predict the successive growth. However, the sample sizein this study was very restricted, while it was vague whether employing thequarterly data and Johansen (1988) vector error correction model wouldcompletely abstract from the high‐frequency factors affecting the stockmarket, bank, and growth nexus to centre on the long‐term economicgrowth.



In line with this, Rousseau and Wachtel (2000) employed paneltechniques with annual data with the intention of exploring the relationshipamong the stock markets, banks and growth. They utilized the M3/GDP toestimate the bank development and the Levine and Zervos (1998) measuresof stock market size and activity, which they devalued by the price index ofthe national stock exchange for eradicating the price changes from theirmeasure of how well the stock market functioned. Rousseau and Wachtel(2000) made use of the difference panel estimator—introduced by Arellanoand Bond (1991) and Holtz‐Eakin et al. (1988)—to determine the growthregression equation for eliminating any biases engendered by theunobserved country‐specific effects as well as instrumenting the right‐hand‐side variables (the differenced values of the original regressors) bymaking use of the lagged values of the original regressors for removing thepotential parameter inconsistency caused by the simultaneity bias. Rousseauand Wachtel (2000) finally indicated that the stock market and bankdevelopment both predicted subsequent growth.

Yet, it needs to be highlighted that some problems still exist. First of all,the goal is to examine the relationship among the stock markets, banks andeconomic growth. Secondly, Alonso‐Borrego and Arellano (1999) indicatedthat the instruments in the difference panel estimator are commonly frail,bringing biases in finite samples and poor precision asymptotically.However, the current econometric developments facilitate the utilizationof the statistical procedures which provide control on such problems.

The panel econometric techniques were exploited in the current paper,which tend to mitigate the statistical weaknesses associated with the currentgrowth studies as well as use some new data to reassess the relationshipbetween the stock markets, banks and economic growth. In particular, thisstudy was aimed at investigating whether the measures of the stock marketand bank development separately hold a positive and significant relationshipwith the economic growth. Furthermore, this research estimated whether thestock markets and banks indicators can conjointly enter the growthregression significantly. Concerning the data, the same basic measures ofbank and stockmarket development were used here, similar to that of Levineand Zervos (1998), while this study endeavoured to boost the previousexertions by deflating the data more cautiously. In practice, the indicators ofthe financial development were regularly measured, at the end of the period,even though such indicators were commonly divided by the GDP, whichwascalculated over the period. This problem was resolved in this research, sinceit could have resulted in high miscalculations among the countries with highinflation. Methodologically, a panel was firstly created with the annual datafrom 1990 to 2009, following which the dynamic heterogeneous panelestimator was utilized, which had been previously introduced by Pesaranand Smith (1995) and Pesaran et al. (1999).



3. Data and Econometric Methodology

The focus of the dynamic panel‐data literature has been recently shiftedtowards the panels wherein the amount of cross‐sectional observations (N)and the sum of the time‐series observations (T) are enormous. In reality, thechief contributor to such a shift has been declared to be the accessibility ofsuch data with higher frequency. For instance, several cross‐national andcross‐state datasets have been observed to be so large in T that every nation(or state) can be assessed separately.

The asymptotics of large‐N, large‐T dynamic panels are different fromthe asymptotics of traditional large‐N, small‐T dynamic panels. Customarily,the small‐T panel estimation depends on the fixed or random‐effectsestimators or even a combination of the fixed‐effects (FE) estimators andinstrumental variable estimators, such as the Arellano and Bond’s (1991)generalizedmethod‐of‐moments (GMM) estimator. Actually, the mentionedmethods necessitate pooling of the individual groups as well as onlypermitting the intercepts to vary across the groups. Still, one major finding ofthe large‐N–large‐T studies is that the assumption related to the homogeneityof the slope parameters is frequently unsuitable, as shown through thestudies conducted by Pesaran and Smith (1995), Im et al. (2003), Pesaranet al. (1999) and Phillips and Moon (2000).

To compound the problem, non‐stationary has been also identified as abig concern with the upsurge of the time observations inherent in large N.Two central new techniques have been suggested by Pesaran et al. (1995,1999) to be used for approximating the non‐stationary dynamic panelswherein the parameters are heterogeneous across the groups: the mean group(MG) and pooled mean group (PMG) estimators. The MG estimator (seePesaran and Smith, 1995) depends on reckoning N time‐series regressionsand averaging of the coefficients; on the other hand, the PMG estimator (seePesaran et al., 1999) depends on a combination of the pooling and averagingof the coefficients.

In addition, the PMG estimators permit the short‐run coefficients, theadjustment speed and the error variances to vary across the countries, yet itlevies homogeneity on the long‐run coefficients. In further detail, throughthe PMG procedure, this study could estimate the subsequent restrictedversion of the growth equation on the annual data for 10 Islamic countries(including Bahrain, Indonesia, Oman, Yemen, Iran, Kuwait, Pakistan,Jordan, Malaysia and the Kingdom of Saudi Arabia) chiefly from 1990 to2009.

3.1. Data

In this study, the effects of the stock market and bank development onthe economic growth were explored with a panel of 10 Asian Islamic



countries1 (based on the availability of the variables at the World BankData, 2011) from 1990 to 2009. Once we moved from pure cross‐sectionaldata into a panel, it was possible to use the time‐series dimension of the dataas well as meticulously contending with simultaneity. While the theoriesrevolve around the long‐run relationships between the stock markets, banksand the economic growth, the leading theory hinges on the role that stockmarkets and banks probably play in decreasing the informationalasymmetries along with lessening the transactions costs. There are nodirect measures related to the degree to which the markets and banksenhance the information and transactions costs over a wide cross‐section ofthe countries. Therefore, we employed the proxy measures related to thebanking system size as well as the stock market activities to measure thecross‐country differences in the stock market and bank developmentwhile identifying the lack of a direct link between the theory andmeasurement.

The financial system can be practically divided into two components,namely the stock market and the banking system. Accordingly, anymethodology on the association between the financial development andeconomic growth has to embrace the banking system and the stock marketdevelopment. Yet, the concept of the stockmarket development mystifies thestakeholders and practitioners. Therefore, four indicatorsmight be employedfor examining the stock market development (e.g. Demirgüç‐Kunt andLevine, 1996), including (i) the market capitalization, (ii) the volatilityestimated by the standard deviation of the stock market, (iii) the indicators ofthe institutional development and finally (iv) the regulation indicators.Because of the fact that the banking system has to be involved, it can be thenestimated by the ratio of the domestic credit to the GDP or the ratio of thenominal money supply (monetary aggregate M2) to nominal GDP or bankcredit that is equivalent to the bank claims on the private sector by the bankdeposit divided by the GDP.

The turnover ratiowas employed to estimate the market liquidity whichis equivalent to the value of the shares trades on the domestic exchangesdivided by the total value of the listed shares. Then it signposts the tradingvolume of the stock market proportionate to its size. Certain models haveprojected that the countries having liquidity markets would engenderdisincentives to long‐run investments as selling one’s stake in the firm is arelatively daunting task. Contrariwise, more liquid stockmarkets are capableof decreasing the disincentives to the long‐run investment because suchmarkets offer the investors a ready‐exit option, raising more efficient

1We choose Islamic countries to carry out our empirical investigations not only because veryfew studies have been devoted to the region, but also the focus on a set of Islamic countries isparticularly interesting, since there might be some heterogeneity in the functioning of the finance‐growth nexus that can be captured by focussing on narrower sets of countries with similarcharacteristics.



resource allocation as well as a quicker growth (Levine, 1991; Bencivengaet al., 1995).

Moreover, in this study we tried to experiment with the other measuresrelated to the stock market development which had been employed byLevine and Zervos (1998) and Rousseau and Wachtel (2000). It needs to beasserted that the Value traded will be equivalent to the trade value of thedomestic shares on the domestic exchanges divided by the GDP. It isconfirmed that the value traded suffers from the two latent drawbacks, asfollows.

Primarily, it does not estimate the market liquidity while it tends tomeasure the trading relative to the size of the economy. Also, the marketswill forestall a higher economic growth by higher share prices as they arefuturistic.

The value traded is capable of increasing without a surge in the quantityof transactions because the value traded is the product of quantity and price.Contrarily, the turnover ratio is not disadvantaged by such a downsidebecause both the numerator and the denominator encompass the price. In thisresearch, the market capitalization was also considered, which wasequivalent to the value of the listed shares divided by the GDP. Its maindrawback, however, is that the theory does not recommend that the merelisting of shares is bale to affect the resource allocation and growth. Asasserted by Levine and Zervos (1998), the market capitalization should notbe regarded as a worthy predictor of the economic growth. To conclude, themarket capitalization ratio was deflated in this research, whichwasmeasuredat the end of period by the end‐of‐the‐period price deflators and the flowvariables (GDP and trading variables) by a deflator for thewhole period. As amatter of fact, such a function would eradicate the potential miscalculationsprompted by the inflation.

We imitated Levine and Zervos (1998) in measuring the bankdevelopment while employing the bank credit, which was equal to thebank claims on the private sector by the bank deposit divided by the GDP.Nonetheless, it is stated that the bank credit is unable to directly estimate thedegree to which banks facilitate the information and transaction costs.Contrasting various studies on the finance and growth in which the ratio ofM3 to the GDP has been used as an empirical proxy of the financialdevelopment, the bank credit variable is used to segregate the bank credit tothe private sector; consequently, it eliminates the credits by the developmentbanks and loans to the government and public enterprises. Moreover, theend‐of‐period credit variables were deflated in this research, as stated earlier,by the end‐of‐period deflators and the GDP flow variables by a deflator forthe entire period.

Furthermore, the other potential determinants of the economic growthwere controlled in our regressions with the intention of appraising thestrength of the independent association between the stock markets and the



growth as well as the bank development and economic growth. The initialreal GDP per capita in the US$ was included in this research in the simpleconditioning information set for exerting control on the convergence. Yet,the simple conditioning information set was employed in the policyconditioning information set in addition to either (i) the share of the exportsand imports to the GDP (in percentage) or (ii) the ratio of the governmentexpenditures to the GDP (in percentage).

The definitions of the variable along with the related descriptivestatistics are demonstrated in Table 4. Indeed, an extensive variation of thebanks and stock market development exists across the sample, which is aninclusive variation across the sample countries. It is observed that Pakistanentailed a max‐value turnover ratio of 497% of the GDP in 2003 (themaximumvalue), whereas Iran’s turnover ratio was only 1.8 per cent in 1993(the minimum value). Also, it is discerned that the banks in Kuwait loaned185 per cent of the GDP to the private sector in 1991 (the maximum value)while the Saudi Arabia’s financial intermediaries loaned only 3.98 per cent in2008 (the minimum value).

3.2. Model specification

In a panel, to gauge the relationship between the stock marketdevelopment, the bank development and the economic growth, the meangroup (MG) estimators were used in our research while using the pooledmean group (PMG) introduced for dynamic heterogeneous panel models byPesaran and Smith (1995) and Pesaran et al. (1999). It should be highlightedthat the average long‐run coefficients in the mean group case can be attainedin three ways:

(a) From the mean of the long‐run group‐specific coefficients.(b) From the average of the group‐specific short‐run coefficients.

Table 4: Variable Definitions and Summary Statistics 1990–2009

Variable Symbol Definition

Descriptive Statistics

Obs Mean Min Max

Economic growth Y Real gross domesticproduct per capita

200 41,198 579 2732075

Banks development C Bank credit (bank creditto private sector/GDP)

198 57.4 �3.98 184.8

Stock marketsdevelopment

S Turnover ratio (trades ofshares on domesticexchanges/total valueshares)

200 62.4 1.8 497.4



(c) From the mean coefficients in the group‐specific cointegratingregressions.While the assumptions are very strong, they necessitate that the group‐

specific parameters have to be distributed independently of the regressors,which are firmly exogenous. In particular, the equation assessed for eachgroup (country) has the autoregressive distributed lag (ADRL) formpresented below:

Y it ¼ ai þ g iY it�1 þ biX it þ uit; i ¼ 1; 2; 3;…;N ; t ¼ 1;…; Tð1Þ

where Y denotes the logarithm of the real GDP per capita and X shows the setof explanatory variables other than the lagged per capita GDP and ourindicators of the stock market, the bank development, the governmentconsumption and trade openness are included. Also, eit stands for the errorterm while the subscripts i and t respectively denote the country and the timeperiod. It should also be noted that the subsequent equation was estimatedfor the purpose of our research:

Y it ¼ ai þ b1iY it�1 þ g1iCit þ g2iCit�1 þ d1iGit

þ d2iGit�1 þ m1iOit þ m2iOit�1 þ u1iSit þ u2iSit�1

þ eit; i ¼ 1; 2; 3;…; 10; t ¼ 1900;…; 2009

ð2Þ

Yit denotes the real GDP per capita, Cit is the bank credit over the GDP, Git

stands for the government consumption over the GDP, Oit shows the exportand import over the GDP and finally Sit signifies the turnover ratio all inlogarithms. The long‐run parameter ri for country i is as follows:

ri ¼bi

1� g ið3Þ

The long‐run parameter can be defined for the entire explanatoryvariable in the model such as the bank credit, the turnover ratio, thegovernment consumption and the trade openness. The MG estimator for theentire panel can be obtained by:

r ¼ 1

N

XN

i¼1

rið4Þ

a ¼ 1

N

XN

i¼1

aið5Þ



Pesaran et al. (1999) permitted the heterogeneous short‐run coefficientsin the PMG model as well as allowed for the intercepts and error variancesto freely vary across groups, while allowing the imposed restraints on thelong‐run coefficients to resemble across the groups. It needs to be signifiedthat the merit of the PMG is its determination of the long‐run and short‐rundynamic relationships. The model can be estimated as a system if the datapermit on the basis of a combination of pooling and averaging thecoefficients. Seemingly, the PMG estimation method lodges an intermedi-ate locus between the MG method (both the slope and intercepts arepermitted to vary across countries) and the fixed‐effects method (the slopesare fixed, and the intercepts are permitted to differ). The unrestrictedspecification for the ARDL system of equations for t¼ 1, 2, …, T timeperiods and i¼ 1, 2,…,N countries for the dependent variable Y can be seenas follows:

Y it ¼Xp

j¼1

li;jY i;t�j þXq

j¼1

g 0ij X i;t�j þ mi þ eitð6Þ

where Xi,t�j denotes (k� 1) the vector of the explanatory variables for groupiwhilemi indicates the fixed effect. It is possible to reparameterize the modelas a vector error correction model system (VECM):

DY it ¼ uiðY i;t�1�b0iX i;t�jÞþXp�1

j¼1

lijDY i;t�jþXq�1

j¼1

g 0ijDX i;t�jþmiþ eitð7Þ

where bi denotes the long‐run parameters, while ui indicates the equilibrium(or error) correction parameters. Having the elements of b to remaincommon across the countries can be counted as the PMG’s limitation. It isaffirmed that the entire dynamic and the ECM terms can easily vary undersome regularity (Blackburne and Frank, 2007). The pooled mean groupparameters are steady and asymptotically normal for both stationary andnon‐stationary repressors. Both MG and PMG estimations necessitate theselection of a proper lag length for the individual country equations. Thisselection was fulfilled in this study by making use of the Schwarz BayesianCriterion (SBC) or the Akaike Information Criterion (AIC).

Once the ARDL is (0, 2, 3, 0, 2), we found the least AIC (the value was2.98), indicating that the bank credit with two lags, the governmentconsumption with three lags and the turnover ratio by two lags werestationary. Moreover, the real GDP per capita and trade openness variableswere I(0).

It needs to be accentuated that the hypothesis on the homogeneity of thelong‐run policy parameters could not be presumed a priori. In this context, a



Hausman‐type test could be employed for examining the effects of theheterogeneity on the means of the coefficients. In actual fact, if theparameters are homogenous, the PMG estimates would be more effective ascompared to the MG. The difference in the estimated coefficients betweenthe MG and PMG are not significantly different under the null hypothesis ofthe Hausman test while indicating that the PMG is more efficient.

Additionally, in order to facilitate the comparison, the results achievedthrough employing the mean group (MG) and the dynamic fixed effects(DFE) will be reported. It needs to be underscored that the results will differquite noticeably across the methodologies providing that the MG procedureis the least restrictive; therefore, it would be possibly inefficient. The DFEpermits the individual intercepts to differ across the countries while itresembles the GMM procedure.

4. Results and Discussion

TheMG, PMGandDFEmodels were assessed in this research bymeansof the econometric software STATA 11.0. The entire estimations acquiredfrom the MG, PMG and DFE estimators are displayed in Table 5 with twooptions—with the time trend and without time trend—for the specificationwhich engendered the best effect in view of the specification tests (Bassaniniand Scarpetta, 2001).

Lower standard errors and slower speed of adjustment were engenderedby the constraint of the common long‐run coefficients (i.e. from MG toPMG). Such an outcome was anticipated assuming that the MG estimatorsare acknowledged to be inefficient. It must be maintained that the resultsmight be sensitive to the choice of the lag length because this was an ARDLmodel. In the subsequent section, an ARDL(0, 2, 3, 0, 0) was imposed for theAkaike Information Criterion (AIC) and the Schwarz Bayesian Criterionwith the aim of acquiring the optimal lag length for different variables. Sucha specification is reliable with the strong balanced panel and very large T, andthe data used in our study can fulfil the mentioned assumptions.

In both models, it was confirmed by the Hausman test statistic that thelong‐run homogeneous coefficient restrictions could not be rejected at the 1per cent significance level, suggesting the manifestation of a long‐runhomogeneous relationship between the countries. In contrast, the restrictioncould not be rejected by the Hausman test statistics at the conventionalstatistical level. According to a sensitivity analysis, the coefficientsappraised via the PMG approach were shown to be robust to changes inthe lag structure of the main variables in both options.

The model with the common long‐run coefficient (PMG) was preferredin this study because of the convergence theory in the economic growth.Indeed, there is a robust indication that the economic growth was influenced



significantly and positively by all of the variables, except for the tradeopenness. Although the obtained results fulfil the theoretical assumption, themagnitude of effects seems to be far more imperative.

Based on the estimated effect of the foreign aid on the economic growth,a 1 per cent increase in turnover ratio would cause a 0.368 per cent upsurge ineconomic growth. Additionally, bank development exerts the mostsubstantial effect. Therefore, a 1 per cent surge in the bank credit wouldcause a 0.538 per cent rise in the economic growth. Nonetheless, for thegovernment consumption, the rate is rather high, as the economic growtheccentrically upsurges by 2.821 per cent, corresponding to a 1 per centincrease in government consumption.

Table 5: Impact of Stock Market and Bank Development on Economics Growth by MGand PMG Estimator (with and without Time Trend)

Dep. variable: lrgdp

Without time trend ARDL(0, 2, 3, 0, 0)

With time trend ARDL(0, 2, 3, 0, 0)

Meangroup

Pooledmeangroup

Hausmantest

Dynamicfixedeffects

Meangroup

Pooledmeangroup

Hausmantest

Dynamicfixedeffects

Convergence coefficient �0.365�� 0.186� 0.14�� 0.482 � �0.207�� 0.136 ��(1.91) (2.72) (7.48) (2.59) (2.06) (6.96)

Long‐run coefficientsC 0.422 0.538�� 1.57 �0.559 0.453 0.350 4.4 �0.752

(0.42) (2.04) (0.81) (1.29) (0.21) (1.53) (0.49) (1.58)G 2.003 2.821�� 0.213 1.144 2.12�� 0.273

(0.97) (7.03) (0.34) (0.48) (4.7) (0.36)O 0.265 0.130 �0.412 0.541 0.3� �0.307

(0.32) (0.94) (0.88) (0.72) (1.8) (0.62)S 0.249� 0.368�� 0.274�� 0.455� 0.32�� 0.329��

(1.87) (5.48) (1.85) (1.80) (5.38) (1.38)Short‐run coefficients

DC 0.332 �0.073 �0.44�� 0.013 0.011� �0.383��(0.81) (0.18) (3.37) (0.29) (1.67) (2.75)

DC2 �0.121 �0.16 0.113 �0.055 �0.167 0.079(0.34) (0.50) (1.32) (0.14) (0.53) (0.88)

DG �1.84 �0.724�� 0.253 0.25 �0.74� �0.394(2.49) (1.97) (1.05) (0.832) (1.87) (1.50)

DG2 0.708�� 0.288 0.173 �0.98 0.2 0.271(0.13) (0.66) (0.63) (1.12) (0.45) (0.95)

DG3 �0.281� �0.215 0.077 0.26 �0.16 �0.099(1.68) (1.61) (0.7) (0.95) (1.22) (0.89)

DO �0.871�� 0.294 �0.62�� 0.73 �0.28 �0.611��(2.14) (0.96) (8.9) (1.76)� (1.01) (8.9)

DS 0.026 0.22 0.055�� 0.040 0.005 0.059��(0.58) (0.76) (2.23) (0.61) (1.00) (2.41)

grend �0.036 �0.005(1.14) (1.00)

No. of countries 10 10 10 10 10 10No. of observations 168 168 168 168 168 168

All equations include short‐term dynamics and a constant country‐specific term.z‐Values are in parentheses. ��,�� and �indicate significance levels at 1, 5 and 10 per cent respectively.



Moreover, it is indicated by the results of the error correctionmodel that,in the short run, an insignificant relationship exists between the financialdevelopment and economic growth. Indeed, there is a noticeable differencebetween the short‐run and long‐run effects of the government consumptionon the economic growth, yet the results for the employment and foreigndirect investment are observed not to be conclusive. Simultaneously, theshort‐run average response in the DFE model for the bank and stock marketdevelopment is significant at the 1 and 5 per cent levels; nonetheless, this rateis weaker in comparison with the long‐run coefficient, whereas it is negativefor the bank credit with a value of �0.44 from �0.559 as the long‐runcoefficient, but it is jointly insignificant.

The probability that a linear time trend might influence the data is apriori rather high. Introduction of the (common) yearly time variable hasbeen claimed to be a customary method for testing the results’ robustness inthe company of a suspected linear time trend.

All of the slope coefficients would indicate higher standard errors if thetime trend is imposed in the estimation while the mentioned coefficientsincrease the measured speed of the convergence at a significant rate, yet itdoes not alter the sign related to the estimated long‐run coefficients. Likebefore, the restriction cannot be rejected under such a conditionat the conventional statistical level by the Hausman test statistics; moreover,the coefficients measured using the PMG approach are shown to be robust tothe changes in the lag structure of the main variables. The bank developmentwill be insignificant after adding the time trend to the model; however, apositive and significant long‐run coefficient will be acquired for all of thevariables; in line with this, although the time trend is insignificant, it issigned negatively. The stock market development measured coefficientvalue is 0.32, implying that an additional year of education is projected toincrease the long‐run steady‐state level of output per capita by about 0.32 percent in the most reliable estimates. Nevertheless, by taking into account thecoefficient of the government consumption, it is suggested that 1 per cent ofits rate elevates the long‐run growth by around 2.12 per cent.

On the contrary, because of the downward bias in the dynamicheterogeneous panels, the DFE estimators resulted in a very minor speed ofconvergence. It is also found that the sign and significance of the long‐runcoefficients are influenced on the condition that the short‐term dynamics arerestricted. Even though the coefficients on the bank credit and tradeopenness are shown to be signed negatively, they are insignificant in two ofthe scenarios. It needs to be confirmed that this finding is compliant with thepreceding surveys which had been based on the DFE estimators. Likewise,strong proof exists that the economic growth is positively influenced by thestock market development. Furthermore, if the turnover ratio indicates asurge of 1 per cent, the economic growth would heighten by 0.274 and 0.329per cent in the models with the time trend and without it, respectively.



It is also observed through the result that the bank credit, the turnoverratio and the government consumption variables are significant while theyare capable of positively promoting the economic growth in the long run.Nevertheless, adding a linear time trend does not alter this conspicuousfeature in place of the bank credit variable.

Again, it is asserted that the probability that a nonlinear trend mayinfluence the data can be considered to be a priori relatively high. For this, acustomary method for testing the results’ robustness depends on theintroduction of (common) dummies in the company of a suspected nonlineartime trend. Nonetheless, the mentioned solution denotes that the commonAsian financial crisis shock has influenced all of the countries included in thesample. At this point, specifications were considered in this researchwith theintention of simulating a nonlinear time trend with two dummy variablesfrom 1990–1998 and 1999–2009; by doing so, country‐specific nonlineartrends were permitted.

The PMG, MG and DFE estimates under the model with and withoutdummies for the Asian financial crisis are represented in Table 6, indicatingthat the results significantly differ with regard to the estimationmethod, fromMG (the least restrictive, but potentially not efficient) to PMG and DFE,which only permits the intercepts to differ across countries. While the meangroup (MG) results are provided in column 1, the pooledmean group (PMG)results are shown in column 2 and the results of the Hausman test related tothe constant long‐run coefficients are given in column 3 in two groups,namely with and without dummies. The standard errors as well as theconvergence speed along with the size of the estimated long‐run parameterswill be all reduced by moving from MG to PMG, merely levying the long‐run homogeneity to the income variable (lrgdp).

It should be confirmed that the null hypothesis of a constant long‐runcoefficient across the counties is not rejected by the Hausman test statistics atthe 1 per cent significant level; hence, application of the more efficient PMGestimator is suggested. Furthermore, the dynamic fixed effects (DFE) resultshave been provided in column 4 for establishing comparisons. It is noted thatthe long‐run coefficients have predicted values while they are significantstatistically. Moreover, the coefficients for the bank and the stock marketdevelopment are�0.32 and 0.259 in value under PMG, and it can be claimedthat such coefficients are significant at the 1 per cent level. It was also foundthat the coefficient related to the government consumption and tradeopenness was 3.512 and �0.53, while this can be considered to be totallysignificant. The long‐run coefficient implies that by increase of 1 per cent inbank credit, real GDP per capita decreases by 0.32 per cent, and also byincrease of 1 per cent in turnover ratio, the real GDP per capita increases by0.259 per cent. It was also found that the estimated error correctioncoefficients were very robust as well as high enough to indicate a reasonablespeed of adjustment to the long run.



It needs to be accentuated that the speed of convergence wassignificantly decreased by moving from the PMG to DFE estimators(model with dummy) because of a downward bias in the dynamicheterogeneous panel data from 0.227 to 0.13, while there are changes in thesignificance of the long‐run coefficients over both specifications. Likewise,neither of the coefficients was shown to be significant at the 10 per cent level

Table 6: Bank and Stock Market Development on Economics Growth by MG and PMGEstimator (with and without Dummy for Asian Financial Crisis)

Dep. variable:lrgdp

With dummy ARDL(0, 2, 3, 0, 0)

Without dummy ARDL(0, 2, 3, 0, 0)

Meangroup

Pooledmeangroup

Hausmantest

Dynamicfixedeffects

Meangroup

Pooledmeangroup

Hausmantest

Dynamicfixedeffects

Convergencecoefficient

�0.926�� 0.227�� 0.13 �� 0.365�� 0.186�� 0.142 ��

(3.29) (2.00) (6.29) (1.91) (0.007) (7.48)Long‐run coefficientsC �4.896 �0.32�� 3.01 �0.887� 0.422 0.538�� 1.57 �0.559

(0.88) (93.54) (0.55) (1.72) (0.42) (2.04) (0.814) (1.29)G 5.128 3.512�� 0.538 2.003 2.821�� 0.213

(1.12) (131.6) (0.64) (0.97) (7.03) (0.34)O 6.64 �0.53�� 0.274 0.265 0.130 �0.412

(1.09) (55) (0.52) (0.32) (0.94) (0.88)S 0.470 0.259�� 0.266� 0.249 0.368�� 0.274��

(1.18) (180.7) (1.62) (1.87)� (5.48) (1.85)Short‐run coefficientsDC 0.269 �0.278 �0.331�� 0.332 �0.073 �0.442��

(0.66) (0.67) (2.31) (0.81) (0.18) (3.37)DC2 �0.185 �0.117 0.071 �0.121 �0.16 0.113

(0.43) (0.26) (0.81) (0.34) (0.50) (1.32)DG �2.130�� 1.29�� 0.468� �1.84 �0.724�� 0.253

(2.68) (2.62) (1.79) (2.49) (1.97) (1.05)DG2 1.614�� 0.591�� 0.331 0.708�� 0.288 0.173

(2.40) (2.45) (1.17) (0.13) (0.66) (0.63)DG3 �0.579�� 0.31�� 0.118 �0.281� �0.215 0.077

(2.59) (3.83) (1.06) (1.68) (1.61) (0.7)DO �0.150 �0.177 �0.63�� 0.871�� 0.294 �0.62��

(0.22) (0.058) (9.09) (2.14) (0.96) (8.9)DS 0.075 0.034 0.054�� 0.026 0.0.22 0.055��

(0.85) (1.01) (2.21) (0.58) (0.76) (2.23)No. of countries 10 10 10 10 10 10No. of

observations168 168 168 168 168 168

dummy 1990–1998

0.010 �0.038 �0.163

(1.00) (1.00) (1.09)dummy 1990–

2009�0.464 �0.84 �0.101

(1.14) (1.3) (0.68)

All equations include short‐term dynamics and a constant country‐specific term.z‐Values are in parentheses.��,�� and �indicate significance levels at 1, 5 and 10 per cent, respectively.



once the DFE was applied as a consequence of employing theheteroskedasticity‐consistent standard errors.

The short‐run financial development coefficients tell a different story.Under PMG, each reported short‐run coefficient is themean of the individualcountry‐specific coefficients and thus it represents the average responseacross countries. Therefore, the PMG results indicate that financialdevelopment does not have a significant effect on economic growth. Atthe same time, the short‐run average response for government consumptionis significant at the 1 per cent level but weaker than the long run’s coefficient,while that for trade openness is positive but jointly insignificant.

According to the PMG estimation (efficient) results demonstrated inTable 7, it can be claimed that the financial development imposes astatistically and economically long‐run effect on the economic growth;contrarily, the bank development impact on the growth is observed to benegative, indicating that the bank’s development would exert a negativeimpact on the growth following the Asian financial crisis. In addition,the stock market development has a positive and significant influence onthe economic growth. By referring to the point estimates under PMG, it canbe declared that any rise equal to 1 per cent in the bank credit would diminishthe long‐run growth by around 0.3 per cent. Nevertheless, the coefficientsrelated to the turnover ratio reveal the fact that a 1 per cent increase in suchcoefficients will lead to rise of about 0.26 per cent to the long‐run growth. Itis also observed that the average short‐run response associated with the bankcredit and turnover ratio is not significant, yet their average short‐runresponse revealed the equivalent sign as the long run.

Table 7: Stock Markets, Banks and Growth, Cross‐Country Regression (Pooled OLS)

Regressions Variables (1) (2) (3)

Constant 0.435��(1.97)

0.018(0.942)

0.534��(2.37)

Logarithm of initial income per capita 0.935��(61.7)

0.922��(60.5)

0.930��(60.8)

Government consumption 0.167��(3.31)

Trade openness 0.063�(1.84)

Bank credit 0.027(0.79)

0.025(0.76)

0.002(0.06)

Turnover ratio �0.002(0.12)

0.018(0.95)

0.011(0.60)

R‐squared 0.95 0.95 0.96Wald test for joint Significance (p‐value) 0.000 0.000 0.000Countries 10 10 10Observations 188 188 188

In the regression, all variables are included as log(variable). � Significant at the 10 per cent level; �� at the 5per cent level; �� at the 1 per cent level. t Values are reported in parentheses.



4.1. Pooled ordinary least squares regressions (robustness check)

The robustness of the association between the financial developmentand the economic growth is inspected against the alternative method in thissection utilizing the pooled OLS regression results related to the stockmarkets, banks and the economic growth. It needs to be underscored thatconsistent and fairly robust estimates related to the long‐run structuralrelationship could be engendered by the PMG methodology. Table 7indicates the pooled OLS regression of the economic growth during theperiod 1990–2009with a single observation per country. There are indeed 10countries in the sample, while the amount of observation equates to 188 (10observations were dropped by attaining the lag for the GDP, while 2 otherobservations were unavailable for the turnover ratio of Saudi Arabia in 1990and 1991). The real per capita GDP growth is the dependent variable here,and the regressions encompass the bank credit and the turnover ratio.Correspondingly, the regressions sequentially control both the governmentconsumption and trade openness. It is noted that the t values related to thecoefficient estimates are stated in parentheses. The pooled OLS regressionsfail to establish a strong and positive relationship between the stock marketdevelopment, the bank development and the economic growth; while thesign was changed by adding new control variables both bank development(bank credit) and stockmarket development (turnover ratio) have not enteredsignificantly each of the three regressions at the 0.05 significance level.

Table 7 also shows that the bank credit and the turnover ratio are jointlysignificant as shown by the p‐value of less than 0.001 on the Wald test interms of the joint significance. It is admitted that the coefficients’ sizes aresmall economically. Although the mentioned counterfactual examples hadnot been observed as the exploitable elasticity, they fail to indicate aneconomically meaningful association between financial development andthe economic growth. It is also discerned that, in the simple pooled OLSregressions, the coefficients’ sizes greatly resemble the results obtained byemploying more sophisticated dynamic panel estimators.

5. Conclusions and Recommendations

The effects of the bank and stock market development on economicgrowth has been highlighted through previously conducted surveys, but it isperceived that, because of the short span of the data or the misspecificationproblem, the results are unreliable to some extent. The current research wasan attempt to primarily provide a financial background on the Islamic financeindicators to enable the readers to become more familiar with the topic andthe economic rationale behind its financing mode characteristics. Moreover,in this research, the relationship between the financial development andgrowth in 10 Islamic countries was empirically examined during the period



of 1990–2009. This study aimed to examine the specific effect of the bankingsector development on the economic growth measured by the quantity of thecredit issued to the private sector by banks over the GDP and the stockmarket development, which were estimated by the trades of shares on thedomestic exchanges over the total listed value shares. The empirical studyhere was executed by employing a balanced panel data from 10 AsianIslamic countries. In addition, the mean group (MG), panel mean group(PMG) and the dynamic fixed effect (DFE) were utilized with the aim ofobtaining strong evidence between the economic growth and theenvironmental factors such as the government consumption as well as thetrade openness for taking control of the potential simultaneity bias.

According to the empirical results obtained here, the stock markets andbanks were found to be significantly substantial for the economic growth.The fact is that both the bank and stock market development constantly wereentered jointly significantly in all system panel estimators (PMG estimation)used in this study. These findings obtained here are significantly inaccordance with the models predicting that well‐functioning financialsystems are able to moderate the information and transaction costs;accordingly, they can improve the resource allocation and economic growth.In addition, if a linear time trend is added to the model, it is discerned that nosignificant relationship exists between banking in the long run and thegrowth. Seemingly, the concept that the banks fail to prompt economicgrowth is reinforced. We additionally realized that, for some specifications,in the models containing the Asian financial crisis, the bank’s indicator issignificantly negatively associated with the growth. Another finding is thatthe Islamic finance background stock market development continuouslyexerts a positive effect on the economic growth, as confirmed theoretically.

Moreover, the government consumption variable was observed throughthe results to be always significant, positively contributing to the economicgrowth in the long run; nonetheless, the trade openness just inmodel with thefinancial crisis significantly negatively affects the economic growth in thelong run; if not, the mentioned variable would be insignificant.Econometrically, this paper’s techniques improve significantly upon theexisting studies on the link between banks, stock markets and economicgrowth. We were also able to control the biases induced by simultaneity,reverse causation and the unobserved country‐specific effects throughemploying instrumental variables for extracting the exogenous componentof the bank and the stock market development; simultaneously, however, theinformational and consistency loss were avoided by exploiting the initialvalues.

In view of the empirical work, this research tends to constantly layemphasis on the fact that the methods all suffer from some problems, butwhen a problem arises, the whole study of finance and growth is related tothe proxies for financial development. Taking the theory into consideration,



the financial systems affect growth by decreasing the information andtransactions costs, which in turn enhances the acquisition of the informationon the firms, the corporate governance, risk management, resourcemobilization and financial exchanges. It needs to be underscored that, toofrequently, empirical measures related to the financial development wouldfail to directly evaluate the mentioned financial functions. There is an urge torun more studies on enhancing the cross‐country indicators of the financialdevelopment as well as the number of innovative enterprises in the localbanking sector, their sizes (estimated by revenue or the number ofemployees), their sectoral specialization and their degree of internationali-zation for the environment features the productive banking industry, eventhough a rising amount of country‐specific surveys endeavour to developfinancial development indicators which are more meticulously linked to thetheory.

To conclude, it is proposed that future research should be directedtowards the determinants offinancial development to the extent that financialsystems exert a first‐order impact on the economic growth. Indeed, a morecomplete understanding the determinants of the financial development isrequired. Likewise, the human capital should be considered as a majorcontrol determinant of the growth that can be estimated by the initialsecondary enrolment rate suggested for the future line of research.



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Non‐technical summary

The current research endeavoured to examine the relationship betweenthe banks and the stock markets development and the economic growth byutilizing a sample of 10 Asian Islamic countries over a changing periodwhile adopting some recently pooled mean‐group techniques developed forthe dynamic panels. In this study, the independent effects of both the equitymarket and the bank development imposed on the growth were tested.Generally, we report PMG estimation across different control variables forthe overall financial development that are important for economic growth inthe long run for Islamic countries. Once the simultaneity bias was controlled,our results indicated that both the stock markets and banks developmentexerted a positive effect on the economic growth. While the stock marketdevelopment plays a significant positive role in the economic growthprocess, the bank development fails to do so. It is also observed that theeffect on the stock market is strongly positive following the Asian financialcrisis, whereas such an effect on the bank development is the opposite. It ishighlighted that the findings of this study are in accordance with the models,forecasting that the well‐functioning financial systems are able to ease thecosts related to the information and transaction while they accordinglyimprove the resource allocation and economic growth.



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The Stock Markets, Banks and Growth Nexus: Asian Islamic Countries

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