Stock markets, banks, and economic growth: Empirical evidence from the MENA region

Research in International Business and Finance 21 (2007) 297–315

Stock markets, banks, and economic growth: Empirical evidence from the MENA region夽 Samy Ben Naceur a,b,∗ , Samir Ghazouani c a

Institut des Hautes Etudes Commerciales (IHEC), 2016 Carthage Pr´esidence, Tunisia b International Monetary Fund (IMF), Washington, DC, United States c Laboratoire d’Economie et Finance Appliqu´ ees (LEFA), and Institut Sup´erieur de Comptabilit´e & d’Administration des Entreprises (ISCAE), Campus Universitaire de Manouba, 2010 Manouba, Tunisia Received 20 February 2005; received in revised form 5 May 2006; accepted 15 May 2006 Available online 27 June 2006

Abstract Over the last four decades, a wide theoretical debate is concerned with the fundamental relationship between financial development and economic growth. Recent studies shed some light on the simultaneous effect of banks and financial system development on growth rather than a separate impact. The empirical study is conducted using an unbalanced panel data from 11 MENA region countries. Econometric issues will be based on estimation of a dynamic panel model with GMM estimators. Thus, peculiarities of MENA region countries will be detected. The empirical results reinforce the idea of no significant relationship between banking and stock market development, and growth. The association between bank development and economic growth is even negative after controlling for stock market development. This lack of relationship must be linked to underdeveloped financial systems in the MENA region that hamper economic growth. Then, more needs to be done to reinforce the institutional environment and improve the functioning of the banking sector in the MENA region. Based on these results, other regions at the same stage of financial development such as Africa, Eastern Europe or Latin America should improve the functioning of their financial system in order to prevent their economies from the negative impact of a shaky financial market. © 2006 Published by Elsevier B.V. JEL classification: E44; O16; C33 Keywords: Bank development; Stock market development; Economic growth; Dynamic panel data models; MENA region

夽 This paper was written when Prof. Samy Ben Naceur was visiting the International Monetary Fund from February to April 2005 under the IMF/GDN program. The views in the paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. ∗ Corresponding author. E-mail addresses: [email protected] (S.B. Naceur), [email protected] (S. Ghazouani).

0275-5319/$ – see front matter © 2006 Published by Elsevier B.V. doi:10.1016/j.ribaf.2006.05.002

298

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

1. Introduction Theory gives contradictory predictions about the incidence of financial system development on economic growth, and about the separate impact of banks on growth and financial markets on growth. Boyd and Prescott (1986) argue that banks ease information frictions and therefore resource allocation while Stiglitz (1985) and Bhide (1993) defend the idea that banks are more efficient than equity markets in improving resource allocation and corporate governance. However, some models1 stress that competitive stock markets reduce the counterproductive monopoly power of banks and boost innovation projects. Finally, some theories argue that banks and stock markets contribute together to economic growth by improving information dissemination and reducing transaction costs. Most of the studies about the association between financial system development and economic growth omit stock markets development. We have to wait the publication of a study conducted by Levine and Zervos (1998) who empirically assess the relationship between both stock marketss and banks development and economic growth. However, their work suffers from many econometric problems. Although recent works have tried to solve some of the statistical shortcomings observed in this approach, statistical and conceptual issues remain. Rousseau and Wachtel (2000) provide a huge contribution to the growth literature by using panel data techniques. In order to study the relationship between stock markets, banks and economic growth, they use the difference panel estimator method developed by Arellano and Bond (1991) and show that both stock markets and banks development contribute to spur economic growth. This paper uses new econometric techniques in the panel data context that solve statistical drawbacks with available data from 11 MENA countries observed over the period [1979–2003] in order to examine the relationship between stock markets, banks and economic growth. We choose MENA countries to carry out our empirical investigations not only because very few studies have been devoted to the region, but also because the size and the structure of the financial systems differ sensibly between these countries and because MENA countries embarked since mid 1980s in far reaching financial reforms. Besides, the results stemming from MENA region could be of interest to other developing countries in the same stage of financial development for instance like African, Eastern European and Latin American countries that are reforming a great deal their financial systems. More specifically, we test whether stock markets and banks development each have a positive impact on economic growth after controlling for simultaneity bias, omitted variables, and the routine inclusion of lagged dependent variables in growth regressions and controlling for many other growth determinants. We also examine whether variables related to banks and stock markets jointly enter the growth regression significantly taking advantage of the finding in recent growth studies that emphasize the importance of using panel data analysis in examining cross-country growth dynamics. The rest of the paper is organised as follows. In Section 2, we discuss the relatively recent literature on finance and economic growth. Section 3 discusses the data sources, definitions of the variables used in our empirical work as well as the econometric modelling. Empirical results are presented in Section 4. Some concluding remarks are presented in Section 5.

1

See Allen and Gale (2000).

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

299

2. Related literature There are at least four ways in which financial system development contributes to economic growth. They are extensively described in the surveys provided by Pagano (1993) and Levine (1997, 2002). First, financial intermediaries may lower the costs of gathering and processing information and thereby improve the allocation of resources (e.g. Boyd and Prescott, 1986). Such information’s improvement about all economic agents can boost economic growth. Besides, banks may also spur the rate of technological innovation by selecting those entrepreneurs with the greatest chances of launching successful ventures (e.g. King and Levine, 1993). Second, Bencivenga and Smith (1993) show that banks that alleviate the corporate governance problem by lowering monitoring costs will reduce credit rationing and thereby spur growth. Third, financial intermediaries and security markets provide vehicles for trading, pooling and diversifying risk. Thus financial systems that allow agents to hold a diversified portfolio of risky projects will induce society to shift towards projects with higher expected returns with positive incidence on economic growth (e.g. Gurley and Shaw, 1955; Greenwood and Jovanovic, 1990). Fourth, financial systems that encourage the mobilization of savings by providing attractive instruments and saving vehicles can profoundly affect economic development. Acemoglu and Zilibotti (1997) were very explicit. With large and indivisible projects, financial arrangements that collect resources from disparate savers to be invested in a diversified portfolio of risky projects make it easier to reallocate investment toward higher return activities with positive implications on economic growth. In summary, theory on finance and growth focuses on particular functions provided by the financial system—producing ex ante information, monitoring investment, exerting corporate governance, facilitating trading, diversification and risk management and pooling savings—and how these impact on economic growth through resource allocation decisions.2 Empirical investigations about the relationship between financial sector development and economic growth began to appear with Goldsmith (1969). He sought to assess whether finance exerts a causal influence on growth and whether the mixture of banks and stock markets operating in an economy impacts on economic growth. Looking at decade averages for 35 countries from 1860 and 1963, Goldsmith graphically documented positive correlations between financial system development and economic growth. Recent years investigations have given rise to a vivid interest in empirical research on the finance–growth relationship. In particular, the paper by King and Levine (1993) provided the starting point for intensified research, which received a major impetus by the IMF and World Bank data sources. An overview of the literature dealing with cross-country studies, pure timeseries investigations, and country case studies can be found in Theil (2001), Wachtel (2003), and Levine (2002). Instead, we will provide an overview of the evidence on panel data approaches in the finance–growth relationship and on studies dedicated to banks, stock markets and economic growth in particular. Before presenting the evidence on the banks, stock markets and economic growth relationship, we need to briefly describe the theory dedicated to this particular aspect of the literature. In this regard, theory provides conflicting predictions about whether banks and stock markets are substitutes, complements, or whether one is more conducive to growth than the other. Work on growth through stock markets development has been scanty. We start our evidence by looking at the paper of Atje and Jovanovic (1993) that assess the impact of stock markets and

2

A comprehensive survey is provided by Levine (2002).

300

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

banks development on subsequent economic growth for 40 countries over the period [1980–1988]. They find a large effect of stock markets development as measured by the value traded divided by GDP on subsequent development, but they fail to find a similar effect for bank lending. Using a similar approach, Levine and Zervos (1998) have focused on the relationship between economic growth and financial system development using both banks and stock markets indicators. They tested this relationship for a sample of 42 countries over the period [1976–1993] using crosssectional regressions. They found that the initial level of stock markets development liquidity and the initial level of banking development are positively and significantly associated with longterm economic growth, productivity growth and capital accumulation. They also find that stock markets size, as measured by market capitalization divided by GDP, is not correlated with growth indicators. However, Harris (1997) shows that this relationship is at best weak by estimating again the same model for 49 countries over the period [1980–1991]. Conversely, Ram (1999) provides contrary evidence for both developed and developing countries that there is “. . . a negligible or negative association between financial development and growth”. Besides, the evidence from a panel of Central and East European countries (e.g. Dawson, 2003) casts further doubt on the conventional wisdom that financial development promotes growth. In a time-series setting, Arestis et al. (2001) use quarterly data on five developed countries and find that both banks and stock markets development lead to economic growth. They also find that the impact of banking sector development is substantially larger than that of stock markets development. More recently, Thangavely and Jiunn (2004) empirically examine the dynamic relationship between financial development and economic growth in Australia in terms of bankbased and market-based financial structure. They find that financial intermediaries and financial markets have different impacts on economic growth given their diverse roles in the domestic economy. Using VAR models, Hondroyiannis et al. (2005) assess empirically the relationship between the development of the banking system and the stock markets, and economic performance for the case of Greece over the period [1986–1999]. The empirical results show that both banks and stock markets financing can promote economic growth in the long-run although their effect is small. Furthermore, the contribution of stock markets in financing economic growth appears to be substantially smaller compared to bank finance. Van Nieuwerburgh et al. (2006) investigate the long-term relationship between financial market development and economic development in Belgium. They find strong evidence that stock markets development caused economic growth in Belgium, especially in the period between 1873 and 1914. Institutional changes affecting the stock exchange explain the time-varying nature of the link between stock markets development and economic growth. In order to correct for the simultaneity bias, Levine (1999), and Levine et al. (2000) introduce an instrumental variable (the legal origin) that explains cross-country differences in financial development but is uncorrelated with economic growth. They find that the strong link between financial development and economic growth is not due to simultaneity bias. Rousseau and Wachtel (2000), and Beck and Levine (2004) extend the Levine and Zervos (1998) approach of stock markets, banks and growth by using panel techniques (GMM estimator). Rousseau and Wachtel (2000) use annual data and the difference estimator. Beck and Levine (2004) use data averaged over 5-year periods and the system estimator to reduce potential biases related to the difference estimator, and extend the sample through 1998. Both studies show that banking and stock markets development explain altogether subsequent growth. In a pair of papers, Rioja and Valev (2004a,b) take up the question of non-linearity in the finance–growth relationship using GMM dynamic panel techniques and a number of bank based

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

301

financial measures. Rioja and Valev (2004a) consider the effect of financial development on growth as well as the sources of growth by grouping countries according to their income per capita. Focusing on their results for the effect of financial development on economic growth, finance in low-income countries generally has a negative but insignificant impact on growth, while for the medium- and high-income countries the correlations are positive, with the largest effect occurring in the high-income group. Rioja and Valev (2004b) divide countries based on level of financial development. For low levels of financial development, the results paint an uncertain picture, as the effect of finance on growth varies depending on the financial measure used, with results ranging from negative and insignificant to an economically large and significant positive effect. In the same vein, Ketteni et al. (2004) study the relationship between financial development and economic growth in order to explore possible non-linearities. They use the same data set as previous researchers but employ nonparametric estimation techniques. They find that, in contrast to recent research, the finance–growth relationship is linear when account is taken of the nonlinearity between initial per capita income and human capital on the one hand, and economic growth on the other. Christopoulos and Tsianos (2004) investigate the long-run relationship between financial depth and economic growth, trying to utilize the data in the most efficient manner via the conduction of panel unit root tests and panel cointegration analysis. In addition, they use threshold cointegration tests, and dynamic panel data estimation for a panel-based vector error correction model. The longrun relationship is estimated using fully modified OLS. For 10 developing countries, the empirical results provide clear support for the hypothesis that there is a single equilibrium relation between financial depth, growth and ancillary variables, and that the only cointegrating relation implies unidirectional causality from financial depth to growth. Finally, Bolbol et al. (2005) study the Egypt’s financial system and its relation to total factor productivity (TFP) during the period [1974–2002], which is as far as we are concerned the first published paper in the MENA region that analyses the simultaneous impact of the development of stock markets and banking sector on economic growth. The results show that bank-based indicators have a negative effect on TFP unless they are associated with a threshold level of per capita income; whereas the effect of market-based indicators is positively reinforced by private net resource flows. 3. Data and econometric methodology 3.1. Data and measurement Data were extracted from various sources. Arab Monetary Fund was a main source for data on Arab countries. We consult the capital market unit database to collect stock markets indicators from 1994, and the economic and technical department database for other economic data series. As for the stock markets data prior to 1994, we collect them from world development indicators and local stock markets. Our original intention was to include all MENA countries, but given that some countries have not yet created stock markets (e.g. Iraq, Libya, Sudan, Syria and Yemen), and other countries established stock markets very recently (UAE), the sample covered only 11 countries. Beside, data were not available for a uniform period for each country, and many countries have established their stock markets recently. Therefore, the number of observations is expected to vary across countries leading to estimations over an unbalanced panel data. The number of time observations ranges from 9 annual observations for Lebanon to 25 observations for Jordan. For the most

302

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

other countries, the periods of observations cover mainly the 1980s and 1990s.3 In summary, our data contains 11 MENA countries observed over the period of [1979–2003] with a minimum of nine observations. We note that estimations will be done on 5-year overlapping data in order to maximize the time-series content of our regressions and to avoid the business cycle effects. Our econometric investigations with panel data described in the next sub-section use a regression specification given by the following expression: yit = ηi + α Zit + β Fit + εit ,

i = 1, . . . , n,

t = 1, . . . , Ti

(1)

yit refers to the growth of real per capita GDP in the ith country for some time-period, which is our measure of economic growth. Fit includes variables that measure stock markets and banking development. Beck et al. (1999) outline three key stock markets indicators in measuring its size, activity and efficiency. stock markets capitalization to GDP ratio (MC) measures the size of stock markets as it aggregates the value of all listed shares in the stock markets. It is assumed that the size of the stock markets is positively correlated with the ability to mobilize capital and to diversify risk. However, the size of the stock markets does not provide any indication of its liquidity. To measure stock markets liquidity, we use value traded variable (VT), which equals the value of the trades of domestic stocks divided by GDP. Liquidity in the stock markets reduces the disincentive to investment as it provides more efficient resource allocation and hence economic development. We use also the turnover ratio (TR), which equals the value of trades of shares on national stock markets divided by market capitalization to capture the efficiency of the domestic stock markets. More efficient stock markets can foster better resource allocation and spur growth (e.g. Levine, 1991; Bencivenga et al., 1995). Thus, taken together, these three measures of stock markets development provide more information about a nation’s stock markets than if one uses only a single indicator. We use a composite index of stock markets development (SMINDEX) using a formula, which is similar to the algorithm developed by Demirg¨uc¸-Kunt and Levine (1996). Specifically, construction of SMINDEX follows a two-step procedure. First, for each country i and each time t, transformed variables of market capitalization, value traded, and turnover ratios are computed. We define the transformed value of each variable X4 as follows: Xitt =

¯ Xit − X ¯ |X|

(2)

¯ is the average value of variable X across all countries in the panel over the period of observation X for each one. Second, we take a simple average of the transformed values of market capitalization, value traded, and turnover ratios obtained by expression (2) in order to provide the overall stock markets index SMINDEX. To measure bank development, the primary measure we use is private credit (PCREDIT) which equals the value of credits by financial intermediaries to the private sector divided by GDP following Levine and Zervos (1998), Rousseau and Wachtel (2000), and Beck and Levine (2004). Unlike many past measures, this indicator excludes credits issued by the central banks. In order to assess the robustness of our results, we use another measure of bank development for instance liquid liabilities (LIQ) which equals the liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of bank and non bank financial intermediaries) divided by GDP. LIQ complements PCREDIT variable because it measures the size of financial intermediaries and 3 4

See Appendix A. X indicates variables MC, VT or TR.

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

303

does not focus in the intermediation of credit to the private sector. However, this indicator reflects the overall size of financial sector and does not distinguish between the allocation of capital to private sector and to various governmental agencies. In this respect, it may not inform accordingly on the ways that financial services are provided. Since, the two indicators are incomplete in their representation of the banking sector, we use a composite index of bank development (BINDEX) based on a formula that is similar to the one developed to obtain a stock markets index (expression (2) above). Zit is a standard set of conditioning variables that includes the logarithm of initial income per capita (IIC) to control for convergence. According to neoclassical theory, the coefficient associated to per-capita income represents the convergence effect and thus should be negative.5 According to endogenous-growth models, there is no convergence effect, since economies do not depart from their steady states, and therefore the coefficient is expected to be zero. On the other hand, we use the ratio of exports plus imports to GDP (TO),6 the foreign direct investment (FDI), and the black market premium (BMP)7 to capture the degree of openness of an economy. Additional conditioning variables include the inflation rate (IR) and the ratio of government consumption to GDP (GC)8 as indicators of macroeconomic stability. We have also controlled for wars and other political turmoil by adding a dummy variable (TURMOIL),9 for oil prices (OIL),10 and for financial crises by including a dummy variable (FCRISIS). Last, to account for the difference of legal system,11 we include a dummy variable (LEGSYS) that takes the value of 1 when the country follows French civil law system and 0 otherwise. Finally, ηi is an unobserved country specific effect, and εit is the error term for each observation (Table 1). 3.2. Highlights from the data Table 2 presents some summary statistics about the principle variables used in the econometric analysis. These descriptive statistics are given for each country of the panel and are calculated over its own period of observation. Firstly, growth of real per capita GDP ranges in average between very low levels for Kuwait and Jordan, even negative average annual growth for Saudi Arabia, and high levels exceeding 2 percent in annual average for Egypt, Iran and Tunisia. 5 If convergence is confirmed, then a country with a relatively lower level of initial per-capita GDP will grow faster, since it is that much farther away from its steady state and must catch up. 6 As discussed by Edwards (1993), the literature on endogenous growth argue that economies that are more open to international trade can grow more rapidly by expanding their markets and becoming more efficient. 7 The black market premiums are from Picks Currency Yearbook through late 1980s and then from the World Currency Yearbook (International Currency Analysis Inc.). It is a general indicator of policy, price, and trade distortions and therefore is a useful variable to use in assessing the independent relationship between growth indicators and measures of development. 8 Theory and some evidence suggest a negative relationship between macroeconomic instability and economic activity (e.g. Fischer, 1993; Easterly and Rebelo, 1993; Bruno and Easterly, 1995). More specifically, as Barro and Sala-i-Martin (1995) point out, the government consumption variable is intended to capture public expenditures that do not directly affect productivity but will entail distortions on private decisions. The coefficient associated to this variable is expected to be negative. 9 Given the political background of this region, it may be meaningful to add a dummy variable. Many authors find that political instability is negatively associated with economic growth. See Barro and Sala-i-Martin (1995) for evidence and citations. 10 For these oil-rich countries in the MENA region, the oil price is likely to play a significant role in both economic development and the growth of stock markets. Hence, it is sensible to incorporate this variable. 11 The law and finance view emphasizes the role of the legal system in shaping financial development and thus economic growth (e.g. La Porta et al., 2000).

304

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

Table 1 List of variables Variable

Definition

Source

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Initial income per capita (IIC) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL) Political turmoil (TURMOIL) Financial crises (FCRISIS) Legal system (LEGSYS)

Annual growth of per capita real GDP Total market value of all listed shares over GDP Value of trades of domestic stocks over GDP Value of trades of shares over market capitalization Average of MC, VT and TR Domestic credit to private sector over GDP M3 over GDP Average of PCREDIT and LIQ GDP converted to international dollars (in logarithm) Total amount of exports and imports over GDP Total amount of foreign direct investment over GDP Indicator of policy, price and trade distortions Increasing rate of consumer price index over 1-year period Government final consumption expenditure over GDP Annual average of international oil prices Dummy variable reflecting political instability Dummy variable reflecting financial crises Dummy variable to differentiate the local legal system

WDI AMF WDI WDI Authors WDI WDI Authors WDI WDI WDI WCY WDI WDI WDI Authors Authors Authors

Market capitalization is considered as a measure of the size of stock markets. Since it is an indicator of the ability of an economy, via its stock markets, to mobilize capital and diversify risk, Bahrain seems to have the best performance in the region according to this criterion. Many countries exhibit a ratio of market capitalization below 20 percent like Tunisia and Lebanon. The two other measures of stock markets development are considered as measures of the liquidity of stock markets. According to these two indicators Kuwait and Turkey shelter the more liquid stock markets in the region. All the other countries present low levels for these ratios with averages often below 4 percent. When we consider the aggregation of the information on the stock markets size and liquidity, values obtained for the stock markets index show that Kuwait has the higher developed stock markets in the region (2.031). Turkey is ranked second with a value of about 1.7. Stock markets of all the other countries seem to be weakly developed since SMINDEX gives very low values for Bahrain, Jordan and Saudi Arabia, even negative for Egypt, Iran, Lebanon, Morocco and Tunisia. The indicators of banking development, that is liquid liabilities and credit to private sector, are considered in order to measure to overall size of the banking sector. Values obtained for each indicator as well as values obtained for to composite indicator BINDEX show that the banking sector in Lebanon and Jordan are the best ones in the region. They seem to be more structured and well developed in comparison to their counterparts in the other countries. Table 3 provides some indications about the empirical correlations between growth and stock markets and banking development indicators. All these correlations are negative. They do not exceed 14 percent especially for indicators of the banking system development. On the other hand, positive correlations between market capitalization and value traded as well as turnover signify a positive relationship between the size and the liquidity of stock markets. But, values are at low levels. As mentioned by Demirg¨uc¸-Kunt and Levine (1996), this suggests that the different indicators capture different aspects of stock markets development. We note also the highly positive correlation between credit to private sector and liquid liabilities (0.626). Finally, an interesting

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

305

Table 2 Summary statistics Country

Period

Variables

Mean

Bahrain

[1989–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

1.43 95.34 3.84 3.85 0.16 56.76 72.51 0.08 160.081 0 0 1.089 21.29 20.42

Egypt

[1981–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

Iran

[1993–2003]

Jordan

[1979–2003]

S.D.

Minimum

Maximum

4.21 17.32 2.48 1.81 0.28 11.65 10.29 0.17 21.49 0 0 1.56 2.071 4.54

−4.43 71.2 1 1.5 −0.22 29.84 58.52 −0.29 128.49 0 0 −1.43 17.56 13.08

10.13 142.1 9.33 8.5 0.84 75.86 95.24 0.43 210.16 0 0 6.17 24.55 28.89

2.44 13.58 2.42 13.51 −0.6 40.29 88.96 0.024 51.12 1.72 8.19 11.73 12.79 21.96

1.76 11.93 3.35 12.43 0.38 12.62 6.24 0.14 11.35 1.019 25.94 6.84 3.21 6.14

−1.14 1 0 2.8 −0.95 26.19 80.59 −0.16 35.32 0.29 −10 2.26 9.74 13.08

7.099 36.81 11.2 53.3 0.25 61.57 106.59 0.36 82.18 3.39 104.85 23.86 19.018 34.27

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

2.16 15.19 1.85 16.035 −0.57 28.36 42.92 −0.41 43.22 0.083 111.55 22.1 13.51 20.64

2.11 9.95 1.5 6.53 0.15 4.41 2.64 0.054 6.23 0.11 121.39 10.98 0.91 5.22

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO)

0.62 59.85 9.18 14.99 0.14 68.063 105.71 0.42 120.049

5.93 17.86 6.92 9.65 0.51 8.35 18.27 0.19 15.24

−0.72 2.15 0.5 7.5 −0.72 21.93 38.58 −0.51 34.15 0.0029 0 11.27 11.96 13.08 −16.51 36 1.7 4.8 −0.51 50.45 77.49 0.05 84.75

5.69 35.34 5.2 26.4 −0.29 35.35 48.26 −0.34 52.58 0.34 400 49.66 14.61 28.89 14.55 111.18 26.2 44.39 1.47 76.71 141.93 0.72 154.64

306

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

Table 2 (Continued ) Country

Period

Variables

Mean

S.D.

Minimum

Maximum

Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

1.47 2.81 5.67 25.57 22.84

2.14 3.17 5.91 2.86 6.68

−0.56 −0.31 −0.2 21.86 13.08

9.25 10.34 25.71 34.36 35.95

Kuwait

[1993–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

0.46 56.19 45.42 60.66 2.031 56 86.8 0.17 91.5 0.11 0 1.71 28.91 20.64

5.67 15.66 39.57 39.66 1.99 19.057 7.23 0.21 3.77 0.38 0 1.12 4.27 5.22

−7.43 36.98 7.8 18.6 −0.027 27.025 71.88 −0.11 87.18 −0.43 0 0.15 21.88 13.08

8.55 87.095 118.017 144.9 6.029 79.85 98.7 0.49 98.067 1.12 0 3.56 35.86 28.89

Lebanon

[1995–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

1.23 10.34 1.093 8.15 −0.75 78.33 177.07 1.02 57.84 1.24 0 3.71 13.34 21.59

1.77 4.69 1.23 5.79 0.16 11.76 33.38 0.34 9.46 0.52 0 4.69 3.067 5.34

−1.8 3.6 0.34 2.8 −0.9 57.91 126.93 0.47 49.072 0.31 0 0 9.81 13.08

4.5 19.51 4.1 21 −0.38 92.018 225.66 1.4 77.11 1.88 0 11 18.92 28.89

Morocco

[1983–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

1.48 15.65 1.8 10.52 −0.64 38.97 67.53 −0.14 59.0026 1.7 3.29 4.49 17.27 20.91

5.038 14.49 2.23 9.85 0.3 15.25 13.94 0.24 6.28 2.0093 4.12 3.12 1.74 5.29

−8.21 1.6 0 2.7 −0.95 11.69 48.53 −0.55 50.34 0.0035 0 0.62 15.35 13.08

10.28 43.83 7.4 45.9 0.05 58.66 92.28 0.21 70.68 8.28 13.27 12.45 21.048 29.64

Oman

[1989–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX)

1.024 20.25 4.58 18.53 −0.38

2.21 10.062 6.37 18.67 0.51

−2.33 9.37 0.9 5.6 −0.8

6.3 45.1 24.5 79.4 1.16

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

307

Table 2 (Continued ) Country

Period

Variables

Mean

Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

32.46 32.88 −0.44 88.36 0.65 0 0.31 27.72 20.51

S.D.

Minimum

Maximum

8.25 3.21 0.1 3.45 0.48 0 1.52 5.27 4.5

22.86 27.81 −0.57 80.99 0.03 0 −1.14 20.68 13.08

47.32 39.35 −0.24 92.32 1.35 0 4.62 38.14 28.89

Saudi Arabia

[1991–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

−0.044 38.059 12.71 30.5 0.25 55.068 47.076 −0.11 66.19 0 0 0.73 26.74 20.42

2.82 11.54 19.25 33.04 1.24 4.089 3.61 0.063 5.79 0 0 2.0056 2.88 4.8

−3.27 28.71 1.73 7.4 −0.47 49.98 42.98 −0.19 56.47 0 0 −1.35 23.6 13.08

5.77 73.25 74.82 137.02 4.28 63.81 53.95 0.0091 75.52 0 0 4.87 34.38 28.89

Tunisia

[1987–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

2.78 10.77 1.13 8.024 −0.75 67.16 52.9 0.052 89.069 2.085 3 4.83 16.25 19.96

2.25 5.46 1.091 7.19 0.17 1.79 3.99 0.035 6.12 1.19 4.62 2.15 0.54 4.49

−2.13 4.33 0.1 0.89 −0.92 63.23 48.53 −0.015 70.64 0.6 0 1.98 15.55 13.08

5.62 21.84 3.7 23.29 −0.38 70.34 60.049 0.11 98.87 3.86 16.55 8.22 17.26 28.89

Turkey

[1988–2003]

Growth of real per capita GDP (y) Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL)

1.53 20.54 26.72 115.4 1.69 19.28 36.5 −0.55 45.48 0.6 1 68.058 12.27 20.071

5.24 14.82 23.95 67.37 1.75 3.41 10.87 0.093 11.67 0.45 4.45 19.075 1.94 4.61

−8.99 1.26 0.1 5.5 −0.91 14.79 24.073 −0.66 30.47 0.35 −9.091 25.3 7.61 13.08

6.82 61.47 89.93 225.98 5.34 26.3 59.96 −0.38 65.025 2.25 9.1 106.26 15.17 28.89

308

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

Table 3 Correlations between growth and financial system variables

y MC VT TR SMINDEX PCREDIT LIQ BINDEX

y

MC

VT

TR

SMINDEX

PCREDIT

LIQ

BINDEX

1 −0.119 −0.08 −0.015 −0.08 −0.317 −0.117 −0.139

1 0.284 0.043 0.423 0.409 0.18 0.314

1 0.763 0.95 0.024 −0.018 0.0012

1 0.862 −0.284 −0.208 −0.268

1 −0.008 −0.055 −0.038

1 0.626 0.88

1 0.921

1

Calculations are done over the whole sample composed by 176 observations. Similar tables by country are available upon request.

result concerns the positive correlation between stock markets development and banking system development. Such result is also confirmed for the correlation between market capitalization and either credit to private sector or liquid liabilities (0.409 and 0.18, respectively). 3.3. Econometric modelling According to the available data, the treatment of incomplete panels is imperative. Indeed, the available panel data set for the 11 MENA region countries is unbalanced since each variable is observed over varying time-period length. On the other hand, introduction of initial income per capita in the model puts the specification (1) inside the context of dynamic panel models, and efficient estimators are given through the generalised method of moments (GMM). This method, developed by Arellano and Bond (1991), provides convergent estimators and derives from the instrumental variables principles. It also makes up for problem of correlation between the lagged dependent variable yit−1 included in the vector Zit , defined above, and the error term εit as well as between some explanatory variables (Zit and/or Fit ) and the unobserved country specific term ηi . The GMM procedure is based on a set of orthogonality conditions, which may occur between the error terms and a set of instrumental variables. According to this principle, the GMM estimator must be able to reduce to zero the empirical counterpart of these conditions. The most efficient estimator is obtained when the model (1) is transformed into the following difference equation12 : (yit − yit−1 ) = α (Zit − Zit−1 ) + β (Fit − Fit−1 ) + (εit − εit−1 ), i = 1, . . . , n,

t = 2, . . . , Ti

(3)

In this specification, the country specific effect is dropped out, but a new kind of bias arises since (yit−1 − yit−2 ) is correlated with the transformed error term (εit − εit−1 ). Hence, Arellano and Bond (1991) proposed the following moment conditions:

12

E(Zit−s (εit − εit−1 )) = 0,

for s ≥ 2;

t = 3, . . . , Ti

(4)

E(Fit−s (εit − εit−1 )) = 0,

for s ≥ 2;

t = 3, . . . , Ti

(5)

Note that the vector (Zit − Zit−1 ) contains the component (yit−1 − yit−2 ).

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

309

With these conditions in mind, the so-called difference estimator is provided after running two steps. In the first one, the error terms are assumed to be independent and homoscedastic across countries and over time. The residuals retained at this step serve to construct consistent estimate for the variance–covariance matrix. Thus the difference estimator is asymptotically more efficient than the first step estimator. Besides the estimation procedure, Arellano and Bond (1991) construct an interesting test in order to validate no second-order serial correlation for the error terms of the first-differenced equation given by expression (3). The importance of this test is due to the fact that the consistency of the GMM estimator depends on the assumption that E(εit εit−2 ) = 0. The appropriate statistic of the test is asymptotically standard normal under the null hypothesis and is defined as follows: N=

ˆε−2 ˆε∗ √ ˆε

(6)

where εˆ −2 is the vector of residuals lagged twice, and εˆ ∗ is a vector of trimmed εˆ to match εˆ −2 . A Sargan specification test is also conducted which is a test of over-identifying restrictions. Under the null hypothesis, the Sargan statistic is asymptotically distributed as χ2 with p–k degrees of freedom and is written down as: −1  n     S = ˆε W Wi ˆεi ˆεi Wi W  ˆε (7) i=1

W is the chosen matrix of instruments. p indicates the number of columns in W, and k the number of parameters to be estimated. The Sargan test is used to verify independence between the instruments and the error term. The null hypothesis in this case is that the instruments and the error term are independent. The Difference-Sargan test is used to verify that the error term is not serially correlated as assumed. Under the null hypothesis, there is no second-order serial correlation. Thus a failure to reject the null hypothesis for both tests would be clear evidence in favour of the fact that the instruments are indeed valid. Both the Sargan and Difference-Sargan tests are distributed as χ2 under the null hypothesis. 4. Empirical results Several specifications of the dynamic panel model were estimated using the econometric methodology presented above.13 Table 4 reports GMM-in level estimates of these specifications. There are 11 countries in the sample observed over the period ranging from 1979 to 2003. The dependent variable is real per capita GDP growth. In column (1), the composite index BINDEX is included in the regression as a measure of bank development as well as the index SMINDEX as a measure of stock markets development. In columns (2)–(7), we have also included the usual measures of financial development that is MC, TR, VT, PCREDIT and LIQ. All the regressions also control sequentially for government consumption (GC), trade openness (TO), foreign direct investment (FDI), inflation (IR), black market exchange premium (BMP), oil prices (OIL), the legal system (LEGSYS) and political and financial crisis (variables TURMOIL and FCRISIS, respectively). Firstly, results of estimation confirm the joint significance of the engaged coefficients apart from specifications given in column (1). This is confirmed by means of test F of the null hypothesis that 13

An appropriate algorithm was written on STATA 8 software.

310

Variables

(1)

(2)

(3)

−0.026 (0.034)

(4)

Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Initial income per capita (IIC) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL) Political turmoil (TURMOIL) Financial crises (FCRISIS) Legal system (LEGSYS) Constant

−6.347** (2.617) −0.166*** (0.054) −0.738** (0.349) −0.011 (0.012) 0.003 (0.079) 0.073 (0.128) 0.171*** (0.056) 0.188 (1.062) 0.581 (0.817) Dropped −0.081 (0.343)

−4.234* (2.527) −0.165*** (0.049) −0.841*** (0.327) −0.016 (0.01) 0.032 (0.056) 0.123 (0.13) 0.129** (0.05) 0.876 (0.994) 0.652 (0.875) Dropped 0.318 (0.362)

−4.057 (2.618) −0.147*** (0.051) −0.693** (0.323) −0.009 (0.012) −0.033 (0.074) 0.067 (0.13) 0.16*** (0.054) 0.057 (0.987) 0.583 (0.775) Dropped 0.135 (0.349)

Time dummies

Included

Included

Included

F-statistic Sargan test (statistic S) Serial correlation test (statistic N) Number of countries Number of observations

−0.002 (0.037)

(5)

***

5.96 47.44 1.43 11 97

(7)

0.039 (0.039)

−0.004 (0.021)

0.113 (0.695)

−9.224* (5.621)

(6)

0.009 (0.395) 0.003 (0.024) −0.049 (0.097)

−0.026 (0.097)

−0.067 (0.099)

−0.025 (0.068) 0.096 (0.129) 0.174*** (0.052) 0.421 (0.991) 0.588 (0.972) Dropped 0.098 (0.34)

−7.504*** (2.459) −0.179*** (0.055) −0.884** (0.377) −0.021** (0.011) 0.065 (0.063) 0.104 (0.129) 0.169*** (0.059) 0.215 (1.104) 0.704 (0.962) Dropped −0.03 (0.364)

−7.11*** (2.417) −0.161*** (0.056) −0.788** (0.351) −0.016 (0.012) 0.02 (0.077) 0.059 (0.129) 0.165*** (0.057) −0.425 (1.058) 0.843 (0.841) Dropped −0.135 (0.338)

−7.273*** (2.573) −0.175*** (0.056) −0.774** (0.358) −0.012 (0.012) −0.006 (0.079) 0.099 (0.129) 0.191*** (0.058) 0.2 (1.108) 0.885 (0.853) Dropped −0.077 (0.332)

Included

Included

Included

Included

−0.226*** (0.067) −0.209*** (0.069) −0.197*** (0.066)

***

7.33 51.64 1.3 11 97

***

7.18 50.24 1.51 11 97

−4.942* (2.639) −0.153*** (0.05) −0.702** (0.323)

7.22 47.8 1.56 11 97

***

***

5.63 47.11 1.52 11 97

***

5.97 47.81 1.49 11 97

5.72*** 43.98 1.53 11 97

Estimations are done on overlapped data. Standard errors of the coefficients are reported in parentheses. *** , ** , and * indicate significance levels at 1, 5, and 10 percent, respectively. For Sargan test, the null hypothesis is that the instruments used are not correlated with the residuals. For the test for autocorrelation, the null hypothesis is that the errors in the first-difference regression exhibit no second-order serial correlation.

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

Table 4 GMM-in level estimates of the relationship between stock markets, banks and growth in MENA; one-step results

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

311

all coefficients except the constant are zero. On the other hand, the Sargan test (statistic S) confirms for the whole specifications no correlation between the used instruments and the residuals. The test for auto-correlation (statistic N) confirms no second-order correlation also for the whole of the specifications. The empirical results show that the impact of banking sector is always negative with significance varying with the nature of the measure introduced either for banking development or stock markets development. Individually, coefficients associated to BINDEX (column (1)) and LIQ (columns (2)–(4)) are negative and significant at a high level. On the other hand, coefficients associated to stock market measures are not significant. The correlation is negative in presence of liquid liabilities but positive in presence of credit to private sector. So, the financial sector has no positive impact on economic growth whether we take financial market or banking sector development variables. The insignificant and negative association between bank development and growth may be directly linked to the overwhelming public sector in the credit allocation. Therefore, MENA countries need to improve the credit allocation process by privatising national banks, by strengthening credit regulation and by reinforcing competition in the banking sector. For control variables, the negative convergence effect is confirmed significantly at various levels of significance except for specification (3). The positive effect of openness is not detected from these regressions. Rather a negative effect is obtained and is significant for the coefficients associated to TO and FDI. Also, we obtained no statistical significance for macroeconomic stability (variables GC and IR). Finally, oil price has a positive and significant impact on economic growth. As expected, this result means that an increase of oil price in the MENA region is a stimulus to growth. These results may be explained by the high degree of financial repression and a weak equity market that is unable to support a sustainable economic development in the MENA region but also to sluggish and unbalanced growth, which weaken any relationship between financial development and economic growth. The lack of contribution of stock markets in the development process is mainly due to a relatively new and generally small capital markets in the MENA region when compared with other developing countries. In other words, stock markets in MENA countries do not reach a threshold that will enable them to contribute to economic growth. Besides, the negative effect could be related to the deviation of financial resources from the real sector to stock markets speculation. Singh (1997) argues that stock markets, even in developed economies, do not perform the monitoring, screening and disciplinary role very well. In emerging markets, including the transition economies, it is even worse since the regulatory infrastructure is badly developed (e.g. Singh, 1997, p. 775). Moreover, in most transition economies the stock markets are very thin. This may lead to excessively volatile share prices. According to Singh (1997), stock price volatility may seriously hamper economic development. Recent developments in the Asian financial markets seem to confirm this. He also points out, in contrast to the analysis of Cho (1986), that equity markets have much more problems with asymmetric information than banks. The reason is that stock markets very often provide investors with short-term finance, whereas banks, especially group-banks, have long-run relationships with firms. In other words, stock markets may suffer from short-termism. This short discussion on the role of stock markets in the process of economic development strongly suggests that, in principle, stock markets may fulfill an important role in inducing growth. However, a prerequisite seems to be that the regulatory infrastructure is well developed and that measures are taken to reduce extreme volatility of stock prices, which is not the case for the MENA countries. Since Gulf countries have specificities relative to extreme abundance of oil deposits, estimation of the same specifications excluding these countries were done. Table 5 provides the results of

312

Variables

(1)

(2)

(3)

−0.078 (0.052)

(4)

Market capitalization (MC) Value traded (VT) Turnover (TR) Stock market index (SMINDEX) Credit to private sector (PCREDIT) Liquid liabilities (LIQ) Bank development index (BINDEX) Initial income per capita (IIC) Trade openness (TO) Foreign direct investment (FDI) Black market premium (BMP) Inflation rate (IR) Government consumption (GC) Oil prices (OIL) Political turmoil (TURMOIL) Financial crises (FCRISIS) Constant

−7.373** (3.523) −0.139*** (0.067) −1.114*** (0.396) 0.074 (0.047) −0.022 (0.095) 0.34 (0.35) 0.312*** (0.099) −0.303 (1.199) 2.419** (1.039) 0.302 (0.428)

−6.946** (3.191) −0.158*** (0.051) −0.797** (0.356) 0.041 (0.042) 0.019 (0.056) −0.287 (0.298) 0.369*** (0.086) −0.732 (0.972) 2.439** (1.187) −0.089 (0.474)

−3.069 (3.915) −0.155*** (0.057) −0.668** (0.338) 0.045 (0.048) −0.113 (0.099) −0.191 (0.258) 0.226** (0.101) −0.715 (1.099) 2.139* (1.145) 0.037 (0.455)

Time dummies

Included

Included ***

F-statistic Sargan test (statistic S) Serial correlation test (statistic N) Number of countries Number of observations

−0.09 (0.088)

−1.328 (1.511)

−23.394*** (8.909)

***

10.1 31.94 0.02 6 67

(5)

(6)

(7)

−0.069 (0.067) 0.026 (0.091) −0.038 (0.046)

−0.089** (0.035) 0.009 (0.149)

−0.047 (0.183)

−0.126 (0.167)

−5.05 (3.32) −0.187*** (0.052) −0.647** (0.327) 0.031 (0.044) −0.043 (0.083) −0.263 (0.249) 0.261*** (0.086) −0.582 (1.083) 2.074* (1.089) −0.299 (0.409)

−8.765** (3.595) −0.215*** (0.075) −0.784* (0.435) 0.061 (0.047) 0.072 (0.069) −0.219 (0.298) 0.412*** (0.106) −1.299 (1.185) 1.879 (1.406) −0.369 (0.45)

−7.649* (3.971) −0.252*** (0.083) −0.787* (0.477) 0.069 (0.054) 0.081 (0.11) −0.006 (0.327) 0.368*** (0.116) −1.244 (1.289) 2.469** (1.221) −0.202 (0.489)

−7.865** (3.758) −0.242*** (0.076) −0.85* (0.463) 0.071 (0.052) 0.051 (0.1) 0.102 (0.339) 0.351*** (0.105) −0.442 (1.451) 2.682** (1.321) −0.117 (0.427)

Included

Included

Included

Included

Included

***

***

−0.291*** (0.088) −0.264*** (0.093) −0.248*** (0.083)

13.29 28.17 0.27 6 67

10.25 31.87 1.41 6 67

12.54 32.5 0.01 6 67

***

8.97 26.85 −0.3 6 67

***

7.49 27.16 1.39 6 67

8.03*** 27.14 0.58 6 67

Estimations are done on overlapped data. Standard errors of the coefficients are reported in parentheses. *** , ** , and * indicate significance levels at 1, 5, and 10 percent, respectively. For Sargan test, the null hypothesis is that the instruments used are not correlated with the residuals. For the test for autocorrelation, the null hypothesis is that the errors in the first-difference regression exhibit no second-order serial correlation.

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

Table 5 GMM-in level estimates of the relationship between stock markets, banks and growth in MENA (excluding oil-countries); one-step results

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

313

these estimations. We observe that the results and conclusions are similar to those obtained for the whole sample. So similarities between the two groups of countries could be confirmed. Despite the difference of the economic structure of these two groups, the financial systems seem to impact homogenously on economic growth. 5. Conclusions Using a sample of 11 MENA region countries over a varying period, this study tries to identify the relationship between banks and stock markets development and economic growth. It tests the independent impact of both equity market and bank development on growth. Generally, we report using GMM estimates and across different control variables that the overall financial development is unimportant or even harmful for economic growth in the MENA region which is counter-intuitive and need to be explained away by reference to theory. This lack of relationship must be linked either to underdeveloped financial systems in the MENA region that hamper economic growth or to unstable growth rates in the region that affect the quality of the association between finance and growth. As a matter of policy implications, we need to draw some proposals according to the results. It is obvious that an improvement of the performance of financial system in the region is crucially needed in order to enable financial development to be growth stimulator. Therefore, MENA countries need to improve the credit allocation process by privatising national banks, by strengthening credit regulation and by reinforcing competition in the banking sector. Also, a prerequisite seems to be that the regulatory infrastructure is well developed and that measures are taken to reduce extreme volatility of stock prices in order to enable stock markets in the MENA region to spur economic growth. Appendix A. Sample description

Bahrain Egypt Iran Jordan Kuwait Lebanon Morocco Oman Saudi Arabia Tunisia Turkey

[1989–2003] [1981–2003] [1993–2003] [1979–2003] [1993–2003] [1995–2003] [1983–2003] [1989–2003] [1991–2003] [1987–2003] [1988–2003]

References Acemoglu, D., Zilibotti, F., 1997. Was Prometheus unbending by chance? Risk, diversification, and growth. J. Polit. Econ. 105, 709–775. Allen, F., Gale, D., 2000. Comparing Financial Systems. MIT Press, Cambridge. Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment. Rev. Econ. Stud. 58, 277–297. Arestis, P., Demetriades, P., Luintel, K.B., 2001. Financial development and economic growth: the role of stock markets. J. Money Credit Bank. 33, 16–41.

314

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

Atje, R., Jovanovic, B., 1993. Stock markets development. Eur. Econ. Rev. 37, 632–640. Barro, R., Sala-i-Martin, X., 1995. Economic Growth. McGraw-Hill, New York. Beck, T., Levine, R., 2004. Stock markets, banks and growth: panel evidence. J. Bank. Finance 28, 423– 442. Beck, T., Levine, R., Loayza, N., 1999. Finance and sources of growth. World Bank Policy Review Working Paper No. 2057. Bencivenga, V., Smith, B., 1993. Some consequences of credit rationing in an endogenous growth model. J. Econ. Dynam. Control 17, 97–122. Bencivenga, V., Smith, B., Starr, R., 1995. Transaction costs, technological choice, and endogenous growth. J. Econ. Theory 67, 53–117. Bhide, A., 1993. The hidden costs of stock market liquidity. J. Financ. Econ. 34, 1–51. Bolbol, A., Fatheldin, A., Omran, M., 2005. Financial development, structure, and economic growth: the case of Egypt, 1974–2002. Res. Int. Bus. Finance 19, 171–194. Boyd, J., Prescott, E., 1986. Financial intermediary-coalitions. J. Econ. Theory 3, 211–232. Bruno, M., Easterly, W., 1995. Inflation, crisis and long-run growth. NBER Working Paper No. 5209. Cho, Y.J., 1986. Inefficiencies from financial liberalization in the absence of well-functioning equity markets. J. Money Credit Bank. 18, 191–199. Christopoulos, D.R., Tsianos, E.G., 2004. Financial development and economic growth: evidence from panel unit root and cointegration test. J. Dev. Econ. 73, 55–74. Dawson, R.J., 2003. Financial development and growth in economies in transition. Appl. Econ. Lett. 10, 833– 836. Demirg¨uc¸-Kunt, A., Levine, R., 1996. Stock market development and financial intermediaries: stylized facts. World Bank Econ. Rev. 19, 291–322. Easterly, W., Rebelo, S., 1993. Fiscal policy and economic growth. J. Monet. Econ. 32, 417–458. Edwards, S., 1993. Openness, trade liberalization and growth in developing countries. J. Econ. Lit. 31, 1358–1393. Fischer, S., 1993. The role of macroeconomic factors in growth. J. Monet. Econ. 32, 485–512. Goldsmith, R.W., 1969. Financial Structure and Development. Yale University Press, New Haven. Greenwood, J., Jovanovic, B., 1990. Financial development, growth and the distribution of income. J. Polit. Econ. 98, 1076–1107. Gurley, J.G., Shaw, E.S., 1955. Financial aspects of economic development. Am. Econ. Rev. 45, 515–538. Harris, R.D.F., 1997. Stock markets and development: a re-assessement. Eur. Econ. Rev. 41, 139–146. Hondroyiannis, G., Lolos, S., Papapetrou, E., 2005. Financial markets and economic growth in Greece, 1986–1999. J. Int. Financ. Markets Inst. Money 15, 173–188. Ketteni, E., Mamuneas, T., Savvides, A., Sterigos, T., 2004. Financial development and economic growth: is the finance–growth relationship non-linear? Manuscript, Department of Economics, University of Cyprus. King, R.G., Levine, R., 1993. Finance and growth: Schumpeter might be right. Quart. J. Econ. 108, 717– 738. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 2000. Investor protection and corporate governance. J. Financ. Econ. 58, 3–29. Levine, R., 1991. Stock markets, growth and tax policy. J. Finance 46, 1445–1465. Levine, R., 1997. Financial development and economic growth: views and agenda. J. Econ. Lit. 35, 688–726. Levine, R., 1999. Law, finance, and economic growth. J. Financ. Intermed. 8, 36–67. Levine, R., 2002. Bank-based or market-based financial systems: which is better. J. Financ. Intermed. 11, 398–428. Levine, R., Zervos, S., 1998. Stock markets, banks and economic growth. Am. Econ. Rev. 88, 537–558. Levine, R., Loayza, N., Beck, T., 2000. Financial intermediation and growth: causality and causes. J. Monet. Econ. 46, 31–77. Pagano, M., 1993. Financial markets and growth: An overview. Eur. Econ. Rev. 37, 613–622. Ram, R., 1999. Financial development and economic growth: additional evidence. J. Dev. Stud. 35, 164–174. Rioja, F., Valev, N., 2004a. Does one size fit all? A re-examination of finance and growth relationship. J. Dev. Econ. 74, 429–447. Rioja, F., Valev, N., 2004b. Finance and the sources of growth at various stages of economic development. Econ. Inq. 42, 27–40. Rousseau, P.L., Wachtel, P., 2000. Equity markets and growth: cross country evidence on timing and outcomes. J. Bank. Finance 24, 1933–1957.

S.B. Naceur, S. Ghazouani / Research in International Business and Finance 21 (2007) 297–315

315

Singh, A., 1997. Financial liberalization, stock markets and economic development. Econ. J. 107, 771–782. Stiglitz, J., 1985. Credit markets and the control of capital. J. Money Credit Bank. 17, 133–152. Thangavely, S.M., Jiunn, A.B., 2004. Financial development and economic growth in Australia: an empirical analysis. Empirical Econ. 29, 247–260. Theil, M., 2001. Finance and economic growth: a review of theory and the available evidence. European Commission Economic Paper No. 158. Van Nieuwerburgh, S., Buelens, F., Cuyvers, L., 2006. Stock market development and economic growth in Belgium. Explor. Econ. Hist. 43, 13–38. Wachtel, P., 2003. How much do we really know about growth and finance? Econ. Rev. 88, 33–48.