Port Econ J DOI 10.1007/s10258-016-0123-8 O R I G I N A L A RT I C L E

Financial market development, global financial crisis and economic growth: evidence from developing nations Rubi Ahmad 1 & Oyebola Fatima Etudaiye-Muhtar 1,2 & Bolaji Tunde Matemilola 3 & Amin Noordin Bany-Ariffin 3

Received: 6 August 2015 / Accepted: 30 August 2016 # ISEG 2016

Abstract Emerging and frontier markets in Africa have witnessed various economic and financial reforms aimed at integrating the domestic markets into the global financial market to attract investment. Whether these reforms promote high economic growth remains inconclusive. The paper applies the pooled mean group estimation technique to empirically re-investigate the link between financial market development, global financial crisis, and economic growth in selected African economies. The results strongly support our hypotheses that stock market and banking sector development promotes economic growth in the selected countries. Moreover, financial crisis reduce the positive effects of both the stock market and banking sector developments on economic growth. The study suggests that both the banking sector and stock market are important to deliver the long-run economic growth that the African region desired. Moreover, effort should be made to enact policy measures that would ensure development of the stock market which has received inadequate attention. Keywords Financial development . Financial crisis . Dynamic heterogeneous panel . Growth . Africa JEL classification G10 . O11 . O16

* Bolaji Tunde Matemilola [email protected]

1

Department of Finance and Banking, University of Malaya, 50603 Kuala Lumpur, Malaysia

2

Department of Accounting and Finance, University of Ilorin, Ilorin, Nigeria

3

Faculty of Economics and Management, Universiti Putra Malaysia, 43400 Serdang, Malaysia

R. Ahmad et al.

1 Introduction African economies have faced challenges in economic growth. One reason for this is an inadequate and inefficient financial system (Allen et al. 2014). The financial markets in the continent have been characterised by illiquidity, constraints in financing for firms, and other systemic imperfections (Allen et al. 2011). Since the mid 1980s, to promote economic growth, some African programmes aimed at developing financial markets. This was done to open the markets to international capital inflows and world market integration for economic growth to take place (Hassan et al. 2011). Stock market reforms include relaxing restrictions on foreign investments, enhancing investor protection laws and corporate governance, shifting from a manual trading system in the stock exchange to an automated trading system, and cross listing of several companies on the various exchanges. Similarly, the banking sector reforms include strengthening commercial banks’ capital bases, improving risk- and corporategovernance practices, and adopting technology-based banking such as mobile and internet-based banking. Despite these actions, the level of development of these markets is still not commensurate with other developing and emerging markets, and the region has the lowest indicators of financial depth in the world (Allen et al. 2011; Andrianaivo and Yartey 2010; Beck et al. 2011). Integration of these markets with the global market made economies vulnerable to the contagion effect of the 2007–2008 global financial crisis through its impact on the stock market and banking sector (Boorman and Christensen 2010; Brambila‐Macias and Massa 2010; Murinde 2009). Earlier studies use descriptive statistics to show that the value of market indicators fall, and regional economic growth decline after the crisis, but an empirical investigation to determine the extent of the effect of the crisis on economic growth through its impact on the markets was overlooked. In line with the identified empirical gap, this study examines the extent of the effects of the 2007–2008 global financial crises on economic growth through its impact on financial market development (stock market and banks) in a focused study of nine (9) African economies. These countries are classified as emerging and frontier economies by Morgan Stanley Capital International (MSCI) 2013 indices. 1 The focused country research is motivated by two main studies: 1) Fan et al. (2011) assert that a focused country study controls data quality and enables in-depth analysis of the effect of major factors on various issues better than a cross-country study. 2) Narayan et al. (2011) show that countries in the same area share a more homogenous set of financial system development indicators. This implies that the analysed countries have similar levels of development. Our results indicate that banking sector development has a significant positive effect on economic growth. Likewise, stock market development has a significant positive effect on economic growth, and lends support to the finance-growth theory. Moreover, financial crisis reduces the positive effect of stock market and banking sector developments on economic growth. Our study makes two contributions to the literature. Firstly, it addresses the dearth of studies emanating from the African continent by focusing on the effects of financial 1

These countries are Botswana, Egypt, Ghana, Kenya, Mauritius, Morocco, Nigeria, South Africa, and Tunisia. Three of these countries are in the emerging market category.

Global financial crisis and economic growth

crisis on the financial development-growth relationship, targeting emerging/frontier economies. This is important because, while previous studies (Allen and Giovannetti 2011; Aryeetey and Ackah 2011; Beck et al. 2011; Boorman and Christensen 2010; Mlachila et al. 2013) identified the channels through which the crisis was transmitted to economic growth, they did not produce empirical evidence to indicate the extent of the effect of the crisis on growth as its impact on the markets. Secondly, we apply the Pooled Mean Group (PMG) estimation technique that account for heterogeneity across countries overlooked by some prior studies when investigating financial development and growth relationship. Unlike fixed or random effects models or generalized method of moments (GMM) that require all slopes to be identical across countries, PMG allows the intercepts, short-run coefficients and error variances to be different across countries but constraints the long-run coefficients to be identical (Pesaran et al. 1999). These desirable features of PMG are consistent with the intuition from economic growth theory. For example, within the Solow (1956) growth model, there is an assumption that all countries have access to same technology, therefore long-run production function parameters would be identical; but the speed of adjustment to the steady state growth may not be identical. The rest of the paper is structured as follows. Section 2 briefly reviews existing theoretical and empirical literature on financial development, financial crisis, and economic growth. Section 3 describes the data and estimation methods employed for the study. Section 4 discusses the results of the empirical analysis. Section 5 concludes and offers relevant policy implications based on the findings of the study.

2 Theoretical and empirical literature on financial development, financial crisis, and economic growth 2.1 Financial development and economic growth There has been considerable debate in literature over the role played by financial development in the growth of the economy of countries. From extant literature, three strands of research have been identified: the “supply-led” group, the “demand-led” group, and the “feedback” group. Firstly, the supply-led (also known as “financegrowth” nexus) argues that growth in the financial system leads to growth in the economy because the existence of the financial sector makes possible the channeling of resources from the surplus units to the deficit units for productive purposes. This is achieved through a reduction in information asymmetries that improves efficiency in resource allocation. It is further argued that this process leads to growth in other sectors of the economy thus enhancing economic growth (Schumpeter 1912; Levine 1997). The second school of thought, which is the “growth-finance” or “demand-following” group, holds the view that the growth in the economy affects development in the financial system. They argue that as the economy grows and becomes more sophisticated, the financial system must respond to the changes and demands of the economy in terms of requirements for financial intermediation. Thus, the financial system simply responds to developments in the actual sector (Robinson 1952). The third group in this line of research argues that financial system development and economic growth complement each other and are referred to as the “feedback” group. This suggests a

R. Ahmad et al.

two way causal relationship such that in a country with a well-developed financial system, technological changes and product and service innovation enhance economic growth. This pressures the financial system to respond to the ever-changing needs of the economy thus stimulating higher economic performance. Brasoveanu et al. (2008) and Luintel and Khan (1999) argue that this relationship could lead to feedback causality. Following the finance-growth theory, studies have tried to establish the channels through which financial system affects growth. Two channels identified by Ang (2008) and Levine (1997) are the quantitative and qualitative channels. The quantitative channel measures how the financial system accumulates and re-allocates capital to productive investments, translates into growth and directly affects investment. The qualitative channel shows how the financial system enhances growth through providing efficient credit facilities and financial services, thereby indirectly affecting the level of investment. Turning to empirical studies, Pereira (2008) notes that financial development promotes economic growth because it reduces financing constraints when financial development is at early stages. Djalilov and Piesse (2011) present empirical evidence supporting the above assertion of finance–growth nexus. They examined the pre- and post-independence financial and economic data (1992 to 2008) of twenty-seven countries in Central Asia to identify the impact of economic and financial development on economic growth. The study found that the financial system is an effective determinant of growth even though the effect is not sustained. Employing three variables (finance, financial spread, and bank credit to the private sector as ratio of gross domestic product [GDP]) to represent the financial sector and a combination of regression, correlation, and Granger causality for analysis, they show that the impact of the financial system on growth differed among the countries studied due to institutional features present in each country. Hassan et al. (2011) also investigated the role of financial development in accounting for economic growth in low- and middle-income countries for the period 1980 to 2007. Using panel data regression and variance decomposition of annual GDP per capita for a sample of one hundred and sixty eight countries, they found a positive relationship between financial development and economic growth in developing countries unlike that obtained in developed countries. The study used six variables as proxies for financial development: 1) domestic credit provided by the banking sector expressed as percentage of GDP, 2) domestic credit to the private sector as a percentage of GDP, 3) the ratio of liquid liabilities (M3) to GDP, 4) gross domestic savings to GDP, 5) trade to GDP, and 6) government final consumption expenditure to GDP. The findings suggest that the level of development of the country is important when investigating the relationship between financial development and economic growth. Focusing on the roles of banks and stock markets in ameliorating information asymmetry and reducing transaction costs, Beck and Levine (2004) investigated the impact of stock markets and banks on economic growth for the period 1976 to 1998. The study employed a dynamic panel regression model on forty developed and developing countries. The findings from the study showed that stock market development and banking sector development have a positive impact on economic growth. Variables used as proxies for the financial markets were the stock market turnover ratio and bank credit to the private sector.

Global financial crisis and economic growth

Akisik (2013) investigated the relationship between accounting regulation, financial development, and economic growth in a sample of fifty-one developed and emerging countries for the period 1997 to 2009. Using the generalised method of moment estimation technique, the study provided evidence of a strong relationship between accounting regulation and economic growth through an indirect and direct impact on financial development. The study established a positive relationship between economic growth and financial development (stock market and banking sector variables), the sign of the relationship changed to negative when financial development variables interacted with accounting regulation. They attributed this to the countries’ high level of regulation of accounting practice. Zhang et al. (2012) examined the link between financial development and economic growth using the generalised methods of moment’s estimates for dynamic panel data of 286 Chinese cities with city level data for the period 2001 to 2006. The result of the study shows a positive relationship between financial development and economic growth after controlling for other factors that affect growth. They argue that this was a result of the various financial sector reforms undertaken by China when it became a member of the World Trade Organisation in 2001. The result of Zhang et al. (2012) is in contrast to that of Liang and Teng (2006) who investigated the relationship between financial development and economic growth in China for the period 1952 to 2001. The result of Liang and Teng (2006) supports the growth-finance theory. Similarly, Bojanic (2012) investigated the relationship between financial development and trade on economic growth in the Bolivian economy over a seventy-year period, 1940 to 2010. He found the existence of a positive long run relationship between financial development and economic growth. The direction of causality was found to be uni-directional in nature running from financial development to growth. This implies that financial development in Bolivia is a determinant of economic growth. In a single country study, Wolde-Rufael (2009) re-examined the causal relationship between financial development and economic growth in Kenya for the period 1966 to 2005. The study found a bi-directional causality between financial development and economic growth in Kenya, thus supporting the ‘feed-back’ hypothesis: finance and growth complement each other. No evidence supported the ‘supply-led’ and ‘demand-following’ hypothesis. This is similar to the result obtained in Brasoveanu et al. (2008) and Hondroyiannis et al. (2005) for Romania and Greece, respectively. In all of the literature reviewed above with the exception of single country studies, both developed and developing countries were combined in the dataset either as crosssectional data or panel data. The problem with this according to Narayan et al. (2013) is that such studies may lead to biased results because the results may be driven by the developed countries’ data. Thus, generalising the findings to all the countries in the panel may be misleading. Unlike prior studies, we apply the pooled mean group estimation technique to re-investigate the link between financial market development, global financial crisis, and economic growth in selected African economies. 2.2 Financial development, financial crisis, and economic growth According to Doman and Doman (2013) the recent financial crisis further strengthen the attempt to explain the mechanism of the dependencies in global financial market. Previous studies identified that the impact of financial crisis on African economies is

R. Ahmad et al.

felt through two main channels: financial system channels and trade channels (Allen and Giovannetti 2011; Aryeetey and Ackah 2011; Hussain et al. 1999). Noting that the impact of the crisis was felt more through the trade channel than the financial channel, Allen and Giovannetti (2011) argue that this is because of the low level of financial development in Africa which protected the countries from the direct impact of the crisis. On the other hand, Aryeetey and Ackah (2011) single out capital markets and the banking sector as direct channels through which the crisis had an impact on African economies. They argue that this is a result of opening the markets/integrating the markets with international financial markets. Although, the literature reviewed in the previous section examined the relationship between financial development and economic growth, none of the studies account for the effect of financial crisis on economic growth through its impact on financial market indicators. Fukuda (2012), recognising that the financial crisis may have an impact on the relationship between financial development and growth, examined the causality between financial development, economic growth, and financial crisis in five Asian economies for the period 1982 to 2007. The study found that finance has a positive effect on growth, but that further financial development can lead to financial crisis with a negative effect on economic growth. They attributed this to the bilateral causality between finance and financial crisis. Similar to what was obtained in Fukuda (2012), an earlier study by Loayza and Ranciere (2006) found that both volatility and crisis aspects of financial development are important determinants of growth. In a sample of seventy-five countries for the period 1960 to 2000, financial crisis had a negative effect on economic growth in the short run. They argued that as the economies develop, financial development weakens the system as shown by episodes of systemic banking crisis and overall financial volatility. These factors (banking crisis and financial volatility) result in the negative relationship with growth.

3 Methodology 3.1 Data This study investigates the long-run relationship between financial development, global financial crisis, and economic growth in a focused panel study of nine emerging and frontier economies in Africa for the period 1987 to 2012. This ensures an annual observation of 26 years for each country. Data were obtained from World Development Indicators. Table 1 summarizes the variables, source, and definition. Saci and Holden (2008) assert that the process of determining the variables to use to proxy financial development is difficult. Referring to the literature, they also note that, though correlated and viewed as being inadequate when used individually, variables present a bigger picture when used collectively as measures of financial development. Levine (2005) also points out that no uniformly accepted proxy was used to represent financial development. Consequently and following Beck and Levine (2004), we used three separate variables to proxy financial market development: 1) stock market capitalization, 2) stock market turnover ratio, and 3) bank credit. The development in the stock market is

Global financial crisis and economic growth Table 1 Definition and source of variables Variable name

Source

Definition

Market Capitalization (MC)

World Development Indicators, World Bank

The value of listed shares as a share of GDP

Turnover Ratio (STO)

World Development Indicators, World Bank

The value of traded shares on domestic exchange divided by the value of listed shares

Bank Credit (BC)

World Development Indicators, World Bank

The total domestic credit to the private sector by deposit money banks as a share of GDP

Inflation (INF)

World Development Indicators, World Bank

The annual inflation rate

Growth (GDPPCGR)

World Development Indicators, World Bank

The annual growth rate of real GDP per capita

Trade Openness (TO)

World Development Indicators, World Bank (WDI)

The total value of exports and imports as a share of GDP

Financial Crisis

A dummy variable for which crisis period has a value of 1, and 0 for non-crisis period.

Investment (INV)

World Development Indicators

Gross Investments (% of GDP)

Government Consumption Expenditure (GE)

World Development Indicators

General government final consumption expenditure (% of GDP)

Real Interest Rate (RIR)

World Development Indicators

Real interest rate (annual %)

proxied by the stock market capitalization (calculated as the value of the listed shares divided by GDP) and stock market turnover ratio (calculated as the value of traded shares on the domestic exchange divided by the value of listed shares). The stock market capitalization is an indicator that represents the size of the market; the turnover ratio indicates the level of liquidity in the market. Higher levels of both indicators suggest higher levels of stock market development. Bank credit (calculated as the ratio of domestic credit by banks to the private sector to GDP) represents banking sector development. According to Beck and Levine (2004), bank credit is preferred to other indicators of banking sector development such as the ratio of liquid liabilities (M3) to GDP because it isolates bank credit to the private sector and therefore excludes credits by development banks and loans to the government and public companies. We use a dummy variable as proxy for financial crisis to capture the effect of the 2007–2008 financial crises on economic growth. A dummy variable equal to one if financial crisis occurs, and zero if there is no financial crisis. Year 2008 is chosen as the crisis year because the impact of the crisis, which emanated from the United States of America, was not immediately felt in Africa (Boorman and Christensen 2010). Economic growth is proxied by the log of annual growth rate of real GDP per capita. We control for other determinants of economic growth such as inflation and trade openness in the regression model following Saci and Holden (2008).

R. Ahmad et al.

3.2 Estimation In order to estimate the regression model, we apply the pooled mean group (PMG) estimation technique proposed by Pesaran et al. (1999) which is considered suitable for the analysis of dynamic panels. This is because it accommodates the long run equilibrium and the heterogeneous dynamic adjustment process. PMG estimation solves heterogeneity bias common in traditional panel fixed and random effects estimations. All traditional panel models have a basic assumption that at least some of the parameters are the same across the panel. For large time periods, Pesaran et al. (1999) shows that the traditional panel technique including panel generalized method of moment can produce inconsistent results, and a misleading estimate of the average values of the parameters in dynamic panel data model, except if the slope coefficients are truly identical. The pooled mean group allows the intercepts, short-run coefficients and error variances to differ across countries, but it constraints the long-run coefficients to be similar across countries (Das 2011). PMG is developed for a dynamic panel data model where the time period is greater than the cross-sectional units, and it estimates the model as a system based on a combination of pooling and averaging of the variable coefficients (Asteriou 2009). Our baseline specification expresses annual growth rate of real GDP per capita (GDPPCGR) as a function of its traditional determinants as stated below: GDPPCGRit ¼ β1 þ β 2 M C it þ β 3 FCDUM it þ β 4 M C*FCDUM it þ β5 BC it

ð1aÞ

þ β6 BC*FCDUM it þ β7 IN F it þ β8 T Oit þ β 9 INV it þ β10 RIRit þ β11 GEit þ μit

GDPPCGRit ¼ β 1 þ β2 ST Oit þ β3 FCDU M it þ β 4 ST O*FCDUM it þ β5 BC it þ β6 BC*FCDU M it þ β 7 IN F it þ β8 T Oit þ β 9 INV it þ β10 RIRit þ β11 GE it þ μit

ð1bÞ

where MC is stock market capitalization, STO is stock turnover, BC is bank credit, INF is inflation, TO is trade openness, INV is investment, RIR is real interest rate, and GE is government final consumption expenditure. Table 1 gives the exact definition of the variables. Adopting from Law and Bany-Ariffin (2008) and Pesaran et al. (1999) the autoregressive distributed lag (ARDL) model’s unrestricted specification for the dependent variable GDPPCGR (y) is 0

Δyit ¼ φ i yi:t 1 þ β i X i;t−1 þ

p−1 X

i ¼ 1; 2…N; t ¼ 1; 2…:T

j¼1

λi j Δyi; t− j þ

q−1 X j¼0

0

γ i j ΔX i;t− j þ μi þ εit

ð2Þ

where yit (annual growth rate of real GDP per capital) is a scalar dependent variable, Xit is the k x 1 vector of explanatory variables (stated in equation 1) for the group i, μi

Global financial crisis and economic growth

represent the fixed effects, φi is a scalar coefficient on the lagged dependent variable, βi’s is the k x 1 vector of coefficients on explanatory variables, λij’s are scalar coefficients on lagged first differences of dependent variables, and γij’s are k x 1 coefficient vectors on first-difference of explanatory variables and their lagged values. This study assumes that the error terms ɛit’s are independently distributed across i and t, with zero means and variances σ 2 i > 0. In addition, assuming that φi < 0 for all i, therefore there exists a long-run relationship between yi t and X i t: 0

yit ¼ θi X it þ ηit i ¼ 1; 2…:N; t ¼ 1; 2…:T

ð3Þ

where, θ′i = β′i/φi is the k x 1 vector of the long-run coefficients, and ηit’s are stationary with possibly non-zero means (this includes fixed effects). Eq. (2) can be rewritten as: Δyit ¼ φ i ηi;t −1 þ

p−1 X j¼1

λi j Δyi; t− j þ

q−1 X j¼0

0

γ i j ΔX i;t− j þ μi þ εit

ð4Þ

Where ηi,t −1, is the error correction term given by (3), hence ϕi is the error correction coefficient that measures the speed of adjustment towards the long-run equilibrium. The PMG estimator proposed by Pesaran et al. (1999) restricts the longrun coefficients to be the same over the cross-section, but allows the short-run coefficients and error variances to be different across groups; that is, θi = θ for all i. The hypothesis of homogeneity of the long-run policy parameters cannot be assumed a priori and we tested it empirically in all specifications by a Hausman-type test (Hausman 1978). The pooled maximum likelihood estimation is used in computing the group-specific short-run coefficients and the common long-run coefficients. The estimators are represented as: . . . XN XN N ; β N ; λ N; j ¼ 1::::::; p−1 φ ¼ β ¼ λ j i j PMG PM G i¼1 i i¼1 i i¼1 . ð5Þ XN N; j ¼ 0; :::::; q−1: ¼ γ i¼1

ϕPMG ¼ γ j PMG

XN

ij

Given that the time period (T) is larger than the number of countries (N) in the panel data for this study and that the PMG estimation technique better accounts for heterogeneity across countries, we consider it (PMG) to be appropriate for this study. The PMG also allows the intercepts, short-run coefficients and error correction mechanism to differ across the countries, but restricts the long-run to be the same across the countries (Das 2011).

4 Results Before estimating (1), it is necessary to determine the order of integration of the variables used in the analysis by using some panel unit root tests. We employ some widely used first generation panel unit root test (Baltagi 2005). In order to test for the presence of unit roots in the panel data series included in our study, we apply the panel

R. Ahmad et al.

unit root test proposed by Levine et al. (2002), (hereafter LLC), Im et al. (2003), (hereafter IPS), and Maddala and Wu (1999), (hereafter MW). In LLC, IPS, and MW, the null hypothesis is non-stationary. However, LLC stated the null hypothesis that each individual time series has a unit root against the restrictive alternative hypothesis that each individual time-series is stationary. Conversely, IPS allows heterogeneity and their null hypothesis states that each series in the panel contains a unit root, but the alternative hypothesis maintains that each series is stationary. The Maddala and Wu (1999) test has the added advantage because it is valid for individual Augmented Dickey-fuller Test (ADF) with different lag lengths. Table 2 reports outcomes of the panel unit root test and it shows that the null hypothesis of a unit root cannot be rejected for most variables in levels, except inflation. However, this hypothesis is rejected when the series is in first differences (see Table 3). These results clearly indicate that the variables in levels are non-stationary while all the variables are stationary in first differences. In other words, all the variables (except inflation) are integrated of order one. Therefore, we proceed to the pooled mean group estimation. Tables 4 and 5 reports estimates for two separate proxies of stock market development (market capitalization and stock market turnover), using the pooled mean group (PMG) estimator that imposes common long-run effects. These tables’ present estimates of the long-run coefficients, the adjustment coefficient, and the joint Hausman test statistics. The lag order is first chosen for each country on the unrestricted model with lag one for the long-run independent variables (Pesaran et al. 1999). The adjustment coefficients (-0.763 and -0.752) for models 1 and 2 has the expected sign and they are significant at the one percent level. Similarly, the adjustment coefficients (-0.801 and -0.787) for models 3 and 4, respectively, carry the expected sign and they are significant at the one percent level. These results show that there is an adjustment

Table 2 Panel unit root tests (Levels) Series

LLC

IPS

Maddala-Wu

Real GDP growth rate (RGDPGR)

–0.705 (0.054)

–0.099 (0.718)

14.932 (0.705)

Market Capitalization (MC)

–1.580 (0.067)

–1.149 (0.125)

21.481 (0.255)

Stock Market Turnover (STO)

–1.717 (0.103)

–0.589 (0.312)

11.235 (0.361)

0.596 (0.724)

0.533 (0.703)

17.974 (0.454)

Inflation (INF)

–1.714 (0.135)

–1.200 (0.421)

24.838 (0.759)

Trade Openness (OP)

–1.137 (0.178)

–1.519 (0.164)

18.940 (0.989)

Investment (INV)

–1.997 (0.107)

–1.859 (0.106)

28.892 (0.059)

Government Final Consumption Expenditure (GE)

–0.633 (0.264)

–1.885 (0.143)

15.031 (0.178)

Bank Credit (BC)

Real Interest Rate (RIR)

–32.571 (0.000)* –28.219 (0.000)* 68.477 (0.000)*

Notes: a Probability values are reported in parentheses. b The maximum lag order considered to perform the tests was 3. c * Indicate the rejection of the null hypothesis. The standard IPS tests are distributed as N (0, 1) and the Maddala-Wu Fisher type test is distributed as X2 with 2 N degrees of freedom. All the tests are conducted using Eviews 7.0 with a time trend included in the specification. The critical values for the Maddala-Wu tests are from Eviews 7.0. For IPS tests, the one-tailed 5 % critical value is -1.64. Source: Authors’ calculations

Global financial crisis and economic growth Table 3 Panel unit root tests (first difference) Series ΔReal GDP per capital growth (ΔGDPCGR) ΔMarket Capitalization (ΔMC) ΔStock Market Turnover (ΔSTO) ΔBank Credit (ΔBC) ΔInflation (ΔINF) ΔTrade Openness (ΔTO) ΔInvestment (ΔINV) ΔGovernment Consumption Expenditure (GE) ΔReal Interest Rate (RIR)

LLC

IPS

Maddala-Wu

–8.544 (0.000)*

–14.095 (0.000)*

65.558 (0.000)*

–6.967 (0.000)* –8.525 (0.000)* –6.014 (0.000)* –8.552 (0.000)* –8.562 (0.000)* –11.078 (0.000)* –8.117 (0.000)*

–7.051 (0.000)* –8.332 (0.000)* –7.150 (0.000)* –11.543 (0.000)* –8.672 (0.000)* –3.200 (0.000)* –10.507 (0.000)*

82.395 (0.000)* 96.935 (0.000)* 83.912 (0.000)* 142.603 (0.000)* 103.301 (0.000)* 43.717 (0.000)* 116.854 (0.000)*

–80.654 (0.000)*

–10.733 (0.000)*

81.898 (0.000)*

Notes: Refer to the notes under Table 2

dynamics from short-run to long-run in growth equation across the sampled African countries. The joint Hausman test statistics fail to reject the null hypothesis, which

Table 4 Pooled Mean Group (PMG) estimation Model 1

Model 2

Long-run coefficients

Long-run coefficients

Market Capitalization (MC) Market Capitalization*FCDUM

0.034*** –0.044**

(4.29) (–2.85)

Financial Crisis Dummy (FCDUM)

–0.329

(–0.31)

– – –0.527

– – (–0.51)

Stock Market Turnover

–

–

0.023**

(2.17)

Stock Market Turnover * FCDUM

–

–

–0.049**

(–2.98)

Bank Credit

(2.30)

0.038**

(2.54)

Bank Credit * FCDUM

–0.038**

(–2.04)

–0.008**

(–2.13)

Inflation

–0.163***

(–4.71)

–0.105**

(–2.78)

0.073*** 0.031**

(3.24) (2.72)

Trade Openness Investment (INV) Government Expenditure (GE)

0.036***

0.064*** 0.091**

(3.08) (2.63)

0.294**

(2.08)

0.299**

(2.92)

Real Interest Rate (RIR)

–0.058**

(–2.65)

–0.140***

(–5.10)

Error correction adjustment

–0.763***

(–7.19)

–0.752***

(–7.36)

Hausman-test

0.690

(0.999)

1.410

(0.997)

Pesaran’s test of cross-dependence

1.490

(0.150)

1.177

(0.220)

Breusch-Pagan Heteroskedasticity

0.660

(0.418)

1.360

(0.243)

Wooldridge Serial Correlation Test

0.214

(0.560)

0.207

(0.151)

Notes: a The dependent variable is real GDP growth rate b Figures in parentheses are test statistics except for Hausman tests (for long-run homogeneity), Pesaran’s test of cross-sectional dependence, Breusch-Pagan Test, and Wooldridge Serial Correlation Test, which are p-values. c ** and *** indicate significance at the 5 and 1 % significance levels, respectively. N = 9, T = 26. Source: Authors’ calculations

R. Ahmad et al. Table 5 Pooled Mean Group (PMG) estimation (Robust test: excluding South Africa) Model 3

Model 4

Long-run coefficients

Long-run coefficients

0.025***

(2.70)

–

–

Market Capitalization*FCDUM

–0.046***

(–2.39)

–

–

Financial Crisis Dummy (FCDUM)

–0.260

(–0.65)

Market Capitalization (MC)

–0.497

(–0.10)

Stock Market Turnover

–

–

0.020**

(2.26)

Stock Market Turnover * FCDUM

–

–

–0.058**

(–2.11)

Bank Credit

0.038***

(2.42)

0.037***

(2.35)

Bank Credit * FCDUM

–0.007**

(–2.09)

–0.028**

(–2.44)

Inflation

–0.093***

(–4.71)

–0.102**

(–2.72)

Trade Openness

0.050***

(2.31)

0.061***

Investment (INV)

0.104**

(3.25)

0.085**

(2.43)

Government Expenditure (GE)

0.373***

(2.62)

0.496***

(2.61)

(2.85)

Real Interest Rate (RIR)

–0.127**

(–4.42)

–0.153***

(–5.38)

Error correction adjustment

–0.801***

(–6.41)

–0.787***

(–5.49)

Hausman-test

0.580

(0.869)

0.780

(0.978)

Pesaran’s test of cross-dependence

1.528

(0.115)

1.738

(0.102)

Breusch-Pagan Heteroskedasticity

0.566

(0.405)

1.7605

(0.132)

Wooldridge Serial Correlation Test

0.114

(0.453)

0.214

(0.122)

Notes: a Refer to the notes under Table 4. b ** and *** indicate significance at the 5 and 1 % significance levels, respectively

indicates that the data do not reject the restriction of common long-run coefficients across the sampled African countries. Therefore, the pooled mean group (PMG) estimation is appropriate to investigate the link between financial development and economic growth in this study. Although the mean group (MG) approach is less restrictive than the pooled mean group (PMG), PMG estimator is consistent and more efficient when the assumptions of common long-run coefficients are valid. The Hausman test confirms that the assumptions of common long-run coefficients are valid. We also conduct Pesaran’s test of cross-sectional dependence and the results (p-values are greater than 0.05) indicate absence of cross-sectional dependency problem. Breusch-Pagan Heteroskedasticity and Wooldridge Serial Correlation Tests indicate that there is no evidence of heteroskedasticity and serial correlation problems. The pvalues are greater than 5 %, and the null hypotheses that there is heteroskedasticity and serial correlation problems are rejected, respectively. Moreover, the study interact financial development proxies with the financial crisis dummy to indirectly examine the effects of the global financial crisis on the relationship between financial development and economic growth. The PMG results show that both market capitalization and stock market turnover are statistically significant and positively related to economic growth (see Tables 4 and 5). Similarly, bank credit is statistically significant and has a positive relationship with economic growth. Moreover, the interaction of financial development proxies and

Global financial crisis and economic growth

financial crisis dummy (i.e. Market Capitalization*FCDUM, Stock Market Turnover * FCDUM and Bank Credit * FCDUM) are statistically significant and negatively related to economic growth. These results suggest that financial crisis reduce the positive effect of stock market development on economic growth. Financial crisis also reduce the positive effects of banking sector development (bank credit) on economic growth. The findings are also robust when South Africa as possible outlier (more developed) sample is excluded (see Table 5). The findings provide support to the argument that financial market development is important for growth in the sampled African countries. Our results are consistent with Wolde-Rufael (2009) and Zhang et al. (2012) that find financial development is positively related to growth in Kenya and Bolivia respectively. Conversely, our results are inconsistent with Naceur and Ghazouani (2007) that find a negative relationship between financial development and economic growth in 10 MENA countries. The significant and positive relationship between stock market capitalization and economic growth is also consistent with the result of Narayan et al. (2013) for a regional panel of 12 African countries but inconsistent for bank sector development. As expected, the coefficient for inflation (as control variable) is significantly and negatively related to economic growth in all the models. Thus, the paper confirms negative effects of inflation on economic growth, in the long-run. Narayan et al. (2013) also report negative effects of inflation on economic growth. Moreover, investment, trade openness and government expenditure are positively related to economic growth which is consistent with previous findings in the literature. Our results suggest that countries that undertake investments and open up to international trade are able to stimulate economic growth. This is consistent with Loayza and Ranciere (2006) arguments that countries that are more open have a greater ability to catch up to leading technologies of the rest of the world which are used to increase output and stimulate economic growth.

5 Conclusion and policy recommendations Although earlier studies have investigated the finance-growth relationship for countries in Africa (individual and panel studies), there is a dearth of studies of the effect of the 2007– 2008 financial crisis on economic growth through its impact on financial market development. Our paper attempts to fill this gap in the literature by investigating the link between financial market development and economic growth for a panel of nine emerging and frontier markets in Africa after accounting for the recent global financial crisis. Our pooled mean group (PMG) results show a positive relationship between financial development proxies and economic growth. Specifically, stock market capitalization, stock market turnover, and banking sector credit have significant positive relationships with economic growth. Moreover, the financial crisis dummy reduces the effects of financial market development proxies on economic growth. The overall result supports the finance-growth link and it is consistent with prior findings in the literature. Our findings have important policy implications. Firstly, the coefficient of the bank credit variable is slightly higher compared with the coefficient of the stock market capitalization and stock market turnover variables. This implies that banking sector development appears to have more impact on economic growth in the sampled

R. Ahmad et al.

countries. Although, available limited capital used to finance profitable investment projects in these groups of countries mainly comes from the banking sector; the banking sector in most African countries fails to adequately exercise its role of intermediation due to very high interest rate spreads which make credit expensive. Our study suggests that the banking sector should broaden its financial intermediation role by reorganizing the banking system and putting in place innovative savings and borrowing instruments adapted to local needs. Banking sector that performs its financial intermediation role effectively is able to redistribute financial resources extending credits to the private sectors to fund profitable investment which stimulate economic growth. Secondly, both the banking sector and stock market are important to deliver the long-run economic growth that the African region desired. Investment climate in most African countries has not been conducive to broad based financial development and economic growth evidenced by levels of instability and resultant capital flights from both the money and stock markets. Hence, rather than focus on banking sector development only as it has been the practice over the years, effort should be made to enact policy measures that would ensure development of the stock market. The stock market if well-developed may be an important source of long-term capital needed to complement bank credit. Consequently, more resources would be available to carry out profitable investment project, which according to Solow (1956), is the main driver of long-run economic growth. These policy implications is in accordance with Moss et al. (2007) argument that financial market development is important to stimulate investments in Africa, and that there is a need to improve the equity markets through increased liquidity and size of African markets. Financial crisis reduce the positive effects of financial development on economic growth which suggest that policy makers need to respond quickly to financial crisis in future by mobilizing domestic resources and adopting appropriate monetary or fiscal policies measures. However, responding quickly to financial crisis will require functioning institutions which is lacking in most African countries, at the moment. Our paper has some limitations. The traditional panel unit root tests applied are unable to address possible structural break problem in the data. Additionally, the focus of this paper is not on possible reduction of the crisis effects on financial development and growth relationship, over the sample period after 2008. Nevertheless, studies that explore this area of research are welcome. Given that the impact of financial development on economic growth in the literature is either positive or negative, future research may investigate the threshold effects in the growth-finance relationship using African sample data. Another avenue for future research is to explore whether the effect of finance on growth is permanent or transitory.

References Akisik O (2013) Accounting regulation, financial development, and economic growth. Emerg Markets Finance Trade 49(1):33–67 Allen F, Giovannetti G (2011) The effects of the financial crisis on Sub-Saharan Africa. Rev Dev Fin 1(1):1– 27 Allen F, Otchere I, Senbet LW (2011) African financial systems: A review. Rev Dev Fin 1(2):79–113

Global financial crisis and economic growth Allen F, Carletti E, Cull R, Senbet L, Valenzuela P (2014) The African financial development and financial inclusion gaps. J Afr Econ 23(5):614–642 Andrianaivo M, Yartey CA (2010) Understanding the growth of African financial markets. Afr Dev Rev 22(3):394–418 Ang JB (2008) Survey of recent developments in the literature of finance and growth. J Econ Surv 22(3):536– 576 Aryeetey E, Ackah C (2011) The global financial crisis and African economies: impact and transmission channels. Afr Dev Rev 23(4):407–420 Asteriou O (2009) foreign aid and economic growth: new evidence from a panel data approach countries. J Policy Model 31(1):155–161 Baltagi BH (2005) Econometric analysis of panel data. Wiley, West Sussex Beck T, Levine R (2004) stock markets, banks and growth: panel evidence. J Bank Financ 28(3):423–442 Beck T, Maimbo SM, Faye I, Triki T (2011) Financing Africa: through the crisis and beyond. World Bank, Washington, DC Bojanic AN (2012) The impact of financial development and trade on the economic growth of Bolivia. J Appl Econ 15(1):51–70 Boorman J, Christensen B (2010) The impact of the global financial crisis on emerging and frontier markets in Africa. Glob J Emerg Mark Econ 2(1):69–90 Brambila‐Macias J, Massa I (2010) The global financial crisis and Sub‐Saharan Africa: the effects of slowing private capital inflows on growth. Afr Dev Rev 22(3):366–377 Brasoveanu LO, Dragota V, Catarama D, Semenescu A (2008) Correlations between capital market development and economic growth: the case of Romania. J App Quant Methods 3(1):64–75 Das A (2011) External resources and saving rates: a pooled mean group analysis for developing countries. J Econ Behav Stud 31(1):51–62 Djalilov K, Piesse J (2011) Financial development and growth in transition countries: a study of central Asia. Emerg Markets Fin Trade 47(6):4–23 Doman M, Doman R (2013) Dynamic linkages between stock markets: the effects of crisis and globalization. Port Econ J 12(2):87–112 Fan JPH, Wei KCJ, Xu X (2011) Corporate finance and governance in emerging markets: a selective review and an agenda for future research. J Corp Fin 17(2):207–214 Fukuda T (2012) Financial development, economic growth and financial crisis in Asian emerging economies. Res Appl Econ 4(2):1–22 Hassan MK, Sanchez B, Yu J-S (2011) Financial development and economic growth: new evidence from panel data. Quart Rev Econ Fin 51(1):88–104 Hausman JA (1978) Specification test in econometrics. Econometrica 46(6):1251–1271 Hondroyiannis G, Lolos S, Papapetrou E (2005) Financial markets and economic growth in Greece, 1986– 1999. J Int Financ Mark Inst Money 15(2):173–188 Hussain MN, Mlambo K, Oshikoya T (1999) Global financial crisis: an African perspective. Afr Dev Rev 11(2):199–232 Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115(1):53–74 Law SH, Bany-Ariffin AN (2008) institutional infrastructure and economic performance: dynamic panel data evidence. Transit Stud Rev 15(3):542–557 Levine R (1997) Financial development and economic growth: views and agenda. J Econ Lit 35(2):688–726 Levine R (2005) Finance and growth: theory and evidence. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier Science, The Netherlands, pp 865–934 Levine A, Lin C, Chu CJ (2002) Unit root test in panel data: asymptotic and finite-samples properties. J Econ 108(1):1–24 Liang Q, Teng J (2006) Financial development and economic growth: evidence from China. China Econ Rev 17(4):395–411 Loayza NV, Ranciere R (2006) Financial development, financial fragility, and growth. J Money Credit Bank 38(4):1051–1076 Luintel R, Khan M (1999) A quantitative re-assessment of the finance-growth nexus: evidence from a multivariate VAR. J Dev Econ 60(2):381–405 Maddala GS, Wu (1999) A comparative study of unit root test with panel data and a new simple test. Oxf Bull Econ Stat 61(S1):631–652 Mlachila M, Dykes D, Zajc S, Aithnard H, Beck T., Ncube M, Nelvin O (2013) Banking in Sub-Saharan Africa: challenges and opportunities. Luxembourg: European Investment Bank. Retrived from http://www.eib.org/attachments/efs/economic_report_banking_africa_en.pdfon 09/10/2014

R. Ahmad et al. Moss T, Ramachandran V, Standley S (2007) Why doesn't Africa get more equity investment? frontier markets, firm size and asset allocations of global emerging market funds. Working Paper 112, Center for Global Development, Washington, DC Murinde V (2009) Global financial crisis: implications for Africa’s financial system, paper prepared for the European Development Report (ERD) 2009 Conference on “Financial markets adverse shocks and policy responses in fragile countries”, 21-23 May. Accra, Ghana Naceur S, Ghazouani S (2007) Stock markets, banks and growth: empirical evidence from the MENA Region. Res Int Bus Financ 21(2):297–315 Narayan PK, Mishra S, Narayan S (2011) Do market capitalization and stocks traded converge? new global evidence. J Bank Financ 35(10):2771–2781 Narayan PK, Mishra S, Narayan S (2013) The short-run relationship between the financial system and economic growth: new evidence from regional panels. Int Rev Financ Anal 29:70–78 Pereira MCC (2008) The effects of households’ and firms’ borrowing constraints on economic growth. Port Econ J 7:1–16 Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc 94(446):621–634 Robinson J (1952) The generalisation of the general theory, in the rate of interest and other essays. J. Robinson London, Macmillan Saci K, Holden K (2008) Evidence on growth and financial development using principal components. Appl Financ Econ 18(19):1549–1560 Schumpeter J (1912) Theorie der Wirtschaftlichen, Entwicklung [The Theory of Economic Development], Leipzig: Dunker and Humblot; translated by redevers Opie. Harvard University Press, Cambridge Solow R (1956) A contribution to the theory of economic growth. Q J Econ 70:65–94 Wolde-Rufael Y (2009) Re-Examining the financial development and economic growth nexus in Kenya. Econ Model 26(6):1140–1146 Zhang J, Wang L, Wang S (2012) Financial development and economic growth: recent evidence from China. J Comp Econ 40(3):393–412