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«WP/09/182 Understanding the Growth of African Financial Markets Mihasonirina Andrianaivo and Charles Amo Yartey © 2009 International Monetary Fund ...»

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Blundell and Bond (1998) argue that lagged levels of variables are likely to be weak instruments for current differenced variables when the series are close to random walk. In these conditions, the differenced GMM estimates are likely to be biased and inefficient. They suggest the more efficient system GMM estimator that combines the difference equation and a levels equation in which suitably lagged differenced variables are the appropriate instruments. The system GMM is consistent and more efficient than the difference estimator so long as there is no significant correlation between the differenced regressors and country fixed effects. The efficiency gains of the system GMM estimator will depend how close the series is to a random walk (see appendix for details). 19

B. Modelling the Determinants of Stock Market Development

Cadeleron-Rossell has developed a behavioral structural model of stock market development in which economic growth and stock market liquidity are considered the main determinants of stock market development. To examine the validity of the model, he used annual observations from 1980–87 from 42 of the main active stock markets in the world and found that stock market liquidity and economic growth are indeed important determinants of stock sense of precision under some circumstances. The usual practice is to estimate with the two-step estimator but base hypothesis tests on the one-step estimator’s statistics.

The System GMM estimation of Blundell and Bond (1998) requires the series of y i,1, y i, 2..... y i,T to be mean

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market growth. Yartey (2008) modified the Calderon-Rossell model to incorporate other financial, economic, and institutional variables that might affect stock market development.

Following Yartey (2008), we estimate the following baseline equation:

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where M i,t is a vector of macroeconomic variables, including GDP per capita, real interest rate, and domestic savings and investment; Fi,t is a vector of financial variables including stock market liquidity, private credit, and financial openness; and I i,t is a vector of institutional variables, including political risk rating. 20 The sample consists of 17 African countries that had stock market activities between 1990 and 2006.

The Dependent Variable

The dependent variable, stock market capitalization, represents the deflated value of listed shares relative to GDP. 21 The assumption is that overall market size is positively correlated with the ability to mobilize capital and diversify risk economy-wide. Since market capitalization is measured at the end of the year and GDP is measured over the year, the measure has a stock flow problem. To solve it, we use the average of two consecutive yearend market capitalizations to estimate the mid-year value.

Explanatory Variables

Table 1b (Appendix) summarizes the explanatory variables used for the stock market regressions. They include measures of macroeconomic environment with lagged gross domestic savings as a percentage of GDP and lagged values of real interest rates. In further estimations, real interest rate is replaced by lagged inflation and budget deficits as a percentage of GDP. We include political risk as a measure of the institutional environment. 22 Two lags of endogenous variables are used to address the reverse causality problem and any endogeneity biases.

Note that unit root tests such as Levin et al. (2002) and Im et al. (2003) show that most variables are I(0), therefore it does not allow us to undertake panel cointegration. For variables which are I(1), results vary sometimes according to the method used, therefore it does not help us to be certain about the non stationarity of the variables, to be able to use their first differences in the model.

The stock market capitalization variable is the ratio of the value of listed shares to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is stock market capitalization, P_e is end-of period CPI, and P_a is average annual CPI.

Explanation and justification of variables are given in the section on modeling banking sector development.

Stock market liquidity

Liquidity is the ease and speed at which economic agents can buy and sell securities. More liquid markets channel more savings and also encourage investment in long-term projects that potentially have higher yields. Therefore, using lagged values of total value traded as a percentage of GDP, we expect liquidity to be positively correlated with the measure of stock market development.

Banking sector development

Yartey (2008b) demonstrated that well-developed financial intermediaries promote development of the stock market, but the relationship is not linear in emerging market countries. We therefore include a measure of banking sector development as an explanatory variable, using deposit money bank credit to the private sector as a percentage of GDP.

We improve on previous studies by taken into account endogeneity, not only by running fixed-effect estimations on lagged values of endogenous variables but also by implementing GMM estimation that corrects this endogeneity using instrumental variables estimations. We use lagged values of explanatory variables as instruments in the GMM specification (see appendix for further details).





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The first empirical analysis we conduct is to examine the determinants of banking sector development in Africa. In the first set of regressions, we use credit to the private sector relative to GDP as the dependent variable. The results are presented in Table 2. Model 1 is the baseline model, which has GDP per capita, reserve requirements, trade openness, creditor rights, standard deviation of inflation, and political risk as the explanatory variables. The analysis shows that political risks, creditor rights protection, and trade openness are positively associated with banking sector development, and reserve requirements and inflation volatility are negatively associated with it. Economic instability negatively affects banking sector growth because inflation volatility reduces bank activities and assets. The negative impact of financial repression on banking sector development confirms the financial liberalization thesis that restrictions on interest rates and mandated credit allocation inhibit the growth of the banking sector.

An analysis on the impact of current inflation on development of the banking system found a positive and significant effect. We attempt to understand this puzzle by removing episodes of very high inflation (outliers) from the regression in Model 2 and find that current inflation is now negative and statistically significant. 23 In Model 4, we examine the impact of capital account liberalization on banking sector development using the Chinn-Ito index and find that capital account liberalization negatively affects banking sector development.

The result of Model 1 found trade openness to be positive and statistically significant in explaining banking sector development. There is an argument, the simultaneous openness hypothesis (Baltagi, Demestriades and Law, 2008), that argues that the simultaneous opening of trade and financial accounts is the key to successful financial development. Testing this hypothesis in Models 3 and 5, we find that trade openness negatively affects bank credit when reserve requirements are high. The analysis also shows that simultaneous opening of trade and capital account tends to promote banking sector development. Overall, the results show that financial openness accompanied by trade openness is likely to promote financial development.

In Model 6, we examine the impact of law and order on banking sector development. The result is in line with the view (see, for instance, La Porta et al., 1997) that sound legal systems promote financial development. We also interact financial openness with law and order to understand the earlier results that capital account liberalization negatively affects banking sector development. Here we find this variable to be significant, indicating that capital account liberalization positively influences banking sector development only in countries with good legal systems. Private capital flows, such as inward remittances, are positively associated with banking sector development (Model 7). 24 To check the robustness of our results, we use alternative measure of banking sector development, namely commercial bank assets relative to total assets of the banking system.

The result (see Table 3) is similar to that obtained using private credit relative to GDP as the dependent variable.

B. Determinants of Stock Market Development

Stock market development is measured by market capitalization as a percentage of GDP.

Table 4 presents the results of the fixed effects estimation. Model 1 is our baseline regression model, which includes variables such as GDP per capita, bank credit, stock market liquidity, real interest rate, and gross domestic savings. We find that bank credit, stock market Further analysis is needed to understand the relationship between inflation and financial market development.

Threshold regressions would help uncover the nature of relationship between inflation and financial development.

We examine the impact of some of the components of political risks on banking sector development. We also find law and order and government stability to be important in explaining it.

liquidity, gross domestic savings, and GDP per capita are significant and have positive effects on stock market development. Real interest rate has a negative effect but it is not statistically significant. Income level is an important determinant of stock market development—a 1 percentage point increase in GDP per capita speeds stock market development by 0.2 percentage points.

We also find that banks and stock markets are complements rather than substitutes, confirming earlier empirical findings: a 1 percentage point increase in bank credit as a percentage of GDP increases stock market development by 0.5 percentage points. Our result is different from Yartey’s finding (2008a) of a non-monotonic relationship between banking sector and stock market development. 25 His view is that at the early stages of its development, the banking sector is a complement to the stock market in financing investment, but as they both develop, the two begin to compete with each other as vehicles for financing investment.

Stock market liquidity and domestic savings relative to GDP are also important for stock market development in Africa. When value traded as a percentage of GDP increases by 1 percentage point, stock market capitalization increases by 0.2 percentage points. Finally, a 1 percentage point increase in gross domestic savings as a percentage of GDP increases stock market development by 0.2 percentage points.

To examine the effect of inflation on stock market development we use last year’s inflation instead of the real interest rate in Model 2. GDP per capita, bank credit, gross domestic savings, and stock market value traded are all significant and positive. Inflation has an unexpected positive sign but is statistically significant. To control for this surprising result, we remove inflation swings larger than 20 percent (Model 3) and find that inflation levels do not significantly affect stock market capitalization. 26 When we replace inflation with budget deficits as a percentage of GDP, we find no conclusive evidence of any impact on stock market development.

Model 4 investigates the effect of capital account liberalization on stock market development. Capital account liberalization is measured using the Chinn-Ito index of financial openness. We find that financial openness has a negative effect on stock market development. In line with the current thinking on the subject, we examine whether the effect of capital account liberalization would change in countries with higher income or a better institutional quality. We do this by interacting financial openness with income level and later with institutional quality. The analysis (see Models 5 and 7) shows that capital account Yartey’s 2008a result was based on a sample of emerging market countries with relatively higher levels of stock market development.

Inflation volatility has no significant impact on stock market development. We later use inflation greater than 100 and find that it is not statistically significant in explaining stock market development.

liberalization has a negative impact generally, but, in countries with sufficiently high income and low political risk, capital account liberalization has a positive effect on stock market development.

In Model 6, we look at the impact of political risk on stock market development in Africa. It appears to be positive and significant. A 1 percentage point improvement in the ICRG political risk rating increases stock market development by 0.9 percentage points. This result suggests that resolution of political risk is important. Bank credit, domestic savings and stock market liquidity are also all positive and significant. The problem with the concept of political risk is that it tells us very little about where to direct institutional policy. To remedy this deficiency, we study the impact of some of the components of the index of political risk on stock market development. Our results show that law and order and government stability are the most important for Africa. 27 One interesting result of the paper is that capital account liberalization negatively affects stock market development except in countries with high income and better institutional quality. We investigate whether there are threshold levels of real GDP per capita and political risk below which the effect of capital account liberalization on stock market capitalization is negative. Further analysis suggests that the marginal effect of capital account liberalization on stock market capitalization is positive if real GDP per capita is above 7.2 in logarithm corresponding to roughly US$1,300.



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