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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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Even though financial development comprises development of the banking sector, stock markets, and other financial intermediaries, the literature is mainly concerned with banks.

We study the determinants of stock market development on the one hand and the determinants of banking sector development on the other, because, although they are related, some variables have been shown to be specific to stock market development and others are more relevant to the banking sector. For instance, capital account liberalization and shareholder rights protection are more important for stock market development, and reserve requirements and creditor rights protection for banking sector development.

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We use panel data techniques to study the main determinants of banking sector development in Africa. The estimation sample comprises 53 African countries and covers the recent period 1990 to 2006. Based on the theoretical and empirical literature, we estimate the following

equation:

yi,t   0 yi,t 1  1M i,t   2 Li,t  3 I i,t  i   i,t, where M i,t is a vector of macroeconomic variables, including GDP per capita, inflation volatility, and domestic savings and investment; Li,t is a vector of liberalization variables including reserve requirements, trade openness, and financial openness; and I i,t is a vector of institutional variables, including creditor rights and political risk rating. 7 The Dependent Variable We use two alternative measures of bank development—credit to the private sector relative to GDP and bank assets relative to total banking system assets—to enhance the robustness of our results. 8 Credit to the private sector by deposit money banks and other financial intermediaries relative to GDP is used because the measure isolates credit issued to the private sector, as distinct from credit issued to governments, government agencies, and public enterprises. Bank assets relative to GDP measures the degree to which commercial banks rather than central banks allocate savings because banks are more likely to identify profitable investments, monitor managers, and manage risk.

Explanatory Variables

Table 1a (Appendix) summarizes the explanatory variables used in the banking sector regressions. Two lags of endogenous variables are used to address the reverse causality problem and any endogeneity biases. As mentioned, we first introduce the lagged values of income levels and inflation volatility to control for income and macroeconomic stability. We also control for openness by introducing a measure of trade openness. We use the creditor rights index and the political risk rating to proxy institutions.

Financial repression

Like Bonis and Stachinni (2009), we use bank reserve requirements to proxy financial repression. Reserve requirements, which include bank free reserves, represent the ratio of domestic currency holdings and deposits with the monetary authorities to claims on other governments, nonfinancial public enterprises, the private sector, and other financial institutions. 9 This measure, which is more relevant for banking sector development, follows the literature on financial repression, which stresses the negative effects on financial deepening of credit controls, interest rate ceilings, and high reserve requirements. Therefore, we expect the sign of reserve requirements to be negative.

All variables are in logarithm, as in Levine (2002), which makes it possible to interpret the estimated coefficients as elasticities.

Bank credit to the private sector is measured using the following deflation method:

{(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is bank credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI.

They are measured by the ratio of bank liquid reserves to bank assets.

Protection of creditor rights

We also use the creditor rights index developed by Djankov, McLiesh, and Schleifer (2005).

This index ranges from 0 (weak creditor rights) to 4 (strong) and is constructed from 1978 to

2003. The measure aims at assessing collateral and bankruptcy laws, following La Porta et al. (1997). The use of this variable is another way to examine the impact of institutional quality on financial development because it refers to the country’s performance in providing a good environment for secure financial transactions.

Income level

We test the effect that income has on financial intermediation and stock market development using lagged values of real GDP per capita. In fact, high incomes are usually accompanied by better education, better property rights, and all good business environment, all of which support financial development. High income could also explain the enforcement of legal rights and the quality of accounting standards, which are important for stock market development (La Porta et al., 1997).

Macroeconomic environment Lagged values of savings or investments are used because financial intermediaries and stock markets allocate savings to investment projects. Therefore, we expect these variables to be positive determinants of banking sector development. 10 Macroeconomic stability Macroeconomic volatility as well as bank willingness to lend can restrain participation in the market. Because profitability is likely to diminish in an unstable environment with frequent changes in policies, we expect lagged values of inflation, inflation volatility to have a negative impact on financial market development. 11 We also consider the impact of lagged values of real interest rates.





Financial openness

Previous studies have found financial openness to be an important determinant of financial development. It helps alleviate the negative effects of financial repression by both reducing The correlation of savings and investments with incomes is low in our sample.

Inflation affects the severity of the frictions that arise in the process of allocating credit and capital. For instance, by lowering real rates, high levels of inflation exacerbate the adverse selection problem on the credit market, leading to more credit rationing (to prevent lenders from misrepresenting their types and misallocating funds) and to less funds for investment and growth (Khan, Senhadji and Smith, 2006).

the cost of capital and increasing its availability. Financial openness can also improve the financial infrastructure by weeding out inefficient institutions. We use the index of capital account liberalization developed by Chin and Ito (2006) as a measure of financial openness.

Trade openness

Following Rajan and Zingales (2003b), we include lagged value of the sum of imports and exports relative to GDP as a measure of trade openness. Trade openness can be linked to financial development because openness may influence the demand for external finance through specialization and sectoral structure or technology transfer and innovation and can therefore require more use of external finance. Since openness may be associated with higher risks (external shocks and foreign competition), financial markets that can diversify risks and cushion adverse shocks and cash flow problems can evolve with trade activities (Svaleryd and Vlachos, 2002 ; Chinn and Ito, 2006). 12 Capital flows We test the effects on financial development of financial flows, such as foreign direct investments and remittances. Aggarwal, Demirguc-Kunt, and Martinez Peria (2006) found that worker remittances are a significant determinant of financial development.

Political and legal institutions This paper measures institutional quality using political risk, a composite index from the International Country Risk Guide (ICRG). 13 The composite political risk index is a 100 point scale. Higher rating (maximum, theoretically, is 100) indicate lower risk, and vice versa. 14 The advantage of the ICRG indicators is that they have been used extensively in the literature (see Knack and Keifer, 1995; and Yartey, 2007) and have distinct categories of political risk that allow researchers to be reasonably specific in what they are measuring. 15 Because goodquality institutions are expected to foster financial development, we expect their values to be Financial development can also influence the development of trade, because better-developed financial markets will tend to have a comparative advantage in manufacturing (Beck, 2002, 2003).

The ICRG Risk Rating System assigns a numerical value to a predetermined range of risk components, according to a weighted scale, for each country covered by the system. Each scale is designed to award the highest value to the lowest risk and the lowest value to the highest risk. All countries are assessed on the same basis to allow for comparability.

The definition of the components of the institutional index is from the International Country Risk Guide. See The International Country Risk Guide for a detailed explanation and computation of the various components of our index of institutional quality.

Erb et al. (1996b) and Diamonte et al. (1996) find that changes in political risk are related contemporaneously to stock returns, using quantitative indicators that proxy for the notion of political risk.

positively correlated with financial development. 16 The problem with the political risk indicator is that it gives us very little guidance on the aspects of institutions that policy should be directed to. 17 To avert this problem, the paper studies the impact of four of the political risk components on stock market development: law and order, bureaucratic quality, democratic accountability, and corruption.

Estimating Method

Given the nature of our dataset this paper uses panel data techniques for estimating the regression models. All the relationships studied can be characterized by the joint endogeneity of most of the variables involved. That is, most explanatory variables in our model are either simultaneously determined with the dependent variable or have a two-way causal relationship with it. We recognize that there may also be unobserved country-specific effects, and ignoring them may produce inconsistent estimates given that they are likely to be correlated with the explanatory variables. If there is correlation between the right-hand-side variables and the country-specific effect, estimation methods like ordinary least squares will not be consistent because the assumption of strict exogeneity of the explanatory variables is violated. Also the orthogonality condition between the error term and the regressors is not likely to be met for either the generalized least squares (GLS) or the fixed effects estimator to produce consistent estimates. Though it can be met through appropriate differencing of the data, because the equation contains endogenous regressors as well as the effects of lagged endogenous variables, the error term in the differenced equation is correlated with the lagged dependent variable through contemporaneous error terms. Under these conditions, neither the fixed effect nor the GLS estimator will produce consistent estimates.

Arellano and Bond (1991) propose using a dynamic panel data estimator based on the GMM methodology that optimally exploits the linear moment restrictions implied by the dynamic panel model. The dynamic GMM estimator is an instrumental variable estimator that uses as instruments both lagged values of all endogenous regressors and lagged and current values of all strictly exogenous regressors. Equations can be estimated using the levels or the first differences of the variables. For the difference estimator, the variables are measured as first differences and their lagged values are used as appropriate instruments. 18 Because the other variables relating to law and finance (such as legal origin) and endowment (such as geography, religion, etc.) and information sharing (such as private and public registries) are both invariant in time and are cross- sectional, we do not include them, under the assumption that they are captured by the fixed effects introduced in the panel regressions and because some of their impacts can be captured by the institutional variables that we introduce in the model. Moreover, they are differenced away with the first differenced models and therefore cannot be identified.

The four measures are chosen because of their importance in past studies. Yartey (2007a) found that law and order, democratic accountability, and bureaucratic quality are important for stock market development in Africa.

Arellano and Bond proposed two estimators, one-step and two–step, with the two-step being the optimal estimator. The two estimators will be asymptotically equivalent if the error terms are spherical. There is a tendency for the two-step estimator to underestimate the standard errors of estimates and hence provide a false (continued…) However, before proceeding with the GMM, the following identifying assumption is necessary.

 We assume that there is no second-order serial correlation in the first differences of the error term. The consistency of the GMM estimator requires that this condition be satisfied. Given the construction of the instruments as lagged variables the presence of second-order serial correlation will render such instruments invalid.

 The specification tests for the GMM estimator are the Sargan test of over- identifying restrictions and the test of lack of residual serial correlation. The Sargan test, which is based on the sample analog of the moment conditions used in the estimation process, evaluates the validity of the set of instruments and therefore determines the validity of the assumptions of predeterminacy, endogeneity, and exogeneity. Since in this case the residuals examined are those of the regressions in differences, first-order serial correlation is expected by construction and thus only second and higher order serial correlation is a sign of misspecification.



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