One of the greatest challenges in this study was the availability of data for the SADC member countries. To efficiently model the effect that the stock market has on a country’s growth one would ideally need to have both a long time-series as well as sufficient cross- sections of data. However, the SADC region presents difficulties in that many of the region’s exchanges were only recently established, and offer very little time-series data (Allen and Ndikumana, 2000:140). In what follows, section 4.1 presents the data used for the stock market development and economic growth nexus. Section 4.2 discusses the empirical method, that is, the econometric methodology, while section 4.3 presents concluding remarks.
The primary data source for this study is the World Bank’s World Development Indicators. By being able to draw data from a single source we overcome the consistency and measurement problems associated with Levine and Zervos’s (1998) use of two different data source. Also, we include several measures of stock market development (as opposed to a single composite measure used by Levine and Zervos). This more disaggregated approach is also recommended by Demirguc-Kunt and Levine (1996). Nine out of the ten SADC countries with stock exchanges have the relevant data. As such, data was obtained for the following countries: Botswana, Mauritius, Malawi, Namibia, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe. Mozambique was excluded from the regression sample due to a lack of data. The analysis covers the period from 1980 to 2011. This was grouped into eight data points based on averages of the following sub periods: 1980-1983, 1984-1987, 1988-1991, 1992-1995, 1996-1999, 2000-2003, 2004-2007 and 2008-2011. All the sub period have four observations, which were in turn averaged to obtain the data for each sub period. However, because many of these exchanges were only recently established, many gaps were found within the data. Where less than four observations exist for a period, the available observations were averaged to obtain the data for the period. For instance, in a country if two observations were found for a sub period say 1992-1995, the average of the two is used for that period. But if only one observation is available, the only observation is used for the period. Thus, following this process we obtained a panel data of four data points for each country.
This study will focus on the stock market development and growth nexus, using the real GDP growth rate as a proxy for economic growth and stock market capitalization as a percentage of GDP as a proxy for stock market development. The other variables used in the model are described as follows:
GDP Growth Rate (GDP)
GDP growth rate is the annual percentage growth rate of GDP at market prices based on constant local currency. It is used as the dependent variable as a proxy for economic growth. It is expected that as the stock market develops it will have a positive effect on economic growth (Mohtadi and Agarwal, 2000:9). It is expected that as the economy grows, it will produce a positive effect on the size of the stock market and as such it is also expected that the stock market will also grow (Naceur et al., 2007:482). In addition, the growth of an economy should create new demand for financial services while also offering a better business environment, thus prompting the growth of the stock market and hence a positive effect on stock market development is expected (Yartey, 2008:15).
Market Capitalization Ratio (MCR)
This is calculated by summing the value of all listed shares and dividing by GDP. It is assumed that the overall market size is positively correlated with the ability to mobilize capital and diversify risk on an economy-wide basis (Agarwal, 2000:50). The MCR is used as an explanatory variable. Here it is expected to have a positive effect on economic growth.
When analyzing stock market liquidity, two different indicators were used. These are:
Total Value of Shares Traded Ratio (VTR): This variable equals the value of shares traded on the exchange divided by GDP (Mohtadi and Agarwal, 2000:6). This ratio is used to measure the value of equity transactions relative to the size of an economy, and as such should positively reflect liquidity on an economy-wide basis. The total value traded ratio complements the market capitalization ratio: although a market may be large, there may be little trading. We expect a positive relationship between the values of shares traded ratio and economic growth.
Turnover Ratio (TR): This variable equals the value of total shares traded divided by market capitalization. That is to say it measures the value of equity transactions relative to the size of the stock market (Mohtadi and Agarwal, 2000:7). Although the turnover ratio is a measure of liquidity, high turnover may also indicate the presence of low transaction costs. The turnover ratio complements the market capitalization ratio. A large but inactive market will have a large market capitalization ratio but a small turnover ratio. Turnover also complements the total value traded ratio. While the total value traded ratio captures trading relative to the size of the economy, turnover measures trading relative to the size of the stock market. A small liquid market will have a high turnover ratio but a small total value traded ratio. As such liquid stock markets allow investors to change their financial positions relatively quickly and cheaply, while also facilitating investment projects and making them less risky (Levine, 1991:1447). Hence we expect the turnover ratio to have a positive influence in the model.
Inflation refers to the persistent rise in the general price level of goods and services in an economy over a certain period of time. In an effort to take into account any macroeconomic instability and its effects on both economic growth and stock market development, the change in price level is used as an explanatory variable. It is expected that the higher the inflation variable, the less incentive investors or companies would have in investing in the stock market, and thus negatively affecting economic growth (Garcia and Liu, 1999:43). However, it has been noted that stock markets also provide some form of a hedge against inflation and hence one may expect a positive relationship between stock market development and inflation (Yartey, 2008:16). Therefore, it is not impossible to expect stock markets in countries with volatile macroeconomic conditions to also have volatile equity indexes and hence market capitalization (Garcia and Liu, 1999:43).
Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Gross capital formation as a percentage of GDP is used in this study as a proxy for the investment rate, and it is used as an explanatory variable because it is believed that the investment rate plays an important role in economic growth (Agarwal, 2000:51). Furthermore, since stock markets are in retrospect financial intermediaries that intermediate savings to investment projects, it is logical to expect that the investment rate will be an important determinant of stock market development. As such, we expect the investment rate to have a positive impact in the model (Yartey, 2008:16).
Financial Intermediary Development
As both banks and stock markets intermediate savings towards investment, they can either be seen as substitutes or complements. Numerous authors including that of Boyd and Smith (1996) and Demirguc-Kunt and Levine (1996) have addressed this issue, with the vast majority concluding that they are generally seen as complements rather than substitutes and grow simultaneously (Garcia and Liu, 1999:40). In this study we use two indicators of financial intermediary development. The first one is the private credit by deposit money banks to GDP, or simply the credit to the private sector. It refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable that establish a claim for repayment. It has been empirically shown by Demirguc-Kunt and Levine (1996) that stock market development and bank development are positively correlated. Secondly, liquid liabilities to GDP are used to show the effect the financial sector has on both growth and stock market development. Liquid liabilities are also known as M3. Liquid liabilities consist of demand deposits and interest bearing liabilities and non-bank financial intermediaries, and have been shown to have a positive impact on both economic growth and stock market development, thus we expect the same relationship to prevail (Garcia and Liu, 1999:41).
4.2 EMPIRICAL METHOD
A panel data approach is employed in this study that covers nine countries within the SADC region. Specifically, the study uses a fixed effects panel data model. The fixed effects approach is relatively simple and is defined according to the following regression:
Yit = αi + βXit + ε it, i = 1,… ,N; t = 1,… ,Ti ………………..…… (4.1)
Where Yit is the dependent variable, Xit is the vector of k explanatory variables, and αi, i =1… N, are the constant coefficients that are specific to individual countries. Therefore, it is assumed that their presence allows for differences across the countries under study via any variations in these constant terms. These individual country coefficients are estimated together with the vector of slope coefficients ‘β’ (Naceur et al., 2007:483).
Hsiao (1986) also suggested that in the scenario whereby there are some individual attributes which have been omitted and which are correlated with the other variables in the model, the fixed effects model produced unbiased estimates. As such, a panel data approach generally offers greater flexibility in the specification of the model rather than other single cross- country regressions, and hence will reduce misspecification of the model. Next we look at the model separately.
This model examines the stock market development and economic growth nexus directly. In this analysis, three regressions were estimated under different specifications in terms of their weightings. These are as follows:
Yit = αi + β1LMCRit+ β2INFit + β3LGCFit + β4LPCit + β5LLLit + β6GDPit-1 + ε it ………….. (4.2)
Yit = αi + β1LVTRit+ β2INFit + β3LGCFit + β4LPCit + β5LLLit + β6GDPit-1 + ε it ………….. (4.3)
Yit = αi + β1LTRit+ β2INFit + β3LGCFit + β4LPCit + β5LLLit + β6GDPit-1 + ε it ………….. (4.4)
Where Y is the real per capita GDP growth rate set as a proxy for economic growth, LMCR is the market capitalization ratio, LVTR is the total value of shares traded ratio, and LTR is the turnover ratio. These ratios were run individually in the regressions, while also including other control variables that may have an impact on economic growth. Specifically, the control variables included are: the rate of inflation change (INF), the log of gross capital formation (LGCF) which was used as a proxy for the investment rate, two indicators of financial intermediary development (LPC and LLL), and while also including the lag of GDP. Furthermore, these regressions were estimated under both cross-section weights and period weights to allow for country specific heteroskedasticity and period heteroskedasticity (Eviews manual, 2008:543).
This chapter discusses the data and the empirical model used for the analysis. The analysis cover the period 1980-2011 which was further divided into eight sub periods: 1980-1983,1984-1987, 1988-1991,1992-1995,1996-1999, 2000-2003, 2004-2007,2008-2011. The average of each sub periods was then obtained in order to obtain the data point for each period. The chapter further specifies the model and describes the econometric techniques used for the estimation, namely a fixed effects panel data approach. The model focuses on the relationship between stock market development and growth.
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