Thus, this study allowed individual intercepts and slope coefficients across countries. This study includes a group of 16 low-income countries 15 low-income African countries and one non-African low-income country classified by the World Bank. The selection of the countries and the study time period is restricted to the availability of the data.
To measure the development of the financial sector in an economy, a number of indicators have been proposed in the literature. Likewise, studies have also used stock market development as the indicator of financial sector development. Thus, the selection of variables is related to the countries financial system, depending on whether the country features a financial system oriented on banks or on the stock market.
Since the stock market in low-income countries is underdeveloped, an indicator of stock market development for financial development is not used in this study. Following Beck et al. The private credit stands for the value of credits by banks to the private sector divided by GDP. They argued that private credit isolates credit issued to the private sector, as opposed to credit issued to governments, government agencies and public enterprises.
Credit to the private sector has also been used by a number of studies as a proxy for financial development Beck et al. Renminbi's rise, financial system regulation, and boosting GDP by empowering women. This study has further used macroeconomic variables: trade openness as import plus export to GDP, gross fixed capital formation as a percentage of GDP, labour force defined as the percentage of the economically active population ages 15 and older to the total population, and inflation in terms of consumer price index to control the finance-growth relationship.
The trade openness measured by imports plus exports to GDP presents the actual status of economic activities within a country. Trade grants a country access to the advancements in technological knowledge of its trade partners.
Yanikkaya argued that trade grants developing countries access to investment and intermediate goods that are vital to their development processes. Similarly, inflation, on the other hand, does not only affect growth, but it also affects the financial activities of the country by affecting the interest rates, which has a direct effect on deposit collection and mobilization activities of banking and financial institutions.
Likewise, capital and labour are the two important pillars of every theory of economic development. These variables have also been used extensively in the literature to control the finance-growth relationship [see: inflation Beck et al. Table 1 presents the descriptive statistics of the variables used in this study.
Notes: This table presents a year average of all the variables used in this study. Table 1 shows that variation does not exist on a large scale amongst the sample countries. It indicates that Tanzania has the largest economy amongst sample countries. However, in the case of development of the banking sector in terms of the flow of credit to the private sector, Nepal stands significantly at the top. The countries below the average are Madagascar It shows that Chad has the lowest average credit to the private sector as a ratio of GDP with 3.
This indicates the urgency in the formulation of policies that enhance the flow of credit to the private sector.
It indicates that economy of Togo is not benefited from international trade. However, Burundi has the smallest volume of international trade throughout the study period. Similarly, data show that average gross capital formation as a percentage of GDP is lowest for Guinea-Bissau 9.
The gross fixed capital formation is highest for Tanzania The labour force defined as the percentage of the economically active population ages 15 and older to the total population is also highest for Tanzania. The data analysis procedures involve a total of following four steps: checking the level of integration of the variables, testing for the long-run cointegrating relationship amongst the variables, estimating long-run cointegrating parameters and finally testing for the short run causality between financial development and economic growth. Since this study seeks to analyze the long-run cointegrating relationship between financial development and economic growth, it is important to verify that all the variables are integrated at least of order one in level.
It is because most of the cointegration techniques in panel data require variables to be integrated at least of order one 3 i. Therefore, it is required to apply panel unit root test on the variables before going for further analysis. The literature shows that Im et al. However, these tests for a unit root in panel data do not address the issue of cross-sectional dependence, though they allow for individual unit root process in a panel Pesaran, So, it is important to test for cross-sectional dependence before performing the first-generation unit root tests like Im et al.
To this end, this study used a second-generation panel unit root test, cross-sectional augmented IPS CIPS test, by Pesaran to address the cross-sectional dependence. Once the order of integration is determined within the variables, the next step is to perform the cointegration test amongst financial development, economic growth, and control variables. Taking into consideration the panel data and the time period of the study, this study used panel cointegration test by Pedroni This test provides seven test statistics; first four are known as panel cointegration statistics and that are within-dimension statistics: the panel v-statistic, panel rho-statistic, panel PP-statistic nonparametric , panel ADF-statistic parametric ; and the last three are known as group mean panel cointegrating statistics and that are between-dimension statistics: group rho-statistic, group PP-statistic nonparametric and group ADF-statistic parametric.
This test tests the null hypothesis of no cointegration. This section also provides an overview of the econometric techniques used in this study to analyze the finance-growth relationship in a dynamic panel of 16 low-income countries.baffrivacorme.cf
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The extant literature shows that there are a number of techniques that can be used to estimate the relationship between variables in a dynamic panel. Raising the issue of simultaneity and endogeneity, Beck et al. However, Christopoulos and Tsionas argued that it is doubtful whether the GMM system generates a structural long-run relationship or a spurious one, since this technique does not take integration and cointegration phenomenon into the account.
Similarly, another technique of panel data analysis includes Instrumental Variables IV approach, especially to account endogeneity and serial correlation in the error term. The fixed effect and random effect on an OLS setting are also highly used estimation techniques in a panel data-set. However, these estimation techniques do not also take integration and cointegration phenomenon into the account, which is critical issues in a time series panel.
The literature also argued that endogeneity and heterogeneity are the major issues that should be taken care of while estimating the long-run coefficients in a dynamic panel see Beck et al. Therefore, it is important to apply panel data analysis techniques that address the issues of integration and cointegration properties of the data along with endogeneity and heterogeneity amongst the variables and countries, respectively. Once the panel estimates of long-run parameters are calculated, this study further applied FMOLS to estimate the long-run estimates across the countries for the robustness of the result.
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