Recent studies (Allen and Ndikumana, 2000; Odhiambo, 2004; Gondo, 2009; and Sunde, 2011) on the relationship between finance and economic growth in the South African context have yielded mixed findings. Similarly, policies aimed at improving the performance of the financial sector should also be pursued as the findings show that there is complementarity between financial development and economic growth in the long term.
B ACKGROUND
Robert Lucas (1988) famously noted that economists "badly overstate" the role of finance by claiming that it leads to economic growth. To summarize these debates, the voluminous literature on the role of finance in economic growth reveals many empirical inconsistencies.
P ROBLEM S TATEMENT
The question of the direction of causality between economic growth and financial development, regardless of the financial development indices used, remains unanswered. The present study aims to study the relationship and determine the direction of causality between financial development and economic growth by using more proxies of finance to obtain more conclusive results.
R ESEARCH O BJECTIVES
The institutional quality variable that is responsible for the impact of freedom and democracy on financial development and economic growth after regime change in SA is considered in this study. The dummy variable is introduced to capture the impact of pertinent financial reforms in SA to promote economic growth (Adusei, 2012).
C ONTRIBUTION AND RELEVANCE OF THE STUDY
Studies that attempted to test the importance of institutional quality include Rodrik and Wacziarg (2005), Haber (2008), Hasan et al. Last but not least Moyo et al. 2014) notes that implementing reforms in the financial sector is beneficial because it stimulates innovation within the financial sector while promoting efficiency, leading to higher economic growth.
O UTLINE OF THE STUDY
This chapter also presents the problem statement, research objectives, and the contribution and relevance of the study. The second chapter presents an analysis of the South African financial sector, its historical development and performance.
I NTRODUCTION
A N A NALYSIS OF THE S OUTH A FRICAN FINANCIAL SECTOR
15 while defending the value of the Rand as well as controlling inflation (Akinboade and Makina, 2006; Akinboade and Kinfack, 2015). Figures 2.2.1 and 2.2.2 below illustrate the development in the indicators for the financial sector from 1965-2013 and the annual growth rate in SA.
C ONCLUSION
He argues that the sector's performance should be linked to the regulatory regime to help improve its allocative efficiency and performance in light of the announced changes to the regulatory framework.
I NTRODUCTION
T HEORETICAL U NDERPINNING
Another strand of literature concerns the threshold effect of financial development on economic growth, which makes the relationship between finance and growth somewhat ambiguous (Bose and Cothren, 1996; Deidda and Fattouh, 2002). According to this argument, there is a threshold level of financial development that is associated with an increase in economic growth.
E MPIRICAL L ITERATURE
Introduction
The third school of thought maintains that a "feedback" bidirectional causal relationship exists between financial development and economic growth. The different views on the relationship between financial development and economic growth reveal significant differences of opinion.
Country-Specific Evidence
Odhiambo (2004) examined whether financial development drives economic growth in South Africa using annual time series data for the period 1960–2000. The study found evidence of bidirectional causality between financial sector development and economic growth in South Africa. Wolde-Rufael (2009) examined the causal relationship between financial development and economic growth in Kenya using annual time series data for the period 1966 to 2005.
Cross-Country Evidence
Ghirmay (2004) used the VAR approach to investigate the causal relationship between economic growth and the level of financial development in 13 sub-Saharan African countries. The results of the estimates do not reveal a long-term relationship between financial development and economic growth. Mhadhbi (2014) empirically examined the causal relationship between financial development and economic growth in 27 middle-income countries, including SA.
T IME SERIES VERSUS CROSS - SECTIONAL OR PANEL ANALYSIS
He found bidirectional causality between economic growth and finance even for countries with a well-established financial sector like SA. Demetriades and Hussein (1996) further argue that causality theory is rooted in time series analysis and thus it is difficult to infer causality other than a simultaneous correlation between economic growth and financial development. Another issue that makes cross-sectional study estimates invalid is the assumption that institutions and other important characteristics that determine economic growth are similar (homogeneous) across countries and that the marginal responses of economic growth to each index of financial intermediation are also constant; this assumption is not true.
C ONCLUSION
36 On the other hand, Masih, Al-Elg and Madani (2009) argue that although time series methods are considered an improvement on cross-sectional methods, their main weakness is the use of error correction or variance decomposition techniques, which are mainly based on the cointegrating vector estimation, which lacks theoretical support.
I NTRODUCTION
D ATA D ESCRIPTION AND SOURCES
C HOICE OF THE FINANCIAL DEVELOPMENT PROXIES AND CONTROL VARIABLES
M ODEL S PECIFICATION
The ratio of gross fixed capital formation as a share of GDP, public expenditure as a share of GDP, also known as public expenditure on final consumption, and trade openness as a share of total trade in GDP. Among the conditional variable, the stock of capital, embedded and approximated by the ratio of investment in fixed assets to GDP after decomposing the above equation (4). The variables are as previously explained in Table 4.2.1, and all are expressed in the natural logarithm (ln) to maintain variance stationarity (Chang, 2002; Masih, Al-Elg, & Madani, 2009), except for the institutional quality variable ( Q ) and a dummy variable for.
M ODEL V ARIABLES AND E XPECTATIONS
From an investment perspective, if the financial sector is healthy, all activities related to investment will strengthen the growth of the economy, and the relationship with economic growth is therefore expected to be positive. Pereira and Teles (2011) also argue that the longer the reign of a democratic regime, the greater the economic growth. If the sign of a reform dummy in the financial sector is positive and significant, it implies that reforms of the financial sector have contributed positively to economic growth in SA.
M ETHOD OF ANALYSIS
- Test of stationarity
- Optimal Lag length selection
- Cointegration
- Vector Error Correction Model (VECM)
- Granger Causality Test
VECM seeks to show the speed of adjustment back to long-run equilibrium after a short-run shock without loss of long-run information (Jalil and Ma, 2008). If the coefficient of the index of institutional quality is positive and significant, it means that the transition of SA from apartheid to democracy has had a positive impact on the level of economic growth of the country. Finally, to determine the direction of causality between the variables, pairwise Granger causality tests will be conducted between financial development indicators and real GDP.
I NTRODUCTION
S UMMARY OF DESCRIPTIVE S TATISTICS AND C ORRELATIONS
S TATIONARITY
The results of the ADF, PP and KPSS tests showed that not all variables are stationary at the level at all levels of significance. The results of the unit roots show that the variables are stationary in first differences, which means that they maintain stationarity after differentiation. Based on the fact that all variables are integrated in the same order, I (1), cointegration tests were performed using Johansen's procedure.
C OINTEGRATION T EST R ESULTS
50 completed; this involves using the trail and maximum eigenvalue tests to determine the number of cointegrating relationships or the number of cointegrating ranks in order to verify whether there is a long-run relationship between non-stationary variables. Therefore, cointegration warrants the construction of a VECM to model the long-run and short-run relationship between the variables.
V ECTOR E RROR C ORRECTION M ODEL R ESULTS
Tests for Robustness
- Serial Correlation test
- Heteroscedasticity
- Normality test
- Stability of the VECM model
The above results indicate the absence of serial correlation considering that the observed p-squared value is 0.4515. This value implies that 45.15% is more than 5% and therefore the null hypothesis of serial correlation is rejected. From the table above, the p-value of 0.3933 implies that 39.33% is more than 5%, and thus the model does not suffer from heteroscedasticity.
S HORT R UN A NALYSIS
M ULTIVARIATE OLS M ODEL
56 The signs of the coefficients are positive, with the exception of the coefficients for domestic credit to the private sector as a percentage of GDP (lnDp) and trade openness, which are negative and statistically significant. The signs of the coefficients of the ratios of financial indicators, such as the ratio between broad money supply and GDP, the ratio between liquid liabilities of banks and GDP, and the ratio between domestic credits of the financial sector and GDP, are positive and highly statistically significant. Government spending has a positive and significant impact on economic growth, which is in line with a priori expectations.
G RANGER C AUSALITY BETWEEN FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH
Real GDP does not determine the ratio of the broad money supply as a percentage of GDP. The ratio of banks' liquid liabilities to GDP is not Granger Cause real GDP. Real GDP does not determine the ratio of banks' liquid liabilities as a percentage of GDP.
C ONCLUSION
The relationship between finance and growth exists and that finance only responds to developments in the real sector of the economy. The results show that government expenditure (lnG) causes Granger economic growth (real GDP), while on the other hand there is unidirectional causality from institutional quality to real GDP. The general conclusion of the research on the direction of causality between economic growth and finance is that there is bidirectional causality.
C ONCLUSION
P OLICY R ECOMMENDATIONS
Financial Development and Economic Growth: A Literature Review and Empirical Evidence from Sub-Saharan African Countries: South African Journal of Economic and Management Sciences, 12(1), pp.11-27. Financial development and economic growth in Mainland China: A note on testing the demand- or supply-following hypothesis. Financial Development and Economic Growth in Sub-Saharan African Countries: Evidence from Time Series Analysis.
Causality Between Financial Development and Economic Growth: Using Vector Error Correction and Variance Decomposition Methods in Saudi Arabia. A New Proxy for Financial Development and Economic Growth in Middle-Income Countries: A Bootstrap Panel Granger Causality Analysis.
L AG O RDER S ELECTION AND S UMMARY S TATISTICS
T ABLE 5.4.1: C OINTEGRATION T EST R ESULTS
Sportest indicates 1 cointegrating eqn(s) at the 0.05 level * indicates rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values. Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * indicates rejection of the hypothesis at the 0.05 level.
T ABLE 5.5.1: VECM T ESTS R ESULTS
T ABLE 5.6.1: W ALD TEST FOR S HORT R UN A NALYSIS
T ABLE 5.8.1 M ULTIVARIATE OLS M ODEL R ESULTS
