Financial repression takes the form of such financial distortions as interest rates controls (ceilings), reserve requirements and directed credit. McKinnon (1993, pp.11) defines financial repression as:
“When governments tax (through reserve requirements) and otherwise distort their domestic capital markets (through interest controls and directed credit), the economy is said to be financially repressed”.
Another argument is that a high degree of financial repression is associated with high inflation or seigniorage (Bencivenga and Smith, 1992). Moreover we assume that, as the volume of credit provided to the government increases crowding out the credit provided to the private sector, the extent of financial repression is intensified. Based on these arguments, we select eight elementary variables of financial repression (see Appendix 4).
Through Equations 3 and 4, the topic of the finance-growth nexus is addressed, that is, whether the causation runs finance-growth or growth^finance or bilaterally (finance-growth). We are also concerned with what impacts financial crisis and financial repression exhibit on economic growth and financial development. Another vital issue is represented by Equation 5, through which the causalities between financial crisis and other underlying variables are investigated. Emotional Intelligence
We conduct Granger causality analysis through the methods of vector error correction model (VECM) and autoregressive distributed lag (ARDL). According to Engle and Granger, cointegrated variables in the vector autoregression (VAR) system must have an error correction representation in which an error correction term (ECT) is incorporated into a model. In the context of assessing the finance-growth nexus, while a simple VAR estimation just indicates that one variable Granger causes the other variable without information of causal direction (e.g., whether finance is positive or negative to growth), both VECM and ARDL show a definite direction through the sign of each underlying variable’s coefficient in the cointegrating space. Moreover VECM imposes a strict condition that all underlying variables be integrated of order 1 (I), whereas ARDL can be performed even with the mixture of I and I (Pesaran and Pesaran, 2009). Thus these two techniques stand on different fundamentals of cointegration. Importantly, since the structural break literature was initiated by Perron, the accuracy of conventional unit root and Johansen cointegration tests (i.e., the VECM estimation) has been challenged as the presence of structural break can mimic the unit root stationary autoregressive process. Hence, using both VECM and ARDL can attach more robustness to the analysis.