First of all, for India and Malaysia, their finance-growth causality is detected as bilateral in the VECM assessment, whereas their ARDL estimates reject the cointegrating relationship in EG-ARDL (where EG is the dependent variable) and thus suggest the causal link of growth^-finance. Recognizing these results, we conclude that the finance-growth nexus is primarily bidirectional but more inclining towards growth^finance in India and Malaysia. As far as Korea’s finance-growth nexus is concerned, while the VECM results support the causal link of growth^finance, the ARDL results demonstrate the bilateral causality. However, since the weak exogeneity test results are insignificant in Korea’s EG-ARDL and FD-ARDL (where FD is the dependent variable), we highlight the stronger evidence of finance-growth in Korea’s VECM outcomes. And as far as Indonesia and Thailand are concerned, their finance-growth nexus cannot be investigated through ARDL, as the bounds test results reject the long-run causality between finance and growth in the two countries. Nonetheless the VECM estimates clearly show that the causality runs finance-growth in Indonesia and growth^finance in Thailand, respectively. Subsequently, the conclusions of the five countries’ finance-growth nexus are summarized in Table 6. As we can see, a variation across countries is observed even though the same variables and methodology are employed for all the countries. The demand-leading hypothesis — economic growth leads to higher financial development but not vice versa — is supported by Thailand’s results. Experiential learning
24 models are estimated for the five Asian countries whose sample periods are the same as those in the BP test (see Table 2). While some models indicate the evidence of heteroscedasticity, non-normality and functional form problem, all the models are free from autocorrelation at the 10% significance level or better. If heteroscedasticity is detected, the results are computed by the White heteroscedasticity adjusted standard error.
Unit root and Cointegration Tests
For examining stationarity in each series, both the ADF- and PP tests identify that all the countries’ EG, FD, FC and FR are non-stationary in their levels (except a few results) but become stationary after taking the first difference. Thus all the underlying variables are confirmed as /. Subsequently, the Johansen cointegration test (with unrestricted intercepts and no trends) is conducted treating FR as an exogenous I variable in the cointegrating vector (Note 15). Before conducting the cointegration test, the lag order of each country’s estimation is selected as the Johansen test highly depends on the choice of lag length. Checking the test statistics at the maximum order of four, we choose three lags for Korea, Malaysia and Thailand and four lags for India and Indonesia, respectively. Then the trace statistics in Table 3 report that, there is a single cointegration relationship (r = 1) among EG, FD and FC at the 10% level or better in all countries. The bounds test is implemented at the maximum lag order of either four (for India and Indonesia) or three (for Korea, Malaysia and Thailand); we refer to the statistics of the lag order selection in the VECM assessment. The test statistics in Table 4 reveal that, there is cointegration relationship in: all EG, FD and FC for Korea; FD and FC for India and Malaysia; and only FC for Indonesia and Thailand (Note 16). Indeed, although several F-statistics in Table 4 are judged as inconclusive in the bounds test, the presence of cointegration has been detected through the conventional unit root tests (i.e., the ADF and PP tests). Next while we seek the lag length of each underlying variable, both AIC and SBC give us only the lag selections that seem to cause autocorrelation in both India and Indonesia’s models. Hence, the orders of the two countries are manually set as presented in Table 4. For the other three countries, Korea’s models are selected by SBC and Malaysia and Thailand’s models by AIC, respectively. Satisfaction level of teachers
It has been generally agreed that a structural break exists in time series data. In fact, visually checking the EG (real per capita GDP) plots in Appendixes 5 to 9, India seems to have a break around 1991, whereas the other four countries have prominent breaks over the period 1997-1998. We therefore consider it important to take the element of structural break into our analysis for obtaining more plausible estimates. To this end, the structural break in economic growth dummy (SBGD) is allocated by seeking structural break(s) in each country’s EG series through the test developed by Bai and Perron (hereafter the BP test).
The BP test specifies multiple structural changes in a linear regression model estimated by least squares, treating the dates of structural breaks as unknown and endogenous events. The rationale for performing the BP test is that it allows us to determine break points statistically and objectively not setting the break dates based on a priori information.
To eliminate autocorrelation in estimation, each EG-VAR has already been included: SGD and PCD for India; SGD and SFD for Indonesia; SFD and SFCD for Korea; SGD and SFCD for Malaysia; and SGD, SFD and SFCD for Thailand (Note 11). As reported in Table 2, the sample periods differ across the five countries due to data availability. Subsequently, we check the lag order selection statistics of each EG-VAR and set three lags for Korea, Malaysia and Thailand and four lags for India and Indonesia. Exhaust Emissions
As the initial step for the VECM estimation, the existence of unit root in each underlying variable is assessed by both the Augmented Dickey-Fuller (ADF) test and the Phillips and Perron (PP) test. After confirming that all underlying are I, we perform the Johansen cointegration test to check whether there is a cointegrating relationship among underlying variables so that the number of cointegrating vectors (r) is determined. For avoiding autocorrelation in estimation, we properly allocate: SGD (the shock in economic growth dummy) which takes the value of one for negative EG growth periods otherwise zero; SFD (the shock in financial development dummy) which is one for negative FD growth periods, otherwise zero; and SFCD (the shock in financial crisis dummy) which takes the value of one for positive FC growth periods otherwise zero. Moreover, PCD is the pre-crisis dummy that takes the value of one for 1990Q1 to 1990Q4 and zero for other periods in India’s analysis. For the other four countries, PCD is not included. Finally, the allocation of SBGD (the structural break in economic growth dummy) is discussed below in Bai and Perron test. Stock Market
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).
One issue in the empirical literature is that there is no single indicator that sufficiently captures all aspects of financial deepening. As a result, most studies — including pioneering works of King and Levine and Demetriades and Hussein and recent ones — separately examine the relationship between economic growth (mostly real per capita GDP) and each of several financial development variables (e.g., liquidity liabilities (M3) and domestic credit provided to the private sector). Another issue is that banking and stock market — two major constituents of financial development — have been independently assessed in the literature. Such studies as Levine and Zervos and Arestis et al. investigated the effect of stock market development on economic growth. Meanwhile, there are few studies that consider financial development as an integrated phenomenon consisting of banking and stock market, despite the increasing proportion of the latter in a financial system. Taking into account these issues, we argue that financial development — as a single phenomenon — should be measured by combining several elements. And five elementary variables of financial development, which are commonly used in the empirical literature, are selected and integrated to make the financial development indicator (FD) (see Appendix 1) (Note 4). The ratio of money supply to GDP (MTG) is picked up to measure the degree of financial depth in the simplest manner. We are also concerned with the financial size- and activity (liquidity) proxies (BATG, PCTG, SKTG and SVTG) suggested by Beck et al.. With these proxies, the impacts of two financial channels (banking and stock market) and their two aspects (size and activity) are approximated. Sales personnel