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Using Islamic Banks, Commercial Banks And Dual Banks

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We conduct a raft of additional tests to ensure the robustness of our findings. First, our sample consists of Islamic banks, commercial banks and dual banks, thus to ascertain how MLG work among the three categories, we re-run equation (1) by separating the sample to three sub-samples: Islamic banks (IBs); commercial banks (CBs); and dual banks (DBs). The results reported in models 2, 3, 4 in Table 6 and models 11, 12 and 13 in Table 7 respectively. The results are basically the same with slight different in the coefficients significant. Nevertheless there is negative impact of GOWN on risk disclosure however this relation is insignificant. Also the results show the FOWN and CC has more impact in IBs rather than CBs and DBs. Finally, the …show more content…

Third, we test the robustness of our results by re-regressing equation (1) and equation (2) using weighted RDI as alternative risk disclosure index. The results reported in Table 7 are mostly the same with those results reported in Table 6 with slight different in the coefficients significant, therefore these findings indicates that our results are robust whether RDI is un-weighted or weighted. Finally, and to test potential endogeneity problems which have been debated to be a common problem in CG studies (Elshandidy & Neri, 2015; Larcker & Rusticus, 2010; Mollaha & Zamanb, 2015; Ntim et al., 2013; Ntim & Soobaroyen, 2013), we uses 3SLS because it is more efficient than 2SLS (Belsley, 1988; Larcker & Rusticus, 2010; Zellner & Theil, 1962). Three-stages least squares (3SLS) methodology consist of three steps: estimates MLG instrumental values in first step, estimates the covariance matrix for MLG instrumental values based on the residuals in second step and finally, performs GLS regression based on covariance matrix (Dennis & Taisier, 2014; Mollaha & Zamanb, 2015). Therefore, the model to be estimated is specified as: (2) Where everything remains unaffected as identified in equation (1) except that we use the covariance matrix from the second step

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