Variables | Panel OLS | GMM |
---|
Coefficient | t-statistics | Coefficient | t-statistics |
---|
Corruption | 0.103 | 2.778 (***) | 0.016 | 0.462 |
NPLt−1 | – | – | 0.843 | 74.461 (***) |
Capitalization | 0.497 | 31.146 (***) | 0.132 | 12.114 (***) |
Credit Disclosure Index | 0.079 | 2.361 (**) | − 0.078 | − 3.787 (***) |
GDP Growth | − 0.068 | − 4.726 (***) | − 0.043 | − 4.827 (***) |
Inflation | − 0.004 | − 0.880 | − 0.001 | − 0.171 |
Public Debt | − 0.001 | − 0.216 | − 0.001 | − 0.370 |
Remittance | 0.015 | 1.213 | − 0.003 | − 0.004 |
Trade Openness | 0.004 | 2.511 (**) | 0.001 | 0.628 |
Unemployment | 0.061 | 4.608 (***) | 0.012 | 1.523 |
Constant | 0.095 | 0.340 | 0.126 | 0.462 |
Adjusted r-square | 0.289 | 0.746 |
F-value | 145.71 (***) | 3013^ (***) |
Observations | 3200 | 2844 |
Hausamn | 58.05 (***) | – |
- We have performed both fixed and random effect regression based on the model: \({\text{NPL}}_{it} = \alpha_{i} + \beta_{1} {\text{Corruption}}_{it} + \mathop \sum \nolimits_{i = 1}^{i} \beta_{2} {\text{Controls}}_{it} + \varepsilon_{it}\). We have also tried to capture the lag impact of NPL using the GMM model: \(Y_{it} = \alpha + \beta Y_{i,t - 1} + \gamma {\text{ Corruption}}_{it} + \mathop \sum \nolimits_{k = 1}^{k} \delta_{k} {\text{Corruption}}_{t}^{k} + \varepsilon_{it}\). Here, NPL is the dependent variable and is measured by the ratio of non-performing loans to total gross loans. A detailed description of the measurement variable and control variables are provided in Table 4. Hausman test score indicates that the fixed effect model is appropriate for the study. Therefore, we report fixed effect regression scores in Table 6.
- ^ represent j-statistic score with the related p value in the parenthesis. Asterisk ** and *** represent significance level at 5 and 1% respectively.