The effect of Brain drain on AI government readiness (system GMM models)
| AI_GOV | System GMM (model 1) | System GMM (Model 2) | System GMM (Model 3) | System GMM (Model 4) | System GMM (Model 5) | System GMM (model 6) |
|---|---|---|---|---|---|---|
| L.AI_GOV | 0.496*** (0.114) | 0.624*** (0.100) | 0.367** (0.144) | 0.420*** (0.127) | 0.468*** (0.136) | 0.522*** (0.110) |
| BRAIN_DRAIN | −1.028*** (0.349) | −1.129** (0.451) | −0.828** (0.402) | −0.799** (0.402) | −0.677* (0.386) | −0.943** (0.429) |
| ICT_SPEC | 0.745*** (0.217) | 0.612** (0.282) | 0.733** (0.293) | 0.944*** (0.305) | 0.930*** (0.318) | 0.460** (0.216) |
| GDPPPG | 0.344*** (0.077) | 0.373*** (0.075) | 0.294*** (0.074) | 0.315*** (0.074) | 0.324*** (0.077) | 0.328*** (0.078) |
| GOVEXPG | 0.258* (0.146) | 0.320** (0.140) | 0.245* (0.147) | 0.277** (0.123) | 0.268** (0.132) | 0.286** (0.143) |
| REG_QUAL | 4.504*** (1.599) | – | – | – | – | – |
| POL_STAB | – | 4.115** (1.712) | – | – | – | – |
| CTRL_CORRUPTION | – | – | 4.447*** (1.459) | – | – | – |
| RULE_LAW | – | – | – | 5.134*** (1.659) | – | – |
| GOVERNANCE | – | – | – | – | 1.194** (0.468) | – |
| GOV_INTEGRITY | – | – | – | – | – | 0.115** (0.054) |
| Constant | 25.784*** (6.726) | 20.214*** (6.883) | 34.576*** (8.447) | 28.732*** (6.791) | 30.583*** (8.319) | 21.978*** (5.655) |
| AR(1) test (p-value) | 0.015 | 0.011 | 0.011 | 0.017 | 0.007 | 0.005 |
| AR(2) test (p-value) | 0.153 | 0.234 | 0.164 | 0.175 | 0.170 | 0.164 |
| Hansen test (p-value) | 0.373 | 0.084 | 0.229 | 0.162 | 0.475 | 0.146 |
| AI_GOV | System GMM (model 1) | System GMM (Model 2) | System GMM (Model 3) | System GMM (Model 4) | System GMM (Model 5) | System GMM (model 6) |
|---|---|---|---|---|---|---|
| L.AI_GOV | 0.496*** (0.114) | 0.624*** (0.100) | 0.367** (0.144) | 0.420*** (0.127) | 0.468*** (0.136) | 0.522*** (0.110) |
| BRAIN_DRAIN | −1.028*** (0.349) | −1.129** (0.451) | −0.828** (0.402) | −0.799** (0.402) | −0.677* (0.386) | −0.943** (0.429) |
| ICT_SPEC | 0.745*** (0.217) | 0.612** (0.282) | 0.733** (0.293) | 0.944*** (0.305) | 0.930*** (0.318) | 0.460** (0.216) |
| GDPPPG | 0.344*** (0.077) | 0.373*** (0.075) | 0.294*** (0.074) | 0.315*** (0.074) | 0.324*** (0.077) | 0.328*** (0.078) |
| GOVEXPG | 0.258* (0.146) | 0.320** (0.140) | 0.245* (0.147) | 0.277** (0.123) | 0.268** (0.132) | 0.286** (0.143) |
| REG_QUAL | 4.504*** (1.599) | – | – | – | – | – |
| POL_STAB | – | 4.115** (1.712) | – | – | – | – |
| CTRL_CORRUPTION | – | – | 4.447*** (1.459) | – | – | – |
| RULE_LAW | – | – | – | 5.134*** (1.659) | – | – |
| GOVERNANCE | – | – | – | – | 1.194** (0.468) | – |
| GOV_INTEGRITY | – | – | – | – | – | 0.115** (0.054) |
| Constant | 25.784*** | 20.214*** | 34.576*** | 28.732*** | 30.583*** | 21.978*** |
| AR(1) test ( | 0.015 | 0.011 | 0.011 | 0.017 | 0.007 | 0.005 |
| AR(2) test ( | 0.153 | 0.234 | 0.164 | 0.175 | 0.170 | 0.164 |
| Hansen test ( | 0.373 | 0.084 | 0.229 | 0.162 | 0.475 | 0.146 |
Note(s): Standard errors in parentheses; ***, ** and * denote significance at 1, 5 and 10 percent level respectively. This table reports results of system GMM (each column represents a separate regression model), based on xtabond2 Stata command, with orthogonal (to use the forward orthogonal deviations transform instead of first differencing), collapse (to create one instrument for each variable and lag distance, rather than one for each period, variable and lag distance) and robust (with Windmeijer’s finite-sample correction for two-step covariance matrix) options. The instruments are the independent variables and their lag1 and/or lag2
Source(s): Authors’ processing
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