Convergence, technological interdependence and spatial externalities: a spatial dynamic panel data analysis

This study adopts a spatial dynamic panel data approach and spatial quasi-maximum likelihood to re-estimate the speed of growth convergence in 91 countries based on technological interdependence and spatial externalities. We perform a conditional Lagrange multiplier test for spatial error dependence and find some differences to previous studies. First, the switch from a cross-sectional to a dynamic panel data framework enables the estimated rate of conditional convergence to be higher, more accurate and more appropriate for realistic and theoretical expectations. Second, the spatial Durbin model (SDM) is a general form of simplified model that considers spatial error correlation, and its likelihood ratio test for the theoretical model of 'learning by doing' effect provides further evidence. Finally, statistical tests find that spatial correlation not only occurs in each variable, but also appears in the error term. Thus, the SDM does not exist in the assumptions associated with the spatial error, which are not necessarily correct.