Convergence in Industrial Subsidies:Following the Crowd or Rational Learning?
Industrial policy is an important starting point for achieving high-quality economic development.This paper studies the herding behavior of local governments in making industrial policies using a novel identification strategy,metrics and hand-collected data on official networks.We find that,local governments adjust their industrial policies in order to catch up with their successful neighbors,and such an effect reflects policy learning between connected officials.The results of the evaluation of policy convergence show that,overall,the learning effect of industrial policy is negative.However,policy learning taking into account the factor endowments,or the target industry being learned is more in line with the local comparative advantages,can significantly improve the learning effect.The conclusions of this paper provide feasible policy suggestions for local governments on how to efficiently learn from others,attract investment,and promote high-quality regional economic development.
万方数据
维普
