The paper estimates cross-province growth regressions for China over the period of economic reform. It first addresses the problem of model uncertainty by adopting two approaches to model selection, Bayesian Model Averaging and the automated General-to-Specific approach, to consider a wide range of candidate predictors of growth in China. The first-stage model selection results identify a role for conditional convergence, physical and human capital formation, population growth, degree of openness, and institutional change. Starting from this baseline equation, the growth impact of physical and human capital is examined in some detail using panel data system GMM. For instance, 'investment in innovation', foreign direct investment, and private investment are found to be particularly important. Secondary school enrolment contributes to growth, and higher education enrolment even more so. Finally, some illustrative counterfactual predictions are conducted to answer the question: why has China, as a whole, and indeed all its provinces, grown so fast?
Economic Growth
,Convergence
,Physical Capital
,Human Capital
,China
