Productive capabilities: An empirical analysis of their drivers

Recent contributions to the growth and trade literature have argued that the structure of an economy, as measured by its productive capabilities, is a key determinant for inter-country differences in development. Productive capabilities have been shown to be highly predictive of future economic growth, yet the country-level variables associated with them remain relatively unknown. In this paper, we empirically explore what variables are systematically associated with productive capabilities using a model averaging framework that can handle a very large number of potential explanatory variables without the need for arbitrary model selection. In order to estimate our dynamic panel specification, we propose a novel Bayesian averaging of classical estimates procedure based on the simple and efficient bias-corrected least squares dummy variable estimator. Our baseline and robustness analysis consider a large number of variables, sample periods and model priors. We find that there is persistence (as measured by the lagged dependent variable) and that variables, such as commodity terms of trade, energy availability, government consumption, capital per worker, arable land and capital inflows show a strong and robust association with capabilities.