Assessment of agricultural productivity change at country level: A stochastic frontier approach

Abstract

In this paper, we estimate agricultural productivity change at country level based on the same data employed by the United States Department of Agriculture (USDA), the current reference data source, using a stochastic frontier model instead of the growth accounting method. The use of a stochastic frontier model is motivated by the opportunity to overcome the limitation of USDA estimates which rely on approximated and imputed input cost shares, and of the growth accounting method in general, which ignores technical inefficiency. We found that, in general, USDA estimates are higher in absolute value than ours but in substantial agreement, confirming the different theoretical foundations of the two methods and suggesting the empirical validity of both of them. Furthermore, our results show that the assumption of constant returns to scale made by many authors appears just a simplification and not a real property of the production processes of the various countries. This work has the value to provide, for the first time in the literature, a comparison between agricultural productivity changes estimated with different methodologies, and an additional data source that can be employed in a large variety of longitudinal economic analyses at country level.