Bayesian stochastic frontier analysis of agricultural productivity efficiency in clmv

Abstract

This paper examines the agricultural productivity efficiency in four countries consists Cambodia, Laos, Myanmar, and Vietnam (CLMV). The Bayesian Stochastic Frontier analysis is used to estimate in this study, this method has several advantages over the traditional method called Stochastic frontier analysis (SFA). The Bayesian method provide more information to be estimation under the uncertainty of parameters. The data consider the period 1991-2019 which comprises 4 countries for 29 years, with 116 observations. The results show that most of the average elasticity variables of agricultural input have a positive association with the agricultural output, this implies that the production frontier is well behave and increase in inputs. It can be concluded that the agricultural outputs of Cambodia, Laos, Myanmar and Vietnam (CLMV) countries in this sample were sensitive to changes in agricultural land followed by changes in agricultural fertilizer and labor. Therefore, the recommendation policy for these countries is governments should focus on enhance the productivity by increasing the technology or innovation in the CLMV countries.