An analysis of grain production decline during the early transition in Ukraine: A Bayesian inference

This study aims to quantify some of the reasons for the grain output decline in the early years of transition in Ukraine. We find that the decline in the use of production inputs accounts for over one half of the total output decline, while weather effects account for about 35% of the decline. The rest is attributable to a decline in the technical efficiency of collective farms during the transition years. The choice of the Bayesian paradigm for estimation was made to improve the reliability in the estimation of standard errors of functions of model parameters. Because we used noninformative priors where possible, posterior medians of parameters are roughly comparable to those that might have been obtained within a frequentist framework. In this paper, we did not model individual farm weather due to lack of data. However, the analysis showed that explicit modeling of weather effects is important and improves technical efficiency analysis. More detailed weather data may improve the precision of estimation. An intriguing extension of this work is to model explicitly the decline in factor use. The quantities of inputs used in production went down because of the break down of state distribution systems and growing prices, and may have been determined significantly by individual farm responses. Explicit modeling of input quantities used would require farm-level information on input prices, uncertainties in delivery systems, and other information on factors affecting acquisition of production inputs during the early transition.