Abstract
This paper presents a four-component stochastic frontier model in which the frontier function is represented by an unknown smooth input distance function, and inefficiency is decomposed into persistent and transient inefficiencies. Furthermore, the pre-truncation mean and variance of the transient inefficiency are functions of the environmental variables. By differentiating the four-component input distance frontier with respect to the time trend, total factor productivity (TFP) growth is estimated under the semiparametric smooth coefficient framework, and is decomposed into six components, i.e., technical change, scale component, allocative component, external component, efficiency change, and residual component. The empirical example focuses on the Lithuanian dairy sector with multiple outputs. Our results show that there are some persistent and transient inefficiencies in Lithuanian dairy farms. However, these farms maintained TFP growth of 2% per annum on average during 2004-2016, and much of it is attributed to the technical change and scale components.
Impact Factor: 3.806 (2018 Journal Citation Reports, Clarivate Analytics, 2019)
Balezentis, T.; Sun, K. 2020. Measurement of Technical Inefficiency and Total Factor Productivity Growth: A Semiparametric Stochastic Input Distance Frontier Approach and the Case of Lithuanian Dairy Farms. European Journal of Operational Research. Vol. 285, Issue 3, 1174-1188; ISSN 0377-2217; https://doi.org/10.1016/j.ejor.2020.02.032; [Science Citation Index Expanded, Scopus].