Technical efficiency of Kansas arable crop farms: a local maximum likelihood approach
采用局部最大似然方法评估堪萨斯州耕地农场的技术效率，发现该方法得到的效率值高于传统DEA和SFA模型，有助于识别低效农场并制定管理策略。
Abstract This study uses local maximum likelihood (LML) methods recently proposed by Kumbhakar et al. (2007) to assess the technical efficiency of arable crop Kansas farms. LML techniques overcome the most relevant limitations associated to mainstream parametric stochastic and nonparametric frontier models. LML allows deriving farm‐level frontier parameter estimates. The relevance of using localized estimates is evidenced by the observed heterogeneity in production technologies. Technical efficiency scores derived from the LML approach [0.905] are higher than those of the DEA model under CRS [0.808] and SFA [0.804] and close to DEA‐VRS [0.917] ratings. Deriving reliable information about farm efficiency performance is relevant to identify inefficient farms and define adequate policy and management strategies. The use of refined methods has thus important implications.