Latent Class Modelling for a Robust Assessment of Productivity: Application to French Grazing Livestock Farms
扩展了潜在类别随机前沿模型，结合Färe-Primont指数测算生产力变化，并应用于法国三种放牧牲畜农场，发现考虑异质性后生产力变化差异不大，但不同类别间存在差异。
Abstract Our objective is to extend the latent class stochastic frontier (LCSFM) model to compute productivity change, using the robust transitive productivity Färe‐Primont index. The application is to three types of grazing livestock farms in France over the period 2002–2016. The LCSFM identified two classes of farms, intensive farms and extensive farms. Results indicate that productivity change and its components show only small differences between the LCSFM and the pooled model that does not account for heterogeneity. Differences across classes exist, but depend on farm type.