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.

中文翻译：

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用于生产力稳健评估的潜在类建模：在法国放牧养殖场中的应用

我们的目标是使用稳健的传递生产力 Färe-Primon 指数扩展潜在类随机前沿 (LCSFM) 模型以计算生产力变化。该应用程序适用于 2002-2016 年期间法国的三种放牧养殖场。LCSFM 确定了两类农场，集约化农场和粗放农场。结果表明，生产力变化及其组成部分仅显示 LCSFM 与未考虑异质性的合并模型之间的微小差异。不同等级之间存在差异，但取决于农场类型。