This paper applies alternative panel data models to a cross‐sectional dataset that contains observations at the plot level for a sample of wine‐grape farms in Central Chile. The input–output data as well as key attributes of the production system are at the plot level, at which individualized management exists. However, plots belonging to a particular farm are also subject to overall centralized (farm‐level) management. Thus, this data configuration offers the possibility of analyzing technical efficiency (TE) both across plots and across farms. A Generalized True Random Effects model, which permits the separate identification of farm‐level and plot‐level inefficiency while controlling for unobserved farm‐level heterogeneity, shows that TE varies across farms but not among plots within the same farm. Geographical location also affects grape production and agro‐climatic conditions influence production levels, with grape farms located on cooler zones producing significantly less than their counterparts in warmer zones, as expected. The analysis underscores the value of using recent methodologies typically applied to panel data when cross‐sectional information is available for individual plots within a farm unit or in similar settings.

中文翻译：

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农场一级影响的地块级技术效率：智利酿酒师的证据

本文将替代面板数据模型应用于横截面数据集，该数据集包含对智利中部葡萄酒葡萄农场的样地水平观察。生产系统的输入-输出数据以及关键属性位于情节级别，存在个性化管理。但是，属于特定农场的地块也要接受整体集中（农场一级）的管理。因此，此数据配置提供了在地块和农场之间分析技术效率（TE）的可能性。广义真实随机效应模型可以在控制未观察到的农场级异质性的同时，单独识别农场级和地块级的低效率，该模型显示TE在不同农场之间变化，但在同一农场内的不同地块之间则不同。地理位置也影响葡萄产量，而农业气候条件也影响产量水平，位于较凉爽地区的葡萄农场的产量明显低于较温暖地区的葡萄农场。当横截面信息可用于农场单位内或类似环境中的单个地块时，分析强调使用通常应用于面板数据的最新方法的价值。