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Latent Class Modelling for a Robust Assessment of Productivity: Application to French Grazing Livestock Farms
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EN
Article de revue
Este ítem está publicado en
Journal of Agricultural Economics. 2021, vol. 72, n° 3, p. 760-780
Resumen en inglés
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 ...Leer más >
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.< Leer menos
Palabras clave en inglés
Efficiency
Färe-Primont
France
grazing livestock farms
latent class stochastic frontier
productivity
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Proyecto ANR
Développement et construction d'un Centre d'Accès Sécurisé Distant aux données confidentielles (CASD) pour la recherche française en sciences sociales et en économie. - ANR-10-EQPX-0017
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