Abstract
This article proposes an extension to the typical random-coefficients frontier model that allows the incorporation of firm management indicator(s) in the distribution of firms’ technology parameters. Such a modelling approach does not only relax the homogeneous technology assumption but also empirically tests for the factors that may be responsible for variation in firms’ technology parameters. The proposed approach is used to measure the technical efficiency of German dairy farms for the period 2001–2009. Estimation is performed using Bayesian techniques. The empirical findings suggest that German dairy farms achieve high levels of technical efficiency, while farms’ degree of intensification indeed drives several technology parameters. Furthermore, model comparison based on Bayes factors reveals that the employed model outperforms a simple stochastic frontier model and a random-coefficients stochastic frontier model.
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Notes
A formal model comparison is performed in the application that follows between the Cobb–Douglas and a translog specification.
An illustration of estimating a stochastic frontier model using Maximum Likelihood can be found in Abid and Goaied (2017).
Data source: EU-FADN DG AGRI.
Formal model comparisons based on Bayes factors suggest that a Cobb-Douglas specification is favored by the data when compared to a translog in all three models. The results can be provided upon request.
An estimated parameter is statistically significant if it’s corresponding 90% Interval does not contain zero.
This can be infered by whether the 90% Interval of the estimates with respect to the intensification variable contain zero.
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Skevas, I. A Hierarchical Stochastic Frontier Model for Efficiency Measurement Under Technology Heterogeneity. J. Quant. Econ. 17, 513–524 (2019). https://doi.org/10.1007/s40953-018-0144-5
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DOI: https://doi.org/10.1007/s40953-018-0144-5