Estimating technical efficiency under unobserved heterogeneity with stochastic frontier models: application to northern German dairy farms

Abdulai, Awudu; Tietje, Hendrik

doi:10.1093/erae/jbm023

Cited by 102 publications

(100 citation statements)

References 14 publications

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“…al. (2005b), Abdulai and Tietje (2007), Faust and Baranzini (2014)). This comparison, usually performed by calculating a rank correlation coefficient of the index of efficiency, will ignore the possibility that the values obtained with the time invariant models reflect something different from those obtained from models with time varying inefficiency.…”

Section: Introductionmentioning

confidence: 99%

Persistent and transient productive inefficiency: a maximum simulated likelihood approach

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe productive efficiency of a firm can be seen as composed of two parts, one persistent and one transient. The received empirical literature on the measurement of productive efficiency has paid relatively little attention to the difference between these two components. Ahn, Good and Sickles (2000) suggested some approaches that pointed in this direction. The possibility was also raised in Greene (2004), who expressed some pessimism over the possibility of distinguishing the two empirically. Recently, Colombi (2010) and Kumbhakar and Tsionas (2012), in a milestone extension of the stochastic frontier methodology have proposed a tractable model based on panel data the promises to provide separate estimates of the two components of efficiency. The approach developed in the original presentation proved very cumbersome actually to implement in practice. Colombi (2010) notes that FIML estimation of the model is 'complex and time consuming.' In the sequence of papers, Colombi (2010), Colombi et al. (2011Colombi et al. ( , 2014, Kumbhakar, Lien and Hardaker (2012) and Kumbhakar and Tsionas (2012) have suggested other strategies, including a four step least squares method. The main point of this paper is that full maximum likelihood estimation of the model is neither complex nor time consuming. The extreme complexity of the log likelihood noted in Colombi (2010), Colombi et al. (2011Colombi et al. ( , 2014) is reduced by using simulation and exploiting the Butler and Moffitt (1982) formulation. In this paper, we develop a practical full information maximum simulated likelihood estimator for the model. The approach is very effective and strikingly simple to apply, and uses all of the sample distributional information to obtain the estimates. We also implement the panel data counterpart of the JLMS (1982) estimator for technical or cost inefficiency. The technique is applied in a study of the cost efficiency of Swiss railways.

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Section: Introductionmentioning

confidence: 99%

Persistent and transient productive inefficiency: a maximum simulated likelihood approach

2015

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“…Measurement of technical efficiency (TE) provides useful information on competitiveness of farms and potential to improve productivity, with the existing resources and level of technology (Abdulai and Tietje, 2007). Moreover, investigating factors that influence TE offers important insights on key variables that might be worthy of consideration in policy-making, in order to ensure optimal resource utilisation.…”

Section: Introductionmentioning

confidence: 99%

Assessment of technical efficiency and its determinants in beef cattle production in Kenya

Otieno¹,

Hubbard²,

Ruto³

2014

J. Dev. Agric. Econ.

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The stochastic metafrontier method is applied to estimate technical efficiency levels in beef cattle production in Kenya. Subsequently, a Tobit model is used to assess factors that might influence efficiency. Results show that the average efficiency level is 0.69, suggesting that there is considerable scope to improve beef production in Kenya. Considering the importance of the livestock enterprise to rural livelihoods and its potential role in poverty reduction, there is need for appropriate development strategies for enhanced efficiency. In particular, livestock development policies should focus on provision of technology-related services. For instance, promoting use of controlled cattle crossbreeding methods would enhance productivity gains. Effective institutional support is also necessary in order to improve efficiency, including improved access to market contracts, better farm management skills and off-farm income opportunities.

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“…However, there are many technical factors and policy programs besides inputs and outputs that can affect TE. Although TE studies for the dairy industry are abundant (Ahmad and Bravo-Ureta, 1996;Mbaga et al, 2003 Abdulai and Tietje, 2007), to our knowledge few studies have considered the effect of public policies and federal programs on TE of dairy farms.…”

Section: Introductionmentioning

confidence: 99%

Technical efficiency of US dairy farms and federal government programs

Murova

Chidmi

2011

Applied Economics

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Estimating technical efficiency under unobserved heterogeneity with stochastic frontier models: application to northern German dairy farms

Cited by 102 publications

References 14 publications

Persistent and transient productive inefficiency: a maximum simulated likelihood approach

Persistent and transient productive inefficiency: a maximum simulated likelihood approach

Assessment of technical efficiency and its determinants in beef cattle production in Kenya

Technical efficiency of US dairy farms and federal government programs

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