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A comparison of stochastic frontier approaches for estimating technical inefficiency and total factor productivity

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  • James Carroll
  • Carol Newman
  • Fiona Thorne

Abstract

This article compares standard stochastic frontier models for panel data with a number of recently developed models which attempt to control for unobserved heterogeneity in the inefficiency component. Results are used to construct a generalized Malmquist Total Factor Productivity (TFP) index for the Irish tillage sector. While our application yields similar general TFP trends across models, it is evident that this new class of model leads to fewer theoretical inconsistencies in the production frontier. Furthermore, inefficiency estimates across models are critically compared and the potential benefits of controlling for unobserved heterogeneity are highlighted.

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  • James Carroll & Carol Newman & Fiona Thorne, 2011. "A comparison of stochastic frontier approaches for estimating technical inefficiency and total factor productivity," Applied Economics, Taylor & Francis Journals, vol. 43(27), pages 4007-4019.
    • Handle: RePEc:taf:applec:v:43:y:2011:i:27:p:4007-4019
    DOI: 10.1080/00036841003761918
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    Cited by:

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    2. A. Tonini, 2012. "A Bayesian stochastic frontier: an application to agricultural productivity growth in European countries," Economic Change and Restructuring, Springer, vol. 45(4), pages 247-269, November.
    3. Cathal O'Donoghue & Thia Hennessy, 2015. "Policy and Economic Change in the Agri-Food Sector in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 46(2), pages 315-337.
    4. Boris E. Bravo‐Ureta & Víctor H. Moreira & Javier L. Troncoso & Alan Wall, 2020. "Plot‐level technical efficiency accounting for farm‐level effects: Evidence from Chilean wine grape producers," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 811-824, November.
    5. Tovar Reanos, Miguel & Martinez-Cillero, Maria, 2021. "Technical efficiency and equity effects of environmental payments in Ireland," Papers WP711, Economic and Social Research Institute (ESRI).
    6. Kexin Li & Jianxu Liu & Yuting Xue & Sanzidur Rahman & Songsak Sriboonchitta, 2022. "Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand," Agriculture, MDPI, vol. 12(8), pages 1-17, July.
    7. A. Wondemu Kifle, 2016. "Working Paper 237 - Decomposing Sources of Productivity Change in Small-Scale Farming in Ethiopia," Working Paper Series 2332, African Development Bank.
    8. Castiglione, Concetta & Infante, Davide & Zieba, Marta, 2024. "Is it worth subsidising the cultural sector? New insights from Italian theatre companies," Journal of Policy Modeling, Elsevier, vol. 46(1), pages 20-38.
    9. Chen, Hong & Wang, Xi & Singh, Baljeet, 2021. "Transient and persistent inefficiency traps in Chinese provinces," Economic Modelling, Elsevier, vol. 97(C), pages 335-347.
    10. Lungelo P. Cele & Thia Hennessy & Fiona Thorne, 2023. "Regional technical efficiency rankings and their determinants in the Irish dairy industry: A stochastic meta‐frontier analysis," Agribusiness, John Wiley & Sons, Ltd., vol. 39(3), pages 727-743, July.
    11. Michael Danquah and Bazoumana Ouattara, 2018. "Comparison of Stochastic Frontier Approaches for Estimating National Efficiency: An Application to Sub-Saharan African Countries," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 43(3), pages 119-142, September.
    12. Asif Reza Anik & Sanzidur Rahman & Jaba Rani Sarker, 2017. "Agricultural Productivity Growth and the Role of Capital in South Asia (1980–2013)," Sustainability, MDPI, vol. 9(3), pages 1-24, March.

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