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Measuring technical efficiency in the stochastic varying coefficient frontier model

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  • Giannis Karagiannis
  • Vangelis Tzouvelekas

Abstract

Due to the assumption that the best practice methods refer to each input separately instead of the whole set of inputs used by a firm, the benchmark technology as defined in the stochastic varying coefficient frontier model may be infeasible and theoretically improper whenever the maximum response coefficients are not coming from the same production unit. To overcome this problem, we propose alternative measures of output‐oriented and single‐factor technical efficiencies inspired from the maximum likelihood formulation of the nonneutral frontier model. The empirical results indicate that there are significant differences between the two in terms of the estimated efficiency scores but not significant differences we detected in terms of the efficiency ranking.

Suggested Citation

  • Giannis Karagiannis & Vangelis Tzouvelekas, 2009. "Measuring technical efficiency in the stochastic varying coefficient frontier model," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 389-396, July.
    • Handle: RePEc:bla:agecon:v:40:y:2009:i:4:p:389-396
    DOI: 10.1111/j.1574-0862.2009.00386.x
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    Cited by:

    1. Pede, Valerien O. & McKinley, Justin & Singbo, Alphonse & Kajisa, Kei, 2015. "Spatial Dependency of Technical Efficiency in Rice Farming: The Case of Bohol, Philippines," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205456, Agricultural and Applied Economics Association.
    2. Dios-Palomares, Rafaela & Martínez-Paz, José M., 2011. "Technical, quality and environmental efficiency of the olive oil industry," Food Policy, Elsevier, vol. 36(4), pages 526-534, August.
    3. Tankari, Mahamadou Roufahi, 2014. "Quelles zones cibler pour accroître l’efficacité agricole en Ouganda?," MPRA Paper 53396, University Library of Munich, Germany.
    4. Singbo, Alphonse G. & Emvalomatis, Grigorios & Alfons, Oude Lansink, 2013. "Assessing the impact of crop specialization on farms’ performance in vegetables farming in Benin: a non-neutral stochastic frontier approach," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149172, Agricultural and Applied Economics Association.
    5. A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.
    6. Tandon, Suranjali & Rao, R. Kavita, 2019. "Frontier analysis for State Excise in India," Working Papers 19/276, National Institute of Public Finance and Policy.
    7. A. Peyrache & A. N. Rambaldi, 2017. "Incorporating temporal and country heterogeneity in growth accounting—an application to EU-KLEMS," Journal of Productivity Analysis, Springer, vol. 47(2), pages 143-166, April.
    8. Valerien O. Pede & Francisco J. Areal & Alphonse Singbo & Justin McKinley & Kei Kajisa, 2018. "Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines," Agricultural Economics, International Association of Agricultural Economists, vol. 49(3), pages 301-312, May.
    9. Jerzy Marzec & Andrzej Pisulewski, 2020. "Pomiar efektywności zróżnicowanych technologicznie gospodarstw rolnych w Unii Europejskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 111-137.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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