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A Metafrontier Analysis on the Performance of Grain-Producing Regions in Norway

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  • Habtamu Alem

    (A Research Scientist, Department of Economics and Society, Norwegian Institute of Bioeconomy Research (NIBIO), Raveien 9, 1430 ÅS, Norway)

Abstract

Previous application of the stochastic frontier model and subsequent measurement of the performance of the crop sector can be criticized for the estimated production function relying on the assumption that the underlying technology is the same for different agricultural systems. This paper contributes to estimating regional efficiency and the technological gap in Norwegian grain farms using the stochastic metafrontier approach. For this study, we classified the country into regions with district level of development and, hence, production technologies. The dataset used is farm-level balanced panel data for 19 years (1996–2014) with 1463 observations from 196 family farms specialized in grain production. The study used the true random effect model and stochastic metafrontier analysis to estimate region level technical efficiency (TE) and technology gap ratio (TGR) in the two main grain-producing regions of Norway. The result of the analysis shows that farmers differ in performance and technology use. Consequently, the paper gives some regionally and farming system-based policy insights to increase grain production in the country to achieve self-sufficiency and small-scale farming in all regions.

Suggested Citation

  • Habtamu Alem, 2021. "A Metafrontier Analysis on the Performance of Grain-Producing Regions in Norway," Economies, MDPI, vol. 9(1), pages 1-10, February.
    • Handle: RePEc:gam:jecomi:v:9:y:2021:i:1:p:10-:d:491145
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    References listed on IDEAS

    as
    1. James Odeck, 2007. "Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2617-2630.
    2. Zhimin Huang & Susan Li, 2001. "Stochastic DEA Models With Different Types of Input-Output Disturbances," Journal of Productivity Analysis, Springer, vol. 15(2), pages 95-113, March.
    3. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    4. Víctor Moreira & Boris Bravo-Ureta, 2010. "Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: a stochastic meta-frontier model," Journal of Productivity Analysis, Springer, vol. 33(1), pages 33-45, February.
    5. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    6. Gudbrand Lien & Subal C. Kumbhakar & J. Brian Hardaker, 2010. "Determinants of off‐farm work and its effects on farm performance: the case of Norwegian grain farmers," Agricultural Economics, International Association of Agricultural Economists, vol. 41(6), pages 577-586, November.
    7. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514.
    8. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
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    Cited by:

    1. Roengchai Tansuchat, 2023. "A Copula-Based Meta-Stochastic Frontier Analysis for Comparing Traditional and HDPE Geomembranes Technology in Sea Salt Farming among Farmers in Phetchaburi, Thailand," Agriculture, MDPI, vol. 13(4), pages 1-23, March.
    2. Habtamu Alem, 2021. "The Role of Technical Efficiency Achieving Sustainable Development: A Dynamic Analysis of Norwegian Dairy Farms," Sustainability, MDPI, vol. 13(4), pages 1-11, February.

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