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Pomiar efektywności zróżnicowanych technologicznie gospodarstw rolnych w Unii Europejskiej

Author

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  • Jerzy Marzec
  • Andrzej Pisulewski

Abstract

Celem niniejszego opracowania jest określenie charakterystyk procesu produkcyjnego gospodarstw rolnych specjalizujących się w uprawach polowych w państwach członkowskich Unii Europejskiej. W pracy wykorzystano dane regionalne FADN. W związku z występującym zróżnicowaniem między regionami w pracy wykorzystano modele uwzględniające tę heterogeniczność. W szczególności rozważono dwa sposoby modelowania heterogeniczności: deterministyczny oraz stochastyczny. Odzwierciedleniem pierwszego sposobu jest wykorzystanie w niniejszej pracy modelu funkcji produkcji typu translog, który pozwala, żeby elastyczności produkcji względem nakładów czynników produkcji zależały od wielkości nakładów. Natomiast stochastyczny sposób modelowania heterogeniczności reprezentuje stochastyczny model graniczny z losowymi parametrami. Zastosowanie powyższej koncepcji pozwoliło na zbudowanie modelu funkcji produkcji typu Cobba i Douglasa (C–D) z indywidualnymi parametrami. Estymacji parametrów czterech modeli dokonano za pomocą podejścia bayesowskiego. Otrzymane wyniki jednoznacznie wskazują, że najlepszym modelem okazał się model C–D z indywidualnymi parametrami. Ponadto zaobserwowano, że dla średniej unijnej najwyższa elastyczność produkcji występuje względem nakładów materiałów, a najniższa względem areału. Natomiast dosyć zaskakującym wynikiem jest wysoki poziom średniej efektywności technicznej (0,95) przy bardzo niewielkim rozproszeniu tych ocen.

Suggested Citation

  • 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.
    • Handle: RePEc:sgh:gosnar:y:2020:i:3:p:111-137
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    References listed on IDEAS

    as
    1. Czyżewski, Bazyli & Matuszczak, Anna & Brelik, Agnieszka, 2018. "Endogeniczna wartość dóbr publicznych na obszarach wiejskich: przypadek Pomorza Zachodniego," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2018(5).
    2. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    3. Lajos Baráth & Imre Fertő, 2017. "Productivity and Convergence in European Agriculture," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(1), pages 228-248, February.
    4. Komorowska, Dorota, 2017. "Wyniki Produkcyjne I Ekonomiczne Gospodarstw Specjalizujących Się W Uprawach Polowych," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2017(6).
    5. 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.
    6. V. Eldon Ball & Jean‐Pierre Butault & Carlos San Juan & Ricardo Mora, 2010. "Productivity and international competitiveness of agriculture in the European Union and the United States," Agricultural Economics, International Association of Agricultural Economists, vol. 41(6), pages 611-627, November.
    7. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    8. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    9. Anthony Rezitis, 2010. "Agricultural productivity and convergence: Europe and the United States," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 1029-1044.
    10. Derek Headey & Mohammad Alauddin & D.S. Prasada Rao, 2010. "Explaining agricultural productivity growth: an international perspective," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 1-14, January.
    11. V. Ball & Jean-Christophe Bureau & Jean-Pierre Butault & Richard Nehring, 2001. "Levels of Farm Sector Productivity: An International Comparison," Journal of Productivity Analysis, Springer, vol. 15(1), pages 5-29, January.
    12. Antonio Alvarez & Julio del Corral, 2010. "Identifying different technologies using a latent class model: extensive versus intensive dairy farms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(2), pages 231-250, June.
    13. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108423380, January.
    14. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    15. Grigorios Emvalomatis, 2012. "Productivity Growth in German Dairy Farming using a Flexible Modelling Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(1), pages 83-101, February.
    16. Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1973. "Transcendental Logarithmic Production Frontiers," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 28-45, February.
    17. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
    18. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-323, March.
    19. Xueqin Zhu & Alfons Oude Lansink, 2010. "Impact of CAP Subsidies on Technical Efficiency of Crop Farms in Germany, the Netherlands and Sweden," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(3), pages 545-564, September.
    20. Eric Njuki & Boris E. Bravo-Ureta & Christopher J. O’Donnell, 2019. "Decomposing agricultural productivity growth using a random-parameters stochastic production frontier," Empirical Economics, Springer, vol. 57(3), pages 839-860, September.
    21. Kalirajan, K P & Obwona, M B, 1994. "Frontier Production Function: The Stochastic Coefficients Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(1), pages 87-96, February.
    22. Laure Latruffe & Kelvin Balcombe & Sophia Davidova & Katarzyna Zawalinska, 2004. "Determinants of technical efficiency of crop and livestock farms in Poland," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1255-1263.
    23. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    24. Marzec, Jerzy & Pisulewski, Andrzej, 2019. "The Measurement of Time Varying Technical Efficiency and Productivity Change in Polish Crop Farms," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 68(1), March.
    25. 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.
    26. Gerdessen, Johanna C. & Pascucci, Stefano, 2013. "Data Envelopment Analysis of sustainability indicators of European agricultural systems at regional level," Agricultural Systems, Elsevier, vol. 118(C), pages 78-90.
    27. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
    28. Ioannis Skevas, 2019. "A Hierarchical Stochastic Frontier Model for Efficiency Measurement Under Technology Heterogeneity," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 513-524, September.
    29. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    More about this item

    Keywords

    funkcja produkcji; stochastyczne modele graniczne; podejście bayesowskie; porównania międzynarodowe; model z losowymi parametrami;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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