IDEAS home Printed from https://ideas.repec.org/a/ora/journl/v1y2020i2p293-303.html
   My bibliography  Save this article

Technical, Cost And Allocative Efficiency In The Hungarian Dairy Farms

Author

Listed:
  • KOVÁCS Krisztián

    (University of Debrecen, Faculty of Economics and Business, Institute of Applied Economic Sciences, Debrecen, Hungary)

  • SZÜCS István

    (University of Debrecen, Faculty of Economics and Business, Institute of Applied Economic Sciences, Debrecen, Hungary)

Abstract

The general aim of the research was to explore the main indicators of the dairy sector in Hungary, and then define and systematize their efficiency and the factors relevant concerning dairy farms. Moreover, the objective is to introduce the most commonly used methods for measuring technical efficiency, which can explore hidden reserves within the dairy sector. To achieve the research objective, first the main indicators of the industry will be introduced, which will be explained in the first part of the literature section. The Hungarian dairy sector production trends and indicators where mainly came from FAOSTAT, EUROSTAT, KSH (Hungarian Central Statistical Office) and AKI (Research Institute of Agricultural Economics) databases. For my assessment, I will use the most reliable and comprehensive domestic agricultural economics database, the AKI- FADN (Farm Accountancy Data Network) database. In accordance with my objective, based on the database, a representative sample was selected in my analyses to represent the national dairy sector. More than 6800 data points were analysed in the different DEA models, which includes data from about more than 950 dairy farms in Hungary. Based on the secondary database (FADN), I created a technical efficiency inputs in four main economic areas (current assets, fix assets, human resources and livestock) which were characterized by using dairy farms efficiency differences of different size, year and regional categories. The model input variables comes from Hungarian FADN database. I confirmed that the used efficiency methods for measuring complex efficiency level were higher in my sample in the case of the large-sized farms than for small and medium-sized farms. The average technical efficiency of the Hungarian dairy sector during the examined 10 years was 64.6%, which means that the farmers can scale down their inputs with 35.4%, without changing the level of output (efficiency reserve). Large and small farms regarding the livestock number are more efficient (93.3%) than the medium sized farms (83.0%) and the small sizes farms (65.8%) maybe, because the medium and small farms are mixed profile farms.

Suggested Citation

  • KOVÁCS Krisztián & SZÜCS István, 2020. "Technical, Cost And Allocative Efficiency In The Hungarian Dairy Farms," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 293-303, December.
    • Handle: RePEc:ora:journl:v:1:y:2020:i:2:p:293-303
    as

    Download full text from publisher

    File URL: http://anale.steconomiceuoradea.ro/volume/2020/n2/027.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Solís & Boris E. Bravo‐Ureta & Ricardo E. Quiroga, 2009. "Technical Efficiency among Peasant Farmers Participating in Natural Resource Management Programmes in Central America," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 202-219, February.
    2. Begum, Ismat Ara & Buysse, Jeroen & Alam, Mohammad Jahangir & Van Huylenbroeck, Guido, 2009. "An application of Data Envelopment Analysis (DEA) to Evaluate Economic Efficiency of Poultry Farms in Bangladesh," 2009 Conference, August 16-22, 2009, Beijing, China 50900, International Association of Agricultural Economists.
    3. Loren W. Tauer, 1998. "Productivity of New York Dairy Farms Measured by Nonparametric Malmquist Indices," Journal of Agricultural Economics, Wiley Blackwell, vol. 49(2), pages 234-249, June.
    4. Bauer, Paul W. & Berger, Allen N. & Ferrier, Gary D. & Humphrey, David B., 1998. "Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods," Journal of Economics and Business, Elsevier, vol. 50(2), pages 85-114, March.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, September.
    7. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krisztián, Kovács & Ratnesh, Pandey, 2017. "Hungarian Dairy And Beef Production Sector Technical Efficiency Comparsion Using Dea," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 11(3-4), December.
    2. Thanh Ngo & Kan Wai Hong Tsui, 2022. "Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines," Operational Research, Springer, vol. 22(4), pages 3411-3434, September.
    3. Sailesh Tanna & Fotios Pasiouras & Matthias Nnadi, 2011. "The Effect of Board Size and Composition on the Efficiency of UK Banks," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 18(3), pages 441-462, November.
    4. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    5. Prakash, Navendu & Singh, Shveta & Sharma, Seema, 2021. "Technological diffusion, banking efficiency and Solow's paradox: A frontier-based parametric and non-parametric analysis," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 534-551.
    6. Van Meensel, Jef & Lauwers, Ludwig & Van Huylenbroeck, Guido & Van Passel, Steven, 2010. "Comparing frontier methods for economic-environmental trade-off analysis," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1027-1040, December.
    7. Helmi Hammami & Thanh Ngo & David Tripe & Dinh-Tri Vo, 2022. "Ranking with a Euclidean common set of weights in data envelopment analysis: with application to the Eurozone banking sector," Annals of Operations Research, Springer, vol. 311(2), pages 675-694, April.
    8. Silvia Saravia-Matus & T. S. Amjath-Babu & Sreejith Aravindakshan & Stefan Sieber & Jimmy A. Saravia & Sergio Gomez y Paloma, 2021. "Can Enhancing Efficiency Promote the Economic Viability of Smallholder Farmers? A Case of Sierra Leone," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    9. Simona Alfiero & Laura Broccardo & Massimo Cane & Alfredo Esposito, 2018. "High Performance Through Innovation Process Management in SMEs. Evidence from the Italian wine sector," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 87-110.
    10. María Victoria Uribe‐Bohorquez & Jennifer Martínez‐Ferrero & Isabel‐María García‐Sánchez, 2019. "Women on boards and efficiency in a business‐orientated environment," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(1), pages 82-96, January.
    11. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    12. J. Cummins & Hongmin Zi, 1998. "Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry," Journal of Productivity Analysis, Springer, vol. 10(2), pages 131-152, October.
    13. Sufian, Fadzlan & Abdul Majid, Muhamed Zulkhibri, 2007. "Consolidation and efficiency: Evidence from non-bank financial institutions in Malaysia," MPRA Paper 12128, University Library of Munich, Germany, revised 01 May 2007.
    14. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    15. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2015. "Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 244(2), pages 511-518.
    16. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.
    17. Moravcikova Dominika & Krizanova Anna & Svabova Lucia, 2018. "Evaluation of the Effectiveness of Selected Slovak Brands on the Principle of DEA Models with the Possibility to Optimise them," Economics and Culture, Sciendo, vol. 15(1), pages 22-34, June.
    18. John C. Topuz & Ali F. Darrat & Roger M. Shelor, 2005. "Technical, Allocative and Scale Efficiencies of REITs: An Empirical Inquiry," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 32(9‐10), pages 1961-1994, November.
    19. Nadimi, Reza & Tokimatsu, Koji, 2019. "Potential energy saving via overall efficiency relying on quality of life," Applied Energy, Elsevier, vol. 233, pages 283-299.
    20. Vittadini, Giorgio & Sturaro, Caterina & Folloni, Giuseppe, 2022. "Non-Cognitive Skills and Cognitive Skills to measure school efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).

    More about this item

    Keywords

    DEA; technical efficiency; cost efficiency; allocative efficiency; dairy farms; Hungary;
    All these keywords.

    JEL classification:

    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ora:journl:v:1:y:2020:i:2:p:293-303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catalin ZMOLE (email available below). General contact details of provider: https://edirc.repec.org/data/feoraro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.