IDEAS home Printed from https://ideas.repec.org/a/spr/jqecon/v17y2019i3d10.1007_s40953-018-0144-5.html
   My bibliography  Save this article

A Hierarchical Stochastic Frontier Model for Efficiency Measurement Under Technology Heterogeneity

Author

Listed:
  • Ioannis Skevas

    (University College Cork)

Abstract

This article proposes an extension to the typical random-coefficients frontier model that allows the incorporation of firm management indicator(s) in the distribution of firms’ technology parameters. Such a modelling approach does not only relax the homogeneous technology assumption but also empirically tests for the factors that may be responsible for variation in firms’ technology parameters. The proposed approach is used to measure the technical efficiency of German dairy farms for the period 2001–2009. Estimation is performed using Bayesian techniques. The empirical findings suggest that German dairy farms achieve high levels of technical efficiency, while farms’ degree of intensification indeed drives several technology parameters. Furthermore, model comparison based on Bayes factors reveals that the employed model outperforms a simple stochastic frontier model and a random-coefficients stochastic frontier model.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jqecon:v:17:y:2019:i:3:d:10.1007_s40953-018-0144-5
    DOI: 10.1007/s40953-018-0144-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40953-018-0144-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40953-018-0144-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    2. Stephen Hynes & Eoghan Garvey, 2009. "Modelling Farmers’ Participation in an Agri‐environmental Scheme using Panel Data: An Application to the Rural Environment Protection Scheme in Ireland," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(3), pages 546-562, September.
    3. Ihsen Abid & Mohamed Goaied, 2017. "Benchmarking Banking Efficiency Using a Meta-Profit Function," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(1), pages 45-74, March.
    4. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    5. 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.
    6. 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.
    7. Stijn Reinhard & C. A. Knox Lovell & Geert Thijssen, 2002. "Analysis of Environmental Efficiency Variation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1054-1065.
    8. 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.
    9. 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.
    10. Johannes Sauer & Catherine J. Morrison Paul, 2013. "The empirical identification of heterogeneous technologies and technical change," Applied Economics, Taylor & Francis Journals, vol. 45(11), pages 1461-1479, April.
    11. 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.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jerzy Marzec & Andrzej Pisulewski, 2021. "Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 152-161.
    2. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, 2023. "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(11), pages 436-445.
    3. 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.
    4. Ioannis Skevas, 2023. "A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1221-1247, August.
    5. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, . "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 0.

    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. 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.
    2. Jerzy Marzec & Andrzej Pisulewski, 2021. "Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 152-161.
    3. Amer Ait Sidhoum & K Hervé Dakpo & Laure Latruffe, 2022. "Trade-offs between economic, environmental and social sustainability on farms using a latent class frontier efficiency model: Evidence for Spanish crop farms," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    4. Antonio Alvarez & Carlos Arias, 2014. "A selection of relevant issues in applied stochastic frontier analysis," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 3-11.
    5. Fertő, Imre & Baráth, Lajos, 2013. "Heterogenitás és technikai hatékonyság - a magyar specializált szántóföldi növénytermesztő üzemek esete [Heterogeneity and technical efficiency - the case of Hungarys specialized arable crop produc," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 650-669.
    6. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," Working Papers 0038, Swiss Economics.
    7. Juan Cabas Monje & Bouali Guesmi & Amer Ait Sidhoum & José María Gil, 2023. "Measuring technical efficiency of Spanish pig farming: Quantile stochastic frontier approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 688-703, October.
    8. Alvarez, Antonio & del Corral, Julio & Tauer, Loren W., 2012. "Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models," Agricultural and Resource Economics Review, Cambridge University Press, vol. 41(3), pages 275-285, December.
    9. Mohamed Chaffai & Patrick Plane, 2017. "Firm Productivity, Technology and Export Status, What Can We Learn from Egyptian Industries?," Working Papers 1134, Economic Research Forum, revised 09 Jun 2017.
    10. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2022. "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach," Empirical Economics, Springer, vol. 62(3), pages 1345-1363, March.
    11. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    12. Ioannis Skevas, 2023. "A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1221-1247, August.
    13. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    14. Skevas, Ioannis & Emvalomatis, Grigorios & Brümmer, Bernhard, 2018. "Productivity growth measurement and decomposition under a dynamic inefficiency specification: The case of German dairy farms," European Journal of Operational Research, Elsevier, vol. 271(1), pages 250-261.
    15. K Hervé Dakpo & Laure Latruffe & Yann Desjeux & Philippe Jeanneaux, 2022. "Modeling heterogeneous technologies in the presence of sample selection: The case of dairy farms and the adoption of agri‐environmental schemes in France," Agricultural Economics, International Association of Agricultural Economists, vol. 53(3), pages 422-438, May.
    16. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    17. Goddard, John & Molyneux, Philip & Williams, Jonathan, 2014. "Dealing with cross-firm heterogeneity in bank efficiency estimates: Some evidence from Latin America," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 130-142.
    18. Pavlos Almanidis & Mustafa U. Karakaplan & Levent Kutlu, 2019. "A dynamic stochastic frontier model with threshold effects: U.S. bank size and efficiency," Journal of Productivity Analysis, Springer, vol. 52(1), pages 69-84, December.
    19. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    20. Guohua Feng & Todd Jewell, 2021. "Productivity and efficiency at english football clubs: a random coefficient approach," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(5), pages 571-604, November.

    More about this item

    Keywords

    Technology heterogeneity; Dairy farms; Bayesian techniques; Technical efficiency; Intensification;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

    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:spr:jqecon:v:17:y:2019:i:3:d:10.1007_s40953-018-0144-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.