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Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models

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Author Info

  • Alvarez, Antonio
  • del Corral, Julio
  • Tauer, Loren W.

Abstract

Agricultural production estimates have often differentiated and estimated different technologies within a sample of farms. The common approach is to use observable farm characteristics to split the sample into groups and subsequently estimate different functions for each group. Alternatively, unique technologies can be determined by econometric procedures such as latent class models. This paper compares the results of a latent class model with the use of a priori information to split the sample using dairy farm data. Latent class separation appears to be a superior method of separating heterogeneous technologies and suggests that technology differences are multifaceted.

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Bibliographic Info

Article provided by Northeastern Agricultural and Resource Economics Association in its journal Agricultural and Resource Economics Review.

Volume (Year): 41 (2012)
Issue (Month): 3 (December)
Pages:

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Handle: RePEc:ags:arerjl:141702

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Web page: http://www.narea.org/
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Related research

Keywords: parlor milking system; stanchion milking system; latent class model; stochastic frontier; Production Economics; Productivity Analysis; Research Methods/ Statistical Methods;

References

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  1. Xu, Xiaosong & Jeffrey, Scott R., 1998. "Efficiency and technical progress in traditional and modern agriculture: evidence from rice production in China," Agricultural Economics, Blackwell, vol. 18(2), pages 157-165, March.
  2. Byma, Justin P. & Tauer, Loren W., 2010. "Exploring the Role of Managerial Ability in Influencing Dairy Farm Efficiency," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 39(3), October.
  3. Kelvin Balcombe & Iain Fraser & Mizanur Rahman & Laurence Smith, 2007. "Examining the technical efficiency of rice producers in Bangladesh," Journal of International Development, John Wiley & Sons, Ltd., vol. 19(1), pages 1-16.
  4. 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.
  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. Kompas, Tom & Che, Tuong Nhu, 2006. "Technology choice and efficiency on Australian dairy farms," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 50(1), March.
  7. 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.
  8. Nobuhiko Fuwa & Christopher Edmonds & Pabitra Banik, 2007. "Are small-scale rice farmers in eastern India really inefficient? Examining the effects of microtopography on technical efficiency estimates," Agricultural Economics, International Association of Agricultural Economists, vol. 36(3), pages 335-346, 05.
  9. George E. Battese & Sohail J. Malik & Sumiter Broca, 1993. "Production Functions for Wheat Farmers in Selected Districts of Pakistan: An Application of a Stochastic Frontier Production Function with Time-varying Inefficiency Effects," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 32(3), pages 233-268.
  10. Bernhard Br�mmer & Thomas Glauben & Geert Thijssen, 2002. "Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 628-644.
  11. Xu, Xiaosong & Jeffrey, Scott R., 1998. "Efficiency and technical progress in traditional and modern agriculture: evidence from rice production in China," Agricultural Economics: The Journal of the International Association of Agricultural Economists, International Association of Agricultural Economists, vol. 18(2), March.
  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, Foundation for the European Review of Agricultural Economics, vol. 37(2), pages 231-250, 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, 02.
  14. Tauer, Loren W., 1993. "Short-Run And Long-Run Efficiencies Of New York Dairy Farms," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 22(1), April.
  15. Ahmad, Munir & Boris E., Bravo-Ureta, 1995. "An econometric decomposition of dairy output growth," MPRA Paper 37678, University Library of Munich, Germany.
  16. 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.
  17. 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.
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Cited by:
  1. 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
    ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 650-669.

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