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

Published online by Cambridge University Press:  15 September 2016

Antonio Alvarez ,
Julio del Corral  and
Loren W. Tauer
Antonio Alvarez
Affiliation:
Department of Economics at the University of Oviedo, in Oviedo, Spain
Julio del Corral
Affiliation:
Department of Economics and Finance at the University of Castilla-La Mancha, in Ciudad Real, Spain
Loren W. Tauer
Affiliation:
Charles H. Dyson School of Applied Economics and Management at Cornell University, in Ithaca, New York
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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.

Keywords

parlor milking systemstanchion milking systemlatent class modelstochastic frontier
Type
Contributed Papers
Information
Agricultural and Resource Economics Review , Volume 41 , Issue 3 , December 2012 , pp. 275 - 285
Copyright
Copyright © 2012 Northeastern Agricultural and Resource Economics Association 

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References

Ahmad, M., and Bravo-Ureta, B.E. 1995. “An Econometric Decomposition of Dairy Output Growth.” American Journal of Agricultural Economics 77(4): 914–921.Google Scholar
Aigner, D., Lovell, C.A.K., and Schmidt, P. 1977. “Formulation and Estimation of Stochastic Frontier Production Function Models.” Journal of Econometrics 6(1): 21–37.Google Scholar
Alvarez, A., and del Corral, J. 2010. “Identifying Different Technologies Using a Latent Class Model: Extensive versus Intensive Dairy Farms.” European Review of Agricultural Economics 37(2): 231–250.CrossRefGoogle Scholar
Alvarez, A., del Corral, J., Solis, D., and Pérez, J.A. 2008. “Does Intensification Improve the Economic Efficiency of Dairy Farms?” Journal of Dairy Science 91(9): 3693–3698.Google Scholar
Amsler, C., Lee, Y.H., and Schmidt, P. 2009. “A Survey of Stochastic Frontier Models and Likely Future Developments.” Seoul Journal of Economics 22(1): 5–27.Google Scholar
Bagi, F.S. 1984. “Stochastic Frontier Production Function and Farm-Level Technical Efficiency of Full-Time and Part-Time Farms in West Tennessee.” North Central Journal of Agricultural Economics 6(1): 48–55.Google Scholar
Balcombe, K., Fraser, I., Rahman, M., and Smith, L. 2007. “Examining the Technical Efficiency of Rice Producers in Bangladesh.” Journal of International Development 19(1): 1–16.Google Scholar
Battese, G.E., and Coelli, T.J. 1992. “Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India.” Journal of Productivity Analysis 3(1/2): 153–169.Google Scholar
Battese, G.E., Malik, S.J., and Broca, S. 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 32(3): 233–268.Google Scholar
Bravo-Ureta, B.E. 1986. “Technical Efficiency Measures for Dairy Farms Based on a Probabilistic Frontier Function Model.” Canadian Journal of Agricultural Economics 34(3): 399–415.Google Scholar
Brümmer, B., Glauben, T., and Thijssen, G. 2002. “Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries.” American Journal of Agricultural Economics 84(3): 628–644.CrossRefGoogle Scholar
Byma, J.P., and Tauer, L.W. 2010. “Exploring the Role of Managerial Ability in Influencing Dairy Farm Efficiency.” Agricultural and Resource Economics Review 39(3): 505–516.Google Scholar
Cabrera, V.E., Solís, D., and del Corral, J. 2010. “Determinants of Technical Efficiency Among Dairy Farms in Wisconsin.” Journal of Dairy Science 93(1): 387–393.Google Scholar
El-Osta, H.S., and Morehart, M.J. 2000. “Technology Adoption and Its Impact on Production Performance of Dairy Operations.” Review of Agricultural Economics 22(2): 477–498.Google Scholar
Emvalomatis, G. 2012. “Productivity Growth in German Dairy Farming Using a Flexible Modelling Approach.” Journal of Agricultural Economics 63(1): 83–101.Google Scholar
Fuwa, N., Edmonds, C., and Banik, P. 2007. “Are Small-Scale Rice Farmers in Eastern India Really Inefficient? Examining the Effects of Microtopography on Technical Efficiency Estimates.” Agricultural Economics 36(3): 335–346.CrossRefGoogle Scholar
Greene, W. 2005. “Reconsidering Heterogeneity in Panel Data Estimators of the Stochastic Frontier Model.” Journal of Econometrics 126(2): 269–303.Google Scholar
Greene, W. 2008. “The Econometric Approach to Efficiency Analysis.” In Fried, H., Lovell, C.A.K., and Schmidt, S., eds., The Measurement of Productive Efficiency and Productivity Growth. Oxford: Oxford University Press.Google Scholar
Hoch, I. 1962. “Estimation of Production Function Parameters Combining Time-Series and Cross-Section Data.” Econometrica 30(1): 34–53.Google Scholar
Jondrow, J., Lovell, C.A.K., Materov, I.S., and Schmidt, P. 1982. “On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model.” Journal of Econometrics 19(2/3): 233–238.Google Scholar
Knoblauch, W.A., Putnam, L.D., and Karszes, J. 2005. “Dairy Farm Management Business Summary New York State 2004.” Research Bulletin No. 2005-03, Department of Applied Economics and Management, Cornell University, Ithaca, NY.Google Scholar
Kompas, T., and Che, T.N. 2006. “Technology Choice and Efficiency on Australian Dairy Farms.” Australian Journal of Agricultural and Resource Economics 50(1): 65–83.Google Scholar
Kumbhakar, S.C., and Lovell, C.A.K. 2000. Stochastic Frontier Analysis. Cambridge, UK: Cambridge University Press.Google Scholar
Kumbhakar, S.C., Tsionas, E.G., and Sipiläinen, T. 2009. “Joint Estimation of Technology Choice and Technical Efficiency: An Application to Organic and Conventional Dairy Farming.” Journal of Productivity Analysis 31(3): 151–161.CrossRefGoogle Scholar
Moreira, V.H., and Bravo-Ureta, B.E. 2010. “Technical Efficiency and Metatechnology Ratios for Dairy Farms in Three Southern Cone Countries: A Stochastic Meta-Frontier Model.” Journal of Productivity Analysis 33(1): 33–15.CrossRefGoogle Scholar
Newman, C., and Matthews, A. 2006. “The Productivity Performance of Irish Dairy Farms 1984-2000: A Multiple Output Distance Function Approach.” Journal of Productivity Analysis 26(2): 191–205.Google Scholar
Orea, L., and Kumbhakar, S. 2004. “Efficiency Measurement Using a Latent Class Stochastic Frontier Model.” Empirical Economics 29(1): 169–183.Google Scholar
Sauer, J., and Morrison Paul, C.J. 2013. “The Empirical Identification of Heterogenous Technologies and Technical Change.” Applied Economics 45(11): 1461–1479.Google Scholar
Tauer, L.W. 1993. “Short-Run and Long-Run Efficiencies of New York Dairy Farms.” Agricultural and Resource Economics Review 22(1): 1–9.Google Scholar
Tauer, L.W. 1998. “Cost of Production for Stanchion Versus Parlor Milking in New York.” Journal of Dairy Science 81(2): 567–569.Google Scholar
Xu, X., and Jeffrey, S.R. 1998. “Efficiency and Technical Progress in Traditional and Modern Agriculture: Evidence from Rice Production in China.” Agricultural Economics 18(2): 157–165.Google Scholar