TY - JOUR
T1 - Aggregation issues in the estimation of linear programming productivity measures
AU - Shaik, Saleem
AU - Mishra, Ashok K.
AU - Atwood, Joseph
N1 - Funding Information:
* Saleem Shaik (corresponding author): 504 Richard Barry Hall, Dept of Agribusiness and Applied Economics, North Dakota State University, Fargo, ND-59108, [email protected]. Ashok Mishra: 211 Martin D. Woodin Hall, Dept. of Agricultural Economics and Agribusiness, Louisiana State UniversityAgCenter, Baton Rouge, LA 70803, [email protected]. Joseph Atwood: Dept of Agricultural Economics and Economics, Montana State University, Bozeman, MT-59717, [email protected]. We thank the editor and two anonymous reviewers for their valuable suggestions that greatly improved the exposition and readability of the paper. Shaik’s time on this project was supported by North Dakota State University Experiment Station project, Hatch project ND01397. Mishra’s time on this project was supported by the USDA Cooperative State Research Education & Extension Service, Hatch project # 0212495 and Louisiana State University Experiment Station project # LAB 93872.
PY - 2012/5
Y1 - 2012/5
N2 - This paper demonstrates the sensitivity of the linear programming approach in the estimation of productivity measures in the primal framework. Specifically, the sensitivity to the number of constraints (level of dis-aggregation) and imposition of returns to scale constraints is evaluated. Further, the shadow or dual values are recovered from the linear program and compared to the market prices used in the ideal Fisher index approach. Empirical application to U.S. state-level time series data from 1960-2004 reveal productivity change decreases with increases in the number of constraints. Divergence in productivity measures is observed due to the choice of method imposed, various levels of commodity/input aggregation, and technology assumptions. Due to the piecewise linear approximation of the nonparametric programming approach, the shadow share-weights are skewed leading to the difference in the productivity measures due to aggregation.
AB - This paper demonstrates the sensitivity of the linear programming approach in the estimation of productivity measures in the primal framework. Specifically, the sensitivity to the number of constraints (level of dis-aggregation) and imposition of returns to scale constraints is evaluated. Further, the shadow or dual values are recovered from the linear program and compared to the market prices used in the ideal Fisher index approach. Empirical application to U.S. state-level time series data from 1960-2004 reveal productivity change decreases with increases in the number of constraints. Divergence in productivity measures is observed due to the choice of method imposed, various levels of commodity/input aggregation, and technology assumptions. Due to the piecewise linear approximation of the nonparametric programming approach, the shadow share-weights are skewed leading to the difference in the productivity measures due to aggregation.
KW - Aggregation
KW - Malmquist productivity index
KW - Malmquist total factor productivity index
KW - Share-weights
KW - Single and multiple output and input
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U2 - 10.1016/S1514-0326(12)60008-7
DO - 10.1016/S1514-0326(12)60008-7
M3 - Article
AN - SCOPUS:84862500035
SN - 1514-0326
VL - 15
SP - 169
EP - 187
JO - Journal of Applied Economics
JF - Journal of Applied Economics
IS - 1
ER -