Aggregation Issues in the Estimation of Linear Programming Productivity Measures

This paper demonstrates the sensitivity of the linear programming approach in the estimation of productivity measures in the primal framework using Malmquist productivity index and Malmquist total factor productivity index models. Specifically, the sensitivity of productivity measure to the number of constraints (level of dis-aggregation) and imposition of returns to scale constraints of linear programing 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 to illustrate sensitivity. Empirical application to U.S. state-level time series data from 1960-2004 reveal productivity change decreases with increases in the number of constraints. Further, the input and output shadow or dual values are skewed, leading to the difference in the productivity measures due to aggregation.