Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis

A DEA-based stochastic estimation framework is presented to evaluate contextual variables affecting productivity. Conditions are identified under which a two-stage procedure consisting of DEA followed by regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation in the second stage yields consistent estimators of the impact of contextual variables. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results suggest that DEA-based procedures perform as well as the best parametric method in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision making unit (DMU) productivity. (