Estimating Production and Environmental Efficiency under Uncertainty

The Arrow-Savage-Debreu framework provides a theoretical foundation for analyzing productivity and efficiency under uncertainty. It treats uncertainty while preserving core economic principles and offering insights into producer decision-making. A key challenge is to reconcile ex ante conceptual models with ex post empirical data. We survey econometric and mathematical programming methods used to address this challenge. These methods include stochastic production functions, latent state models, auxiliary-variable methods, and data envelopment analysis techniques. We discuss the strengths and limitations of each method, highlighting how they handle the fundamental challenge of measuring efficiency when production decisions are made under uncertainty but only realized outcomes are observable. Our analysis demonstrates that such measurement complexity necessitates carefully designed empirical approaches to capture the true nature of production and environmental efficiency.