Endogenous Dynamic Efficiency in the Intertemporal Optimization Models of Firm Behavior

Abstract

Existing methods for the measurement of technical efficiency in the dynamic production models obtain it from the implied distance functions without making use of the information about intertemporal economic behavior in the estimation beyond an indirect appeal to duality. The main limitation of such an estimation approach is that it does not allow for the dynamic evolution of efficiency that is explicitly optimized by the firm. This paper introduces a new conceptualization of efficiency that directly enters the firm's intertemporal production decisions and is both explicitly costly and endogenously determined. We build a moment-based multiple-equation system estimation procedure that incorporates both the dynamic and static optimality conditions derived from the firm's intertemporal expected cost minimization. We operationalize our methodology using a modified version of a Bayesian Exponentially Tilted Empirical Likelihood adjusted for the presence of dynamic latent variables in the model, which we showcase using the 1960-2004 U.S. agricultural farm production data. We find that allowing for potential endogenous adjustments in efficiency over time produces significantly higher estimates of technical efficiency, which is likely due to inherent inability of the more standard exogenous-efficiency model to properly credit firms for incurring efficiency-improvement adjustment costs. Our results also suggest material improvements in efficiency over time at an about 2.6% average annual rate, which contrasts with near-zero estimates of the exogenous efficiency change.

Keywords: dynamic efficiency, endogenous efficiency, intertemporal optimization, Bayesian analysis, Markov Chain Monte Carlo, sequential Monte Carlo

JEL Classification: C11, D24, Q10