Econometric Modeling of Input-Driven Output Risk through a Versatile CES Production Function

TL;DR

This paper introduces a generalized CES production function that captures diverse input effects on output risk, addressing limitations of the conventional CES form and improving risk modeling accuracy.

Contribution

It proposes a flexible CES variant that accounts for various input-driven output risks and develops a three-stage NLS estimation method for empirical application.

Findings

The generalized CES captures diverse input effects on output variability.

The proposed estimation method effectively models input-driven production risks.

Empirical applications demonstrate improved risk assessment in agriculture data.

Abstract

The conventional functional form of the Constant-Elasticity-of-Substitution (CES) production function is a general production function nesting a number of other forms of production functions. Examples of such functions include Leontief, Cobb-Douglas, and linear production functions. Nevertheless, the conventional form of the CES production specification is still restrictive in multiple aspects. One example is the fact that the marginal effect of increasing input use always has to be to increase the variability of output quantity by the conventional construction of this function. This paper proposes a generalized variant of the CES production function that allows for various input effects on the probability distribution of output. Failure to allow for this possible input-output risk structure is indeed one of the limitations of the conventional form of the CES production function. This limitation may result in false inferences about input-driven output risk. In light of this, the present paper proposes a solution to this problem. First, it is shown that the familiar CES formulation suffers from very restrictive structural assumptions regarding risk considerations, and that such restrictions may lead to biased and inefficient estimates of production quantity and production risk. Following the general theme of Just and Pope's approach, a CES-based production-function specification that overcomes this shortcoming of the original CES production function is introduced, and a three-stage Nonlinear Least-Squares (NLS) estimation procedure for the estimation of the proposed functional form is presented. To illustrate the proposed approaches in this paper, two empirical applications in irrigation and fertilizer response using the famous Hexem-Heady experimental dataset are provided. Finally, implications for modeling input-driven production risks are discussed.