Estimating Disaggregate Production Functions: An Application to Northern Mexico

This paper demonstrates a robust method for achieving disaggregation in the estimation of flexible-form farm-level multi-input production functions using minimally specified data sets. Since the ultimate goal is to address important questions related to the distributional effects of policy changes, the emphasis is placed on the ability of the model to reproduce the characteristics of the existing production system and to predict the outcomes of these changes at a high level of disaggregation. Achieving this requires the use of farm-level models that are estimated across a wide spectrum of sizes and types, which is often difficult to do with traditional econometric methods, due to limitations of data. In this estimation procedure, it is used a two-stage approach that first generates a set of observation-specific shadow values for incompletely priced inputs, such as irrigation water or family labor, which are used in the second stage, along with the nominal input prices, to produce estimates of crop-specific production functions using Generalized Maximum Entropy (GME) methods. This paper demonstrates this methodology through an empirical application to Mexico, drawing from a small set of cross-section data collected in the northern Rio Bravo regions.