Estimating input-mix efficiency in a parametric framework: application to state-level agricultural data for the United States

Abstract

This paper contributes to the productivity literature by demonstrating novel econometric methods to estimate input-mix efficiency (IME) in a parametric framework. Input-mix efficiency is defined as the potential improvement in productivity with change in input mix. Any change in input-mix (e.g., land to labor ratio) will result in change in productivity. We minimize a nonlinear input-aggregator function (e.g., Constant Elasticity of Substitution) to derive an expression for input-mix efficiency. We estimate a Bayesian stochastic frontier for obtaining mix efficiency using US state-level agricultural data for the period 1960-2004. We note significant variation in input-mix efficiency across the states and regions, attributable to diverse topographic, geographic and infrastructure conditions. Furthermore, comparisons of allocative and mix efficiencies provide insightful policy implications. For example, the production incentives such as taxes and subsidies could help farmers in adjusting their input mix in response to changes in input prices, which can affect the US agricultural productivity significantly. We provide a simple way of estimating mix efficiency in an aggregate-input, aggregate-output framework. This framework can be extended by i) using flexible functional forms; ii) introducing various time-and region-varying input aggregators; and iii) defining more sophisticated weights for input aggregators.

Innovation and Drivers of Productivity: A Global Analysis of Selected Critical Minerals

“…Productivity is often implicitly measured as the ratio of an aggregate output to an aggregate input [37]. The aggregation of inputs and outputs must be performed using aggregators [13,38]. The identification of appropriate aggregator functions is important for the construction of various indexes.…”

“…Applications range from agriculture [23][24][25] to manufacturing [2,26] and the services sector [27][28][29]. There are also other important drivers of TFP, including scale and scope economies and technical change, which need further investigation to identify comprehensive policy insights [13]. For instance, it would be interesting to know whether the uptake of technologies driving the productivity or scale and scope economies (as a result of appropriate output and input mixes) are important levers of TFP in the mining sector.…”

“…Understanding the causes of productivity decline is difficult. Several factors influence mining industry productivity and efficiency, including management approaches, effective resource allocation, scale economies, and, most importantly, innovation [13]. Embracing new technologies and optimal management practices also significantly influences the efficiency and productivity of the mining industry [14].…”

A conceptual framework for operational performance measurement in wholesale organisations

“…Sfez et al (2017) defined efficiency as the ratio between the benefits obtained from the amount of resources consumed. Similarly, Rodr ıguez et al (2016) and Ahmad (2020) consider efficiency as the rational use of resources to maximise benefits. However, according to the objective of the researcher, efficiency was defined as numerical values in different contexts.…”

“…Similarly, Rodríguez et al. (2016) and Ahmad (2020) consider efficiency as the rational use of resources to maximise benefits. However, according to the objective of the researcher, efficiency was defined as numerical values in different contexts.…”