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Estimating Input-Mix Efficiency in a Parametric Framework: Application to State-Level Agricultural Data for the United States

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  • Shabbir Ahmad

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.

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  • Shabbir Ahmad, 2017. "Estimating Input-Mix Efficiency in a Parametric Framework: Application to State-Level Agricultural Data for the United States," Economic Theory, Applications and Issues Working Papers 263149, University of Queensland, School of Economics.
    • Handle: RePEc:ags:uqseet:263149
    DOI: 10.22004/ag.econ.263149
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    1. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
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    4. Timmer, C P, 1971. "Using a Probabilistic Frontier Production Function to Measure Technical Efficiency," Journal of Political Economy, University of Chicago Press, vol. 79(4), pages 776-794, July-Aug..
    5. Efthymios G. Tsionas & Subal C. Kumbhakar & Emir Malikov, 2015. "Estimation of Input Distance Functions: A System Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(5), pages 1478-1493.
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    Cited by:

    1. Shabbir Ahmad & Sriram Shankar & John Steen & Martie-Louise Verreynne & Abid Aman Burki, 2018. "What Drives Smallholders' Productivity in Pakistan's Horticultural Sector?," Discussion Papers Series 597, School of Economics, University of Queensland, Australia.
    2. Svizzero, Serge & Tisdell, Clement, 2019. "Barter and the Origin of Money and Some Insights from the Ancient Palatial Economies of Mesopotamia and Egypt," Economic Theory, Applications and Issues Working Papers 291788, University of Queensland, School of Economics.
    3. Ahmad, Shabbir & Shankar, Sriram & Steen, John & Verreynne, Martie-Louise & Burki, Abid Aman, 2021. "Using measures of efficiency for regionally-targeted smallholder policy intervention: The case of Pakistan’s horticulture sector," Land Use Policy, Elsevier, vol. 101(C).
    4. Ahmad, Shabbir & Steen, John & Ali, Saleem & Valenta, Rick, 2023. "Carbon-adjusted efficiency and technology gaps in gold mining," Resources Policy, Elsevier, vol. 81(C).

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    Keywords

    Agribusiness; Production Economics; Productivity Analysis;
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