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Input Substitution, Productivity Performance and Farm Size

Author

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  • Sheng, Yu
  • Davidson, Alistair
  • Fuglie, Keith
  • Zhang, Dandan

Abstract

This paper develops a theoretical model to examine the relationship between the input elasticity of (technical) substitution and both farm total factor productivity and size. In the presence of ongoing technical change and its factor bias, the ‘income effect’ arising from farms’ cost minimising behaviour enables them to increase productivity by saving inputs or, through the dual equivalent, enlarging farm size. As such, farms with higher elasticities of substitution tend to grow larger and become more productive, which provides a new mechanism through which farm heterogeneity in productivity growth can be examined. Empirical evidence from Australian broadacre agriculture supports this theory and points to important policy implications.

Suggested Citation

  • Sheng, Yu & Davidson, Alistair & Fuglie, Keith & Zhang, Dandan, 2016. "Input Substitution, Productivity Performance and Farm Size," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 60(3), July.
    • Handle: RePEc:ags:aareaj:292460
    DOI: 10.22004/ag.econ.292460
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    Cited by:

    1. Elizabeth Ahikiriza & Joshua Wesana & Xavier Gellynck & Guido Van Huylenbroeck & Ludwig Lauwers, 2021. "Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda," Agriculture, MDPI, vol. 11(9), pages 1-19, August.
    2. Sheng, Yu & Chancellor, Will, 2019. "Exploring the relationship between farm size and productivity: Evidence from the Australian grains industry," Food Policy, Elsevier, vol. 84(C), pages 196-204.
    3. Zhang, Qian & Sun, Zhongxiao & Huang, Wei, 2018. "Does land perform well for corn planting? An empirical study on land use efficiency in China," Land Use Policy, Elsevier, vol. 74(C), pages 273-280.
    4. Will Chancellor, 2023. "Exploring the relationship between information and communication technology (ICT) and productivity: Evidence from Australian farms," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(2), pages 285-302, April.
    5. Yu Sheng & Tom Jackson & Shiji Zhao & Dandan Zhang, 2017. "Measuring Output, Input and Total Factor Productivity in Australian Agriculture: An Industry-Level Analysis," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63, pages 169-193, February.
    6. Juliane Haensch & Sarah Ann Wheeler & Alec Zuo, 2021. "Explaining permanent and temporary water market trade patterns within local areas in the southern Murray–Darling Basin," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(2), pages 318-348, April.
    7. S. C. West & A. W. Mugera & R. S. Kingwell, 2022. "The choice of efficiency benchmarking metric in evaluating firm productivity and viability," Journal of Productivity Analysis, Springer, vol. 57(2), pages 193-211, April.
    8. Qianqian Chen & Ruifa Hu & Yiduo Sun & Chao Zhang, 2020. "How Does Rural–Urban Migration Experience Affect Arable Land Use? Evidence from 2293 Farmers in China," Land, MDPI, vol. 9(11), pages 1-17, October.
    9. Sirkka Schukat & Heinke Heise, 2021. "Towards an Understanding of the Behavioral Intentions and Actual Use of Smart Products among German Farmers," Sustainability, MDPI, vol. 13(12), pages 1-24, June.

    More about this item

    Keywords

    Farm Management; Productivity Analysis;

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