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Analyzing Components of Productivity Growth Using the Bennet-Lowe Indicator: An Application to Welsh Sheep Farms

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  • Frederic Ang

Abstract

This article introduces the Bennet-Lowe Total Factor Productivity (TFP) indicator. The proposed measure is difference-based, additively complete, and transitive. We also develop a general nonparametric framework to exhaustively decompose all Bennet-type TFP indicators, including the one introduced here, into technical change, technical efficiency change, scale efficiency change, and mix efficiency change. This decomposition provides a powerful tool for policy makers to guide financial decisions on research and development, extension, subsidies, and price support. The empirical application focuses on the Welsh sheep sector for the years 2001–2014. The results show that in this 14-year period, Welsh sheep farms increased their TFP by 30.28% on average (2.33% p.a.). However, the exhaustive decomposition shows that TFP growth is not distributed equally across all farms, with an increasing divergence between front-runners and laggards. The negative values of scale efficiency change and mix efficiency change cast doubt on the current subsidy policies.

Suggested Citation

  • Frederic Ang, 2019. "Analyzing Components of Productivity Growth Using the Bennet-Lowe Indicator: An Application to Welsh Sheep Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(4), pages 1262-1276.
    • Handle: RePEc:oup:ajagec:v:101:y:2019:i:4:p:1262-1276.
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    File URL: http://hdl.handle.net/10.1093/ajae/aay111
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    Citations

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    Cited by:

    1. Frederic Ang & Pieter Jan Kerstens, 2023. "Robust nonparametric analysis of dynamic profits, prices and productivity: An application to French meat-processing firms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(2), pages 771-809.
    2. Andrew P. Barnes, 2023. "The role of family life‐cycle events on persistent and transient inefficiencies in less favoured areas," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 295-315, February.
    3. Frederic Ang & Stephen J. Ramsden, 2024. "Analysing determinate components of an approximated Luenberger–Hicks–Moorsteen productivity indicator: An application to German dairy‐processing firms," Agribusiness, John Wiley & Sons, Ltd., vol. 40(2), pages 349-370, April.
    4. Frederic Ang & Kristiaan Kerstens & Jafar Sadeghi, 2023. "Energy productivity and greenhouse gas emission intensity in Dutch dairy farms: A Hicks–Moorsteen by‐production approach under non‐convexity and convexity with equivalence results," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 492-509, June.
    5. 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.
    6. Ang, Frederic & Kerstens, Pieter Jan, 2020. "A superlative indicator for the Luenberger-Hicks-Moorsteen productivity indicator: Theory and application," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1161-1173.
    7. Barnes, Andrew P. & Bevan, Kev & Moxey, Andrew & Grierson, Sascha & Toma, Luiza, 2023. "Identifying best practice in Less Favoured Area mixed livestock systems," Agricultural Systems, Elsevier, vol. 208(C).
    8. Stefano NASINI & Rabia NESSAH, 2021. "Endogenous Learning in Multi-Sector Economies," Working Papers 2021-EQM-08, IESEG School of Management, revised Oct 2023.
    9. Stefan Wimmer & Christian Stetter & Jonas Schmitt & Robert Finger, 2024. "Farm‐level responses to weather trends: A structural model," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(3), pages 1241-1273, May.

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