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Correcting for sample selection in stochastic frontier analysis: insights from rice farmers in Northern Ghana

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  • Shaibu Baanni Azumah

    (University for Development Studies)

  • Samuel Arkoh Donkoh

    (University for Development Studies)

  • Joseph Agebase Awuni

    (University for Development Studies)

Abstract

This study employs stochastic frontier analysis (SFA) correcting for sample selection bias, to determine technical efficiency (TE) and technology gap using cross-sectional data collected from 543 rice farmers in Northern Ghana. The results showed that corrected sample selection TE estimates were marginally higher. Without the appropriate corrections, inefficiency is overestimated, while the gap in performance between irrigation farmers and their rainfed counterparts is underestimated. We recommend that authorities in Ghana should work with development partners, especially in the implementation of small village-dam projects, and also to expand the existing irrigation schemes. Bunds should also be constructed around rice production valleys across northern Ghana so that farmers could expand their farm sizes to increase production. It is important also that the government’s input subsidy programme be structured to cater for experienced and younger farmers who consider agriculture as a business.

Suggested Citation

  • Shaibu Baanni Azumah & Samuel Arkoh Donkoh & Joseph Agebase Awuni, 2019. "Correcting for sample selection in stochastic frontier analysis: insights from rice farmers in Northern Ghana," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-15, December.
    • Handle: RePEc:spr:agfoec:v:7:y:2019:i:1:d:10.1186_s40100-019-0130-z
    DOI: 10.1186/s40100-019-0130-z
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    References listed on IDEAS

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    Cited by:

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    2. Fissha Asmare & Jūratė Jaraitė & Andrius Kažukauskas, 2022. "Climate change adaptation and productive efficiency of subsistence farming: A bias‐corrected panel data stochastic frontier approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(3), pages 739-760, September.
    3. Muhamad Zahid Muhamad & Mad Nasir Shamsudin & Nitty Hirawaty Kamarulzaman & Nolila Mohd Nawi & Jamaliah Laham, 2022. "Investigating Yield Variability and Technical Efficiency of Smallholders Pineapple Production in Johor," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    4. Anita Rosli & Alias Radam & Khalid Abdul Rahim & Amin Mahir Abdullah, 2020. "Technical Efficiency among Pepper Farmers in Sarawak, Malaysia: A Stochastic Frontier Analysis," Asian Journal of Agriculture and rural Development, Asian Economic and Social Society, vol. 10(3), pages 729-739, October.
    5. Ayobami Adetoyinbo & Verena Otter, 2022. "Can producer groups improve technical efficiency among artisanal shrimpers in Nigeria? A study accounting for observed and unobserved selectivity," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-33, December.

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