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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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    1. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    2. Ragasa, Catherine & Dankyi, Awere & Acheampong, Patricia & Wiredu, Alexander Nimo & Chapoto, Antony & Asamoah, Marian & Tripp, Robert, 2013. "Patterns of adoption of improved rice technologies in Ghana:," GSSP working papers 35, International Food Policy Research Institute (IFPRI).
    3. Ragasa, Catherine & Takeshima, Hiroyuki & Chapoto, Antony & Kolavalli, Shashi, 2014. "Substituting for rice imports in Ghana," GSSP policy notes 6, International Food Policy Research Institute (IFPRI).
    4. Daniel Solís & Boris E. Bravo-Ureta & Ricardo E. Quiroga, 2007. "Soil conservation and technical efficiency among hillside farmers in Central America: a switching regression model ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(4), pages 491-510, December.
    5. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    6. repec:fpr:gsspwp:(6 is not listed on IDEAS
    7. Kodde, David A & Palm, Franz C, 1986. "Wald Criteria for Jointly Testing Equality and Inequality Restriction s," Econometrica, Econometric Society, vol. 54(5), pages 1243-1248, September.
    8. Sanzidur Rahman* & Basanta K. Barmon, 2015. "Productivity and efficiency impacts of urea deep placement technology in modern rice production: An empirical analysis from Bangladesh," Journal of Developing Areas, Tennessee State University, College of Business, vol. 49(3), pages 119-134, July-Sepe.
    9. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    10. Shaibu Baanni Azumah & Samuel A. Donkoh & Isaac Gershon K. Ansah, 2017. "Contract farming and the adoption of climate change coping and adaptation strategies in the northern region of Ghana," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(6), pages 2275-2295, December.
    11. Carlos D. Mayen & Joseph V. Balagtas & Corinne E. Alexander, 2010. "Technology Adoption and Technical Efficiency: Organic and Conventional Dairy Farms in the United States," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 181-195.
    12. Renato Villano & Boris Bravo-Ureta & Daniel Solís & Euan Fleming, 2015. "Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 129-154, February.
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    Cited by:

    1. Kozo Harimaya & Koichi Kagitani, 2022. "Efficiency, and economies of scale and scope in Japanese agricultural cooperatives," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 11(1), pages 1-19, December.
    2. 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.
    3. 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.
    4. 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.
    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.
    6. Rosli, Anita & Radam, Alias & Rahim, Khalid Abdul & Abdullah, Amin Mahir, 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 (AESS), vol. 10(03), January.

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