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

Shaibu Baanni Azumah (), Samuel Arkoh Donkoh () and Joseph Agebase Awuni ()
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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

Agricultural and Food Economics, 2019, vol. 7, issue 1, 1-15

Abstract: 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.

Keywords: Rice production; Sample selection; Stochastic frontier; Technical efficiency; Northern Ghana (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1186/s40100-019-0130-z

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