Does agricultural cooperative membership impact technical efficiency of maize production in Nigeria: An analysis correcting for biases from observed and unobserved attributes

The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria,...

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Published inPloS one Vol. 16; no. 1; p. e0245426
Main Authors Olagunju, Kehinde Oluseyi, Ogunniyi, Adebayo Isaiah, Oyetunde-Usman, Zainab, Omotayo, Abiodun Olusola, Awotide, Bola Amoke
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 22.01.2021
Public Library of Science (PLoS)
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ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0245426

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Abstract The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria, this study assesses the impact of agricultural cooperative membership on technical efficiency (TE). We based our estimation approach on the combination of a newly developed sample selection stochastic production frontier model with propensity score matching to control for possible selectivity biases from both observables and unobservables. We estimate stochastic meta-frontiers to examine TE differences between cooperative members and non-members. Our results reveal that TE levels of members are consistently higher than that of non-members. This calls for continued policy incentives targeted at encouraging farmers to form as well as participate in agricultural cooperatives.
AbstractList The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria, this study assesses the impact of agricultural cooperative membership on technical efficiency (TE). We based our estimation approach on the combination of a newly developed sample selection stochastic production frontier model with propensity score matching to control for possible selectivity biases from both observables and unobservables. We estimate stochastic meta-frontiers to examine TE differences between cooperative members and non-members. Our results reveal that TE levels of members are consistently higher than that of non-members. This calls for continued policy incentives targeted at encouraging farmers to form as well as participate in agricultural cooperatives.
[...]the unclear empirical relationship between cooperative membership and TE requires further re-examination. [...]we provide the first attempt, in the Nigeria context, to examine the impact of agricultural cooperatives on TE of maize farmers while making sure that we corrected for potential sources of selection biases from unobserved attributes by employing a selectivity-corrected stochastic frontier technique developed by Greene [22] and propensity score matching approach to correct for the selection bias stemming from observable factors. [...]in reality, farmer’s decision to join cooperatives is non-random which simply implies that such a decision is based on the individual farmer’s self-selection into membership. [...]the duo of unobserved factors (such as risk behaviour and farmer managerial skills) and observed factors (such as household size, literacy level, and age) may affect farmer’s decision to participate which may lead to sample selection bias. [...]we assess the farm managerial, socioeconomic and plot-specific factors affecting the decision of maize farmers to participate in agricultural cooperatives, using a comprehensive survey data that cuts across the six geopolitical zones of Nigeria.
The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria, this study assesses the impact of agricultural cooperative membership on technical efficiency (TE). We based our estimation approach on the combination of a newly developed sample selection stochastic production frontier model with propensity score matching to control for possible selectivity biases from both observables and unobservables. We estimate stochastic meta-frontiers to examine TE differences between cooperative members and non-members. Our results reveal that TE levels of members are consistently higher than that of non-members. This calls for continued policy incentives targeted at encouraging farmers to form as well as participate in agricultural cooperatives.The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria, this study assesses the impact of agricultural cooperative membership on technical efficiency (TE). We based our estimation approach on the combination of a newly developed sample selection stochastic production frontier model with propensity score matching to control for possible selectivity biases from both observables and unobservables. We estimate stochastic meta-frontiers to examine TE differences between cooperative members and non-members. Our results reveal that TE levels of members are consistently higher than that of non-members. This calls for continued policy incentives targeted at encouraging farmers to form as well as participate in agricultural cooperatives.
[...]the unclear empirical relationship between cooperative membership and TE requires further re-examination. [...]we provide the first attempt, in the Nigeria context, to examine the impact of agricultural cooperatives on TE of maize farmers while making sure that we corrected for potential sources of selection biases from unobserved attributes by employing a selectivity-corrected stochastic frontier technique developed by Greene [22] and propensity score matching approach to correct for the selection bias stemming from observable factors. [...]in reality, farmer’s decision to join cooperatives is non-random which simply implies that such a decision is based on the individual farmer’s self-selection into membership. [...]the duo of unobserved factors (such as risk behaviour and farmer managerial skills) and observed factors (such as household size, literacy level, and age) may affect farmer’s decision to participate which may lead to sample selection bias. [...]we assess the farm managerial, socioeconomic and plot-specific factors affecting the decision of maize farmers to participate in agricultural cooperatives, using a comprehensive survey data that cuts across the six geopolitical zones of Nigeria.
Audience Academic
Author Olagunju, Kehinde Oluseyi
Oyetunde-Usman, Zainab
Ogunniyi, Adebayo Isaiah
Omotayo, Abiodun Olusola
Awotide, Bola Amoke
AuthorAffiliation 1 Economics Research Branch, Agri-Food and Biosciences Institute, Belfast, United Kingdom
2 International Food Policy Research Institute (IFPRI), Abuja, Nigeria
5 International Institute of Tropical Agriculture (IITA), Bamako, Mali
3 Natural Resources Institute, University of Greenwich, Chatham Maritime, United Kingdom
4 Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North West University, Mmabatho, South Africa
Universidad de Murcia, SPAIN
AuthorAffiliation_xml – name: 4 Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North West University, Mmabatho, South Africa
– name: Universidad de Murcia, SPAIN
– name: 3 Natural Resources Institute, University of Greenwich, Chatham Maritime, United Kingdom
– name: 2 International Food Policy Research Institute (IFPRI), Abuja, Nigeria
– name: 5 International Institute of Tropical Agriculture (IITA), Bamako, Mali
– name: 1 Economics Research Branch, Agri-Food and Biosciences Institute, Belfast, United Kingdom
Author_xml – sequence: 1
  givenname: Kehinde Oluseyi
  orcidid: 0000-0002-5619-054X
  surname: Olagunju
  fullname: Olagunju, Kehinde Oluseyi
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  givenname: Adebayo Isaiah
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  surname: Awotide
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2021 Olagunju et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Competing Interests: The authors have declared that no competing interests exist.
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SSID ssj0053866
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Snippet The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with...
[...]the unclear empirical relationship between cooperative membership and TE requires further re-examination. [...]we provide the first attempt, in the...
[...]the unclear empirical relationship between cooperative membership and TE requires further re-examination. [...]we provide the first attempt, in the...
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SourceType Open Website
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StartPage e0245426
SubjectTerms Agricultural cooperatives
Agricultural production
Agriculture
Agriculture, Cooperative
Bias
Biology and Life Sciences
Corn
Crop production
Economic aspects
Efficiency
Empirical analysis
Evaluation
Farm management
Farmers
Farming
Food security
Households
Industrial efficiency
People and Places
Research and Analysis Methods
Risk management
Risk taking
Rural areas
Selectivity
Stochasticity
Technology adoption
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Title Does agricultural cooperative membership impact technical efficiency of maize production in Nigeria: An analysis correcting for biases from observed and unobserved attributes
URI https://www.ncbi.nlm.nih.gov/pubmed/33481874
https://www.proquest.com/docview/2479993997
https://www.proquest.com/docview/2480250792
https://pubmed.ncbi.nlm.nih.gov/PMC7822389
https://doaj.org/article/21a267bbc57e4b8cadb76917d90e2b47
http://dx.doi.org/10.1371/journal.pone.0245426
Volume 16
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