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 in | PloS one Vol. 16; no. 1; p. e0245426 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
22.01.2021
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/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. |
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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 – sequence: 2 givenname: Adebayo Isaiah surname: Ogunniyi fullname: Ogunniyi, Adebayo Isaiah – sequence: 3 givenname: Zainab surname: Oyetunde-Usman fullname: Oyetunde-Usman, Zainab – sequence: 4 givenname: Abiodun Olusola orcidid: 0000-0002-4423-9983 surname: Omotayo fullname: Omotayo, Abiodun Olusola – sequence: 5 givenname: Bola Amoke surname: Awotide fullname: Awotide, Bola Amoke |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33481874$$D View this record in MEDLINE/PubMed |
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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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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 |
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