Please use this identifier to cite or link to this item: http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32049
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dc.contributor.authorSallawu, H.-
dc.contributor.authorAbdullahi, A.-
dc.contributor.authorAbduljelil, A. A.-
dc.contributor.authorAdekunle, D. D.-
dc.contributor.authorAdesakin, A. P.-
dc.contributor.authorAdeseun, A. E.-
dc.contributor.authorAuru, A.-
dc.contributor.authorBomodeoku, R. O.-
dc.contributor.authorDakay, B. W.-
dc.contributor.authorJibrin, A.-
dc.contributor.authorMoses, J.-
dc.contributor.authorOguntebi, G. T.-
dc.contributor.authorOkafor, F. O.-
dc.contributor.authorOshin, E. O.-
dc.contributor.authorPeters, A. O.-
dc.contributor.authorNmadu, J. N.-
dc.date.accessioned2026-11-04T18:33:09Z-
dc.date.available2026-11-04T18:33:09Z-
dc.date.issued2026-
dc.identifier.urihttp://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32049-
dc.description.abstractUsing data from 1,614 farming households across 26 Local Government Areas in 13 Nigerian states, a Multidimensional Poverty (MDP) index comprising seven dimensions, i.e., health, nutrition, education, water and sanitation, living standard, social security, employment and income; was computed as the dependent variable. A Beta regression model was applied to determine the influence of socio-economic, infrastructural, occupational, and household-level factors because MDPI is a bounded continuous variable between 0 and 1. The model explained 73.7% of the variation in MDP with significant State-level differences, particularly, Osun, Oyo, Kaduna states, and the FCT exhibited between 10% and 28% higher MDP in comparison to Benue state, the base. Human capital indicators, particularly tertiary education, was associated with up to 3.3% reduction in poverty, echoing findings that education is a pathway out of deprivation. All-season roads and respondents living in urban areas experienced lower poverty, consistent with studies on infrastructure and rural welfare. Energy access emerged as a critical factor: reliance on fuelwood exacerbated poverty, while access to gas, solar, and grid electricity significantly reduced it, supporting evidence on the welfare impacts of modern energy access. Occupational diversification into non-agricultural employment (e.g., civil/public service, forestry, healthcare) was associated with lower poverty, aligning with findings on livelihood diversification. Conversely, reliance on maternity homes and Traditional medicine practitioners was directly associated with deprivation, while access to hospitals mitigated it, reinforcing health–poverty linkages. Numerical variables revealed that higher income and asset values decreased poverty, whereas security shocks, unemployment, and underemployment strongly heightened vulnerability, consistent with recent Nigerian studies. The findings underscore that poverty is multidimensional, shaped by structural, household, and livelihood factors. Policies that expand educational access, improve rural infrastructure, enhance modern energy supply, and foster non-agricultural employment while mitigating security risks are essential pathways for poverty reduction. These results corroborate earlier empirical studies on the critical role of human capital, infrastructure, and livelihood diversification in addressing MDP in Sub-Saharan Africa.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Agricultural Science, Research and Technology in Extension and Education Systemsen_US
dc.relation.ispartofseriesVol. 16 Issue (2);143-162-
dc.subjectBeta regressionen_US
dc.subjectEducationen_US
dc.subjectEnergy accessen_US
dc.subjectInfrastructureen_US
dc.subjectRural householdsen_US
dc.subjectlivelihood diversificationen_US
dc.titleDrivers and determinants of multidimensional poverty among Nigerian farming households.en_US
dc.typeArticleen_US
Appears in Collections:Agricultural Economics and Farm Management

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