Effects of the International Fund for Agricultural Development’s (IFAD) Agri-Hub Initiatives on Youth’s Employment Outcomes in Nigeria

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Effects of the International Fund for Agricultural Development’s (IFAD) Agri-Hub Initiatives on Youth’s Employment Outcomes in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of the International Fund for Agricultural Development’s (IFAD) Agri-Hub Initiatives on Youth’s Employment Outcomes in Nigeria Nathaniel Olutegbe, Omotomiwa Adesanya, Waheed Oni, David Otu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9065263/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the effects of the International Fund for Agricultural Development’s (IFAD) Agri-Hub Nigerian (IAN) project on youth employment status and income change in Nigeria. A multi-stage sampling procedure resulted in 320 beneficiaries and 541 matched non-beneficiaries across the Abuja, Imo, and Oyo hubs. A combination of Propensity Score Matching (PSM) and instrumental variable techniques was employed, generating 300 well-balanced matched pairs. Independent sample t-test, ANOVA, Binary logistic regression and incremental model building (multiple linear regression) were used to isolate the impact on employment status and income changes. Findings reveal higher employment rates among both entrepreneurship and employment track beneficiaries compared to non-beneficiaries. For employment status, beneficiaries (88.3% employed) were significantly influenced by participation intensity (OR = 1.022) and prior income (OR = 1.000), although the entrepreneurship track showed a decreased odds of being employed (OR = 0.050), while non-beneficiaries (38.7% employed) depended on years of agribusiness experience (OR = 3.122) and involvement in crop processing (OR = 2.396). The incremental model building results show that regarding income change and employment status positively predicted gains for both beneficiaries (β = 26,606.47) and non-beneficiaries (β = 16,968.62). Group membership was the strongest predictor of employment for beneficiaries (β = 68.275.96), whereas baseline income was for non-beneficiaries (β = 0.421). Track-level analysis shows higher income gains for entrepreneurship participants (₦156,825) than for those in the wage employment track (₦44,666). The IAN project significantly improved youth employment status, with higher participation rates increasing employment likelihood, and income gains primarily driven by group membership and programme support services. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9065263","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609581050,"identity":"b3fa9c46-391e-43c3-ac83-bdb82792de74","order_by":0,"name":"Nathaniel 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A multi-stage sampling procedure resulted in 320 beneficiaries and 541 matched non-beneficiaries across the Abuja, Imo, and Oyo hubs. A combination of Propensity Score Matching (PSM) and instrumental variable techniques was employed, generating 300 well-balanced matched pairs. Independent sample t-test, ANOVA, Binary logistic regression and incremental model building (multiple linear regression) were used to isolate the impact on employment status and income changes. Findings reveal higher employment rates among both entrepreneurship and employment track beneficiaries compared to non-beneficiaries. For employment status, beneficiaries (88.3% employed) were significantly influenced by participation intensity (OR\u0026thinsp;=\u0026thinsp;1.022) and prior income (OR\u0026thinsp;=\u0026thinsp;1.000), although the entrepreneurship track showed a decreased odds of being employed (OR\u0026thinsp;=\u0026thinsp;0.050), while non-beneficiaries (38.7% employed) depended on years of agribusiness experience (OR\u0026thinsp;=\u0026thinsp;3.122) and involvement in crop processing (OR\u0026thinsp;=\u0026thinsp;2.396). The incremental model building results show that regarding income change and employment status positively predicted gains for both beneficiaries (β\u0026thinsp;=\u0026thinsp;26,606.47) and non-beneficiaries (β\u0026thinsp;=\u0026thinsp;16,968.62). Group membership was the strongest predictor of employment for beneficiaries (β\u0026thinsp;=\u0026thinsp;68.275.96), whereas baseline income was for non-beneficiaries (β\u0026thinsp;=\u0026thinsp;0.421). Track-level analysis shows higher income gains for entrepreneurship participants (₦156,825) than for those in the wage employment track (₦44,666). 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