Distributional Effects of Nigeria’s 2025 Tax Act on the Composition of State Tax Revenues across Taxpayer Groups

Distributional Effects of Nigeria’s 2025 Tax Act on the Composition of State Tax Revenues across Taxpayer Groups | 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 Article Distributional Effects of Nigeria’s 2025 Tax Act on the Composition of State Tax Revenues across Taxpayer Groups Olatunji Ojedokun, Seun Adebanjo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8148801/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 examined the distributional effects of Nigeria’s 2025 Tax Act on the composition of state tax revenue, with a focus on the PAYE–Direct Assessment mix. Using a balanced panel of 37 jurisdictions from 2016 to 2027, outcomes were constructed from NBS State IGR reports and population data, with covariates on e-payment intensity (CBN POS per 1,000 residents), MSME density (SMEDAN–NBS), formal employment shares (NBS Labour Survey), and CAC registrations. Identification relies on a post-Act indicator (2026–2027). Methods combine two-way fixed effects with interactions, an event study, dynamic estimation (System GMM), a causal ML T-learner, and an isolation forest anomaly screen. Results show a post-Act rise in PAYE share (+ 3.9 points), no pre-trend, and stronger effects in states with higher employment and CAC registrations. The T-learner estimates an average treatment effect of ≈ 0.14, highlighting heterogeneity. Policy implications stress digital rails, formalisation, and AI-enabled monitoring. Business and commerce/Economics Social science/Economics Physical sciences/Mathematics and computing PAYE Tax Revenue Two-Way Fixed Effects System GMM T-learner AI-enabled 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. 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Using a balanced panel of 37 jurisdictions from 2016 to 2027, outcomes were constructed from NBS State IGR reports and population data, with covariates on e-payment intensity (CBN POS per 1,000 residents), MSME density (SMEDAN\u0026ndash;NBS), formal employment shares (NBS Labour Survey), and CAC registrations. Identification relies on a post-Act indicator (2026\u0026ndash;2027). Methods combine two-way fixed effects with interactions, an event study, dynamic estimation (System GMM), a causal ML T-learner, and an isolation forest anomaly screen. Results show a post-Act rise in PAYE share (+\u0026thinsp;3.9 points), no pre-trend, and stronger effects in states with higher employment and CAC registrations. The T-learner estimates an average treatment effect of \u0026asymp;\u0026thinsp;0.14, highlighting heterogeneity. 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