Nigeria's 2025 Tax Act in Comparative Perspective: Tax Rates, Compliance, and Fiscal Space with Econometric and Machine-Learning Evidence from Nigeria, South Africa, the United Kingdom, and the United States

Olatunji Dauda Ojedokun, Lagos State University, Faculty of Management Sciences, Department of Business Administration, Nigeria
Seun Adebanjo, Statistical Training and Consultation, NigeriaFollow

ORCID

O. D. Ojedokun: https://orcid.org/0009-0004-8835-8556

S. Adebanjo: https://orcid.org/0000-0002-2423-363X

Keywords

Nigeria 2025 Tax Act, Revenue efficiency, Compliance behaviour, Fiscal space, Machine learning models

Abstract

This study compares Nigeria's 2025 Tax Act with regimes in South Africa, the United Kingdom, and the United States using quarterly panel data (2000–2025) and complementary econometric and machine-learning methods. We estimate fixed-effects difference-in-differences, autoregressive distributed lag/cross-sectionally augmented autoregressive distributed lag error-correction models, dynamic panel generalised method of moments, and local projections to identify short- and long-run effects on revenue efficiency, compliance behaviour, and fiscal space. Predictions for VAT/corporate income tax revenues and gaps are generated with Elastic Net, Random Forest, XGBoost, and LightGBM, and heterogeneity is assessed via generalised random forests and doubly robust learners. Results show a significant, durable postreform rise in Nigeria's revenue efficiency, with the strongest gains where enforcement intensity and digitalisation are high; VAT/corporate income tax gaps shrink, and long-run elasticities confirm cointegration with policy fundamentals. Machine-learning models achieve high out-of-sample accuracy and highlight digitalisation, enforcement, VAT productivity, and tax capacity as dominant predictors. Policy implications emphasise pairing rate design with administrative capacity building.

Recommended Citation

Ojedokun, O., & Adebanjo, S. (2026). Nigeria's 2025 Tax Act in Comparative Perspective: Tax Rates, Compliance, and Fiscal Space with Econometric and Machine-Learning Evidence from Nigeria, South Africa, the United Kingdom, and the United States. Economic and Business Review, 28(3), 176-191. https://doi.org/10.15458/2335-4216.1374