ARDL-VECM ANALYSIS OF ARTIFICIAL INTELLIGENCE ADOPTION AND TAX REVENUE COLLECTION EFFICIENCY IN NIGERIA (2015–2025)

Authors: Unusotame, Okiemute Omenasa & Prof. Okolie, Augustine Oke

ABSTRACT

This study examines the influence of artificial intelligence (AI) adoption on tax revenue collection efficiency in Nigeria, drawing on quarterly time-series data spanning 2015 to 2025 (44 observations). Motivated by Nigeria’s persistent fiscal challenges — including volatile revenue growth, systemic leakages, elevated collection costs, and fragmented government data infrastructure — the study employs an Auto-Regressive Distributed Lag (ARDL) bounds testing framework and a Vector Error Correction Model (VECM), augmented by Johansen co-integration restrictions and Block Exogeneity Wald tests. Government AI Readiness Index Score was used as proxy for AI adoption while Revenue Collection Efficiency is decomposed into six dimensions: Tax Revenue Growth Rate, Financial Leakage Reduction, Operational Efficiency Ratio, Data Governance Score, Taxpayer Compliance, and Transparency and Accountability. Empirical results reject all six null hypotheses, confirming a highly significant long-run equilibrium network among the variables. The error correction term of -1.5885 (p < 0.001) demonstrates an oscillatory over-correction speed of adjustment of 158.8% per period. Long-run Johansen normalization restrictions confirm that transparency and accountability (β = +32.95, p = 0.008) and financial leakage reduction (β = +0.068, p = 0.004) are significant positive drivers of fiscal expansion, while the operational efficiency ratio reveals a massive cost-compressing impact (β = -57.42, p < 0.001). A locked 1-to-1 dynamic equilibrium between data governance and taxpayer compliance confirms that data integrity directly determines behavioral compliance. In the short run, a temporary friction lag accompanies AI deployment before long-term benefits materialize. The study concludes that AI adoption has catalyzed a structural transformation of Nigeria’s revenue administration, recommending aggressive scale-up of machine-learning forensic auditing, mandatory cross-institutional database synchronization, and automated predictive risk profiling to maximize sustainable fiscal performance.

Keywords: Artificial Intelligence; Tax Revenue Efficiency; Financial Leakage; Taxpayer Compliance; Transparency

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