The Road to Tax Collection Digitalization: An Assessment of the Effectiveness of Digital Payment Systems in Nigeria and the Role of Macroeconomic Factors
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Bibliographic record
Abstract
The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, the focus of this study is to assess the implications of digital payment systems in enhancing the effectiveness of tax revenue collection in Nigeria. The analysis spans from the first quarter of 2009 to the fourth quarter of 2023, utilizing the Autoregressive Distributed Lag and Error Correction Model. The research uses the most active digital payment systems that have been in operation during the study period. These electronic payment types include digital cheques (CHQs), Automated Teller Machines (ATMs), Point-of-Sales (POSs), Mobile payment (MPY), and Web-based payment (WPY). These are the predictor variables, while the tax revenue collection (TXC) during this period is the dependent variable. The control variables include information and telecommunication technology penetration rate (ICTPR), inflation, and gross domestic product. The outcomes of this study reveal that, over the long term, a percentage change in CHQs, ATMs, MPY, and ICTPR is linked to a decline of 8.1%, 12.5%, 6.7%, and 22.4% in TXC, respectively. In contrast, WPY indicates a 7.2% positive increase in TXC while inflation exerts a positive increase of 46.7%. The Error Correction Model (ECM) suggests that the deviations from the long-term equilibrium in earlier years are being corrected at a rate of 3.9% in the current year. In the short term, it is noted that digital payment systems do not influence TXC. On the other hand, GDP maintains a significant negative influence on TXC, in both the long- and short-term. Given these results, the study recommends the establishment of a robust information and communication technology (ICT) infrastructure to enhance effective tax collection, even from rural areas and the informal sector. It is also important for the government to develop strategies that will bring the informal sector into the tax net.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it