Taxing high‐net‐worth individuals in Nigeria: Challenges and opportunities for policy‐makers from a preliminary investigation
Bibliographic record
Abstract
Abstract Motivation Nigeria ranks third in Africa for the number of US dollar millionaires, but whether these high‐net‐worth individuals (HNWIs) are contributing their fair share to domestic revenue mobilization is open to question. Although there have been various attempts to improve tax collection in recent years, including the establishment in 2023 of a presidential committee to harmonize fiscal policy across the country's 36 states, some of which are developing compliance strategies for wealthy individuals, very little is known about the impact of these reforms. Purpose To understand what approaches are currently prevalent to improve HNWI compliance across Nigeria and whether they are perceived to be effective. Methods The study is based on 12 semi‐structured interviews with public and private stakeholders from North East Nigeria, analysis of federal and state‐level legislation, data collected from 10 State Boards of the Internal Revenue Service from all Nigerian geopolitical zones in preparation for a two‐day workshop on HNWIs, and discussions with the 26 participants in the workshop. Findings Despite the great diversity in the economic and social structures of the states of Nigeria, legal, administrative, and political challenges faced by the State Boards of the Internal Revenue Service are very similar. Different states have passed subnational legislation that introduces requirements over and above those present in federal legislation to collect the information required to identify HNWIs. However, enforcement is made complex by low tax morale amongst the citizenship and political interference in tax administrative processes. These trends are then discussed in more depth for the particular case of Borno State. Policy implications Given the similarities between the obstacles faced by State Boards of the Internal Revenue Service in taxing HNWIs, there is scope for promoting regional approaches coordinated by the Nigerian Joint Tax Board. More evidence needs to be gathered on the effectiveness of policy measures implemented by particular states and the sharing of experiences across State Boards of the Internal Revenue Service needs to be facilitated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".