BUILDING A SOCIAL CONTRACT: UNDERSTANDING TAX MORALE IN NIGERIA
Bibliographic record
Abstract
An essential part of every country's development process is building a social contract in whichcitizens pay taxes and, in turn, receive public goods and services ( Neil et al., 2021). Nigeria hasone of the world's lowest ratios of Tax to GDP. The available record showed that thecountry's tax to GDP was 6.1 percent in 2019 compared to South Africa of 28.6 percent,Namibia of 30.1 percent, Ghana of 17.6 percent and Ivory Coast of 17.4 percent to mention afew from Africa. The ratio was 46.2 percent in France, 46 percent in Denmark, 44.6 percent inBelgium, 40.6 percent in Cuba, 32.3 percent in Brazil and 32.2 in Canada, to mention a fewEuropean and American countries. Nigeria recorded a total tax collection of about N8.8trillion in 2019, the total taxes collected from oil and non-oil tax plus taxes collected byStates. Nigeria had a nominal GDP of N145.6 trillion as of December 2019.The poor fiscal capacity of Nigeria has been linked to poor tax morale. Some researchersdescribed this non-compliance as a cultural phenomenon, where tax evasion is the normrather than a crime.The tax morale in Nigeria has also been linked to the trust of taxpayers or citizens in thegovernment for effective utilization of the tax revenue to provide for their needs, such asinfrastructures.The social contract places revenue mobilization and revenue utilization on both ends offiscal legitimacy, emphasizing why individuals voluntarily surrender their hard-earnedincome to obtain governance benefits. Tax is the contribution made by members of a societytowards their collective welfare. It follows, therefore, that individuals with a positiveexperience of public service delivery and a feeling of inclusion in governance are morelikely to be encouraged to pay tax.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".