Sources of State Revenue and State Effectiveness: The Nigerian Experience
Bibliographic record
Abstract
Ineffectiveness of states has been linked to poor fiscal-social contract between states and her citizens which is a consequence of how states raise her revenues. Hence, this paper examines the relative impacts of earned and unearned revenues on different measures of state effectiveness in terms of provision of basic public goods and development of economic and political institutions in Nigeria over the period 1996 to 2018, using Autoregressive Distributive Lag (ARDL) estimation technique. The paper found that, on one hand, an increase in earned revenue instigates improvement in provision of health care, while increase in unearned revenue had no significant impact on provision of health. On the other hand, a one-percent (1%) increase in earned revenue had a greater impact on educational enrollment than a 1% increase in unearned revenue. Increase in earned revenue increases state effectiveness while increase in unearned revenue reduces state effectiveness. The paper concludes that, the effectiveness of Nigerian government in provision of basic public goods and development of strong economic and political institutions might improve if government increases their financial resources through taxes than increase in oil revenue.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".