Domestic Resource Mobilization and Under-Five Mortality in Nigeria
Bibliographic record
Abstract
This study considered domestic resource mobilisation and allocation to healthcare service delivery due to the high rate of infant deaths in Nigeria. Value addition in the agricultural sector is captured as a major source of revenue which could be channelled towards increased government expenditure in healthcare. As such, the paper applies vector error correction technique on yearly data for the period 1981 to 2015. It investigates the long-run relationship and short-run dynamics between under-five mortality on the one hand, with female literacy, agricultural productivity, tax revenue, and gross capital formation on the other hand. Outcome of the study indicates, from a long run perspective, under-five mortality is positively related to tax revenue, female literacy rate and gross capital formation. However, there is a negative relationship between under-5 mortality and agricultural productivity. Moreover, as Nigeria diversifies away from crude oil towards agriculture, increased productivity and tax revenues could be channelled towards better health outcomes and rural transformation. Furthermore, enhanced management of tax and better budgeting towards the agricultural sector is recommended. In addition, infrastructure development, agro-allied investments will ensure reduction in agricultural waste and supply costs. The outcomes portend significant relevance for meeting Sustainable Development Goals 2, 3, 4, & 10.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".