Human Capital Investment and Labour Force Participation in Nigeria
Bibliographic record
Abstract
The study investigated the effect of human capital investment on labour force participation in Nigeria from 1990 to 2021. The study used Government Expenditure on Education, Government Expenditure on Health and Government Expenditure on Research and Development to proxy human capital investment as the independent variables while labour force participation rate was used as the dependent variable. Descriptive statistics, unit root test, vector error correction model test were employed to analyze the data. The study reveals that Government Expenditure on Education (XEDU) had a negative and significant impact on Labour force participation (LBFP) in Nigeria; Government Expenditure on health (XHLT) has a positive, but statistically insignificant impact on Labour force participation (LBFP) in Nigeria. The study thus concluded that human capital investment did not promote labour force participation in Nigeria. The study recommends that there is the need for the government in Nigeria to comply with the bench mark of 26% specified by UNESCO. There is the need to improve on the pay package of the health workers and teachers at all levels of education. The government should adopt efficient planning and monitoring.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".