Economic Growth and Demographic Dividend Nexus in Nigeria: A Vector Autoregressive (VAR) Approach
Bibliographic record
Abstract
In demography and population economics discourse, the macroeconomic implications of an upsurge in working age population, notably the labour force, on economic growth has been widely studied and the inherent beneficial impact has become known as demographic dividend. However, the exact mechanism linking the dividend to growth remains a perennial question. This motivates the current study to investigate empirically the dividend-growth nexus in the context of Nigerian economy in a multivariate VAR model spanning between the period 1970 and 2017. Specifically, the paper attempted to answer the question: Is the Nigerian Demographic Dividend an Education-triggered Dividend? Innovation Accounting Techniques was applied to assess the dynamic interactions among the variables. The empirical evidence obtained revealed that the innovation in gross enrollment made much contribution to the variation in economic growth relative to innovation in economic support ratio. The magnitude ranges between 20.09 and 27.54 percent. This result, thus, lend credence to the theoretical view of the education-triggered dividend model which ascribes to education twofold roles of helping to lessen fertility and also enhancing productivity but invalidates the conventional dividend paradigm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".