Economic Growth and Population Ageing in Nigeria: Innovation Accounting Techniques
Bibliographic record
Abstract
While global ageing suggests a triumph of social, economic, and medical advances over diseases, however, in economics regarding the growth implications of ageing, it is a puzzle as to what direction the effect will go. This motivates the current study to investigate empirically the economic growth consequences of population ageing in the context of Nigerian economy spanning between the period 1970 and 2015. Innovation Accounting Techniques was applied to assess the dynamic interactions among the variables. The results obtained revealed that the innovation in life expectancy and change in adult age dependency had the least contribution to the variation in per capita real GDP growth rate. The magnitude ranges between 1.45 and 8.33 percent. These results, thus, lend credence to the pessimistic view which contend that the inequality in a country’s population age structure, particularly, a greater share of the population of the elderly, depresses the country’s productivity level. Hence, the study recommends that any long term growth strategy aimed at boosting per capita income at a sustainable rate over the next ten (10) to fifteen (15) years needs to envision policies and reforms that are likely to foster savings and boost returns on them.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".