Population Pyramid And Economic Growth: An Econometric analysis of Sri Lanka
Bibliographic record
Abstract
Economists are torn between basically three schools of thoughts where the first theory states that the population growth will stimulate the economic growth of a country and other believes that the population growth will bring detrimental or adverse impact to the economic growth. Not only that, but there is another school of thought, which believes that the population growth is a neutral factor in economic growth. Given this diverse of opinions, through this study it is expected to established a firm relationship between the population growth and the economic growth of Sri Lanka. This study developed an econometric model using time series data from 1980 to 2015 and tested the relationship not only the GDP of Sri Lanka, but other significant variables of an economy such as Domestic Savings, Private consumption and Total Investment as well. The results of this study indicate absence of a long term relationship between the population growth and the GDP of Sri Lanka and there will be no any relationship between the other selected variables and the population growth of Sri Lanka. The Granger Causality Analysis found out a unidirectional relationship between the GDP and the population growth, running from population growth to GDP. The study concludes that in Sri Lankan context, the population growth will not have any significant impact on the economic growth.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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".