Effect of Longevity on Economic Growth, Accounting for Variability in Demographic Transition: Evidence for Pakistan using ARDL Bounds Testing Approach
Bibliographic record
Abstract
Rising longevity due to access to better health services affects the growth and composition of the population differently than birth rates. In addition, empirical evidence of the effects of rising longevity on standards of living is ambiguous. From the perspective of developing nations, it is important to understand how rising longevity affects national prosperity, as this allows governments to develop programs for increasing investment in the health sector. This study explicitly tested varying intertemporal impacts of rising longevity on the GDP per capita of Pakistan between 1967 and 2020. An Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration was used to estimate and compare short-run and long-run estimates of longevity. The results indicated that a 1% increase in longevity increased the growth rate of GDP per capita in Pakistan by 0.64% in the long-run. In addition, a 1% increase in life expectancy at birth above 62 years increased economic growth by an additional 0.009%. In general, the estimated effect of increased longevity varied by stages of demographic transition in Pakistan.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".