What rates of productivity growth would be required to offset the effects of population aging? A study of twenty industrialized countries
Bibliographic record
Abstract
A shift in population distribution toward older ages is underway in industrialized countries throughout the world, and will continue well into the future. We provide a framework for isolating the pure effects of population aging on per capita GDP, employ the framework in calculations for twenty OECD countries, and derive the rates of productivity growth required to offset those effects. Taking the twenty countries as a whole, the average productivity growth rate (a simple unweighted arithmetic average) required to just offset aging effects over the full 30 years from 2015 to 2045 would be 4.2 per cent per decade, or approximately 0.4 per cent per year; to achieve an overall increase of 1 per cent in GDP per capita would require an average rate of 15.1 per cent per decade, or 1.4 per cent per year. We consider also some labour-related changes that might provide offsets, for comparison with productivity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".