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Record W2626013774 · doi:10.25336/csp29355

What rates of productivity growth would be required to offset the effects of population aging? A study of twenty industrialized countries

2018· article· en· W2626013774 on OpenAlexaffvenue
Frank T. Denton, Byron G. Spencer

Bibliographic record

VenueCanadian Studies in Population · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProductivityPer capitaEconomicsDeveloped countryOffset (computer science)PopulationPopulation ageingDeveloping countryDemographic economicsPopulation growthDemographyEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.134
GPT teacher head0.487
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes2
Has abstractyes

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