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Record W4213295302 · doi:10.1111/padr.12469

Six Ways Population Change Will Affect the Global Economy

2022· article· en· W4213295302 on OpenAlexfundno aff
Andrew Mason, Ronald Lee

Bibliographic record

VenuePopulation and Development Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
FundersEconomic and Social Research CouncilWilliam and Flora Hewlett FoundationEuropean CommissionInternational Development Research CentreUnited Nations Population FundNational Institute on AgingWorld Bank Group
KeywordsPopulationEconomicsStandard of livingPopulation ageingProductivityPopulation growthDistribution (mathematics)Demographic economicsDemographic changeAffect (linguistics)DebtDevelopment economicsEconomic growthDemographyMarket economyMacroeconomics

Abstract

fetched live from OpenAlex

Abstract New estimates of economic flows by age combined with population projections show that in the coming decades (1) global GDP growth could be slower by about 1 percentage point per year, declining more sharply than population growth; (2) GDP will shift toward sub‐Saharan Africa more than population trends suggest; (3) living standards of working‐age adults may be squeezed by high spending on children and seniors; (4) changing population age distribution will raise living standards in many lower‐income nations; (5) changing economic life cycles will amplify the economic effects of population aging in many higher income economies; and (6) population aging will likely push public debt, private assets, and perhaps productivity higher. Population change will have profound implications for national, regional, and global economies.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.089
GPT teacher head0.256
Teacher spread0.168 · 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 designSimulation or modeling
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

Citations113
Published2022
Admission routes1
Has abstractyes

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