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Record W2608355082 · doi:10.1093/sleepj/zsx050.802

0803 WHY SLEEP MATTERS: THE MACROECONOMIC COSTS OF INSUFFICIENT SLEEP

2017· article· en· W2608355082 on OpenAlexaboutno aff
Marco Hafner, WM Troxel, Jan L Taylor, Christian Van Stolk

Bibliographic record

VenueSLEEP · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsAbsenteeismPresenteeismProductivityPopulationEconomicsHuman capitalDemographic economicsPublic healthSleep (system call)MedicineEconomic growthEnvironmental health

Abstract

fetched live from OpenAlex

The Centers for Disease Control and Prevention (CDC) has declared insufficient sleep a ‘public health problem’. According to the CDC, more than a third of American adults are not getting enough sleep on a regular basis. However, insufficient sleep is not exclusively a U.S. problem, but also affects other industrialised countries such as the United Kingdom, Japan, Germany or Canada. A robust and growing literature has documented the public health consequences of insufficient sleep, in terms of increased morbidity and mortality. However, to date, there has been no comprehensive and cross-national study of the economic implications of sleep loss. Using a macroeconomic modelling approach, we develop a general equilibrium model (so called ‘Overlapping Generations Model’) that simulates various agents in an economy, including individuals, firms and the government, and their interactions over time. In our model, the effect of insufficient sleep is translated into the supply of effective labour units in the economy, which in turn, is affected through three mechanisms related to mortality and productivity: increased mortality risk associated with insufficient sleep which reduces the size of the working population; increased worker absenteeism or presenteeism (i.e., reduced performance while at work), and sub-optimal school performance in younger years which hinders skill development. The human capital effect is taken into account by modelling shifts in the skill distribution at the point in time when individuals enter the labour market. Our findings suggest that the relative estimated loss of economic output is highest for Japan (1.86 to 2.92 % of GDP), followed by the U.S (1.56 to 2.28 % of GDP), the UK (1.36 to 1.86 % of GDP), Germany (1.02 to 1.56 % of GDP) and Canada (0.85 to 1.56 % of GDP). This represents large annual macroeconomic costs related to insufficient sleep across five OECD countries ($ 457 billion to $ 680 billion in total). Substantial research has documented the public health consequences of sleep loss; however, these findings are the first, on a global-scale to demonstrate the significant economic consequences of sleep loss.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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