0803 WHY SLEEP MATTERS: THE MACROECONOMIC COSTS OF INSUFFICIENT SLEEP
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".