Spring Forward = Fall Back? The Effect of Daylight Saving Time Change on Consumers’ Unhealthy Behavior
Bibliographic record
Abstract
Prior research documents deleterious consequences of the annual clock change to daylight saving time in many contexts, but little is known about the effect the policy has on consumer behavior. While policy debates around ending seasonal clock changes continue, millions of consumers worldwide are potentially adversely affected by the time change. Drawing on the notions of sleepiness and self-control, the authors propose a framework of how the onset of daylight saving time increases unhealthy behavior. The hypotheses are tested via two studies cast in the difference-in-differences modeling framework capturing consumption before and after the time change and across consumers who experience the transition versus those who do not. Results of the first study suggest that the onset of daylight saving time increases calorie consumption from packaged snacks that are largely unhealthy, specifically in the evening and on cloudy days. The effect of the end of daylight saving time is not significant, suggesting an overall asymmetric effect of the time change on unhealthy behavior. Study 2 reveals that the onset of daylight saving time decreases fitness center visits, particularly for consumers without healthy consumption habits and with high transaction costs. Analysis of social media data suggests that consumers find the time change disruptive. Overall, the findings imply that public policy makers and businesses should find ways to support consumers around the onset of daylight saving time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".