Why do we never have enough time? Economic inequality fuels the perception of time poverty by aggravating status anxiety
Bibliographic record
Abstract
People in many societies report that they do not have enough time. What makes people feel so rushed? We propose that economic inequality leads to perceived time poverty by increasing status anxiety. Five studies examined this line of reasoning. Study 1 (N = 230) found a positive correlation between economic inequality and perceived time poverty. Study 2 (N = 194) manipulated economic inequality to test the causal link between economic inequality and perceived time poverty. The results showed that people perceived more time poverty in the high (vs. low) economic inequality condition. Study 3 (N = 381) supported the mediating role of status anxiety in the relationship between economic inequality and perceived time poverty using a questionnaire survey. Study 4 (pre-registered; N = 283) manipulated economic inequality in an ecological valid way and yielded further support for the hypotheses. In pre-registered Study 5 (N = 233), a blockage manipulation design was employed to test the mediating effect of status anxiety as a function of economic inequality, which provided causal evidence for the proposed mediator. Our findings suggest that economic inequality serves as a structural societal factor that fuels people's perception of time poverty.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".