The asymmetric impact of leisure externalities on economic growth
Bibliographic record
Abstract
Leisure generates externalities for the economy as a whole, as individuals generally get some (dis-)utility from their leisure-time. However, the sign and the extent of the effect that these externalities have on a specific worker's productivity and on the productivity of all other factors used in combination with labor (hence on long-term economic growth) may be asymmetric across different economic activities. The objective of this paper is to shed light on the impact that sector-specific leisure-time externalities have on the innovation rate, on the sectorial allocation of (skilled) labor, and eventually on the long-run economic growth rate, without making any prior assumption on their sign and magnitude. In the baseline model the growth rate of per capita income moves together with all types of leisure externalities, whereas the innovation rate moves together with (and depends solely on) the R&D-sector-specific leisure externality. From numerical analyses, we conclude that sector-specific leisure-time externalities provide asymmetric effects on the growth rate of real per capita GDP and on the way skilled labor is allocated across different economic activities. The robustness of these conclusions is analyzed by using various definitions of leisure along with different utility functions (including leisure as an argument).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".