THE EFFECTS OF HEALTH STATUS AND HEALTH SHOCKS ON HOURS WORKED
Bibliographic record
Abstract
We investigate the impact of health on working hours. This is in recognition of the fact that leaving the labour market because of persistently low levels of health status, or because of new health shocks, is only one of the possible responses open to employees. We use the first six waves of the Household, Income and Labour Dynamics in Australia (HILDA) Survey to estimate the joint effect of health status and health shocks on working hours. To account for zero working hours, we use a dynamic random effects Tobit model of working hours. We follow Heckman (1981) and approximate the unknown initial conditions with a static equation that utilises information from the first wave of the data. Predicted individual health status is used to ameliorate the possible effects of measurement error and endogeneity. We conclude that overall, lower health status results in fewer working hours and that when they occur, health shocks lead to further reductions in working hours. Estimation results show that the model performs well in separating the time-persistent effect of health status and the potentially more transient health shocks on working hours.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".