Public, private or both? Analyzing factors influencing the labour supply of medical specialists
Bibliographic record
Abstract
Abstract This paper investigates the factors influencing the allocation of time between public and private sectors by medical specialists. A discrete choice structural labour supply model is estimated, where specialists choose from a set of job packages that are characterized by the number of working hours in the public and private sectors. The results show that medical specialists respond to changes in earnings by reallocating working hours to the sector with relatively increased earnings, while leaving total working hours unchanged. The magnitudes of the own‐sector and cross‐sector hours elasticities fall in the range of 0.16–0.51. The labour supply response varies by gender, doctor’s age and medical specialty. Family circumstances such as the presence of young dependent children reduce the hours worked by female specialists but not male specialists. Résumé Public, privé ou les deux? Analyse des facteurs influençant l’offre de travail des médecins spécialistes . Ce mémoire étudie les facteurs influençant l’allocation du temps des médecins spécialistes entre le secteur privé et le secteur public. Un modèle structurel de choix discret d’offre de travail est calibré ans lequel les spécialistes choisissent entre des arrangements caractérisés par le nombre d’heures de travail dans le secteur public et le secteur privé. Les résultats montrent que les spécialistes répondent aux changements dans la nature des gains en réaménageant leurs heures de travail vers le secteur qui offre des gains relativement plus élevés, tout en gardant leurs heures totales de travail inchangées. Les magnitudes des élasticités de l’offre des heures à l’intérieur d’un secteur et entre secteurs se situent dans un intervalle entre 0.16‐0.51. La réponse de l’offre de travail varie selon le genre, l’âge et la spécialité. Le cadre familial, comme la présence de jeunes enfants à charge, tend à réduire les heures travaillées par les femmes mais pas pour les hommes.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".