Notice bibliographique
Résumé
This thesis comprises three essays in labour economics, econometrics and health economics. The first essay uses educational quality outcomes in immigrants’ home countries to explain the variation of immigrants’ rates of return to education in Canada. The second essay explores an econometrics technique (i.e., generalized method of moments) that combines macro level data and micro survey data to reduce the bias and/or variance of estimates. The goal of this essay is to address nonresponse and attrition issues that are commonly encountered in surveys in health services and health economics. The last chapter is an empirical investigation on the association between diabetic patients’ hospitalizations and their family doctor’s payment model. The first chapter uses international test scores as a proxy for the quality of immigrants’ source country educational outcomes to explain differences in the rate of return to schooling among immigrants in Canada. The average quality of educational outcomes in an immigrant’s source country and the rate of return to schooling in the host country labour market are found to have a strong and positive association. However, in contrast to those who completed their education pre-immigration, immigrants who arrived at a young age are not influenced by this educational quality measure. Also, the results are not much affected when the source country’s GDP per capita and other nation-level characteristics are used as control variables. Together, these findings reinforce the argument that the quality of educational outcomes has explanatory power for labour market outcomes. The effects are strongest for males and for females without children. The second chapter explores a technique that combines macro and micro level data. Administrative data in the health sector normally provide censuses of relevant populations but the scope of the variables is often limited and frequently only aggregate summary statistics are publically available. In contrast, survey datasets have a broader set of variables but commonly suffer from nonresponse, attrition and small sample sizes. This paper explores a technique that combines complementary population and survey data using a method of moments technique that matches auxiliary moments of the two data sources in estimating micro-econometric models. We provide Monte Carlo evidence showing that the approach can give appreciable reductions in both bias and variance. We show an example looking at midwife training and another looking at an optometrist’s location of work, to illustrate its use in a health human resource context. This approach could have wide applicability in health economics and health services. The third chapter investigates the impact of a blended capitation model (Family Health Organizations -- FHOs) compared to an enhanced fee-for-service model (Family Health Groups - FHGs) on diabetic patients in Ontario, Canada. Using comprehensive administrative data and primary care reform as a quasi-natural experiment, we construct a panel for diabetic patients and employ a difference-in-differences approach to identify the impact of a change in the general practitioner’s (GP’s) remuneration model on patients’ hospital admissions. We find that on both the intensive and extensive margins, the hospital admissions for senior female patients statistically significantly increased after their GP’s remuneration model changed from FHG to FHO. In contrast, the impacts on male patients and younger female patients were small and not statistically significant. The results provide a cautionary message regarding the differences in practice patterns towards senior diabetic patients between GPs as a function of their payment model.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».