Notice bibliographique
Résumé
This dissertation explores behavioral responses to policy interventions through three empirical studies. The chapters draw on a combination of administrative, experimental, and survey data to examine the effects of immigration policy, information frictions, and the provision of public goods. The overarching theme is to understand agents, be they firms, students, or workers, react to sudden changes in policy or information environments, and how these responses shape their outcomes. The first chapter studies the effects of increased immigration on the performance of local firms and their workers, leveraging a sharp increase in Canada's immigration targets in 2016. The policy led to an influx of predominantly high-skilled workers and generated unexpected variation in the growth of the foreign-born population across regions and nationalities. I quantify firms' exposure to the shock using a shift-share instrument and draw comparisons across firms that operate within the same labor market based on differences in worker origins. I find that employers more exposed to the shock accelerated the hiring of recent arrivals who lacked locally accumulated human capital, increased employment and compensation for both immigrant and native workers, and experienced expansions in both total output and output per worker. These results are consistent with firms benefiting from immigration through workplace ethnic networks, which may help identify workers' productivity characteristics that are otherwise overlooked in the labor market. The second chapter, joint with Marc-Antoine Châtelain, Paul Han, and En Hua Hu, examines how individuals form and update beliefs in the presence of misspecification in the data generating process. Using high-frequency data from a large undergraduate course, the study documents persistent overconfidence in students’ grade expectations, and a systematic overestimation of grading noise. An experimental intervention that provides information about noise leads to a 32% reduction in prediction errors. Structural estimates indicate that at least 25% of prediction errors are attributable to misspecified priors. These results highlight the role of subjective model in belief updating, and suggest that simple interventions can significantly improve information processing. The third chapter, co-authored with Kourtney Koebel, analyzes how universal childcare policy in Québec, which led to sharp increase in demand for their service, affected the labor market for childcare workers. Using Canadian Census data and administrative reports from Québec, we find that the policy roll-out coincided with a sharp decline in caregiver qualifications, offering a potential explanation for the negative effects on children documented in earlier studies. Earnings for workers improved under the policy, counter to concerns that government monopsony power would dampen wage growth. Hourly earnings rose significantly for center-based workers, who were generally covered by the subsidy. Wages for home-based workers, who were largely unsubsidized, remained flat, or declined in regions that saw rapid expansion in regulated care. We also find that this latter group increasingly served lower-income families, raising concerns about unequal access to high-quality care.
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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,004 |
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 ».