Three essays in labour economics and the economics of education
Notice bibliographique
Résumé
This thesis consists of three empirical essays. The first chapter is focused on the economics of gender, and the other two chapters are focused on the economics of education. A common theme in all these three chapters is studying the outcomes of disadvantaged groups in society, with an eye to policy interventions that could improve these outcomes. The first chapter examines whether women face a glass ceiling in the labour market, which would imply that they are under-represented in high wage regions of the wage distribution. I also measure the extent to which the glass ceiling comes about because women are segregated into lower-paying firms (glass doors), or because they are segregated into lower-paying jobs within firms (within-firm glass ceilings). I find clear evidence that women experience a glass ceiling that is driven mainly by their disproportionate sorting across firm types rather than sorting across jobs within firms. I find no evidence that gender differences in sorting across firms can be accounted for by compensating differentials. However, my results are consistent with predictions of an efficiency wage model where high-paying firms discriminate against females.\tThe second chapter estimates the effect of publicly-disseminated information about school achievement on school choice decisions. We find that students are more likely to leave their school when public information reveals poor school-level performance. Some parents’ respond to information soon after it becomes available. Others, including non-English-speaking parents, alter their school choice decisions only in response to information that has been disseminated widely and discussed in the media. Parents in low-income neighbourhoods are most likely to alter their school choice decisions in response to new information. The third chapter measures the extent to which cross-sectional differences in schools’ average achievement on standardized tests are due to transitory factors. Test-based measures of school performance are increasingly used to shape education policy, and recent evidence shows that they also affect families’ school choice decisions. There are, however, concerns about the precision of these measures. My results suggest that sampling variation and one-time mean reverting shocks are a significant source of cross-sectional variation in schools’ mean test scores.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».