Notice bibliographique
Résumé
This dissertation explores key mechanisms underlying earnings inequality through three self-contained essays in labor economics. Each chapter addresses a distinct source of heterogeneity in wage outcomes—firm-specific wage-setting, employer learning and signaling, and occupational sorting—using large-scale administrative and survey data from Canada and the United States.The first essay examines how changes in firm-specific wage premiums contribute to gender earnings inequality. Using Canadian linked employer-employee data from 2001 to 2019, I estimate a gender-specific, time-varying firm fixed effects model to assess the role of wage-setting practices in shaping the gender pay gap. I find that while firms with rising wage premiums expand employment for both men and women, women capture only two-thirds of the wage gains men receive from reallocating to these firms, offsetting nearly one-quarter of the progress in narrowing the gender wage gap. This disparity is driven primarily by two factors: women’s lower likelihood of sorting into firms with rising premiums and their reduced ability to capture those gains relative to equally qualified men within the same firm. These effects are closely linked to gender differences in how wages and employment respond to firm-level productivity shocks.The second essay investigates the role of job market signaling in shaping the returns to education, drawing on canonical models of employer learning. The empirical literature on employer learning posits that employers learn about unobserved ability differences across workers as they spend time in the labor market. This chapter outlines the testable implications of this hypothesis and describes how they have been employed to estimate the relative contributions of job market signaling and human capital to observed returns to education. Although the empirical evidence remains limited, we conclude that signaling accounts for, at most, one-quarter of the measured returns to education.The third essay examines the flattening of the ability–experience profile in the U.S. since the early 2000s, identifying changes in occupational sorting as the primary driver. Using data from the 1979 and 1997 waves of the National Longitudinal Survey of Youth (NLSY), I show that life-cycle returns to cognitive ability have declined among less-educated workers due to increased concentration in lower-return occupations and reduced upward mobility. In contrast, more-educated workers in recent cohorts begin with substantially lower initial returns to cognitive ability but experience steeper growth in ability premiums, ultimately converging with the levels attained by earlier cohorts. This convergence is driven by relative improvements in occupational sorting by ability over the life cycle. These patterns are closely linked to broader structural changes—particularly the post-2000 reversal in the demand for cognitive tasks—which have reshaped occupational allocation and weakened life-cycle returns to ability
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 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,007 |
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 ».