Notice bibliographique
Résumé
In Chapter I, Xing Guo and I study how the U.S. restrictions on skilled immigration affect the Canadian economy and the welfare of American workers. In 2017, there was a policy that tightened the eligibility criteria for U.S. visas and was immediately followed by a trend break in the number of skilled immigrant admissions to Canada. We use quasi-experimental variation introduced by this policy over time and across immigrant groups, along with U.S. and Canadian visa application data, to show that Canadian applications in 2018 were 30% larger than without the restrictions. We then study how the restrictions affected Canadian firms using comprehensive Canadian administrative databases containing the universe of employer-employee-linked records, immigration records, and international trade data. We find that the restrictions increased firms' production, exports, and employment of Canadian workers. Finally, we study the policy’s impact on American workers by incorporating immigration policy into a multi-sector international trade model. With international trade, the increase in immigration to other countries due to the restrictions affects American wages through U.S. exports and consumption prices. We find that the welfare gains for American workers targeted for protection by the 2017 policy are up to 25% larger in a closed economy than in an open economy with the observed trade levels. In Chapter II, Nicolas Morales and I use a detailed establishment-level dataset from Germany to document that large firms allocate a higher proportion of their wage bill to immigrants compared to small firms. We study both analytically and quantitatively the importance of this heterogeneity across firms in the effects of immigration on the welfare of native-born workers. To achieve this, we set up and estimate a model of international trade and immigration where heterogeneous firms choose their immigrant share. We find that our model with no heterogeneity in immigrant share across firms underestimates the native workers' welfare gains by 11%. In chapter III, Alberto Cavallo, Javier Cravino, Andres Drenik and I study how the price of remote work is determined in a globalized labor market using data from a large web-based job platform, where workers from around the world compete for remote jobs. Despite the global nature of the platform, we find that remote wages are higher for workers in regions with higher income per capita. This correlation is not accounted for by differences in workers' observable characteristics, occupations, or differences in the employers' locations. Instead, data on wage histories indicate that remote wages are partly determined by the conditions that workers face in their local labor markets. We also show that remote wages expressed in local currency move strongly with the dollar exchange rate of the worker's country and are highly sensitive to foreign competition. Finally, we identify occupations at high risk of being offshored based on the prevalence of cross-border contracts.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
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 ».