Notice bibliographique
Résumé
When making the decision to move to another country for work, people take into consideration the likelihood of obtaining work, the value of that work (often relative to work in the home country), and the costs faced when moving and living away from home. In order to understand the effects of costs of migration to workers, I build a two-country search model with costs of migration for workers. I focus on move costs and flow cost faced anytime a worker is away from his/her country of origin. Since the characteristics of the labor markets between EU countries and the US and Canada differ along separate dimensions, they can be used to illuminate the importance of costs in a worker’s migration decision. The model in this paper tends to over-predict migration, and implies that costs to workers moving between EU countries are higher than those for moving between the US and Canada. This second result is in contrast with the higher observed migration in the EU, and highlights important general equilibrium effects and the need for better understanding the migration decision. The benefit of the theoretical model employed here is that sending and receiving countries are considered individually. Natives, new migrants, and existing migrants are followed separately, shedding light on distinctions previously shown to matter in determining whether workers are helped or hurt by migration. The model supports empirical findings that the effects of migration on unemployment are sometimes mixed, but typically decrease unemployment overall. Importantly for policy implications, unemployment rates for all groups are lower when workers are permitted to move. This paper fills the gap in existing work by tracking migrants between countries and separates out within-skill wage effects of labor migration in both the sending and receiving countries. Migration can both help and hurt the wages of workers of all migration histories depending on the context, and wage outcomes for workers can vary drastically across a number of labor market characteristics. Differing experiences of migrants across migration and employment histories observed in the data can be predicted with the model, and is strongly influenced by costs to workers in the form of one time move costs and ongoing costs to living away from home, characteristics of the model in this paper which are frequently missing from existing work. The politicized nature of immigration policy and the increase in migration around the world makes it important to separate out myth from truth of the employment effects of immigration. Added to the highly charged nature of the political and news cycle discussion of immigrants is the disagreement in academic circles on the effects of immigration on labor market conditions. Empirical investigations of the effects of migrants on labor markets are necessarily limited, making a theoretical model necessary to weigh the sometimes contradicting effects of increased competition versus market growth. General equilibrium effects in the face of frictional labor markets and migration need to be understood before any policy is implemented responsibly.
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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».