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Essays on the Economics of Immigration

2023· article· en· W6990983858 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholarship@Western (Western University) · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueBody Contouring and Surgery
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationEarningsInequalityImmigration policyEconomic inequalitySocial mobilityRefugeeSocial network (sociolinguistics)Social inequality
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

My dissertation consists of three papers studying the impact of social networks and employment mobility on Canadian immigrants, and the effects of economic conditions and immigration policy changes on international Ph.D. students in Canada. In the first paper, I use the Longitudinal Survey of Immigrants to Canada (LSIC) to examine the effects of social networks on labour market outcomes of newly arrived Canadian immigrants. I find that the presence of initial networks at landing significantly increases the probability of getting a network job and reduces the probability of getting a formal job after landing. Across immigration categories, network effects vary, with the largest effect among the Refugees, followed by the Family Class, and then the Economic Class immigrants. In each class, low-educated immigrants rely more on networks to find a job than high-educated ones. By separating close ties into kinship and friendship, I find that family has stronger effects on employment outcomes. Moreover, the development of the network is important over time. Economic immigrants gain from more diverse networks, while the Refugees improve their employment outcomes by frequently contacting their networks. Finally, social networks play a limited role in determining the quality of immigrants' first jobs. The second paper examines employment mobility and its effects on long-run earnings inequality among Canadian male immigrants using the Longitudinal Immigration Database (IMDB) and linked tax data. Incorporating employment risk and earnings mobility, I find long-run earnings inequality among male immigrants is up to 34 percent lower than the current inequality (a 90/10 ratio of 4.92 versus 7.51). Further, I find that around 70 to 80 percent of the total long-run inequality reduction happens within the first 5 years with the remainder occurring by 15 years. Among immigration categories, the Refugees experience the highest level of both earnings mobility and employment risk, while employment mobility mainly happens at the bottom of the earnings distribution for the Family Class and Economic Class. These findings indicate high current earnings inequality among the immigrant population is not persistent in the long run. This is good news. One concerning factor is that the employment risk is concentrated at the bottom of the earnings distribution, especially for the Refugees. In the third paper, I study the effect of changing economic conditions and immigration policies on international Ph.D. students in Canada. After arriving in a host country, they are prone to economic conditions like domestic students and are also likely to be affected by immigration policies. Using the IMDB, I find that, unlike domestic students, international doctoral students experience a shorter study duration under adverse economic conditions. At the same time, a higher unemployment rate negatively affects international Ph.D. students as it associates with a lower probability of both getting permanent resident (PR) status during the study and remaining in Canada in the following year after finishing their studies. Immigration policies are also found to significantly correlate with the students' outcomes. When PR policies are less restrictive, international students have shorter study durations and are more likely to get PR while studying and stay in Canada after studying. Although there is no evidence that relaxed work permit policies affect the study duration of international Ph.D. students, they are shown to negatively correlate with their probability of getting PR during the study period and to substantially improve the retention likelihood.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,525

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,110
Tête enseignante GPT0,303
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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