Notice bibliographique
Résumé
This thesis consists of three chapters investigating the impact of immigrant language skills on Canadian wages. The first chapter, “Linguistic diversity among Canadian immigrants: 1981-2006”, describes the changes in linguistic diversity among Canadian immigrants, as measured by a preferred linguistic distance measure, the Levenshtein Distance (LD) Index, and documents socio-demographic characteristics of recent immigrants as well as their labour market performance based on their language capital at the time of entry. The LD is an approximation of immigrants’ language skills in the Canadian official languages and represents the “distance” of an immigrant’s reported language to the Canadian official languages. Using the 20% micro-data files of the Canadian Censuses between 1981 and 2006, I assign each immigrant an index number based on two language measures: mother tongue and home language. French and English are defined as the Canadian official languages in Quebec and outside of Quebec, respectively. The main findings suggest that although immigrants’ mother tongues became more “distant” to the Canadian official languages in both regions over time, the language skill of an average immigrant based on home language remained almost the same in Quebec, in particular, between 1981 and 1996. In terms of immigrants’ socio-demographic characteristics and their labour market performance, general patterns were similar across the two regions, although there were significant differences by language groups. In particular, the change in immigrants’ wages by language groups is suggestive of the role of language skills in determining wages. The second chapter, “Immigrant versus native men? Substitutability and the role of linguistic diversity in Canada”, estimates the degree of substitutability between immigrant and native men by incorporating immigrants’ language skills into the analysis and calculates the potential wage effects of immigration on Canadian wages. Using the 20% micro-data files of the Canadian Censuses between 1981 and 2006 and imposing a nested-CES production function on the Canadian economy, I estimate immigrant-native substitutability based on immigrants’ language skills in addition to education levels and years of labour market experience. I use the LD Index to represent immigrants’ language skills by the distance of the mother tongue and home language of an immigrant to English outside of Quebec and to French in Quebec. I define three language groups for immigrants as the high language-skilled, the medium language-skilled, and the low language-skilled. The key findings are as follows. First, home language-based estimates suggest imperfect substitutability in Canada outside of Quebec in some cases. Second, by language skill groups, the low language-skilled immigrants are more likely to be imperfect substitutes for the Canadian-born. Third, the findings for Quebec are substantially different from those for Canada outside of Quebec. My simulations suggest that the long-run effect of immigration on immigrants’ wages was negative between 1981 and 2006 while the long-run effect of immigration on the wages of the Canadian-born was small but positive over the same period. The third chapter, “Gender, linguistic diversity, and labour market substitutability”, uses the same methodology and data sources as in the second chapter to incorporate female workers into the analysis of immigrant-native substitutability. This study estimates the elasticity of substitution between immigrant language groups and natives for female workers and the pooled sample of male and female workers. The findings suggest that the degree of substitutability between female immigrants and female natives is similar to the degree of substitutability between male immigrants and male natives. The main results do not change for the pooled sample. Due to potential differences between language accumulation processes between female and male immigrants, the third chapter also estimates female-male immigrants substitutability based on language skills, education levels, and years of labour market experience. The findings suggest that female and male immigrants are imperfect substitutes outside of Quebec regardless of language measures.
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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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».