Notice bibliographique
Résumé
Chapter 1 of this thesis analyzes the determinants of income inequality in Canada using micro-level data from Canada’s censuses (1991, 1996, 2000, 2006, 2016). First, it is shown that market-income inequality is higher than inequality based on other types of income (annual wage, annual pre-tax income and annual income after-tax). Inequality is highly driven by the gap between the income shares held by the top 1% income group compared with other income percentiles. It is also explained by the large gap between income percentile of the top 25% income group and the bottom 75% income group. The top 30% income group held 60% of the population total income, while the bottom 30% income group held under 9% of the population total income. Inequality is different by province across Canada. From the findings, within-group inequality dominates between-group inequality, regardless of whether groups are defined by education, occupation, gender, age, language, marital status, or citizenship status. Second, analyzing the determinants of inequality, the results suggest that they vary significantly across income groups. The results highlight the contribution of any explanatory factor to inequality and the proportion of inequality explained by all observable characteristics. The largest part (between 64% and 74%) of income inequality is not explained by individual observable characteristics. Third, these determinants are modified by redistributive policies such as taxes and transfers. Chapter 2 brings further light on income inequality dynamics by gender and inves tigates its determinants from static and dynamic points of view. Using Canada income data, this research uses different measures of inequality to provide evidence on the changes in inequality by gender from 1991 to 2016. In this study, unconditional quantile regression based on the Re-entered Influence Function (RIF) is used to assess the impact of individual characteristics on income quantiles. The contribution of each relevant covariate on the Theil index by gender is documented by applying regression-based decomposition of inequality. Finally, RIF-Oaxaca-Blinder decomposition is used to investigate the composite and income structural effects on the changes in inequality measures by gender. Results show that, before 2001, inequality was higher among females than among males, and starting from 2001, the inverse process is observed. The changes in the interquantile differences are not homogeneous along the income distribution for both males and females. The pattern of the effects of covariates on quantiles along the income distribution is gender specific. The findings provide evidence that, in most cases, the income structural effect explains the higher part of inequality. dynamics by gender, even if the size of the impact differs by gender. Furthermore, the composite effect counterbalances the income structural effect most of the time, even if, in some cases, they contribute to the change in inequality measures in the same direction. Chapter 3 investigates the spillover effects of corporate tax across the provinces using Canada’s corporate provincial aggregate data from 1981 to 2019. A dynamic panel model is used to assess the incidence of tax competition within the country. The results show that an increase of statutory taxes in other provinces has a positive effect on the corporate taxable income of a specific province. The results provide the evidence of spillover effects of corporate tax across provinces in Canada. This chapter supports the recommendations proposed by Smart and Vaillancourt (2021) on formula allocation mechanism and by Boadway and Tremblay (2016) on the modernization of business taxation mechanism in Canada.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,008 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,008 |
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 ».