Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".