Three Empirical Essays on Economic Well-being in Canada
Bibliographic record
Abstract
This thesis studies issues related to population aging. In Canada, various policies address this issue. Such policies include encouraging labour force participation to address challenges about the variations in the size of different birth cohorts through immigration or encouraging fertility while ensuring attachment to the labour market. In this thesis, I explore the influence policies have on the economic well-being of individuals at two significant events in a person’s lifetime, namely childbirth and retirement. In Chapter 1, I examine the impact of children on the earnings of mothers through the lens of Québec’s Parental Insurance Plan. Particularly, I explore how the maternity and parental leave benefits available at first birth affect a woman’s earnings loss. The results show that mothers who received a more generous benefit experience, on average, a larger decline in earnings immediately after the birth of their first child. However, under Québec’s plan, there is a substantial recovery in earnings starting four years after their first child’s birth. In Chapter 2, I study immigrants’ retirement and public pension take-up patterns and examine the residency requirements associated with public pension eligibility and entitlement that primarily impact immigrants. Additionally, I examine how reaching the eligibility age for OAS affects the employment and earnings of Canadian seniors differently depending on their immigration status. The analysis reveals that immigrants who arrive in Canada before age 40, especially those in the economic immigration class, have higher employment rates at older ages compared to native-born Canadians. Conversely, immigrants who arrive later in life and face stricter public pension restrictions tend to have lower employment rates. Furthermore, the impact of the age at immigration appears to outweigh the incentives related to public pension eligibility. In Chapter 3, I document disparities in low-income rates between immigrants and non-immigrants at older ages, focusing on the intersectionality of immigration status, racial identity, and gender while observing the changes over two decades. I find a large decline in low-income rates between 2000 and 2020 and a reduction in the gaps between demographic groups. I show that the differences in low-income levels are associated with variations in prime-age employment, earnings, and access to pension benefits, particularly the Canada Pension Plan.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".