Examinations of the Labour Market Outcomes and Housing of Ontario Social Assistance Recipients
Bibliographic record
Abstract
This thesis showcases a series of research examining various aspects of social assistance programs and their recipients using a set of administrative microdata following beneficiaries of the social assistance programs in Ontario, Canada. In Chapter 2, an analysis is carried out examining the disparities between various labour market outcomes for immigrant and non-immigrant social assistance recipients. After controlling for education level and other characteristics, survival analysis shows that likelihood of immigrant recipients exiting social assistance in any given period is statistically significantly lower than that of non-immigrant recipients by around 16%. However, further analysis reveals that immigrant recipients are less likely than non-immigrant recipients to return to social assistance after leaving. This discrepancy between the likelihood of finding employment and the implicit likelihood of retaining employment may be evidence of discrimination faced by immigrants in the Ontario labour market. Within the immigrant portion of the sample, higher age at their time of arrival to Canada leads to lower likelihood of finding employment, but lower likelihood of returning to social assistance, which could point to issues of cultural assimilation or discrimination against immigrants in the hiring process, or the undervaluation of human capital attained outside of Canada. Chapter 3 focuses on active labour market programs to which Ontario Works social assistance beneficiaries are assigned, intended to help them find and keep employment. After controlling for endogenous enrollment and omitted variable bias using an instrumental variable approach, significant differential impacts are found on unemployment spell duration, return rates to social assistance, and future time spent on social assistance across programs. Additionally, the effects of programs when assigned simultaneously are found to be detrimental. Chapter 4 provides a descriptive analysis of the housing situations and levels of residential mobility among recipients of Ontario Works, as well as the Ontario Disability Support Program. Additionally, an examination of those experiencing homelessness while on social assistance is carried out to examine prevalence of homelessness among different subpopulations. Overall, this research aims to shed light on both the living situations and labour market experiences facing those who are the most economically vulnerable in our society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".