Bibliographic record
Abstract
This thesis comprises three chapters that evaluate the effects of income support programs targeting out-of-work or low-income individuals. In Chapter 1, I examine a temporary Unemployment Insurance benefit extension introduced during a downturn caused by a global oil price shock. Using a fuzzy regression discontinuity design and linked administrative data, I find that additional weeks of benefit entitlement significantly increase re-employment earnings, raise the probability of returning to the same industry, reduce entry into self-employment, and boost both pre- and post-tax income as well as government tax revenues. Chapter 2, coauthored with Gustavo J. Bobonis, Aneta Bonikowska, Philip Oreopoulos, and W. Craig Riddell, investigates the medium- and long-term impacts of the Canada Self-Sufficiency Project (SSP) Plus program, which randomly offered intensive employment support services for up to three years to long-term welfare recipients eligible for temporary earnings subsidies. We link study participants to high-frequency survey data and to their federal tax and employer–employee matched records for up to 20 years following random assignment. The intensive services treatment led to a 20–27 percent increase in participants’ annual earnings over the 20-year period and sustained increases in full-time employment during the first decade post-intervention. As potential mechanisms, treated individuals engaged in more job search and job-to-job transitions and secured employment in higher-wage jobs and at higher-paying firms. In Chapter 3, I reexamine the evidence on the maternal labor supply impacts of two reforms to Canada’s system of child benefits: the integration of refundable tax credits for children with welfare programs and the introduction of a universal benefit for families with young children. Using an event study approach, I find that integrating child benefits with welfare increased the labor force participation and full-time employment rates of single mothers, the group most likely to be affected by the reform. The introduction of the universal benefit did not produce labor supply responses among married mothers in the two years following its implementation; further analysis suggests that previous evidence of its impacts may be confounded by the effects of the Great Recession.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".