Three Essays in Health Economics
Bibliographic record
Abstract
This thesis comprises three essays in health economics. Chapter 1, co-authored with Dr. Michel Grignon, examines how minimum wage increases affect access to employer-sponsored prescription drug insurance. Using cross-sectional data linked with provincial minimum wages changes from 2008 to 2019, the study identifies threshold effects: increases of 20–30 cents reduce coverage by about three percent, with the strongest impacts among women, young workers, immigrants, and racial minorities. Chapter 2 evaluates the impact of the Ontario Health Insurance Plan Plus (OHIP+), introduced in 2018 to provide free prescription drug coverage to residents under 25. Applying event study and Difference-in-differences methods with administrative emergency department data, the analysis finds no overall effect on utilization but reveals significant declines among low-income households. This suggests that improved drug access reduced reliance on emergency departments as a substitute source of medication. Chapter 3 investigates how a cancer diagnosis influences household spending patterns by linking the Canadian Cancer Registry with household expenditure survey data. The results show an average decline in total spending of about seven percent following a diagnosis, with the largest reductions in food and income tax expenditures. Although budget shares remain broadly stable, heterogeneity analysis reveals meaningful reallocations across families with and without children, single parents, and younger households. In contrast, subsequent diagnoses generate smaller adjustments.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".