Essays on Health Economics, Health Behaviours, and Labour Outcomes
Bibliographic record
Abstract
This thesis consists of three chapters that investigate issues related to health economics, health behaviours, and labour outcomes. Using the longitudinal data from the National Population Health Survey (NPHS), Chapter 1 examines the association between minimum wage increases and a wide range of health outcomes and behaviours, such as physical health, mental health, chronic conditions, unmet health need, obesity, insurance, smoking, drinking, food insecurity and fruit and vegetable consumption using Difference-in-Difference (DD) and Difference-in-Difference-in-Difference (DDD) models. There is no evidence that minimum wage increases are associated with most health outcomes and behaviours, including better health. There is an association for low-education females with a higher probability of reporting overall fair or poor health, and excess drinking but a lower probability of work absences due to illness and being physically inactive. For low-education men, there is an association with improved mental health and less drinking and smoking. Broadly there is more evidence that minimum wage increases lead to healthier behaviours than evidence of an actual improvement in health, perhaps because of lags effects that are not captured in this analysis. Chapter 2 links the survey data from 2015-16 Canadian Community Health Survey (CCHS) to job characteristics from O*Net to explore the role of job characteristics in explaining the positive association between drinking alcohol and income, which is commonly found in the literature. The study finds that controlling for job characteristics reduces “income return to drinking” substantially (by between one fifth and one half, depending on gender and the measure of alcohol consumption). Last, using data from the Ontario sample of the 2020 CCHS, Chapter 3 estimates the marginal effects of an index of social capital (at the individual or aggregated level) on changes in intentions to get vaccinated. Results show that individual-level social capital is associated with a greater willingness to get vaccinated against Covid-19 at all ages, while aggregate-level social capital is associated with higher vaccination willingness only among older adults.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".