Three essays in health economics: 1. An equilibrium model of waiting lists for medical care. 2. An evaluation of alternative econometric specifications for estimating a tobacco budget share equation. 3. The determinants of expenditures on tobacco in Canada
Bibliographic record
Abstract
This thesis consists of three essays on health economics. Chapter 1 is an introduction. Chapter 2 studies waiting lists for medical care, and Chapters 3 and 4 study the demand for tobacco products. The main contribution of Chapter 2 is a game-theoretic model of waiting times for medical care that provides new insights into how health care waiting times and the number of cases treated may be related. The model demonstrates that charging patients for medical care may not result in decreased waiting times. One policy implication of the model is the potential gains from the sharing of information and co-ordination among health-care providers. Chapters 3 and 4 are on the demand for tobacco products. Estimating the demand for tobacco involves choosing one or more econometric specifications or functional forms. Different econometric specifications can result in conflicting results, raising questions about how to interpret results. A primary objective of Chapter 3 is to identify the similarities and differences between three econometric specifications that have often been applied to tobacco data. A behavioural model is a useful starting point for making these comparisons. In this chapter I compare the results arrived at by applying different specifications to one data set. This data set reports individual's expenditure on tobacco. Chapter 4 examines a data set that reports household expenditure on tobacco. A number of economics papers examine tobacco data from the United States, the United Kingdom and Spain. Chapter 4 examines tobacco data from Canada. One finding indicates that households that do not own their home and consist of one or more unemployed individuals tend to purchase a relatively high amount of tobacco. This information may be of interest to people involved in tobacco policy.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".