Bibliographic record
Abstract
This dissertation is a collection of three essays that use economic tools to address policy-relevant issues related to ageing, population health, and education.The use of economic modelling and econometric analyses has the potential to provide information on the consequences and effectiveness of policy interventions in these areas and enables policymakers to make better informed decisions.Chapter 1 provides an introduction to these topics and is followed by the three essays.In Chapter 2, I analyze how providing informal care to an elderly parent affects the caregiver's labour market outcomes, cognitive ability, and health; and study the influence of the institutional background on the caregiving decision and the effects of caregiving.My results show that negative effects on labour market outcomes can be avoided by the provision of formal care alternatives, but negative effects for caregivers' mental health persist.These findings give useful insights into the optimal provision of formal care in today's ageing societies.Self-reported health measures are commonly collected in numerous surveys but might be influenced by respondents' definitions and frames of reference of health.In Chapter 3, I address the issue of response bias in population surveys by constructing an objective measure of health.I find that using a common definition of health nearly eliminates the reported health differences between the U.S. and Canada.Socioeconomic differences in health are stronger in the U.S., but remain an issue in Canada.Chapter 4 studies the effect of post-secondary education on the continued development of reading proficiency during adolescence and young adulthood.Reading proficiency is essential for labour market success in a knowledge-based economy, but little is known about how advanced reading skills such as text interpretation and text evaluation are developed.The results show that university graduation increases students' reading proficiency relative to high school graduation, which demonstrates the importance of cognitive skill investments later in the life cycle.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".