Bibliographic record
Abstract
This thesis presents four studies on mortality inequality which advance the knowledge of how lifespans differ across social groups and the implications for retirees. The first two studies are based on the concept of distributional differences and propose new methods to study mortality inequality. Specifically, Chapter 2 introduces a statistical distance index to capture the stratification of lifespans among social groups. Empirical evidence from Finland demonstrates its utility and reveals new dimensions of mortality inequalities that traditional measures obscure. Chapter 3 proposes a novel method for investigating the factors that contribute to total lifespan inequality. It finds that in the United States, while racial/ethnic differences in life expectancy contribute little to total-population-level lifespan variance, distributional differences across race/ethnicity explain one fifth of the total lifespan variance. The next two studies explore the implications of mortality inequalities for retirees. Chapter 4 models dynamic work trajectories of older US adults and presents major results of gender and educational inequalities in the United States. It shows that less-educated older adults spent less time working, which compensates for their lower longevity when compared to their more-educated counterparts. Nonetheless, educational inequality in retirement lifespan is substantial and persistent. Chapter 5 looks at how education and preretirement earnings relate to lifetime pensions from age 60 onward, as well as how mortality affects the distribution of lifetime pensions in Sweden. The results show that the greater longevity of socially advantaged groups accounts for up to one quarter of lifetime pension inequality. Chapters 4 and 5 both highlight the importance of social differences in mortality and advocate for greater emphasis on the role of mortality in high-level discussions on old-age policies.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".