Changes in Subjective Well-being with Retirement: Assessing Savings Adequacy in Australia
Bibliographic record
Abstract
Does retirement represent a state of relative prosperity or a time of unanticipated economic hardship? To assess whether individuals are successful in smoothing their well-being across the transition to retirement we analyse measures of relative subjective wellbeing (SWB) in the Australian HILDA Survey. Specifically, this research examines individual's self-reported change in their standard of living, financial security, and overall happiness over the transition to retirement. It is found SWB either improves or remains constant for the large majority of individuals as they retire from the labour force. However, there are significant disparities in changes in well-being with retirement among retirees. In particular, the subset of individuals who are forced to retire early due to job loss or their own health, and who find their income in retirement to be much less than expected, report marked declines in their well-being in retirement.
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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 | 0.000 |
| 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".