The Asset Portfolios of Older Australian Households
Bibliographic record
Abstract
This paper investigates whether there is evidence that households adjust their asset portfolios just prior to retirement in order to maximise their eligibility for a means-tested public pension. To this end, we take advantage of recently available, detailed micro data for a nationally-representative sample of Australian households to estimate a system of asset equations that are constrained to add up to net worth. Our results provide little evidence that in 2006 healthy households or couples were responding to the incentives embedded in the asset and income tests used to determine Australian Age Pension eligibility by reallocating their assets. While there are some significant differences in asset portfolios associated with having an income near the income threshold, being of pensionable age and being in poor health, these differences are often only marginally significant, are not robust across time, and are not clearly consistent with the incentives inherent in the Australian Age Pension eligibility rules. Any behavioral response to the incentives inherent in the Age Pension means test in 2006 appears to be predominately concentrated among single pensioners who are in poor health. In 2002 there is also evidence that healthy households above pension age held significantly more wealth in their homes than did otherwise similar younger households, perhaps suggesting some reduction in the incentives to reallocate assets over time.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".