Wealth Accumulation of US Households: What Do We Learn from the SIPP Data?
Bibliographic record
Abstract
In this paper, I estimate, for US households, age-wealth profiles which allow for cohort effects. I use these to reexamine one of the central empirical propositions of simple life-cycle models: dissaving after retirement. The analysis employs a data set which has not been previously examined in this way: the Survey of Income and Program Participation (SIPP). The main regression results suggest that elderly households do not dissave after retirement. However, an examination of the distribution of wealth at retirement reveals that most households have accumulated very little wealth from which to dissave. Given that about 40% of households are not covered by any occupational pension, social security payments are the main source of retirement income for a large number of households. Even more than the absence of post-retirement dissaving, it is this overall lack of pre-retirement saving which seems to contradict life-cycle models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".