Fast Facts: A Tale of Three Retirement Lifestyles
Bibliographic record
Abstract
Spending in retirement is an increasingly important area of focus of the retirement industry, plan sponsors, and policymakers as more individuals enter retirement. Indeed, in the third quarter of 2020, about 28.6 million Baby Boomers - those born between 1946 and 1964 - reported that they were out of the labor force due to retirement. Yet not enough is understood about how retirees spend their money and, just as importantly, why they spend the way they do.In its Issue Brief, "Why Do People Spend the Way They Do in Retirement? Findings From EBRI's Spending in Retirement Survey," the Employee Benefit Research Institute (EBRI) reported the spending habits and situation of 2,000 individuals ages 62 to 75 at and during retirement. Three types of retirees in particular stood out: (1) highly indebted retirees who described their debt as unmanageable or even crushing; (2) long-term secure retirees, or those retirees who reported they had long-term care insurance; and (3) full-nester retirees, or those reporting that they had at least one child at home with them. These three groups are highly distinct from one another and paint a portrait of starkly different retirement lifestyles depending on these circumstances.EBRI was able to fund development of this research thanks to a generous grant from RRF Foundation for Aging.Click "Download" to read the summary of EBRI's research.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.022 |
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".