Distributional Effects of Means Testing Social Security: Income Versus Wealth
Bibliographic record
Abstract
This paper compares Social Security means tests that would reduce benefits for recipients who fall in the top quarter of the income distribution with means tests aimed at those in the top quarter of the wealth distribution. Both means tests would reduce the average benefits for the affected groups by about $5, 000. The analysis is based on data from the Health and Retirement Study and covers individuals aged 69 to 79 in 2010. About 14.5 percent of retirees in this age group are both in the top quarter of income recipients and in the top quarter of wealth holders. Another 10.5 percent are top quarter income recipients, but not top quarter wealth holders; with an additional 10.5 percent top quarter wealth holders, but not top quarter income recipients. We find that a means test of Social Security based on income has substantially different distributional effects from a means test based on wealth. Moreover, there are substantial differences when a Social Security means test based on income is evaluated in terms of its effects on individuals arrayed by their wealth rather than their income. Similarly, a means test based on wealth will be evaluated quite differently by policy makers who believe that income is the appropriate basis for a means test than by those who believe that means tests should be based on wealth.
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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 | 0.034 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".