Distributional Effects of Means Testing Social Security: An Exploratory Analysis
Bibliographic record
Abstract
This paper examines the distributional implications of introducing additional means testing of Social Security benefits where proceeds are used to help balance Social Security’s finances. Benefits of the top quarter of households ranked according to the relevant measure of means are reduced using a modified version of the Social Security Windfall Elimination Provision (WEP). The replacement rate in the first bracket of the benefit formula, determining the Primary Insurance Amount (PIA), would be reduced from 90 percent to 40 percent of Average Indexed Monthly Earnings (AIME). Four measures of means are considered: total wealth; an annualized measure of AIME; the wealth value of pensions; and a measure of average indexed W2 earnings. The empirical analysis, based on data from the Health and Retirement Study, starts with a baseline benefit for each household, calculated as the product of the average benefit-tax ratio under the current system, multiplied by the taxes paid by the household.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".