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. These means tests would reduce total household benefits by 7 to 9 percentage points, amounting to 15.4 to 16.4 percent of the benefits of affected workers at baseline. We find that the basis for means testing Social Security makes a substantial difference as to which households have their benefits reduced, and that different means tests may have different effects on the benefits of families in similar circumstance. We also find that the measure of means used to evaluate the effects of a means test makes a considerable difference as to how one would view the effects of the means test on the distribution of benefits.
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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 | 0.038 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".