Pathways Taken by New Social Security Disability Insurance and Supplemental Security Income Awardees
Bibliographic record
Abstract
We use administrative data to examine the various milestones achieved and pathways followed by new Social Security Disability Insurance (DI) and Supplemental Security Income (SSI) awardees. Our findings show that 80% of DI-first awardees and 53% of SSI-first awardees either achieved none of the milestones we tracked in the 10 years after their initial award or their only milestone was death or attainment of full retirement age. Furthermore, many DI and SSI awardees who achieved work- or program-related milestones during the analysis period did not make additional progress toward exiting the program. For example, one third of DI-first and one fifth of SSI-first awardees who enrolled in employment services had no other milestones and one quarter of DI-first awardees who completed a trial work period either had no other milestones or their only additional milestone was enrolling in employment services. We also found that approximately one quarter of SSI-beneficiaries who later received DI had their SSI benefits suspended and terminated due to excess income that included DI payments as the only additional milestones. Finally, our analysis reveals great diversity in the paths taken to achieve work- and program-related milestones, which policy makers should consider when designing interventions to help awardees return to work.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".