How Does Contingent Work Affect SSDI Benefits
Bibliographic record
Abstract
Some studies have found that contingent workers – including independent contractors, consultants, and those in temporary, on-call, and “gig economy” jobs – make up an increasing share of the labor force. How does this group of workers interact with Social Security Disability Insurance (SSDI)? This project uses the Health and Retirement Study linked to administrative data on SSDI applications and earnings to answer this question. Specifically, the paper examines how SSDI application, receipt, potential benefits, and insurance status differ for workers who hold contingent arrangements in their 50s and early 60s, compared to those who work in more traditional jobs at those ages. This study is among the first to examine how contingent work is likely to affect participation in a public program, specifically disability benefits. The study finds that SSDI application rates are about one-quarter smaller for older eligible contingent workers than for traditional workers of the same ages. Contingent workers are also about one-third less likely to be awarded disability benefits. The lower application and award rates are likely due in part to contingent workers’ lower eligibility rates and lower potential benefits. The application and award rates are also lower for contingent workers who have a chronic condition, work limitation, or limitation in their Activities of Daily Living. These results suggest that contingent workers would benefit from a greater availability of information and assistance in navigating the SSDI application process.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".