Does Student Loan Forgiveness Drive Disability Application
Bibliographic record
Abstract
Student loan debt in the US exceeds $1.3 trillion, and unlike credit card and medical debt, typically cannot be discharged through bankruptcy. Moreover, this debt has been increasing: the share of borrowers leaving school with more than $50, 000 of federal student debt increased from 2 percent in 1992 to 17 percent in 2014. However, federal student loan debt discharge is available for disabled individuals through the Department of Education's Total and Permanent Disability Discharge (TPDD) mechanism through certification of a total and permanent disability. In July 2013, the TPDD expanded to include receipt of Social Security Disability Insurance (SSDI) or Supplemental Security Income (SSI) as an eligible category for discharge, provided medical improvement was not expected. Using data from the Survey of Income and Program Participation (SIPP) matched to SSI and SSDI applications, we find that SSDI and SSI application rates increased among respondents with student loans relative to rates among those without student loans. Our estimates suggest the policy change raised the probability of applying for SSDI or SSI in a given quarter among student loan-holders by 50% (baseline rate per quarter is approximately 0.3%), generally increasing SSI and SSDI awards. However, these induced award recipients were unlikely to receive the disability designation necessary to obtain student loan discharge. Given that the geographic distributions of student loan indebtedness and historical SSDI/SSI program participation differ, there are strong implications for both the size and location of SSDI and SSI beneficiaries. Furthermore, these findings highlight the importance of learning from policy changes in programs that interact with SSDI and SSI to better understand the drivers of disability program participation.
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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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".