<scp>COVID</scp>‐19 Labor Market Shocks and Withdrawals From Retirement Accounts: Understanding the Moderating Role of Financial Knowledge
Bibliographic record
Abstract
ABSTRACT We explored the relationship between COVID‐19 labor market shocks and the likelihood of hardship withdrawals or plan loans from retirement accounts, which could significantly impact workers' retirement savings. We found that about 14% of working‐age respondents took a hardship withdrawal or plan loan. Those reporting a COVID‐19 labor market shock had odds of a hardship withdrawal as much as 3.8 times as high as otherwise comparable respondents who did not have a shock. Additionally, we found that the relationship to a COVID‐19‐related labor shock was moderated by the objective and subjective financial knowledge of individuals, suggesting a potential role for financial education in alleviating retirement risks. A notable finding is that respondents exhibiting financial knowledge overconfidence were more likely to take a plan loan or a hardship withdrawal than those with appropriate levels of confidence or low levels of confidence. This study offers important insights for policymakers, educators, and practitioners.
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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 | 0.015 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".