Unemployment, Financial Literacy, and Retirement: Evidence From National Data Before and During COVID-19 Pandemic
Bibliographic record
Abstract
Unemployment rates changed dramatically and peaked at 14.7% in April 2020 in the United States. The labor market force might affect households’ retirement differently before and during the COVID-19 pandemic. By utilizing 2018 and 2021 datasets, the study mainly contributes to the following insights related to retirement decisions. First, the current study finds a positive correlation between state-level unemployment rates and retirement. Second, this study finds that both objective and subjective financial literacy, financial confidence, age, and households without a child or a financially dependent child are positively associated with retirement in both pre-pandemic and during the pandemic. Financial market participation, financial risk, and income drop are negatively associated with retirement in pre-pandemic and during the pandemic. We find different significant results regarding the annual income, savings, and the number of children in a household before and during the pandemic. The findings extend the literature on unemployment and retirement. Financial professionals and the government will apply the empirical findings to the practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".