The Economic Implications of the Covid-19 Pandemic on Older Adults in the U.S
Bibliographic record
Abstract
Abstract Older adults were among those most disproportionately affected by the Covid-19 pandemic. Research over previous years has outlined the changes to health, loneliness, and long-term well-being outcomes, in addition to economic impacts. This project recognizes and addresses a gap in the literature pertaining to the role of government economic impact payments (EIP) and labor force participation during the pandemic for older adults. Leveraging the Health and Retirement Study’s 2020 Core Survey and 2021 “Perspectives on the Pandemic” supplement, results highlight the demographic characteristics of the population of respondents (n = 2167) and the distribution of changes to economic and labor outcomes resulting from the pandemic. Most respondents noted having received an EIP and used this payment to primarily pay off existing debt, followed closely by contributing to savings. The majority were not working at the time of receipt and saw few changes to their economic situation throughout the pandemic. However, a stratified analysis of the payment amount showed that women received, overall, 31% less than men. Findings indicate that while labor force participation among this group was low, use of the economic payment indicates a need to address personal debt. Moreover, the gendered difference in total amount received may be the result of several factors, including the division of labor and labor force participation in older generations and the responsibilities of caregiving duties falling to women rather than men over the life course.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".