How Americans used their <scp>COVID</scp>‐19 economic impact payments
Bibliographic record
Abstract
Abstract This study investigates how Americans used their CARES Act Economic Impact Payments (EIP) for their spending needs, spending wants, and financial transactions. The results from a sample of 1,172 Amazon MTurk users collected in July 2020 suggest that EIP use varied across spending and financial transaction categories. Those with job instability, less financial resources, and more people to care for received essential support. A smaller proportion of the population spent at least some of their EIP on their wants. Americans' primarily focused their EIP spending on housing, food, and hobbies. In addition, people were able to improve their financial situation through investing and debt reduction. Decisions to save the EIP were related to economic recovery concerns; a broad policy package and public messaging strategy that offers assurance of economic recovery and stability might enhance policy effectiveness for boosting immediate economic growth through EIP spending.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".