Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments
Bibliographic record
Abstract
The 2020 CARES Act directed large cash payments to households. We analyze house-holds' spending responses using high-frequency transaction data from a Fintech non-profit, exploring heterogeneity by income levels, recent income declines, and liquidity as well as linked survey responses about economic expectations. Households respond rapidly to the re-ceipt of stimulus payments, with spending increasing by $0.25-$0.40 per dollar of stimulus during the first weeks. Households with lower incomes, greater income drops, and lower lev-els of liquidity display stronger responses highlighting the importance of targeting. Liquidity plays the most important role, with no significant spending response for households with large checking account balances. Households that expect employment losses and benefit cuts dis-play weaker responses to the stimulus. Relative to the effects of previous economic stimulus programs in 2001 and 2008, we see faster effects, smaller increases in durables spending, larger increases in spending on food, and substantial increases in payments like rents, mortgages, and credit cards reflecting a shortterm debt overhang. We formally show that these differences can make direct payments less effective in stimulating aggregate consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".