The Coronavirus Stimulus Package: How Large is the Transfer Multiplier
Bibliographic record
Abstract
Abstract In response to the COVID-19 pandemic, large parts of the economy were locked down and, as a result, households’ income risk rose sharply. At the same time, policy makers put forward the largest stimulus package in history. In the United States it amounted to $2 trillion, a quarter of which represented transfer payments to households. To the extent that such transfers were (i) announced in advance and (ii) conditional on recipients being unemployed, they mitigated income risk associated with the lockdown—in contrast to unconditional transfers. We develop a baseline scenario for a COVID-19 recession in a medium-scale heterogeneous agent new Keynesian model and use counterfactuals to quantify the impact of transfers. For the short run, we find large differences in the transfer multiplier: it is negligible for unconditional transfers and about unity for conditional transfers. Overall, we find that the transfers reduced the output loss due to the pandemic by some two percentage points at its trough.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".