A Comparative Evaluation of Fiscal Stabilization Strategies during the Covid-19 Pandemic with Germany as a Reference Point
Bibliographic record
Abstract
Abstract We provide a comparative evaluation of fiscal stabilization strategies during the Covid-19 pandemic. For this purpose, we use Germany’s temporary value-added tax (VAT) rate reduction as a reference point. We construct a credible counterfactual for Germany in a two-step procedure. First, we carry out a careful pre-selection of the donor pool countries to obtain a control group that is highly similar to Germany regarding important post-treatment characteristics. Second, we apply a reweighting scheme on the pre-selected donor countries. The synthetic control group only differs from Germany in the way that it did not implement the temporary VAT rate reduction. Our results indicate that the German VAT cut policy and partial VAT reductions in other countries were relatively ineffective in stimulating consumption with regards to their costs when compared to other measures such as (targeted) direct cash transfers (e.g. implemented in Canada, Denmark, Japan, and the United States). We attribute this to the fact that direct cash transfers are more comprehensible, salient, and actionable, in particular, in a dynamic environment with high uncertainty induced by unclear future economic prospects.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".