Fiscal Policy in the Age of COVID: Does it ‘Get in all of the Cracks?’
Bibliographic record
Abstract
We study the effects of fiscal policy in response to the COVID-19 pandemic at the firm, sector, country and global level.First, we estimate the impact of COVID-19 and policy responses on small and medium sized enterprise (SME) business failures.We combine firm-level financial data from 50 sectors in 27 countries, a detailed I-O network, real-time data on lockdown policies and mobility patterns, and a rich model of firm behavior that allows for several dimensions of heterogeneity.We find: (a) Absent government support, the failure rate of SMEs would have increased by 9 percentage points, significantly more so in emerging market economies (EMs).With policy support it only increased by 4.3 percentage points, and even decreased in advanced economies (AEs).(b) Fiscal policy was poorly targeted: most of the funds disbursed went to firms who did not need it.(c) Nevertheless, we find little evidence of the policy merely postponing mass business failures or creating many 'zombie' firms: failure rates rise only slightly in 2021 once policy support is removed.Next, we build a tractable global intertemporal general equilibrium I-O model with fiscal policy.We calibrate the model to 64 countries and 36 sectors.We find that: (d) a sizable share of the global economy is demand-constrained under COVID-19, especially so in EMs.(e) Globally, fiscal policy helped offset about 8% of the downturn in COVID, with a low 'traditional' fiscal multiplier.Yet it significantly reduced the share of demand-constrained sectors, preserving employment in these sectors.(f) Fiscal policy exerted small and negative spillovers to output in other countries but positive spillovers on employment.(g) A two-speed recovery would put significant upwards pressure on global interest rates which imposes an additional headwind on the EM recovery.(h)Corporate and sovereign spreads rise when global rates increase, suggesting that EM may face challenging external funding conditions as AEs economies normalize.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".