SME Failures Under Large Liquidity Shocks: An Application to the COVID-19 Crisis
Bibliographic record
Abstract
We study the effects of financial frictions on firm exit when firms face large liquidity shocks.We develop a simple model of firm cost-minimization, where firms' borrowing capacity to smooth temporary shocks to liquidity is limited.In this framework, firm exit arises from the interaction between this financial friction and fluctuations in cash flow due to aggregate and sectoral changes in demand conditions, as well as more traditional shocks to productivity.To evaluate the implications of our model, we use firm level data on small and medium sized enterprises (SMEs) in 11 European countries.We confirm that our framework is consistent with official failure rates in 2017-2019, a period characterized by standard business cycle fluctuations in demand.To capture a large liquidity shock, we apply our framework to the COVID-19 crisis.We find that, absent government support, SME failure rates would have increased by 6.01 percentage points, putting 3.1 percent of employment at risk.Our results also show that in the presence of financial frictions and in the absence of government support, the firms failing due to COVID have similar productivity and growth to firms that survive COVID.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".