How Did Swiss Small and Medium Enterprises Weather the COVID-19 Pandemic? Evidence from Survey Data
Bibliographic record
Abstract
The COVID-19 pandemic had unprecedented consequences on businesses and, in particular, small and medium enterprises (SMEs). The aim of this paper is to empirically study the impact of the COVID-19 sanitary crisis on Swiss SMEs two years after the onset of the pandemic. Using a sample of 149 SMEs operating in the French-speaking region of Switzerland, we find that revenue loss across the full sample averaged 14% in 2020 compared to 2019. Our findings show that firm characteristics are not significant in explaining turnover loss while business management strategies such as business restructuring, remote working, and prioritizing employee protection were significantly associated with revenue. Our results suggest that remote work should be gradually introduced in sectors of activity whenever possible to reduce the financial burden of any future pandemic on SMEs. Additionally, we quantify the impact of closure on SMEs and find that firms having reported to have closed partially or completely due to sanitary restrictions were impacted seven times more in terms of revenue loss compared to SMEs that did not cease their activity during the COVID-19 crisis. This paper contributes to our understanding of the magnitude of the financial impact of the COVID-19 pandemic on SMEs and highlights the importance of SMEs adopting efficient business management strategies, and implementing measures to support smaller firms by public authorities in times of crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".