Bibliographic record
Abstract
We use the full population of Belgian firms to examine the unequal impact of Covid-19 on firm growth. In doing so, we compare whether the response of firms to Covid-19 is different from the Great Recession of 2008. We find a significant decline in net employment growth during the first and second quarter of 2020, with an average loss of 4 and 19 percent in aggregate employment, respectively. We show that the aggregate picture masks significant heterogeneity among firms and that the Covid-19 crisis is different than the Great Recession. While small and medium-sized firms performed relatively well during the 2008 crisis, during the 2020 pandemic crisis they were hit harder compared to large firms. We find that the difference stems from the industry-specific effects of the shocks and the nature of the crises. However, we show that what really matters is firm age, which is even more relevant than firm size. Young firms contribute disproportionately to aggregate job creation an destruction and they are respond more to major shocks. Policy should therefore target young firms rather than small firms to weather the crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".