The Central Italy earthquake and its short-term impact on firms
Bibliographic record
Abstract
This paper evaluates the short-term impact of the earthquake that hit Central Italy in 2016 on firms’ economic activity. The analysis is based on corporate balance sheet data and on a methodology that compares the performance of firms located within the seismic area with firms featuring similar characteristics but located outside it. The results show that in 2016, firms within the seismic area experienced a negative effect on revenues of over 5 per cent with respect to the counterfactual group (corresponding to about 20 per cent in the last quarter of the year, when most of the tremors occurred). In 2017, the effects lessened overall. The analysis also shows that the effects were quite heterogeneous. The negative impact is considerable for firms closer to the epicentres (core area) in both years, in particular for firms of smaller size that operate in the service sector, owing to their reliance on local and tourism-related demand. For firms located further from the epicentres (non-core area), which include the main local firms and long-standing manufacturing specializations, the effects were somewhat smaller and vanished in 2017.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".