The Adjustment of Maltese Firms to the Post-crisis Economic Environment: Evidence from a Firm-level Survey
Bibliographic record
Abstract
In contrast to the experience of southern and peripheral economies in the euro area, Malta has weathered the financial crisis relatively well and its labour market remained resilient in the face of shocks. Using information from a firm-level survey conducted in 2014, this paper focuses on the nature of the shocks hitting the economy after the crisis and the reaction of Maltese firms to these shocks. Concerning the latter, a distinction is made between the firms’ decisions to adjust their workforce and on the wages given to new hires compared to incumbents. The empirical analysis is conducted with a multivariate probit framework that controls for both firm and workforce specific characteristics as well as the nature of the shocks faced by the firms. The results highlight the high degree of heterogeneity in demand conditions across sectors although concerns about skill shortages were broad-based.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".