The Effects of Partial Employment Protection Reforms: Evidence from Italy
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We combine matched employer–employee data with firms’ financial records to study a 2001 Italian reform that lifted constraints on the employment of temporary contract workers while maintaining rigid employment protection regulations for employees hired under permanent contracts. Exploiting the staggered implementation of the reform across different collective bargaining agreements, we find that this policy change led to an increase in the share of temporary contracts but failed to raise employment. The reform had both winners and losers. Firms are the main winners as the reform was successful in decreasing labor costs, leading to higher profits. By contrast, young workers are the main losers since their earnings were substantially depressed following the policy change. Rent-sharing estimates show that temporary workers receive only two-thirds of the rents shared by firms with permanent workers, helping explain most of the labor costs and earnings reductions caused by the reform.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it