Bibliographic record
Abstract
An employment tax deduction is frequently used as a public policy tool to stimulate economic growth and recovery. Analysis of the impact of such provisions adopted in the recent past may shed light on the effects of current tax policies. This article aims to estimate the effects of a tax deduction for Italy's <i>imposta regionale sulle attivit‡ produttive</i> (regional tax on productive activities), or IRAP, granted to firms that increased their personnel between 2005 and 2007. The main objectives of the analysis are to assess the increase in, and the permanence of, new employment; to detect any changes in the employment structure of beneficiary firms; and to evaluate the effectiveness of different deduction amounts granted to firms in disadvantaged regions in order to reduce the employment gap. The results of the analysis using a difference-in-difference model indicate that firms enjoying IRAP incentives registered more significant and more enduring changes in the selected indicators as compared with firms not taking the deduction, thus verifying the effectiveness of the provision. The adopted measure provided for larger deductions for disadvantaged regions of southern Italy, but the results do not register a larger increase in employment in those regions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".