Que nous apprennent les données de branches sur les premiers effets du CICE ? Évaluation pour la période 2014-2015t2
Bibliographic record
Abstract
What Do Branches Data Teach Us about the CICE’s Initial Effects ? Evaluation for the Period 2014 – Q2 2015. This paper aimsto highlight the potential effects of the Competitiveness and Employment Tax Credit (CICE) on the French economy over the period from 2014 to the end of the second quarter of 2015. It is based on information from quarterly national accounts by occupational sector. Beginning with an econometric analysis of panel data, we endeavour to find out whether the CICE has had effects on employment, wages and value-added prices. This method enables us to identify and quantify the CICE’s relative cross-sector effects on these variables, but does not allow macroeconomic effects to be deduced. Based on our estimates for 16 occupational sectors, the results show that for a tax credit equal to 1% ofwages and salaries, employment in a sectorwould rise by 0.5% compared to other sectors, and wages by 0.7%. Lastly, using the results of estimates carried out at the same time, we can quantify these relative cross-sector effects at 1.1% for wages and 120,000 jobs (either created or safeguarded).
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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 | 0.044 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".