Inflation and counter-inflationary policy measures: The case of France
Bibliographic record
Abstract
French consumer price inflation (as measured by the HICP) rose by 6.2% in September 2022 as compared to September 2021, against 10% in the euro area. Inflation rose less rapidly in France than in the euro area primarily due to a less rapid rise in energy prices: energy prices contributed to raise annual inflation by 1.9 percentage points in France as compared to 4.4 at the euro area level. Energy price inflation is lower in France partly because the economy is less reliant on gas than other euro area economies, but even more due to significant fiscal measures. The "tariff shield" on gas and electricity prices introduced at the end of 2021 and the rebate on fuel prices have strongly limited inflation. The fact remains that the French economy has been hit by a huge energy shock of the size of the first oil shock in 1974, i.e. amounting to around 3% of GDP. This energy shock is mainly absorbed by government finances, through substantial fiscal measures (3.3 percent of GDP in 2022-23), but also by employees who are suffering a record fall in real wages (-2.2% in real terms in the second quarter of 2022 as compared to a year earlier). Profit margins have remained rather stable since the last quarter of 2021 when inflationary pressure became visible, mainly because real wages cuts have offset productivity losses. In 2023, we expect nominal wages to accelerate (+3.4% in 2022 and +3.8% in 2023), which would remain below inflation (5.3% in 2022 and 5% in 2023). According to our estimates energy price inflation by itself would reduce French GDP by 1.4 percentage points in 2022 and 3.3 in 2023, but fiscal measures introduced to counter the impacts of the energy crisis will soften the economic shock by 0.8 percentage points of GDP in 2022 and 1.5 in 2023.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".