Price and cost competitiveness. Quarterly report on the price and cost competitiveness of the European Union and its Member States. Fourth quarter 2000. ECFIN/44/4/00-EN
Bibliographic record
Abstract
This series of quarterly reports provide a. periodic assessment of the price and cost competitiveness of the euro area and the individual Member States of the European Union.Part 1 offers an overview of international and intra-EU price and cost competitiveness positions.Part 2 is a data section which provides data for the euro area, for each Member State, as well as for five other industrial countries (United States, Japan, Norway, Australia, and Canada).The nominal effective exchange rate (NEER) of a country (or currency area) aims to track changes in the value of that country's currency relative to the currencies of its principal trading partners.It is calculated as a weighted average of the bilateral exchange rates with those currencies.C~anges in cost and price competitiveness depend not only on exchange rate movements but also on cost and price trends.The real effective exchange rate (REER) aims to assess a country (or currency area's) price or cost competitiveness relative to its principal competitors in international markets.It corresponds to the NEER deflated by selected relative price or cost deflators.Countries in the euro area share a single currency and there is no longer any exchange rate between them.For these countries, the terms "nominal effective exchange rate" and "real effective exchange rate" have been replaced by the terms "trade-weighted currency index" and "relative price and cost indicators" but the underlying concepts and their calculation remains the same.The trade-weighted currency indices for individual countries using the euro may diverge because they have different trading patterns.In addition, the relative price and cost indicators may evolve differently due to diverging price and cost trends.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".