Who Supports the <scp>ECB</scp>? Evidence from <i>Eurobarometer</i> Survey Data
Bibliographic record
Abstract
Abstract This paper studies the determinants of the support for the European Central Bank (ECB) in the member countries of the European Monetary Union (EMU) and their evolution from 1999 to 2015. Our contribution is to examine micro‐level sociodemographic characteristics from the Eurobarometer surveys jointly with macroeconomic indicators of trust in a central bank in order to evaluate econometrically their relative importance over time. Pseudo‐panel logit estimates reveal that the former have a dynamically stable, and generally stronger influence taken altogether, when compared with the latter. Interestingly, we find that while expected inflation becomes a positive determinant of trust in the ECB after the global financial crisis (GFC), actual inflation gets no statistical significance. Having taken centre stage in the monetary policy debate in the Euro‐area post‐GFC and especially since 2013, excessive disinflation and risk of deflation attracted strong attention by the public and have consequently affected its perceptions about the ECB. Accordingly, our results emphasise forward lookingness of the EMU population with regard to ‘deflation scares’ in determining trust in the ECB, in addition to disentangling the contributions of the key individual‐level sociodemographic factors, and can duly inform ECB's communication strategy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".