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Record W2309479440

The Costs and Welfare Effects of ECB's Financial Repression Policy: Consequences for German Savers

2015· article· en· W2309479440 on OpenAlexvenueno aff
Karl-Heinz Tödter

Bibliographic record

VenueReview of Economics and Finance · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGerman Economic Analysis & Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMonetary policyInterest rateInflation (cosmology)WelfareMonetary economicsCapital (architecture)Order (exchange)Deadweight lossGermanMacroeconomicsFinanceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

The losses in interest income of German savers as a result of ECB's monetary policy of extremely low interest rates are estimated at around €65bn pa. These losses exceed the financial costs of capital yields taxes and the inflation tax on consumer prices taken together. However, the calculations do not take into account that (especially public) debtors benefit from low interest rates. We develop new measurement concepts and apply an overlapping generation model in order to calculate stepwise the net welfare costs (excess burden) for the German economy. Capital yields taxes have an excess burden of €10bn per year. The excess burden from 1.5% inflation totals at €33bn pa. In comparison, the monetary policy of low interest rates that is conducted by the ECB since 2010 created an excess burden of €37bn or 1.4% of GDP pa. Hence, the cumulated net welfare losses resulting from the ECB policy of ultra-easy money already exceed the primary effects of the financial crisis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.257
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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