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Record W7043201028

The state of financial knowledge in the European Union

2024· other· en· W7043201028 on OpenAlexaboutno aff

Bibliographic record

VenueEconstor (Econstor) · 2024
Typeother
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionInflation (cosmology)ObstacleState (computer science)Financial riskFinancial literacyFinancial marketQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Only one in two individuals in the European Union, on average, is financially knowledgeable. In response to a 2023 survey containing five questions assessing basic financial knowledge, only half of the respondents answered at least three of the five questions correctly. This represents a low level of financial knowledge and an obstacle for individuals to invest in financial markets. The questions most often answered correctly by respondents measured understanding of inflation and the relationship between risk and return. Only one in five respondents answered correctly a question on the relationship between interest rates and bond prices. Regarding inflation, there is a large difference between the least and most educated respondents in terms of answering the relevant question correctly. Gaps in understanding the concept of inflation are also evident between the youngest (18-24) and oldest respondents (55+) and between the poorest and richest households. A gender gap is present in financial knowledge, with 18 percentage points more men than women answering at least three out of five questions correctly, on average in the EU. Those with greater financial knowledge are less financially fragile in that they can still cover their expenses if there is a sudden loss of income, and are more confident that they will have sufficient funds to sustain themselves during retirement. Countries with higher proportions of people who are financially knowledgeable have higher numbers of people who both save with and borrow from financial institutions, an indication that financial knowledge may improve financial inclusion. All EU countries have, or are in the process of putting together, a national financial literacy strategy. There is an urgent need to roll out these strategies, to monitor progress over time and to establish best practices. Particular attention needs to be given to how financial knowledge interacts with digital skills as financial services are increasingly digitalised. Financial literacy strategies should also help close gender and other gaps in knowledge among vulnerable groups, and should ensure that financial education starts early and in schools.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.010
GPT teacher head0.223
Teacher spread0.213 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2024
Admission routes1
Has abstractyes

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