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Record W3208219154 · doi:10.1111/ecpo.12201

The influence of financial and economic literacy on policy preferences in Italy

2021· article· en· W3208219154 on OpenAlexaff
Beatrice Magistro

Bibliographic record

VenueEconomics and Politics · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of Toronto
FundersInstitute for Humane Studies, George Mason UniversityUniversity of Washington
KeywordsFinancial literacyImmigrationProtectionismPensionEconomicsEconomic globalizationLiteracyGlobalizationEconomic integrationInternational economicsPolitical scienceFinanceEconomic growthMarket economy

Abstract

fetched live from OpenAlex

Abstract As populist and protectionist sentiments across the world increase, this paper explores the role that financial and economic literacy plays in shaping individual economic policy preferences. Analyzing original survey data collected in Italy, this study shows that financially and economically literate individuals, regardless of their economic self‐interest, are more likely to prefer remaining in the Eurozone, to favor free trade, EU immigration, non‐EU immigration, and the Fornero pension reform. The author provides preliminary evidence that the lack of differential effects between financially and economically literate winners and losers from globalization and pension reform is driven by longer time horizons. Finally, the author examines different ways to measure financial and economic literacy and finds that there is no evidence of a similar effect when looking at general education, suggesting that financial and economic literacy has distinctive features that more closely capture an individual's ability to evaluate policies.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.227
Teacher spread0.218 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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