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Record W2890399122 · doi:10.3386/w18669

Optimal Financial Knowledge and Wealth Inequality

2013· report· en· W2890399122 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNational Bureau of Economic Research · 2013
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité du Québec à Montréal
FundersNetwork for Studies on Pensions, Aging and RetirementU.S. Social Security AdministrationUniversity of PennsylvaniaRAND Corporation
KeywordsInequalityEconomicsFinanceBusinessFinancial economicsMathematics

Abstract

fetched live from OpenAlex

While financial knowledge is strongly positively related to household wealth, there is also considerable cross-sectional variation in both financial knowledge and net asset levels. To explore these patterns, we develop a calibrated stochastic life cycle model featuring endogenous financial knowledge accumulation. The model generates substantial wealth inequality, over and above that of standard life cycle models; this is because higher earners typically have more hump-shaped labor income profiles and lower retirement benefits which, when interacted with precautionary saving motives, boost their need for private wealth accumulation and thus financial knowledge. Our simulations show that endogenous financial knowledge accumulation has the potential to account for a large proportion of wealth inequality. The fraction of the population which is rationally financially "ignorant" depends on the generosity of the retirement system and the level of means-tested benefits. Educational efforts to enhance financial savvy early in the life cycle so as to produce one percentage point excess return per year would be valued highly by people in all educational groups.

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.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.271
GPT teacher head0.476
Teacher spread0.205 · 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