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Record W3119040406 · doi:10.3386/w25513

Parental Beliefs about Returns to Different Types of Investments in School Children

2019· preprint· en· W3119040406 on OpenAlexaff
Orazio Attanasio, Teodora Boneva, Christopher Rauh

Bibliographic record

VenueNational Bureau of Economic Research · 2019
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité de MontréalStantec (Canada)
FundersBritish AcademyJacobs FoundationUniversity College DublinNuffield Foundation
KeywordsPsychologyDevelopmental psychologyMathematics education

Abstract

fetched live from OpenAlex

Parental investments as well as school quality are important determinants of children's later-life outcomes.In this paper, we shed light on what determines parental investments and study how parents perceive the returns to parental time investments, material investments and school quality, as well as the complementarity/substitutability between the different inputs.Using a representative sample of 1,962 parents in England, we document that parents perceive the returns to 3 hours of weekly parental time investments or £30 of weekly material investments to matter more than moving a child to a better school.Parents perceive the returns to time and material investments to be diminishing and perceive material investments as more productive if children attend higher quality schools.Perceived returns do not differ with the child's initial human capital or gender and, surprisingly, we find no differences in perceived returns by the parents' socioeconomic background.Consistent with parental beliefs playing an important role in parental investment decisions, perceived returns are found to be highly correlated with actual investment decisions.

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)0.008
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.120
GPT teacher head0.414
Teacher spread0.294 · 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

Citations49
Published2019
Admission routes1
Has abstractyes

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