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Record W1510536973 · doi:10.1787/5k92vxbgpmnt-en

Money or Kindergarten? Distributive Effects of Cash Versus In-Kind Family Transfers for Young Children

2012· paratext· en· W1510536973 on OpenAlexaboutno aff
Michael Förster, Gerlinde Verbist

Bibliographic record

VenueOECD social employment and migration working papers · 2012
Typeparatext
Languageen
FieldSocial Sciences
TopicEducation in Diverse Contexts
Canadian institutionsnot available
FundersEuropean Commission
KeywordsCashPovertyDistributive propertyCash transfersDemographic economicsQuarter (Canadian coin)EconomicsPoverty levelValue (mathematics)Child careTransfer (computing)Labour economicsGeographyEconomic growthFinanceMedicineStatistics

Abstract

fetched live from OpenAlex

Public support to families with pre-school children can be in the form of cash benefits (e.g. child allowances) or of “in-kind” support (e.g. care services such as kindergartens). The mix of these support measures varies greatly across OECD countries, from a cash / in-kind composition of 10%/90% to 80%/20%. This paper imputes the value of services into an “extended” household income and compares the resulting distributive patterns and the redistributive effect of these two strands of family policies. On average, cash and in-kind transfers each constitute 7 – 8% of the incomes of families with young children. Both instruments are redistributive. Cash transfers reduce child poverty by one third, with the estimated impacts in Austria, Ireland, Sweden, Hungary and Finland performing above average. When services are accounted for, child poverty falls by one quarter and poverty among children enrolled in childcare is more than halved. This reduction is highest in Belgium, France, Hungary, Iceland and Sweden.

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)0.002
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 score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0280.001

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 head0.040
GPT teacher head0.331
Teacher spread0.290 · 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

Citations36
Published2012
Admission routes1
Has abstractyes

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