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Record W4386222133 · doi:10.1002/ise3.61

Intergenerational transfers in China: What are the patterns of the transfers and when do the transfers occur?

2023· article· en· W4386222133 on OpenAlexaff
Jingjing Xu

Bibliographic record

VenueInternational Studies of Economics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsCounterfactual thinkingChinaEconomicsUpstream (networking)Social securityDemographic economicsTransfer (computing)Overlapping generations modelLongitudinal dataDownstream (manufacturing)Safety netLabour economicsSociologyPolitical scienceDemographyPsychology

Abstract

fetched live from OpenAlex

Abstract China's social safety net is still underdeveloped, hence family support in the form of intergenerational transfers often serves as a substitute for the public transfer system. Using data from the China Health and Retirement Longitudinal Study, this paper finds that both upstream inter‐vivos transfers (from children to parents) and downstream inter‐vivos transfers (from parents to children) are prevalent in urban China. Moreover, the relative income status of the parent and children has an impact on inter‐vivos transfers. To investigate what economic factors generate the observed patterns of inter‐vivos transfers, this paper adopts a general equilibrium life‐cycle model in which overlapping generations are altruistically linked and calibrates the model to match data from urban China. Counterfactual experiments of removing one source of economic risk or modifying the social security replacement rate from the baseline model at a time reveal that intergenerational transfers mainly serve as informal insurance against the income risk of the children.

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)0.003
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.031
GPT teacher head0.286
Teacher spread0.255 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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