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Record W4406190764 · doi:10.1002/hec.4927

Economic Value of Informal Care: Contingent Valuation From the Perspective of Caregivers and Care Recipients in China

2025· article· en· W4406190764 on OpenAlexaff
Hongli Fan, Jinyan Gao, Lu Chen, Zixuan Peng, Peter C. Coyte

Bibliographic record

VenueHealth Economics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Toronto
FundersNational Social Science Fund of China
KeywordsContingent valuationWillingness to payPromotion (chess)ChinaWillingness to acceptValue (mathematics)Health careValuation (finance)Care workEconomicsDemographic economicsBusinessSocioeconomicsEconomic growthFinanceWork (physics)Political scienceMicroeconomics

Abstract

fetched live from OpenAlex

We estimated the monetary value of informal care from the perspectives of informal caregivers and care recipients in China using the contingent valuation method. Data were obtained from a specially designed survey of 1458 informal caregivers and 972 care recipients. The mean for caregivers' willingness to pay (WTP) for reducing informal care by 1 h per week was CNY32.37 (€4.11), while the mean for willingness to accept (WTA) increasing informal care by 1 h was CNY46.21 (€5.87). The mean for care recipients' WTP (WTA) values for increasing or reducing informal care by 1 h per week were CNY28.74 (€3.65) and CNY44.78 (€5.69), respectively. The WTP and WTA values varied according to care hours and tasks, kinship, and living arrangements, and correlated with the characteristics of both caregivers and care recipients. The WTP and WTA values were also sensitive to a broad range of factors such as health, level of education, employment status, and household income. We highlight the contribution made by informal caregivers to elderly care and recommend the promotion of informal care activities to support and incentivize them.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.298
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2025
Admission routes1
Has abstractyes

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