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Record W2249826377 · doi:10.1515/apjri-2013-0012

Welfare Effects of Developing the Reverse Mortgage Market in China: An Individual and Social Perspective

2013· article· en· W2249826377 on OpenAlexaboutno aff
Minan Huang, Bingzheng Chen, Yinglu Deng

Bibliographic record

VenueAsia-Pacific Journal of Risk and Insurance · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWelfareConsumption (sociology)ChinaSocial securitySocial WelfareEconomicsAsset (computer security)PopulationLabour economicsMarket economyMedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract The increasing aging population and social security insufficiency have become serious problems in China. Some developed countries, such as the U.S., Canada, and Japan, have developed reverse mortgage markets as solutions for the aging problem. In this article, a theoretical analysis of the welfare and asset allocation effects from introducing a reverse mortgage market into China, with the overlapping generation model and parameters from Chinese factors, was carried out. The results show that the introduction of a reverse mortgage market improved individual and social welfare. It provides more income for both the older and the younger generations. For the older generation, this can reduce the burden on social security; for the younger generation, this can smooth out lifetime consumption for the individual. By making comparisons among different scenarios, this article shows that the welfare effects from a reverse mortgage market would increase with the aging level of society.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.006
GPT teacher head0.207
Teacher spread0.202 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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