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Record W2938746304 · doi:10.1080/02673037.2019.1585521

Housing affordability, subsidized lending and cross-city variation in the performance of China’s housing provident fund program

2019· article· en· W2938746304 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHousing Studies · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsCentre for International Governance Innovation
FundersFudan University
KeywordsSubsidyChinaBusinessVariation (astronomy)Distribution (mathematics)InequalitySubsidized housingPanel dataEconomicsDemographic economicsLabour economicsFinanceGeographyMarket economy

Abstract

fetched live from OpenAlex

This study examines the cross-city variation in the performance of China’s Housing Provident Fund (HPF) program, a collective saving scheme that provides subsidized lending to support participants’ home purchases. It finds that while the program as a whole is limited in both participation and benefit provision, the level of HPF activities has differed across localities. Panel-data analysis of HPF lending in seven cities reveals that local housing affordability was an important determinant of who benefited from the program. Rising housing price increased the demand for HPF loans. But if price rose too high relative to household income, the share of participants who used HPF loans declined. This shows that as the program currently operates, expanding HPF participation would only increase the inequality in the distribution of program benefits. Finally, we did not find evidence for the counter-cyclic effects that HPF lending was expected to have in relation to bank lending. These findings have important implications for the program’s future reform.

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.

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)0.002
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 score0.014
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.065
GPT teacher head0.298
Teacher spread0.233 · 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