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Record W2093445077 · doi:10.1080/0042098032000065272

Economic and Social Status in Household Decision-making: Evidence Relating to Extended Family Mobility

2003· article· en· W2093445077 on OpenAlexaff
Chin‐Oh Chang, Shumei Chen, Tsur Somerville

Bibliographic record

VenueUrban Studies · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEarningsEconomicsAffect (linguistics)Unit (ring theory)Household incomeMicroeconomicsDemographic economicsPublic economicsLabour economicsGeographySociology

Abstract

fetched live from OpenAlex

Models of the allocation of household resources use as a decision rule either the maximisation of a household utility function or the solution to a Nash-bargaining game. The literature on residential mobility has exclusively used the former to analyse the household's decision to change location. This is despite the strong empirical evidence that allocations in other areas are more consistent with the bargaining model. In this paper micro-data from Taipei, Taiwan, are used to determine which approach is most appropriate for studying housing mobility decisions. The mobility decisions of nuclear and different types of extended family household are compared to test whether the social and economic roles of different generations affect the household decision process, as is consistent with the bargaining approach. Thus, household mobility is analysed with a richer description of household structure than is found in the current literature, which implicitly treats households as either a nuclear family or some smaller unit. The results support the bargaining model of household decision-making. Conditional probabilities differ between nuclear and extended families, when a member of the eldest generation in an extended household is the household head, and when a member of the eldest generation contributes to household earnings. Of these, it is found that economic status is paramount to social status.

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)0.001
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 score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.080
GPT teacher head0.348
Teacher spread0.268 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

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