Economic and social status in household decision making : evidence from extended family mobility
Bibliographic record
Abstract
Models of the allocation of household resources use as a decision rule either the maximization of a household utility function (Becker, 1964) or the solution to a Nash-bargaining game (McElroy and Horney, 1981). The literature on residential mobility has exclusively used the former to analyze 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 we use micro-data from Taipei, Taiwan to determine which approach is most appropriate for studying housing mobility decisions. We compare the mobility decisions of nuclear and different types of extended family households to test whether the social and economic roles of different generations affect the household decision process, as is consistent with the bargaining approach. In doing so, we analyze household mobility 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. Our 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 we find that economic status is paramount to social status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".