Lechene (2001): “Caring and Sharing: Tests Between Alternative Models of Intra-household Allocation
Bibliographic record
Abstract
Several models of intra-household decision making have been suggested in the literature. One important dichotomy is between non-cooperative and cooperative models (including speci…c models of bargaining). The other im-portant distinction is between models that allow for caring and those that do not. We present a framework that includes all suggested models and vari-ants as special cases. We derive the theoretical predictions of these models for the relationship between expenditures on goods and the intra-household distribution of income. We estimate and test between these relationships using Canadian household expenditure data. We conclude that there is ev-idence that both husbands and wives care for each other in the sense that with an unequal distribution of incomes the high income partner behaves as a ‘Becker dictator ’ and there is local income pooling. We further …nd that for about half of the households in our sample (those with more equal incomes) a re-distribution of income would lead to changes in budget allo-cations. We conclude that the data are consistent with a collective model with caring partners. ¤We gratefully acknowledge the contribution of Wasif Rasheed to the research presented
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.002 |
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".