Intrahousehold Resource Allocation and Individual Poverty: Assessing Collective Model Predictions using Direct Evidence on Sharing
Bibliographic record
Abstract
Abstract Welfare analyses conducted by policy practitioners around the world usually rely on equivalised or per capita expenditures and ignore the extent of within-household inequality. Recent advances in the estimation of collective models suggest ways to retrieve the complete sharing process within families using homogeneity assumptions (typically preference stability upon exclusive goods across individuals or household types) and the observation of exclusive goods. So far, the prediction of these models has not been validated, essentially because intrahousehold allocation is seldom observed. We provide such a validation by leveraging a unique dataset from Bangladesh, which contains information on the fully individualised expenditures of each family member. We also test the core assumption (efficiency) and homogeneity assumptions used for identification. It turns out that the collective model predicts individual resources reasonably well when using clothing, i.e., one of the rare goods commonly assignable to males, females and children in standard expenditure surveys. It also allows for identifying poor individuals in non-poor households, while the traditional approach understates poverty among the poorest individuals.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".