Intrahousehold moral hazard frictions and household poverty traps in rural India
Bibliographic record
Abstract
Abstract We empirically study the role of assets held by women in the creation of household wealth using data from rural India. We design a streamlined model of intrahousehold project funding where moral hazard frictions between spouses and women's asset control are the main ingredients. As predicted by the model, the data show that household asset accumulation depends on women's asset control in a non‐monotonic way. Results indicate no presence of multiple equilibrium poverty traps, but do show that exogenous negative shocks will trigger assets aggregation within households where both spouses are present. This resilience mechanism is, however, not found in female headed household as these households have a monotonic relationship between women's wealth control and asset creation. We thus argue that policies to support women's empowerment need to distinguish women based on their individual wealth levels and headship status to enhance household well‐being in remote Indian communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".