Assessing the impact of public transferson private risk sharing arrangements : evidence from a randomized experiment in Mexico
Bibliographic record
Abstract
We adopt a structural approach to studying the effects of public transfers on consumption smoothing, risk sharing and welfare in small village economies. We calibrate the key parameters of a dynamic limited commitment model using data gathered as part of the Mexican Progresa program, and take advantage of the randomized experimental design of the data to validate the model using the treatment sample. The limited commitment model enriched to allow for unobserved heterogeneity in preferences can reasonably well explain consumption dynamics and cross-sectional distributions. The calibrated model correctly predicts the increase in consumption smoothing of transfers recipients, and the decrease in risk sharing between beneficiaries and non beneficiaries of the program. Progresa transfers are found to crowd-out between 3% and 10% of the pre-existing private transfers, but the overall direct e,ect of the subsidy on consumption is welfare improving for all households. Last, we use our structural model to evaluate a counterfactual, fully funded, insurance scheme.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".