MIGRATION, SOCIAL NETWORKS, AND CREDIT: EMPIRICAL EVIDENCE FROM PERU
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We seek evidence of the causal relationship between migration, social networks, and the probability of receiving credit in a developing country where credit markets are weak and internal migration is common. Migrants may face binding asymmetric information constraints as they often lack collateral. Social networks can help mitigate these constraints. Conversely, migrants might face higher liquidity constraints and might, therefore, demand more credit than nonmigrants. The effect of migration on participation in the credit market is thus ambiguous. Compounding this, migration and credit may be jointly determined. We utilize rich data from Peru to establish the net effect of migration on credit and the role that social networks play in this relationship.
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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 it