Measuring Information Frictions in Migration Decisions: A Revealed-Preference Approach
Why this work is in the frame
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Bibliographic record
Abstract
We investigate the role of information frictions in migration. Using novel moment inequalities and data on internal migration in Brazil, we estimate worker preferences and migration costs while allowing for unobserved worker-specific information sets. We find that common estimation procedures overestimate migration costs and underestimate the importance of expected wages in migration decisions. Model specification tests indicate that workers often have limited information on location-specific wages. However, those living in regions with better internet access and larger populations have more precise wage information, and information precision decreases with distance. According to our estimated model, workers’ limited wage information plays a quantitatively important role in reducing migration flows and worker welfare, and limits the effect of policies that reduce migration costs.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it