CERTIFIED TO MIGRATE: PROPERTY RIGHTS AND MIGRATION IN RURAL MEXICO
Bibliographic record
Abstract
Improving security of tenure over agricultural land has recently been the focus of a number of large land certification programs. While the main justification for these efforts was to increase productive investments and facilitate land rental transactions, we show that if access rights were tied to actual land use in the previous regime, these programs can also lead to increased outmigration from agrarian communities. We analyze the Mexican ejido land certification program which, from 1993 to 2006, awarded ownership certificates to 3.6 million farmers on about half the country’s agricultural land. Using the program rollout over time and space as an identification strategy, we show that households that obtained land certificates were 28% more likely to have a migrant member. The effect was larger for households with ex-ante weaker property rights and with larger off-farm opportunities. At the community level, certificates led to a 5% reduction in population, and the effects were larger in lower land quality environments. We show evidence of certificates leading to sorting, with larger farmers staying and land-poor farmers leaving in high productivity areas. We use satellite imagery to determine that, on average, cultivated land was not reduced because of the program, consistent with increases in agricultural labor productivity. Furthermore, in high productivity areas, the certification program led to an increase in cultivated land compared to low productivity areas.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".