Land Reform and Productivity: A Quantitative Analysis with Micro Data
Bibliographic record
Abstract
We assess the effects of a major land policy change on farm size and agricultural productivity using a quantitative model and micro-level data. We study the 1988 land reform in the Philippines that imposed a ceiling on land holdings, redistributed above-ceiling lands to landless and smallholder households, and severely restricted the transferability of the redistributed farm lands. We study this reform in the context of an industry model of agriculture with a nondegenerate distribution of farm sizes featuring an occupation decision and a technology choice of farm operators. In this model, the land reform can reduce agricultural productivity not only by misallocating resources across farmers but also by distorting farmers' occupation and technology decisions. The model, calibrated to pre-reform farm-level data in the Philippines, implies that on impact the land reform reduces average farm size by 34% and agricultural productivity by 17%. The government assignment of land and the ban on its transfer are key for the magnitude of the results since a market allocation of the above-ceiling land produces about 1/3 of the size and productivity effects. These results emphasize the potential role of land market efficiency for misallocation and productivity in the agricultural sector.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".