The Effects of Land Markets on Resource Allocation and Agricultural Productivity
Bibliographic record
Abstract
We assess the effects of land markets on misallocation and productivity by exploiting effective variation in land rentals across time and space arising from a large-scale land certifcation reform in Ethiopia, where land remains owned by the state. Our main fnding from detailed micro panel data is that land rentals substantially reduce misallocation and increase agricultural productivity. Our evidence builds from an empirical difference-in-difference strategy and a calibrated quantitative macroeconomic framework with heterogeneous household-farms that replicates|without targeting|the empirical effects, an outcome that externally validates our model. The empirical effects are nonlinear|impacting more farms farther away from effcient operational scale, consistent with our theory. Further, counterfactual model experiments suggest that the land reform reduces income inequality, is relatively scalable and explains a sizeable proportion of the full extent of misallocation. Additional insights on the role of (in)formality in land markets and its effects on technology adoption are provided
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".