The Transition from Small to Large Farms in Developing Economies: A Welfare Analysis
Bibliographic record
Abstract
Promoting smallholder production systems as a growth and poverty‐reduction strategy versus supporting an institutional framework that enables endogenous and voluntary consolidation of smallholder farms into larger operations is a central debate for economic development and food security in low‐ and middle‐income countries. We propose an integrated conceptual framework to compare the two alternative farming systems for producing a domestic staple commodity, focusing on key economic factors that differentiate them, including labor inefficiency of larger farms, credit constraints for smallholders, and differences in farm–retail price spreads. We derive equilibrium expressions for economic welfare for smallholder farmers and urban consumers under the two farming systems, and parameterize the model based on publicly available data and recent empirical literature. An extensive simulation analysis reveals several key results from transforming to a large‐farm equilibrium: (a) rural household welfare almost always declines; (b) total production of the staple almost always increases; (c) the sum of urban and rural household welfare almost always increases, often by substantial amounts; and (d) rural employment does not decrease, even with modest increases in capital intensity on large farms. Policies to promote farm consolidation, while protecting rural households from welfare losses, for example through income transfers, can achieve Pareto improvements for nearly all of the comparative equilibria studied.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".