Contract Farming and Food Insecurity in an Open Competitive Economy: Growth, Distribution, and Government Policy
Bibliographic record
Abstract
The paper explores the emergence and consequence of contract farming as a new subsector of agriculture in a small open developing economy, applying the theory of finite change in a general equilibrium framework. In this paper, we analyze the entry of a cash crop-producing foreign contract farming (CF) subsector within the agricultural sector of a country. Entry requires a cash crop price that is substantially above the price of the food crop already being produced within the country. CF (a) increases GDP and hence aggregate economic welfare; (b) may make income distribution more skewed; (c) reduces domestic production of food and hence, (d) increases food import and hence food insecurity. Thus, CF might imply a trade-off between inequality and growth. We employ a variant of the 3 × 3 mixed specific factor-Heckscher Ohlin general equilibrium model of production and trade where introduction of a new policy may lead to the emergence of a new sector resulting in finite changes where we show the possibilities of sectoral diversification with combinations of contract farming vis-à-vis traditional agriculture under some plausible conditions. Our results seem to be consistent when compared to some empirically robust conclusions found in the literature and some secondary data available on the FAO website. We also argue that the food insecurity problem gets aggravated as more and more countries engage in contract farming. Policy simulations identify critical parameters confirming the dominance of distribution over the growth effect in terms of a social welfare function. Simulations imply that there could be a food insecurity problem, as rises in GDP could result in increasing inequality so that government—to preserve social welfare—could restrict the extent of contract farming if non-food-producing sectors expand, causing terms-of-trade deterioration of food-importing nations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".