The Missing Food Problem: How Low Agricultural Imports Contribute to International Income and Productivity Differences
Bibliographic record
Abstract
This paper finds an important relationship between the international food trade and cross-country income and productivity differences. Poor countries have low labour productivity in agriculture relative to other sectors, yet predominantly consume domestically-produced food. To understand these observations, I describe and exploit a general equilibrium model of international trade to: (1) measure sectoral productivity and trade costs across countries; and (2) quantify the impact of low poor-country food imports on international income and productivity gaps. Specifically, I expand on Yi and Zhang [2010] and modify an Eaton-Kortum trade model to incorporate multiple sectors, non-homothetic preferences, and labour mobility costs. With this model, I estimate PPP-adjusted productivity from observed bilateral trade data, avoiding problematic price and employment data in poor countries that direct output-per-worker estimates require. I find reasonable trade barriers and labour mobility costs account for the low poor-country imports despite their low productivity. Through various counterfactual experiments, I quantify how easing import barriers and labour mobility costs increases imports and within-agriculture specialization, shuts down low productivity domestic food producers, and lowers the gap between rich and poor countries. I also find an interaction between domestic labour-market distortions and trade barriers not found in the existing dual-economy literature, which largely abstracts from open-economy considerations. Overall, I account for one-third of the aggregate labour productivity gap between rich and poor countries and for nearly half the gap in agriculture.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".