Would freeing up world trade reduce poverty and inequality? The vexed role of agricultural distortions
Bibliographic record
Abstract
Trade policy reforms in recent decades have sharply reduced the distortions that were harming agriculture in developing countries, yet global trade in farm products continues to be far more distorted than trade in nonfarm goods. Those distortions reduce some forms of poverty and inequality but worsen others, so the net effects are unclear without empirical modeling. This paper summarizes a series of new economy-wide global and national empirical studies that focus on the net effects of the remaining distortions to world merchandise trade on poverty and inequality globally and in various developing countries. The global LINKAGE model results suggest that removing those remaining distortions would reduce international inequality, largely by boosting net farm incomes and raising real wages for unskilled workers in developing countries, and would reduce the number of poor people worldwide by 3 percent. The analysis based on the Global Trade Analysis Project model for a sample of 15 countries, and nine stand-alone national case studies, all point to larger reductions in poverty, especially if only the non-poor are subjected to increased income taxation to compensate for the loss of trade tax revenue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".