Buffer or Conduit? Global Agri‐food Value Chains and Food Price Transmission
Bibliographic record
Abstract
Abstract In an era of volatile global markets, stabilizing domestic food prices has become increasingly critical. This paper examines how participation in global agri‐food value chains (GVCs) influences food price transmission through a robust two‐step regression framework. First, we estimate country‐specific long‐run pass‐through (LRPT) coefficients for 173 countries over four separate episodes between 2000 and 2022, quantifying the extent to which changes in international food prices lead to changes in domestic consumer food prices. Then, we regress these estimates on distinct measures of GVC participation: intermediate (two‐sided or mixed) participation versus pure backward or forward trade (reflecting participation in later and earlier stages of production, respectively). Our findings reveal that nations with intermediate GVC participation experience a significant reduction in the transmission of price shocks, while those focused solely on backward or forward trade exhibit greater transmission of international food price shocks. These results challenge the dominant view that deeper global integration invariably increases the transmission of international food price shocks and highlight the potential of intermediate positioning as an effective policy tool for stabilizing domestic food prices. This study provides valuable insights for trade policy design and contributes to the broader discourse on food security.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".