Differentiated Agri-Food Product Trade and the Linder Effect
Bibliographic record
Abstract
Using a generalized gravity equation, this study tests for the Linder effect in differentiated agrifood product trade, i.e. as the demand structures of two countries become more similar, their trade intensity increases. Two proxies of demand structure, the Balassa index and the absolute value of the difference in per capita GDPs of trading partners, are used to capture the Linder effect. In addition, two measures of bilateral trade, the Grubel and Lloyed index, and the value of bilateral trade are used as the dependent variable. The study investigates the role of the Linder effect in explaining the trade of 37 differentiated agri-food and beverage products categorized into eight product groups: cereals; fresh fish; frozen fish; vegetables; fresh fruit; processed fruit; tea and coffee; and alcoholic beverages. The data covers trade across 52 developed and developing countries from 1990 to 2000. The type of proxy used for the Linder effect and the way in which bilateral trade is measured influence the outcome of the statistical tests for the Linder effect. The Linder effect for cereals, frozen fish, vegetables, processed fruits, and tea and coffee, using the value of trade as the dependent variable, is often accepted but it is generally rejected when the GL index is used as the measure of trade intensity. In brief, the results do not provide strong support for the Linder effect in the trade of differentiated agri-food products.---------------------------------------------------------------------------Revised and published as: Haq, Ul Zahoor and Karl D. Meilke. 2009. “Does the Linder effect hold for differentiated agri-food and beverage product trade?” Applied Economics 43(27):4095-4109.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".