credit, including © notice, is given to the source. From Groundnuts to Globalization: A Structural Estimate of Trade and Growth
Bibliographic record
Abstract
JEL No. E00,F43,O4 Starting with Romer [1987] and Rivera-Batiz-Romer [1991] economists have been able to model how trade enhances growth through the creation and import of new varieties. In this framework, international trade increases economic output through two channels. First, trade raises productivity levels because producers gain access to new imported varieties. Second, increases in the number of varieties drives down the cost of innovation and results in ever more variety creation. Using highly disaggregate trade data, e.g. Gabon's imports of Gambian groundnuts, we structurally estimate the impact that new imports have had in approximately 4000 markets per country. We then move from groundnuts to globalization by building an exact TFP index that aggregates these micro gains to obtain an estimate of trade on productivity growth for each country. We find that in the typical country in the world, new imported varieties account for 15 percent of its productivity growth. These effects are larger in developing countries where the median impact of new imported varieties equals a quarter of national productivity growth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.492 | 0.279 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".