The Power of Major Trade Languages in Trade and Foreign Direct Investment
Bibliographic record
Abstract
While the effects of cultural disparity and common institutional foundations on international trade and foreign direct investment (FDI) have been much analyzed, little analysis of languages’ transaction costs has been done in either the international relations or international business literatures. This paper integrates literature from international political economy, international business and economics, and linguistics, to examine the transaction costs of languages under three different measures of language closeness, same language, direct communication, and language distance. Language is both a tool in international economic transactions and a vehicle to transmit cultural values, but our results point out that major trade languages are employed differently in international trade and in FDI. Communication costs, for both FDI and international trade, show a hierarchy, with English the most inexpensive among major trade languages. We introduce the concept of language intensity to explain why communication costs are much more important in FDI than in international trade. Major trade languages may obtain considerable power from their economic use; we examine the asymmetric nature of this power. We empirically test these ideas in gravity equation models.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".