Bibliographic record
Abstract
Abstract Most international commerce is carried out by multinational firms, which use their foreign affiliates both to serve the market of the host country and to export to other markets outside the host country. In this article, I examine the determinants of multinational firms’ location and production decisions and the welfare implications of multinational production. The few existing quantitative general equilibrium models that incorporate multinational firms achieve tractability by assuming away export platforms—that is, they do not allow foreign affiliates of multinationals to export—or by ignoring fixed costs associated with foreign investment. I develop a quantifiable multicountry general equilibrium model, which tractably handles multinational firms that engage in export platform sales and that face fixed costs of foreign investment. I first estimate the model using German firm-level data to uncover the size and nature of costs of multinational enterprise and show that the fixed costs of foreign investment are large. Second, I calibrate the model to data on trade and multinational production for twelve European and North American countries. Counterfactual analysis reveals that multinationals play an important role in transmitting technological improvements to foreign countries and that the pending Canada-EU trade and investment agreement could divert a sizable fraction of the production of EU multinationals from the U.S. to Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".