The Effect of Membership in the European Monetary Union on Trade Between Member Countries (An Empirical Study)
Bibliographic record
Abstract
The question of whether or not to adopt the euro is a very important one, not only for the 13 European Union members that do not share the same currency, but also for future EU candidates. Current literature on the effect of the euro on trade is scarce since the European Monetary Union (EMU) was officially created in 1999, and up until recently there has not been enough data to analyze this issue. This paper aims to estimate the effect of the euro on trade between member countries using the standard gravity model of trade. Using data from current 25 EU members over the period from 1997 to 2004, I show that higher trade volumes between EMU members cannot be attributed to the adoption of the euro. I find evidence that the euro adoption has had a short-run effect on bilateral trade and that this effect is eliminated over a short period of time. My findings suggest that members of the EMU trade on average from 8.8% to 47% more compared to non-members depending on the type of regression used, while members of the Free Trade Agreement trade 61.3% more. The effect of the euro on trade is eliminated as soon as I control for country-pair specific effects that include the FTA effect as well as history of trade relations between two countries. I conclude that the adoption of the euro should be seen as a final step in the European economic and monetary integration for countries that already benefit from relatively high volumes of bilateral trade.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".