The Impact of Eurasian Economic Union Membership on Mutual Trade in Services: What Are the Challenges for Small Economies?
Bibliographic record
Abstract
Despite the fact that a decade has elapsed since the establishment of the Eurasian Economic Union (EAEU), the impact of the EAEU on the economic development of its member states remains a subject of ongoing debate. This article examines the mutual trade in services between the Eurasian Economic Union (EAEU) countries, with the aim of assessing the impact of membership on it. The difference-in-difference model has been applied for impact assessment. The model utilizes data from five EAEU member countries—Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia—capturing periods both before and after their EAEU membership, spanning 17 years in total. The results show that membership in the EAEU has significantly affected the exports of services from Russia and Belarus and has a less significant impact on the exports of services from Kazakhstan to the EAEU. At the same time, it has no significant effect on the exports of services from Kyrgyzstan and Armenia to other EAEU countries. In order to ascertain the challenges that exist, expert surveys among service exporters from Armenia have been conducted. Representatives of companies exporting various services to the EAEU have been selected as experts. The survey results indicate the presence of various barriers, including legal, logistical (for cargo transportation companies), and cultural challenges. These barriers encompass licensing difficulties, technical obstacles related to VAT refunds, a ban on cash payments, and difficulties with financial transfers due to sanctions against Russia. The findings of this research are of practical importance and can serve as a guideline for policymakers in the EAEU.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".