The Impact of European Union's Free Trade Agreements on Türkiye’s Foreign Trade: Trade Diversion
Bibliographic record
Abstract
This study investigates the trade diversion effects stemming from the European Union’s (EU) Free Trade Agreements (FTAs) with third countries—particularly Canada and Algeria—on Türkiye’s import structure. As a non-EU member bound by the EU–Türkiye Customs Union, Türkiye is compelled to align with the Common External Tariff but is systematically excluded from the EU’s bilateral trade negotiations. This institutional asymmetry creates the potential for both direct and indirect trade diversion, whereby third-country exporters route goods through the EU to exploit tariff advantages. Using detailed trade data from 2004 to 2023 and disaggregated product-level analysis across HS chapters (notably Chapters 10, 27, 28, 29, 72, 79, and 84), this study demonstrates that trade diversion is both sector-specific and tariff-sensitive. Empirical findings show strong diversionary patterns in energy-related and agricultural commodities, whereas capital goods and chemical sectors show limited or no diversion. The analysis is supported by a comparative assessment of customs duty structures and trade flows between Türkiye, Canada, and Algeria vis-à-vis the EU. Panel gravity model estimations using Poisson Pseudo Maximum Likelihood (PPML) reveal statistically significant diversion effects, confirming that Türkiye’s exclusion from the EU FTAs with Canada and Algeria has a measurable negative impact on direct import volumes. These econometric results substantiate the descriptive findings and provide robust evidence for policy intervention. The results corroborate the theoretical predictions of Vinerian trade theory and are consistent with existing empirical literature on preferential trade agreements. The study contributes to the broader discourse by offering micro-level evidence from a non-FTA signatory state embedded in a customs union framework. Policy recommendations are offered to mitigate asymmetries in trade governance and to enhance Türkiye’s strategic agency in global commerce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".