Uluslararası ticaret tutarsızlığının ölçümü: Türkiye'nin ihracat farklılıkları için üç aşamalı ampirik bir çerçeve

Öz

Ayna ticaret istatistikleri, uluslararası ticaret raporlamasının tutarlılığını değerlendirmek için yaygın olarak kullanılmakta; ancak ihracatçı ve ithalatçı ülkelerin raporladığı değerler, değerleme yöntemleri, raporlama uygulamaları, yeniden ihracat ve kurumsal farklılıklar nedeniyle sıkça birbirinden ayrışmaktadır. Bu çalışma, 2013–2024 dönemi için 204 partner ekonomiyi kapsayan UN Comtrade verilerini kullanarak Türkiye'nin ihracatındaki ayna farklılıklarını analiz etmek amacıyla bir Uluslararası Ticaret Tutarsızlığı (ITI) çerçevesi geliştirmektedir. Ampirik tasarım; betimleyici, açıklayıcı ve öngörücü yöntemleri üç aşamada bütünleştirmektedir. İlk aşamada kümeleme analizi, ticaret ortakları arasındaki heterojen farklılık rejimlerini belirlemektedir. İkinci aşamada, Türkiye'nin en büyük yirmi ihracat pazarı için panel lojistik regresyon, farklılık riski ile Dünya Bankası Dünya Kalkınma Göstergeleri'nden elde edilen ekonomik ve kurumsal göstergeler arasındaki ilişkiyi değerlendirmektedir. Üçüncü aşamada, WEKA ortamında uygulanan lojistik sınıflandırma ve Random Forest modelleri, on katlı çapraz doğrulama ile öngörü sağlamlığını sınamaktadır. Bulgular, ayna farklılıklarının ortaklar arasında tekdüze değil yapısal nitelikte olduğunu göstermektedir. Ticaret hacmi ve seçilmiş ülke özellikleri, geniş yönetişim göstergelerine kıyasla farklılık riskiyle daha yakından ilişkilidir; makine öğrenmesi modelleri ise orta düzeyde öngörü performansı sunarak ekonometrik bulguları genel olarak desteklemektedir. Çalışma, ayna ticaret farklılıklarının doğrudan ticaret usulsüzlüğü kanıtı olarak değil, raporlama riski göstergesi olarak yorumlanması gerektiği sonucuna ulaşmaktadır.

Anahtar Kelimeler

• Ayna Ticaret İstatistikleri
• Uluslararası Ticaret Tutarsızlığı
• UN Comtrade
• Panel Logit
• Makine Öğrenmesi

Measuring international trade inconsistency: a three-stage empirical framework for Türkiye's export discrepancies

Öz

Mirror trade statistics are widely used to assess the consistency of international trade reporting, yet exporter- and importer-reported values frequently diverge because of valuation conventions, reporting practices, re-exports, and institutional differences. This study develops an International Trade Inconsistency (ITI) framework for analysing mirror discrepancies in Türkiye’s exports using UN Comtrade data for 204 partner economies over 2013–2024. The empirical design integrates exploratory, explanatory, and predictive methods in three stages. First, clustering identifies heterogeneous regimes of discrepancy across trading partners. Second, a panel logistic regression for Türkiye’s twenty largest export destinations evaluates the association between discrepancy risk and economic and institutional indicators from the World Bank’s World Development Indicators. Third, logistic classification and Random Forest models implemented in WEKA assess predictive robustness through ten-fold cross-validation. The results show that mirror discrepancies are structured rather than uniform across partners. Trade volume and selected country characteristics are more closely associated with discrepancy risk than broad governance indicators, while the machine-learning models provide moderate predictive performance and broadly support the econometric findings. The study concludes that mirror trade discrepancies should be interpreted as reporting-risk indicators rather than direct evidence of trade misreporting or fraud. By combining classification, econometric analysis, and predictive validation, the proposed framework contributes to statistical reconciliation, customs risk assessment, and research on international trade data quality.

Anahtar Kelimeler

• Mirror Trade Statistics
• International Trade Inconsistency
• UN Comtrade
• Panel Logit
• Machine Learning

Kaynakça

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