Mirror-Data Discrepancies as Trust Metrics: Quantifying Institutional Integrity in Trade

Linsi et al. expose pervasive data mismatches in trade statistics but stop at diagnosing measurement error. This idea treats those gaps as purposeful signals—e.g., systematic underreporting of imports may indicate corruption or sanctions evasion. By correlating mirror divergences with fraud patterns (Karim & Kudapa, 2022) or dispute frequency (Trucmel et al., 2022), we could rank countries by "statistical integrity." This reframes data errors as institutional features, not bugs. The innovation lies in converting a methodological flaw into a political economy tool, offering a low-cost proxy for trust in interdependence—critical for assessing PTAs (Smirnov & Lukyanov, 2022).

References:

1. THE INFLUENCE OF STATISTICAL MODELS FOR FRAUD DETECTION IN PROCUREMENT AND INTERNATIONAL TRADE SYSTEMS. Md. Rabiul Karim, Sai Praveen Kudapa (2022). American Journal of Interdisciplinary Studies.
2. The Problem with Trade Measurement in International Relations. L. Linsi, Brian Burgoon, Daniel Mügge (2023). International Studies Quarterly.
3. An International Trade Disputes Analysis using Network Theory. Irina-Maria Trucmel, Alexandra Vintilă, Andreea Gabriela Capbun, M. Roman (2022). Proceedings of the International Conference on Business Excellence.
4. International Political Economy of Preferential Trade Agreements. E. Smirnov, S. Lukyanov (2022). World Economy and International Relations.