Understanding trade data quality with the Discrepancy Index

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This blog is part of a series about WITS, the World Integrated Trade Solution, a collaborative trade data platform developed by the World Bank and other institutions. This is the eighth installment of the series—for further reading, here are the first, second, third, fourth, fifth, sixth and seventh installments.



Accurate trade data is essential for understanding global trade patterns, making informed policy decisions, and conducting empirical research. However, discrepancies in trade reporting can arise due to various factors, including logistical issues and deliberate misclassification. These discrepancies can lead to distortions in key trade metrics and negatively impact research findings.

How to measure these discrepancies?

The Discrepancy Index (DI) is a valuable tool for assessing the quality of trade data. By examining discrepancies in reported trade values, the DI provides insights into the accuracy and reliability of trade data, ultimately contributing to better-informed trade policies and research.

How does the Discrepancy Index work?

The Discrepancy Index measures the level of divergence between reported trade data and its corresponding mirror data, which is the data reported by the trading partner. By calculating this discrepancy, the DI helps identify potential issues in trade reporting and assess the overall quality of the data.

Figure 1. Interpretation of the values of the Discrepancy Index, where M is imports and X is exports.



Key contributions of the Discrepancy Index paper

    The World Bank recently published a paper titled Bridging the Gap in Trade Reporting : Insights from the Discrepancy Index. The paper uses Discrepancy Index calculated at the Harmonized System (HS) 6-digit level product to analyze the quality of reported trade data in UN COMTRADE. The paper makes several important contributions to the field of trade data quality. For example:

  • Comprehensive set of indicators: The paper proposes a wide range of country and product-level indicators that capture both the frequency of misreporting and its impact on overall trade value. These indicators provide a nuanced view of trade data quality and help pinpoint specific areas for improvement.

  • Practical application: The paper demonstrates how the DI can be used to analyze discrepancies and resolve data reliability issues in the UN Comtrade database. This practical application showcases the DI's usefulness in real-world scenarios.
  • Identifying trends and patterns: By analyzing general trends in trade reporting, the paper offers empirical insights into the nature and extent of reporting discrepancies. This information is crucial for understanding the challenges in trade data and developing strategies to address them.


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