Why is there a discrepancy in international trade data?

Table of Contents

• Introduction
• A Multitude of Trade Data Sources
• Discrepancies Across Trade Data Sources
• Unpacking the Discrepancies in Trade Data
• Conclusion

Introduction:

International trade data holds a central role in shaping economic analysis and policymaking. However, a closer inspection of various official trade data sources reveals significant disparities in the reported figures. In this comprehensive blog post, we will delve into the intricacies behind these discrepancies, shedding light on the complex processes of data collection and processing in the realm of international trade. It’s important to note that, regardless of these challenges, trade data remains the most authoritative source due to its factual status.

A Multitude of Trade Data Sources:

A plethora of international organizations and academic initiatives contribute to the compilation of international trade data. The most prominent sources encompass:

1. World Bank Open Data
2. IMF Data
3. WTO Statistics
4. UN Comtrade
5. UNCTAD World Integrated Trade Solutions
6. Eurostat
7. OECD.Stat

In addition to these authoritative sources, several academic projects, such as The Correlates of War Project, The NBER-United Nations Trade Dataset Project, and The CEPII Bilateral Trade and Gravity Data Project, supplement trade data. These projects often enhance data quality by amalgamating series from multiple sources.

Discrepancies Across Trade Data Sources:

Discrepancies in trade data are not isolated incidents but rather a recurring challenge. To illustrate, consider the case of China in 2010. The estimated total value of goods exports was reported as $1.48 trillion by the World Bank Data, yet the WTO Data indicated $1.58 trillion, representing a substantial 7% variance. Similar discrepancies manifest in developed countries, where statistical agencies closely adhere to international reporting guidelines. In Italy, for instance, Eurostat figures for the value of exported goods in 2015 exceed those published by the OECD by 10%.

Moreover, bilateral discrepancies persist within trade data sources. For instance, according to IMF data, the value of goods reported as exported by Canada to the US exceeds the value of goods reported as imported by the US from Canada by nearly $20 billion.

Unpacking the Discrepancies in Trade Data:

To comprehend the underpinnings of these disparities in trade data, it’s imperative to consider a multitude of factors:

1. Data Collection Approaches: Two primary methodologies are employed to estimate international merchandise trade— one relies on customs records, often supplemented with data from enterprise surveys and administrative records, while the other draws from macroeconomic data, particularly National Accounts. These distinct approaches often entail different guidelines and concepts for recording trade, engendering inconsistencies.
2. Valuation Differences: Discrepancies can arise due to variations in the valuation methods employed by different countries. For instance, some countries employ “free on board” (FOB) prices for exports, while others employ “Cost, Insurance, and Freight” (CIF) prices for imports.
3. Attribution of Trade Partners: Determining the origin and final destination of merchandise becomes increasingly intricate as global production chains evolve. Countries often grapple with challenges in unambiguously establishing these details, even when guidelines exist.
4. Exchange Rates: The choice of exchange rates for converting values from local currency units to a common currency, typically US dollars, can result in conflicting estimates.
5. ‘General’ vs. ‘Special’ Trade Systems: Differences between statistical territories and actual country borders introduce inconsistencies, particularly in areas like customs-free zones.
6. Other Factors: Timing of recording, confidentiality policies, product classification, and invoicing manipulation for illicit purposes can further contribute to data discrepancies.

Conclusion:

Discrepancies in international trade data are not anomalies; they are pervasive and rooted in multifaceted factors, including inconsistencies in measurement standards and the varying application of protocols by different nations. Researchers and policymakers should exercise prudence when interpreting trade data, understanding that discrepancies often emanate from measurement inconsistencies rather than intentional manipulation. Regardless of these challenges, trade data remains the most authoritative source due to its factual status, serving as the foundation for informed economic decisions and policies.

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