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Production and Measurement of Organic Particulate Matter in the Harvard Environmental Chamber
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Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets.

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Accurate bilateral trade data is crucial for policy and business decisions. This study introduces a novel method to harmonize UN Comtrade data, resolving reporting inconsistencies and improving cross-country comparisons.

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Area of Science:

  • Economics
  • International Trade
  • Data Science

Background:

  • Bilateral trade data is vital for policy and financial decisions.
  • UN Comtrade data has structural issues: reporting differences and varied classification systems.
  • Product-level harmonization is necessary for accurate cross-country trade analysis.

Purpose of the Study:

  • To address structural problems in raw bilateral trade data.
  • To develop a method for harmonizing product classifications and reconciling reporting differences.
  • To create improved, publicly available trade datasets.

Main Methods:

  • Combined a mirroring technique with a data-driven concordance method.
  • Mirroring reconciles importer/exporter differences using country reliability scores and weighted averages.
  • Harmonized product classifications across vintages using market share-based conversion weights.

Main Results:

  • Developed a novel method to reconcile reporting differences and harmonize product classifications.
  • Created publicly available datasets that mitigate inconsistencies in raw trade statistics.
  • Maintained product-level granularity across six decades of trade data.

Conclusions:

  • The developed method significantly improves the accuracy and comparability of bilateral trade data.
  • The harmonized datasets provide a more reliable basis for policy and investment decisions.
  • This approach enhances the utility of international trade statistics for economic analysis.