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Can Mirror Data Help To Capture Informal International Trade?

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  • Céline Carrère
  • Christopher Grigoriou

Abstract

Empirical studies on international trade extensively rely on the use of mirror trade statistics, i.e data reported by trading partners. However, while extensive reviews have been done on how to use mirror data to compensate poor quality data or to proxy transportation costs, very few has been done to see if and how the gap between the declared and mirrored disaggregated bilateral data could be used to capture informal cross border trade. Indeed, beyond the valid logistic reasons to explain why reported bilateral export flows from one country do not match the respective reported imports of its partner country, deliberate misreporting could significantly contribute to explain those discrepancies, either through misevaluation or misclassification of the imported goods, notably to evade tariffs and taxes. This paper proposes a review of the reasons for the gap between matched partner data, before investigating stylized facts from UN-COMTRADE data. Empirical analysis relying on econometrical panel data over a worldwide set of data at the 6 digits level evidences that discrepancies from the mirror data are not erratically driven. A statistically significant relationship between the gap and macroeconomic variables such bilateral distance, gdp per capita, average tariffs, foreign direct investments (FDI), implementation of regional trade agreements (RTA) have been evidenced. Based on these preliminary correlations, a probit has been run on orphan imports (imports reported by importing country without equivalent by exporting country) and predicts accurately up to 68 per cent of these misclassification cases. Thus, part of the gap can be predicted by macroeconomic variables, some of them suggesting a relationship between cross-border trade flows misreporting and fraud opportunities to evade tariffs and taxes.

Suggested Citation

  • Céline Carrère & Christopher Grigoriou, 2014. "Can Mirror Data Help To Capture Informal International Trade?," UNCTAD Blue Series Papers 65, United Nations Conference on Trade and Development.
  • Handle: RePEc:unc:blupap:65
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    3. Mohammad Farhad & Michael Jetter & Abu Siddique & Andrew Williams, 2018. "Misreported Trade," CESifo Working Paper Series 7150, CESifo.
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    5. Andrey A. Gnidchenko & Vladimir A. Salnikov, 2021. "Trade Intensity, Net Trade, and Revealed Comparative Advantage," HSE Working papers WP BRP 244/EC/2021, National Research University Higher School of Economics.
    6. Carton, Christine & Slim, Sadri, 2018. "Trade misinvoicing in OECD countries: what can we learn from bilateral trade intensity indices?," MPRA Paper 85703, University Library of Munich, Germany.
    7. Peyton Michael Ferrier, 2021. "Detecting origin fraud with trade data: the case of U.S. honey imports," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(1), pages 222-245, January.
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    9. Joël CARIOLLE & Cyril CHALENDARD & Anne-Marie GEOURJON & Bertrand LAPORTE, 2017. "Going beyond analysis of internal data to support customs modernization: a case study in Gabon," Working Papers P173, FERDI.
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    14. Roy, Jayjit, 2017. "On the environmental consequences of intra-industry trade," Journal of Environmental Economics and Management, Elsevier, vol. 83(C), pages 50-67.
    15. Cyril Chalendard, 2015. "Use of internal information, external information acquisition and customs underreporting," CERDI Working papers halshs-01179445, HAL.
    16. Mario Gara & Michele Giammatteo & Enrico Tosti, 2018. "Magic mirror in my hand�. how trade mirror statistics can help us detect illegal financial flows," Questioni di Economia e Finanza (Occasional Papers) 445, Bank of Italy, Economic Research and International Relations Area.
    17. Ferrier, Peyton Michael, 2020. "Detecting origin fraud with trade data: the case of U.S. honey imports," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(01), January.
    18. Markowicz Iwona & Baran Paweł, 2019. "Intra-Community Trade Asymmetries-based Clustering and Linear Ordering of Combined Nomenclature Chapters Using Generalized Distance Measure (GDM)," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 50-58, September.
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    21. Lourenço S. Paz, 2022. "Measuring illicit financial flows: A gravity model approach to estimate international trade misinvoicing," WIDER Working Paper Series wp-2022-24, World Institute for Development Economic Research (UNU-WIDER).

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    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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