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Gravity models of trade-based money laundering

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  • Joras Ferwerda
  • Mark Kattenberg
  • Han-Hsin Chang
  • Brigitte Unger
  • Loek Groot
  • Jacob A. Bikker

Abstract

Several attempts have been made in the literature to measure money laundering. However, the adequacy of these models is difficult to assess, as money laundering takes place secretly and, hence, goes unobserved. An exception is Trade-Based Money Laundering (TBML), a special form of trade abuse that has been discovered only recently. TBML refers to criminal proceeds that are transferred around the world using fake invoices that under- or overvalue imports and exports. This article is a first test on the well-known prototype models proposed by Walker and Unger to predict illicit money laundering flows and to apply traditional gravity models familiar in international trade theory. To do so, we use a dataset of Zdanowicz of TBML flows from the US to 199 countries. Our test rejects the specifications of the Walker and Unger prototype models, at least for TBML. The traditional gravity model that we present can explain TBML flows worldwide in a plausible manner. An important determinant is trade in which TBML is hidden. Furthermore, our results suggest that criminals use TBML in order to escape the stricter anti-money laundering regulations of financial markets.

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  • Joras Ferwerda & Mark Kattenberg & Han-Hsin Chang & Brigitte Unger & Loek Groot & Jacob A. Bikker, 2013. "Gravity models of trade-based money laundering," Applied Economics, Taylor & Francis Journals, vol. 45(22), pages 3170-3182, August.
  • Handle: RePEc:taf:applec:v:45:y:2013:i:22:p:3170-3182
    DOI: 10.1080/00036846.2012.699190
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    References listed on IDEAS

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    Cited by:

    1. Mohammed Ahmad Naheem, 2016. "Money laundering: A primer for banking staff," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 13(2), pages 135-156, May.
    2. 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.
    3. Valentina Gullo & Pierluigi Montalbano, 2018. "Where does “dirty” money go? A gravity analysis," Working Papers 5/18, Sapienza University of Rome, DISS.
    4. Bebonchu Atems & John K Mullen, 2016. "Outward FDI from the USA and host country financial transparency," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 25(8), pages 1122-1143, November.
    5. Christian Friedrich & Reiner Quick, 2019. "An analysis of anti-money laundering in the German non-financial sector," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(4), pages 1099-1137, December.
    6. Canh Phuc Nguyen & Binh Nguyen Quang & Thanh Dinh Su, 2023. "Institutional frameworks and the shadow economy: new evidence of colonial history, socialist history, religion, and legal systems," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 50(3), pages 647-675, September.
    7. Emma Galli & Ilde Rizzo & Carla Scaglioni, 2020. "Is transparency spatially determined? An empirical test for Italian municipalities," Applied Economics, Taylor & Francis Journals, vol. 52(58), pages 6372-6385, December.
    8. Brigitte Unger, 2013. "Introduction," Chapters, in: Brigitte Unger & Daan van der Linde (ed.), Research Handbook on Money Laundering, chapter 1, pages 3-16, Edward Elgar Publishing.
    9. Gullo, Valentina & Montalbano, Pierluigi, 2022. "Financial transparency and anomalous portfolio investment flows: A gravity analysis," Journal of International Money and Finance, Elsevier, vol. 128(C).
    10. Acciai, Elia & Belloni, Michele & Della Giusta, Marina & Segre, Giovanna, 2023. "Illicit Shadows: An Economic Analysis of Trade Gaps in Cultural Goods through the Italian Market," IZA Discussion Papers 16282, Institute of Labor Economics (IZA).

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • F10 - International Economics - - Trade - - - General

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