Gravity models of trade-based money laundering

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.

Magic mirror in my hand … How trade mirror statistics can help us detect illegal financial flows

“…Several “offshore” countries, like Cayman and British Virgin islands, are excluded from the analysis due to the lack of mirror data (see footnote 14). Hence, the list of countries (24 in total) for which the synthetic indicator is statistically significant (see Table ) includes many of Italy's main trade partners, mainly European Union (and euro area) Member States: the 24 riskiest countries (according to our indicator) accounted for almost 60% of Italy's external trade in the period of analysis, which is consistent with what most contributions in the relevant literature point out, that is anomalous trade flows are frequently observed vis‐à‐vis major trade partners (see Bhagwati, ; Pitt, ; Ferwerda et al, ). This result can be reconciled with the negative sign of the EU dummy coefficient in Table : anomalous transactions tend to be greater with non‐EU countries (hence the negative sign of the coefficient), but are more often observed with reference to EU Member States, with whom trade flows are more frequent.…”

Behavior Monitoring Methods for Trade-Based Money Laundering Integrating Macro and Micro Prudential Regulation: A Case From China

“…Many questions regarding current forms of money laundering have sprung up and today TBML are becoming more and more popular (Unger & den Hertog, 2012;Ferwerda et al, 2013;Delston & Walls, 2009;Liao & Acharya, 2011;Thanasegaran & Shanmugam, 2007). For this reason, regulation is becoming more important (Deng, Joseph, Sudjianto, & Wu, 2012).…”

“…Second, many econometric methods in economics attempt to measure TBML behavior. Ferwerda et al (2013) claimed that when using traditional methods in economic literature TBML was difficult to access since the TBML is hidden in licit trade and thus proposed an updated gravity model, which means a regression of exports and Gross Domestic Product (GDP), population, border dummy, common language, colonial background and distance of two countries, as opposed to the Walker (1995) and Unger (2007) prototype model. Their basic idea of the normal value of exports matches the inherent scale of two countries.…”

The Global Waste Trafficking and Its Correlates

“…The empirical test relies on a statistical model inspired by the gravity models commonly used in international trade studies (Greaney & Kiyota, 2020;Kabir et al, 2017;Yotov et al, 2016). Besides international trade, the gravity model has proved to be a useful framework in which to explain various illegal cross-border relations, as human trafficking (Akee et al, 2014), trade-based money laundering (Ferwerda et al, 2013;Walker & Unger, 2009), suspicious bank transfers (Cassetta et al, 2014), and firearm trafficking (Kahane, 2013). We also rely on this class of models as a workable empirical strategy to control for characteristics of both importing and exporting countries as well as for relational factors.…”