Estimating Money Laundering Flows with a Gravity Model-Based Simulation

Principal Investigator(s): View help for Principal Investigator(s) Joras Ferwerda, Utrecht University; Alexander van Saase, Utrecht University; Brigitte Unger, Utrecht University; Michael Getzner, Vienna University of Technology

Version: View help for Version V1

Name	File Type	Size	Last Modified
SPAN_code			09/22/2020 10:15:AM

Project Citation:

Ferwerda, Joras, van Saase, Alexander, Unger, Brigitte, and Getzner, Michael. Estimating Money Laundering Flows with a Gravity Model-Based Simulation. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-09-23. https://doi.org/10.3886/E122401V1

Project Description

Summary: It is important to understand the amounts and types of money laundering flows, since they have very different effects and, therefore, need different enforcement strategies. Countries that mainly deal with criminals laundering their proceeds locally, need other measures than countries that mainly deal with foreign illegal investments or dirty money just flowing through the country. This paper has two main contributions. First, we unveil the country preferences of money launderers empirically in a systematic way. Former money laundering estimates used assumptions on which country characteristics money launderers are looking for when deciding where to send their ill-gotten gains. Thanks to a unique dataset of transactions suspicious of money laundering, provided by the Dutch Institute infobox Criminal and Unexplained Wealth (iCOV), we can empirically test these assumptions with an econometric gravity model estimation. We use this information for our second contribution: iteratively simulating all money laundering flows around the world. This allows us, for the first time, to provide estimates that distinguish between three different policy challenges: the laundering of domestic crime proceeds, international investment of dirty money and money just flowing through a country.

Funding Sources: infobox Crimineel en Onverklaard Vermogen (iCOV), a Dutch cooperation network of (among others) national law enforcement agencies, Tax Office, Customs, Financial Intelligence Unit and the Public Prosecutors Office

Scope of Project

Subject Terms: Money Laundering; Gravity model; Simulation

Geographic Coverage: World

Time Period(s): 1/1/2009 – 12/31/2014 (2009 through 2014)

Collection Date(s): 2019 – 2019

Universe: Data about countries and country-pairs

Data Type(s): aggregate data; geographic information system (GIS) data

Collection Notes: All relevant data and code used for the gravity model and the simulation in this study are available in this repository, with one exception: the data on the number and amount of suspicious transactions is confidential. These data are available from the governmental network ‘infobox Criminal and Unexplained Wealth’ in the Netherlands but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request only after permission of the governmental network ‘infobox Criminal and Unexplained Wealth’ in the Netherlands.

Methodology

Response Rate: N/A

Sampling: No sampling applied.

Data Source: All data for the gravity model and the simulation in this study are from public sources as specified in the related paper, with one exception: the data on the number and amount of suspicious transactions is confidential. These data are available from the governmental network ‘infobox Criminal and Unexplained Wealth’ in the Netherlands but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request only after permission of the governmental network ‘infobox Criminal and Unexplained Wealth’ in the Netherlands.

Collection Mode(s): other

Scales: US dollars, count data, kilometers, dummy variables

Weights: N/A

Unit(s) of Observation: Country-year

Geographic Unit: Country

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V1 [2020-09-23]

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