Utrecht University School of Economics (U.S.E.), Kriekenpitplein 21-22, 3584 EC, Utrecht, The Netherlands.
Vienna University of Technology - TU Wien, Karlplatz 13, 1040, Vienna, Austria.
Sci Rep. 2020 Oct 29;10(1):18552. doi: 10.1038/s41598-020-75653-x.
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.
了解洗钱流量的数量和类型很重要，因为它们的影响非常不同，因此需要不同的执法策略。主要处理犯罪分子在当地洗钱的国家需要采取其他措施，而不是主要处理外国非法投资或只是流经该国的肮脏资金的国家。本文有两个主要贡献。首先，我们以系统的方式揭开了洗钱者对国家偏好的实证。以前的洗钱估计使用了假设，即洗钱者在决定将非法所得发送到何处时，会考虑哪些国家特征。由于荷兰犯罪和不明财富研究所（iCOV）提供的可疑洗钱交易的独特数据集，我们可以使用计量经济学引力模型估计对这些假设进行实证检验。我们将这些信息用于我们的第二个贡献：迭代模拟全球所有洗钱流量。这使我们第一次能够提供估计，区分三种不同的政策挑战：国内犯罪收益的洗钱、肮脏资金的国际投资以及只是流经一个国家的资金。
