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Red flags are widely used to minimize the risk of various forms of economic misconduct, among which corruption in public procurement. Drawing on criminal investigations, the literature has developed several indicators of corruption in public procurements and has put them forward as viable risk indicators. But are they genuinely viable, if only corrupt procurements are analysed? Using a dataset of 192 public procurementswith 96 cases where corruption was detected and 96 cases where corruption was not detectedthis paper addresses the identification of significant risk indicators of corruption. We find that only some indicators significantly relate to corruption and that eight of them (e.g. large tenders, lack of transparency and collusion of bidders) can best predict the occurrence of corruption in public procurements. With this paper we successfully tap into one of the most vulnerable areas of criminological researchselecting the right sampleand consequently, our results can help increase the detection of corruption, increase investigation effectiveness and minimize corruption opportunities.

Anti-money laundering policy has become a major issue in the Western world, especially in the United States after 9-11. Basically all countries in the world are more or less forced to cooperate in the global fight against money laundering. In this paper, the criminalization of money laundering is modelled, assuming rational behaviour of criminals, following the law and economics strand of the literature which is described as the economics of crime. The theoretical model shows that a) the probability to be caught for money laundering, b) the sentence for money laundering, c) the probability to be convicted for the predicate crime and d) the transaction costs of money laundering are negatively related to the amount of crime. Under the assumption that these factors are all positively influenced by a stricter anti-money laundering policy, the hypothesis empirically tested in this paper is that anti-money laundering policy deters potential criminals from illegal behavior and therefore lowers the crime rate. Since the data on anti-money laundering policy, used in the literature so far, is not all-embracing, a new unique indicator is constructed by using all the information from the mutual evaluation reports on money laundering of the FATF, IMF and World Bank. This unique dataset is used in an empirical estimation based on a Mundlak specification to test the effect of antimoney laundering policy on the crime rate. Among the four policy areas measuredthe role of laws, the institutional framework, the duties of the private sector in law enforcement, and international cooperation, the latter turned out to be the most important policy area for reducing crime. This should be an extra incentive for countries and international organizations to continue their efforts to promote and develop international cooperation in the fight against money laundering.

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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.