Revaluating the Tanzi-Model to Estimate the Underground Economy

Since the early 1980s, the interest in the nature and size of the non-measured economy (both the informal and the illegal one) was born among researchers in the US. Since then, several models to estimate the shadow and/or the underground economy appeared in the literature, each with its own theoretical pros and cons. In this paper we show that it is possible to overcome earlier expressed criticism of the Tanzi-model (1983). Its lack of a base year without any underground economy can be overcome, by using the natural experiment of the introduction of the Euro. However, this paper also comes up with new criticism. It shows that the crucial relationship of the Tanzi-model between taxes and the demand for cash money is not time robust, hence the model is not useful for estimating the underground economy nowadays. We believe that the change in financial conditions could partially explain the decline in the relevance of taxes as a means to evaluate the underground economy. We build a revised Tanzi model and try to find variables apart from tax evasion incentives in order to explain the underground economy.Underground Economy Estimation, Shadow Economy, Tax Evasion

National Assessments of Money Laundering Risks : Learning from Eight Advanced Countries’ NRAs

The Financial Action Task Force (FATF) requires national governments to demonstrate an understanding of the money laundering risks in the country. Such an understanding is the foundation for effective control of money laundering under the risk-based approach the FATF calls for. The authors analyzed the National Risk Assessments (NRAs) published by eight systemically important countries to test whether they demonstrate that basic understanding and to draw lessons for national governments from those NRAs. The eight show very different conceptualizations, analytic approaches, and products. Each raises serious issues regarding the risk assessment methodology. For example, most relied largely on expert opinion, which they solicited in ways that are inconsistent with the well-developed methodology for making use of expert opinion. They misinterpreted data from suspicious activity reports and failed to provide risk assessments relevant for policy makers. Only one described the methodology employed. Although the challenge of conducting strong risk assessments is great, given the difficulty of estimating the extent of money laundering in any sector, the findings based on this limited sample point to ways to improve substantially on existing practices. The report concludes with a set of suggestions for (international) policy makers and those conducting NRAs for improving risk assessments. Suggestions include increased clarity about the conceptualization of risk, transparency about data and methods so that each country can learn from others, and the adoption of more formal and standardized methods of eliciting expert opinion

Activities, Access Control, and Crime:a Quasi-Experimental Study regarding Entry Gates at Train Stations in the Netherlands

This article discusses a unique “natural experiment,” the introduction of entry gates at Dutch train stations and the potential effects of this on crime in the areas around these stations. A quasi-experimental study was carried out to show that introducing entry gates correlated with a drop in crime in these areas. After entry gates had been introduced, potential offenders could only enter train stations with a valid ticket, which meant that they would be less likely to enter or leave these stations and more likely to choose other places to hang around in or for entering and leaving trains. A dataset was created in which the crime rates around train stations were registered for each month in the years 2013 through 2018. The changing numbers of travelers at each station were also taken into account, as this variable probably correlates with the amount of crime. A two-way fixed-effects model was run on data for about 260 train stations, with and without entry gates, using the relative crime rate per thousand travelers as the dependent variable. Based on this relative crime rate, the use of entry gates was found to coincide with a decrease of 9% in crime, compared to a situation without entry gates. This study can inform policymakers about the potential effects of entry gates in particular and about situational crime prevention in general. Moreover, it illustrates how implementing measures at various locations at different moments enables the effectiveness of such measures to be tested more precisely and with more confidence

Searching for Smurfs: Testing if Money Launderers Know Alert Thresholds

To combat money laundering, banks raise and review alerts on transactions that exceed confidential thresholds. This paper presents a data-driven approach to detect smurfing, i.e., money launderers seeking to evade detection by breaking up large transactions into amounts under the secret thresholds. The approach utilizes the notion of a counterfactual distribution and relies on two assumptions: (i) smurfing is unfeasible for the very largest financial transactions and (ii) money launderers have incentives to make smurfed transactions close to the thresholds. Simulations suggest that the approach can detect smurfing when as little as 0.1-0.5\% of all bank transactions are subject to smurfing. An application to real data from a systemically important Danish bank finds no evidence of smurfing and, thus, no evidence of leaked confidential thresholds. An implementation of our approach will be available online, providing a free and easy-to-use tool for banks

Shedding light inside the black box of implementation: Tax crimes as a predicate crime for money laundering

Even perfect transposition of EU Directives does not necessarily translate into homogeneous rules or application of rules across the European Union. Europeanization literature focused on the formal transposition of EU Directives. Newer studies suggest looking into the black box of how this translates into law in action. The 4th Anti-Money Laundering Directive incorporated taxes as a predicate crime for money laundering. We analyze how and why this Directive has been implemented so differently across EU countries both in the books and in action through a novel dataset. We find that country characteristics can explain formal transposition patterns and influence the domestic adaptation of regulation as well as how practitioners, the second front line of implementation, use these rules in action. We find that corruption, government effectiveness, regulatory quality, tax morale, and tax administrative capacity are important factors to explain lingering differences in the books and in action among EU Member States

Gravity Models of Trade-based Money Laundering

Several attempts have been made in the economics 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 attempt to test well-known prototype models proposed by Walker and Unger to predict illicit money laundering flows and to apply traditional gravity models borrowed from 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 here can indeed explain TBML flows worldwide in a plausible manner. An important determinant is licit trade, the mass 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.Money laundering, international trade, gravity model, Walker model.

Bilateral responsive regulation and international tax competition: An agent‐based simulation

Country‐by‐Country Reporting and Automatic Exchange of Information have recently been implemented in European Union (EU) countries. These international tax reforms increase tax compliance in the short term. In the long run, however, taxpayers will continue looking abroad to avoid taxation and, countries, looking for additional revenues, will provide opportunities. As a result, tax competition intensifies and the initial increase in compliance could reverse. To avoid international tax reforms being counteracted by tax competition, this paper suggests bilateral responsive regulation to maximize compliance. This implies that countries would use different tax policy instruments toward other countries, including tax and secrecy havens. Our agent‐based simulation finds that a differentiated policy response could increase tax compliance by 6.54 percent, which translates into an annual increase of €105 billion in EU tax revenues on income, profits, and capital gains. Corporate income tax revenues in France, Spain, and the UK alone would already account for €35 billion

The effect of anti-money laundering policies: an empirical network analysis

Aim: There is a growing literature analyzing money laundering and the policies to fight it, but the overall effectiveness of anti-money laundering policies is still unclear. This paper investigates whether anti-money laundering policies affect the behavior of money launderers and their networks. Method: With an algorithm to match clusters over time, we build a unique dataset of multi-mode, undirected, binary, dynamic networks of natural and legal persons. The data includes ownership and employment relations and associated financial ties and is enriched with criminal records and police-related activities. The networks of money launderers, other criminals, and non-criminal individuals are analyzed and compared with temporal social network analysis techniques and panel data regressions on centrality measures, transitivity and assortativity indicators, and levels of constraint. Findings: We find that after the announcement of the fourth EU anti-money laundering directive in 2015, money laundering networks show a significant increase in the use of foreigners and corporate structures. At the individual level, money launderers become more dominant in criminal clusters (increased closeness centrality). This paper shows that (the announcement of) anti-money laundering policies can affect criminal networks and how such effects can be tested

A synthetic data set to benchmark anti-money laundering methods

Bank transactions are highly confidential. As a result, there are no real public data sets that can be used to investigate and compare anti-money laundering (AML) methods in banks. This severely limits research on important AML problems such as efficiency, effectiveness, class imbalance, concept drift, and interpretability. To address the issue, we present SynthAML: a synthetic data set to benchmark statistical and machine learning methods for AML. The data set builds on real data from Spar Nord, a systemically important Danish bank, and contains 20,000 AML alerts and over 16 million transactions. Experimental results indicate that performance on SynthAML can be transferred to the real world. As use cases, we present and discuss open problems in the AML literature

The Economics of Crime and Money Laundering: Does Anti-Money Laundering Policy Reduce Crime?

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 measured- the 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.Anti-Money Laundering Policy and Crime