Estimating the Illicit Flows – Asking the Right Questions John Walker CEO, John Walker Crime Trends Analysis Principal Research Fellow, Centre for Transnational Crime Prevention, University of Wollongong, Australia

Illicit Financial Flows Put simply, Illicit Financial Flows from Developing Countries consist of quantities of money derived from crimes committed in those countries. Official definitions of crime vary between countries, although there is consensus about most forms of crime. Contentious areas include business practices considered as fraudulent in some countries but not in others (e.g. payments of bribes and false invoicing), and some areas of excise avoidance (e.g. cigarette smuggling). This paper takes a broad view that acts that would be crimes or illegal business practices in countries with sophisticated legal systems must also be regarded as crimes or illegal business practices in developing countries. Illicit Financial Flows from Developing Countries are, therefore, part of the broader issue of transnational crime and moneylaundering.

Asking the Right Questions about Transnational Crime and Moneylaundering It is difficult to explain the importance of a problem without quantifying it. (Neil Jensen, Director, AUSTRAC, 2005) FinCen’s “overriding objective” under the strategic plan is the development of a “viable model for measuring the magnitude of moneylaundering.” “No assessment of an agency’s or government’s anti-moneylaundering programs can be a true gauge of its effectiveness, unless it is based on an understanding of the breadth of the problem being addressed”. (FinCEN Strategic Plan, 2000) “The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root, and prepare wonderful diagrams. But you must never forget that every one of these figures comes, in the first instance, from the village watchman, who just puts down what he damn pleases”. (English economist Sir Josiah Stamp, 1929) "Striving for perfection is the greatest stopper there is... It's your excuse to yourself for not doing anything. Instead, strive for excellence, doing your best." (British actor, Sir Laurence Olivier) “…..blending dodgy data and heroic assumption and turning them into something particularly useful” (Dutch criminologist, Max Kommer) “Accuracy is meaningless – credibility is everything”. (Me) These are common claims - are they true?

The Right Questions about Transnational Crime and Moneylaundering How Much Crime is there ? How Much Profit is there in the Crime ? What Proportion of the Profits is Laundered ? Where does it go for Laundering ? Other researchers were trying to analyse financial data to identify the extent of moneylaundering, but my approach was quite different. I saw that moneylaundering involved many stages – “placement”, “layering” and “integration” – and the same money could go through many different transactions in the laundering process. Counting financial transaction data was therefore certain to involve double – or even multiple – counting. To measure moneylaundering, we had to start – again – from “how much crime is there?”; “how much profit is there in the crime?”; and then ask “what proportion of the profits are laundered”. Only then is it safe to ask “where does it go for laundering?” and what impact does it have on society?”. How does it impact on Society ?

These are Very important Questions 1. How much crime is there around the world, and where is it based? 2. How big are crime profits around the world, and where are they generated? 3. What factors make crime more profitable in some countries than others? 4. What factors make some countries more attractive to ML than others? 5. How much money is laundered each year around the world? 6. How much harm is caused by crime and ML, and who suffers most?

So who is asking them? Up to the 1980s Most countries compile crime statistics. Only measures recorded crime. Accuracy doubts; rigging by police, politicians; counting rules. Interpol collects crime data from member countries – no consistency, no analysis, not even computerised until the late 1990s. Only measures recorded crime. Crime Victims Surveys developed in the 1970s (USA, UK) to capture data on a common set of definitions and on unrecorded crime. Limited crime types, costly, political risk, little interest in other countries. In the 1980s, the U.N. attempted to compile international crime and justice statistics on a common set of definitions – very problematic, poor response, little consistency, poorly resourced. Transnational crime analysis mostly country-specific, offence specific or confined to studies of mafia, yakuza, drug gangs etc Transparency International experimenting with corruption and bribery indexes.

Measuring the Risks posed by Transnational Crime and Moneylaundering 1988 United Nations Convention against illicit traffic in narcotic drugs and psychotropic substances; EU decides to conduct Europe-wide crime survey. Australia, USA, Canada join in. Questions include “did you report it to police?” and “how much did it cost you?” 1989 FATF Working Group on Statistics and Methods, “Narcotics ML – Assessment of the Scale of the Problem” notes the lack of reliable data to measure ML. 1990 EU Convention on laundering, search, seizure and confiscation of the Proceeds from Crime. FATF’s “Forty Recommendations“ on the prevention of money laundering. U.N. Rome conference agrees to pilot Business Crime survey, including reporting and costs questions. Australia, UK, Netherlands, South Africa. 1991 EU Directive on prevention of the use of the financial system for purpose of money laundering. 1992 Estimates of the Costs of Crime in Australia – a model for the U.N. 1995 Estimates of the Extent of money laundering in Australia – a model for the FATF. 1996-2000 UN Office on Drugs and Crime Refinement of International Crime and Justice Surveys, International Crime Victims Surveys, Business Crime Surveys “Global Report on Crime and Justice” attempts to bring together data on economics of transnational crime Attempts to survey the characteristics of organised crime groups in different countries

Measuring the Risks posed by Transnational Crime and Moneylaundering June 1997 FATF creates “Ad Hoc Group on Estimating the Magnitude of Money Laundering” Sept 1997 Ad Hoc Group’s Chair agrees to draft a paper to suggest methodologies to measure ML – calls for papers from member countries. Feb 1998 Interim report from Chairman Identifies AUSTRAC study as a landmark study, examines four macro-economic methodologies identified by FINCEN-sponsored studies, and concludes that future studies should be [a] confined to FATF members, [b] focused on a wide range of crimes that generate criminal proceeds. Dec 1998 FATF International Meeting of Experts on Estimating the magnitude of ML Disappointing level of participation Most countries’ contributions focused on official statistics Discussion of relationship between underground economic activity and ML Switzerland attempts to have the work focus on ML from drug crimes, arguing that crimes involving theft, fraud and corruption are “regarded as simply transfers of wealth”. Finland paper focuses on fraud. Australian paper presents complete methodology. Conclusions: Need for greater participation from all FATF member countries Initial focus to be on estimating “the supply and demand for illegal drugs”

The Rich Countries’ Club fails to Deliver FATF efforts to “estimate the magnitude of ML” failed on 5 key counts: Focus on incomplete range of crime types – drugs only Focus on incomplete range of illicit drugs - Cocaine, Marihuana, Heroin only Focus on incomplete range of countries - rich north-Atlantic countries only Focus on statistical “purity” Avoidance of “proceeds of crime” logic Intended to fail………….? Need to provoke more constructive discussion. Feb 2000 FATF Technical Workshop for Estimating the Magnitude of Drug Trafficking Proceeds * “The Workshop again suffered from poor attendance of member countries”. “The morning two sessions of the first day … were completely dominated by international organisations and US drug people. The debate was technical and irrelevant for this Ad Hoc Group”. Agreed that Dr Peter Reuter prepare draft report – only “Cocaine, Marihuana and Heroin”, covering “only North America and Europe”, and “won’t cover drug trafficking proceed”. “Peter Reuter proposed to focus only two regions – North America and EU, rather than global estimate, because sightly better data sets available”… “This means that AD Hoc Group will further retreat from its purpose while ML is really worldwide issue”. “UK again induced the workshop to focus on real issue and argued for looking at Australian paper because it has been the only study focussing on intermediate level (trafficking side) and worldwide estimate so far”. “It was quite clear, at this stage, US had no intention to change its mind and other member countries just spent their time.” * Notes from UK Treasury representative

What next? Need to provoke discussion, in the absence of any real progress within the UN or FATF. Can the Australian model be used for other countries around the world?

The 1996 Australian Model Identifies Upper and Lower Bounds TE = Total Economy Costs of crime are part of the Economy. Proceeds of crime are a subset of costs. Some proceeds of crime are laundered, but some laundered money also comes from outside the economy. Terrorist finance may not have criminal origins and is not necessarily laundered. “Known” components are very small subsets of their respective estimated totals. [Not to scale] TC = Total Costs of Crime TP = Total Proceeds of Crime KP = Known Proceeds of Crime TM = Total Money Laundering KM = Known Money Laundering TT = Total Terrorist Financing KT = Known Terrorist Financing I had already demonstrated to FATF my methodology for estimating the extent of ML in and through Australia. The costs of crime are part of the Economy. Proceeds of crime are a subset of the costs. Some proceeds of crime are laundered, but some is simply spent by the criminals and does not go through any laundering-like process. Some laundered money also comes from outside the economy, from crimes committed in other countries. This means that, not only do we have upper and lower limits to our estimates, based on the likely extent and profitability of crime, but we can also use a wide range of other economic data to triangulate towards the most credible estimates. Since September 2001, we have been told that terrorists use laundered money to commit their crimes, but terrorist finance is different from the proceeds of crime – it may not have criminal origins and is not necessarily laundered. “Known” components are very small subsets of their respective estimated totals. This means that, not only do we have upper and lower limits to our estimates, based on the likely extent and profitability of crime, but we can also use a wide range of other economic data, including estimates of the size of the economy and of imports and exports, to triangulate towards the most credible estimates of the extent of economic crime and moneylaundering. Incoming Money Laundering

Estimates of ML in and through Australia (1996) Estimates based on Costs of Crime and Expert Survey Estimated Proceeds of Crime Implied ML Estimates for Australia ($mill) Crime Category/ Min Mid1 Mid2 Max Homicide Max $2.75m <1 Robbery & Extortion $74.4m 22 45 Other Violence Min $3.31m Breaking and Entering $714.4m 14 71 500 Insurance Fraud $1530m 38 77 153 306 Business Fraud $375 - $900m 56 225 540 900 Other Fraud $750m 113 188 600 Motor Vehicle Thefts $533.6m 27 53 187 480 Other Thefts $462- 8 82 116 347 Environmental Crime $5.21- 2 Illicit Drugs $1500m 300 750 1050 1350 Total $5951 - $7661m 402 1394 2328 4536 My methodology involved estimating the proceeds of crime in Australia, and the proportion of those proceeds that are likely to be laundered. I started from the estimates of the extent of recorded and unrecorded crime, and took only the property loss components of the costs, as being equal to the proceeds of crime. These proceeds sometimes have to be heavily discounted, depending on the crime type, because of course if you are selling stolen goods, you will happily accept a lower than market value price for them. However, when the crime is fraud, there are no actual goods to dispose of, so the income from fraud is almost all proceeds of crime. Finding no actual data that could measure the extent of laundering, I conducted an expert survey to determine the proportions of proceeds likely to be laundered. The proportions differed between crime types, and in general the smaller the returns per crime, the less likely that the proceeds would be laundered.

Importance of Triangulating with Other Data Estimates based on Costs of Crime and Expert Survey compared to: Estimates based on Proceeds of Crime Monitoring Estimates Based on Understatement of Income Data Estimates Based on Suspect Financial Transactions Estimates Based on Flows of Finance through Australian Banks and International Transfers I then “triangulated” these resulting estimates with other data, including Estimates based on Proceeds of Crime Monitoring (Police/Prosecutions data) Estimates Based on Understatement of Income Data (Income Tas assessment data) Estimates Based on Suspect Financial Transactions (Austrac data) Estimates Based on Flows of Finance through Australian Banks and International Transfers (Austrac/National Accounts data)

Estimates of ML in and through Australia (1996) Overseas Money laundered overseas $US100-500 billion? Overseas Economy $US20,000 billion Costs of Crime The Australian Economy $380 billion P.o.C. Costs of Crime $11-21 billion ML And these are the resulting estimates: In a total Australian economy of (then) $380 billion, it was estimated that the costs of crime were $11-21 billion per annum, of which $6-8 billion were proceeds of crime. Of these, between $1-4.5 billion was laundered. Looking at the disparities between imports and exports of goods and services and inflows and outflows of financial transactions, it appeared likely that large amounts of money were being laundered in or through Australia, generated by crime in other countries. Unfortunately, because no similar research had been done in other countries, that could not be confirmed. But these estimates have been widely accepted – their accuracy can never be ascertained, but their credibility is unchallenged, and the Australian Government has been comfortable in using them as evidence for the need for AML controls. Australian Money laundered overseas $5.5 billion? Overseas Money laundered in Australia $7.7 billion? Proceeds of Crime $6-8 billion Australian Money laundered in Australia ML $1-4.5 billion

“Just do it…..!” Is there enough basic data to construct a global model?

How much Crime around the World? U.N. Crime & Justice Survey

Triangulate with other Data …Banks and Businesses rarely report crime, because they think it will adversely affect their “image” Source: www.kpmg.com

Measuring the Proceeds of Crime “Crime in Australia costs $A11-21 bn, and profits are $A6-8 bn per year” (John Walker Crime Trends Analysis, 1996) "Illegal grey economy in Czech Republic about 10% of GDP” (Hospodárské Noviny, 2 Apr 98) "$30bill illegal drugs reach the US from Mexico each year" (Chicago Tribune, 25 Mar 98) "Shadow business in Russia's economy may range between 25% -50%" (TASS 17 Mar 98) "UK black economy between 7-13% of GDP" (Sunday Telegraph, 29 Mar 98) "$50-250bn illegally moved from Russia to Western banks in 5 years" (Russian Interior & Economics Ministries, April 99) "Illicit drug sales (in the USA) generated up to $48bn a year in profits" (Congressional hearing, April 99) "Illegal profits total 2-5% of world GDP or $1-3trillion" (Dow Jones News, 12 Mar 98)

About Criminal Income Crime generates income in all countries Income from crime depends on the prevalence of different types of crime and the average proceeds per crime Sophisticated and organised crimes generate more income per crime than simpler and individual crimes Crimes that trade on “forbidden goods” like drugs, arms, pornography, slave labour, copyrights, migration visas etc, are particularly profitable In general, richer countries generate more income per crime than poor ones Income inequality or corruption may support a rich criminal class even in a poor country Not all criminal income is laundered - Even criminals have to eat, sleep, drive fast cars, and pay accountants and lawyers If you like algebra........ Total Criminal Profits to be Laundered = Total Population times GNP/Capita times: 700*(TI Corruption Index)*(Bribery+Embezzlement+Fraud rates) + 500*Drug Trafficking rate + 100*Theft rate + 65*Burglary rate + 50* Drug Possession rate + 20*Robbery rate + 0.2*Homicide rate + 0.1*(Assault rate + Sex Assault rate) It appeared therefore that I had at least the bare bones of the data required. The basic structure took me a weekend to develop, in Excel, and I was lucky that a former AIC colleague had recently completed an international risk assessment, including several measures of economic and political stability, which I was able to access. The assumptions I built into the model about criminal income were: . Crime generates income in all countries Income from crime depends on the prevalence of different types of crime and the average proceeds per crime Sophisticated and organised crimes generate more income per crime than simpler and individual crimes In general, richer countries generate more income per crime than poor ones Income inequality or corruption may support a rich criminal class even in a poor country Not all criminal income is laundered - Even criminals have to eat, sleep, drive fast cars, and pay accountants and lawyers If you like algebra........ Total Criminal Profits to be Laundered = Total Population times GNP/Capita times: 700*(TI Corruption Index)*(Bribery+Embezzlement+Fraud rates) + 500*Drug Trafficking rate + 100*Theft rate + 65*Burglary rate + 50* Drug Possession rate + 20*Robbery rate + 0.2*Homicide rate + 0.1*(Assault rate + Sex Assault rate) All parameters based on my Australian estimates model. The first line here expresses the assumption that criminal income is related to the country’s income per capita. The second expresses the idea that income from fraud is higher in countries where corruption and bribery are common. The other assumptions make the proceeds of different types of crime similar in proportion to those in Australia. These are all supportable assumptions, in the absence of any existing research of this kind.

About Corruption

Resulting Estimates of Money Generation by Crime Type by World Region $US bill/yr Note: the big numbers come from fraud not drugs

Assumptions about Laundering Processes Not all laundered money leaves the country Some countries' finance sectors provide perfect cover for local launderers Countries where official corruption is common provide benign environments for launderers Laundered money seeks countries with attractive banking regimes Tax Havens "No questions asked" banking Countries with stable economies and low risk Trading, ethnic and linguistic links will determine launderers' preferred destinations Other things being equal, "hot" money will be attracted to havens with trading, ethnic, linguistic or geographic links to the generating country If you like algebra........ Attractiveness to money launderers = [GNP per capita] *[3*BankSecrecy+GovAttitude+SWIFTmember-3*Conflict-Corruption +15] Where: GNP per capita is measured in US$, BankSecrecy is a scale from 0 (no secrecy laws) to 5 (bank secrecy laws enforced), GovAttitude is a scale from 0 (government anti-laundering) to 4 (tolerant of laundering), SWIFTmember is 0 for non-member countries and 1 for members of the SWIFT international fund transfer network, Conflict is a scale from 0 (no conflict situation) to 4 (conflict situation exists), Corruption is the modified Transparency International index (1=low, 5=high corruption), And the constant '15' ensures that all scores are greater than zero. The assumptions I built into the model about laundering processes were: Not all laundered money leaves the country Some countries' finance sectors provide perfect cover for local launderers Countries where official corruption is common provide benign environments for launderers Laundered money seeks countries with attractive banking regimes Tax Havens "No questions asked" banking Countries with stable economies and low risk Trading, ethnic and linguistic links will determine launderers' preferred destinations Other things being equal, "hot" money will be attracted to havens with trading, ethnic, linguistic or geographic links to the generating country If you like algebra........ Attractiveness to money launderers = [GNP per capita] *[3*BankSecrecy+GovAttitude+SWIFTmember-3*Conflict-Corruption +15] Where: GNP per capita is measured in US$, BankSecrecy is a scale from 0 (no secrecy laws) to 5 (bank secrecy laws enforced), GovAttitude is a scale from 0 (government anti-laundering) to 4 (tolerant of laundering), SWIFTmember is 0 for non-member countries and 1 for members of the SWIFT international fund transfer network, Conflict is a scale from 0 (no conflict situation) to 4 (conflict situation exists), Corruption is the modified Transparency International index (1=low, 5=high corruption), And the constant '15' ensures that all scores are greater than zero. This measure of attractiveness is based in the gravity model concepts. I actually was not in a position to measure the impacts of trading, ethnic or linguistic links, and chose to use the standard gravity model “square of the distance between the countries” measure, which appeared to work quite well as a first step.

Model Index: Most Attractive to Launderers COUNTRY Score Luxembourg 686 United States 634 Switzerland 617 Cayman Islands 600 Austria 497 Netherlands 476 Liechtenstein 466 Vatican City 449 United Kingdom 439 Singapore 429 Hong Kong 397 Ireland 356 Bermuda 313 Bahamas, Andorra, Brunei, Norway, Iceland, Canada 250-299 Portugal, Denmark, Sweden, Monaco, Japan, Finland, Germany, New Zealand, Australia, Belgium 200-249 Bahrain, Qatar, Italy, Taiwan, United Arab Emirates, Barbados, Malta, France, Cyprus 150-199 Gibraltar, Azores (Spain), Canary Islands, Greenland, Belarus, Spain, Israel 100-149

Triangulation: Attractiveness to ML: Service Exports and Incoming Money Laundering A further source of triangulation for estimates of money laundering is the UN’s statistics on services exports. When computed as a percentage of GNP, these figures clearly show which countries have unusually strong financial services exports. These countries include most of the Caribbean tax havens and some of the larger centres including Luxembourg, Switzerland and Singapore. In many countries, these data can be interpreted as measuring the capacity of the financial sector to support money laundering. The next slide shows how a study of finance and business regulation in each country can measure the “willingness” of a country’s financial sector to support money laundering.

Triangulation: Attractiveness to ML: Banking Risk Analysis TRANSCRIME “Euroshore” project 1. Money laundering punished in your criminal system? 2. Legislation provides for a list of crimes as predicate offences? 3. Predicate offences cover all serious crimes? 4. Predicate offences cover all crimes? 5. Provision allowing confiscation of assets for an ML offence? 6. Special investigative bodies or investigations in relation to ML offences? 1. Is there an anti-ML law in the jurisdiction? 2. Banks covered by the anti-ML law? 3. Other financial institutions covered by the anti-ML law? 4. Non-financial institutions covered by the anti-ML law? 5. Other professions carrying out a financial activity covered by the anti-ML law? 6. ID requirements for the institutions covered by the anti-money law? 7. Suspicious transactions reporting? 8. Central authority (for instance, an FIU) for the collection of suspicious transactions reports? 9. Co-operation between banks or other financial institutions and police authorities? 1. Prohibition to open a bank account without ID of the beneficial owner? 2. Limits to bank secrecy in case of criminal investigation and prosecution? 1. Minimum share capital required for limited liability companies? 2. Prohibition on bearer shares in limited liability companies? 3. Prohibition on legal entities as directors of limited liability companies? 4. Registered office exists for limited liability companies? 5. Any form of annual auditing (at least internal) for limited liability companies? 6. Shareholder register exists for limited liability companies? 1. Extradition (at least of foreigners) for ML offences? 2. Assistance to foreign law enforcement agencies in investigation of ML cases? 3. Law enforcement may respond to a request from a foreign country for financial records? 4. Provision allowing the sharing of confiscated assets for ML offences? 5. The 1988 UN Convention been ratified? CRIMINAL LAW ADMINISTRATIVE REGULATIONS BANKING LAW COMPANY LAW INTERNATIONAL CO-OPERATION PROVISIONS Transcrime & Walker Attractiveness Indices I was an adviser to the Italian research group TransCrime, led by Professor Ernesto Savona, in their work for the European Union, analysing the impacts of the Criminal Law, Administrative Regulations, Banking Law, Company Law and International Co-operation Provisions on moneylaundering. Using a simple questionnaire-type framework, the analysis gives a sort of credit rating to each country’s finance sector, depending on how laundry-friendly or laundry-proof it is. As the chart shows, the results of such analysis can identify those countries whose regulations leave gaps for money launderers to wriggle through. The potential for money laundering through a country’s banking sector is the product of its capacity to launder and its willingness to launder. These charts suggest that it may be possible to measure both of these aspects.

…Putting all this information together... Model’s Top 10 Origins of Laundered Money Rank Origin Amount ($Usmill/yr) % of Total 1 United States 1320228 46.3% 2 Italy 150054 5.3% 3 Russia 147187 5.2% 4 China 131360 4.6% 5 Germany 128266 4.5% 6 France 124748 4.4% 7 Romania 115585 4.1% 8 Canada 82374 2.9% 9 United Kingdom 68740 2.4% 10 Hong Kong 62856 2.2%

Model’s Top 10 Flows of Laundered Money Rank Origin Destination Amount ($USmill/yr) % of Total 1 United States United States 528091 18.5% 2 United States Cayman Islands 129755 4.6% 3 Russia Russia 118927 4.2% 4 Italy Italy 94834 3.3% 5 China China 94579 3.3% 6 Romania Romania 87845 3.1% 7 United States Canada 63087 2.2% 8 United States Bahamas 61378 2.2% 9 France France 57883 2.0% 10 Italy Vatican City 55056 1.9%

Model’s Top 10 ML Destinations Rank Destination Amount ($Usmill/yr) % of Total 1 United States 538145 18.9% 2 Cayman Islands 138329 4.9% 3 Russia 120493 4.2% 4 Italy 105688 3.7% 5 China 94726 3.3% 6 Romania 89595 3.1% 7 Canada 85444 3.0% 8 Vatican City 80596 2.8% 9 Luxembourg 78468 2.8% 10 France 68471 2.4%

Model results compared to Press reports "Illegal grey economy in Czech Republic about 10% of GDP” (Hospodárské Noviny, 2 Apr 98) Model estimates 14.8% of GDP "$30bill illegal drugs reach the US from Mexico each year" (Chicago Tribune, 25 Mar 98) Model estimates $26bill laundered in Mexico each year “More than $2bill is laundered in Poland each year" (National Bank of Poland, reported on 15 Apr 98) Model estimates $3bill laundered in Poland each year "Share of shadow business in Russia's economy may range between 25% -50%" (TASS 17 Mar 98) Model estimates money laundering 15% of Russian GDP "Switzerland is implicated in $500bill of money laundering each year" (Swiss Finance Ministry, reported on 26 Mar 98) Model estimates $59bill - including only "first-stage" laundering. "UK black economy between 7-13% of GDP" (Sunday Telegraph, 29 Mar 98) Model estimates total money laundering 7.4% of UK GDP "$50-250bn illegally moved from Russia to Western banks in 5 years" (Russian Interior & Economics Ministries, April 99) Model estimates $28bn per year from Russia to western banks "Money Laundering in Belarus about 30% of GDP" (European Humanities University, 20 Nov 98) Model estimates 22.2% of Belarus GDP is laundered money "Illicit funds generated and laundered in Canada per year $5-17 bn" (Canadian Solicitor General, Sep 1998) Model estimates $22bill generated and laundered in Canada each year, but also that $63bn of US crime funds laundered in Canada. "Approximately $2.7bn are laundered in Colombia each year" (BBC Monitoring Service, Nov 98) Model estimates $2.1bn laundered in Colombia every year "Illicit drug sales (in the USA) generated up to 48bn a year in profits for laundering" (Congressional hearing, April 99) Model estimates $34.6bn generated and laundered by illicit drug trade in USA "Illegal profits total 2-5% of world GDP or $1-3trillion" (Dow Jones News, 12 Mar 98) Model estimates total global money laundering $2.85 trillion I monitored the internet and press reports to find estimates that could be compared with the results of the model, But after late 1999, I found that most people were lazily copying the results from my website, rather than doing their own research. After late 1999, it became apparent that most published estimates were based on my model

Triangulation: Shadow Economy, Crime and Money Laundering All rich countries have low % shadow economies “Excess” shadow economy might be an indicator of the proceeds of crime. Many of the richest countries with high % shadow economies have significant transnational crime, illicit drug production and corrupt business practices. However, I soon began to find other researchers following interesting aspects of economic crime. Austrian economist Friedrich Schneider published some interesting estimates of shadow economy in a range of different countries. I found that I produced some interesting results by comparing his estimates of shadow economy against GDP per capita. Unsurprisingly, his analysis suggests that the poorer countries have higher percentages of shadow economy than the rich countries, and there appears to be a nice “J”-curve on the graph. Those countries to the left of the curve – lower than expected shadow economy - (blue, including China) tend to have “command” economies in which the shadow economy is suppressed. Those to the right of the line (red) appear to have significant transnational crime, illicit drug production and corrupt business practices. But this analysis seems to identify “excess” shadow economy in some countries, often those with a reputation for “mafia-type” organised crime, including Italy, Russia and Colombia, and the excess can be measured as a proportion of the countries’ GDPs. It is too soon to know whether this form of analysis can successfully identify the proceeds of organised crime in specific countries – but it is at least extremely interesting. On this basis, the shadow economy in Australia would produce around AU$20 billion per year, some of which laundered. Poor countries with low % shadow economies are mostly “command economies” Source: F. Schneider and J. Walker.

Triangulation: Cross-border flow Analysis (Raymond Baker, 2005) Global Flows Low ($US bn) High ($US bn) Drugs $120 $200 Counterfeit goods $80 Counterfeit currency $3 Human trafficking $12 $15 Illegal arms trade $6 $10 Smuggling $60 $100 Racketeering $50 Crime Subtotal $331 $549 Mispricing $250 Abusive transfer pricing $300 $500 Fake transactions Commercial Subtotal $700 $1000 Corruption $30 Grand Total $1061 $1599 This view is supported by Raymond Baker’s interesting work on cross-border flow analysis. It is based only on a review of studies of transnational crime, and the data may not be internally consistent, but this is another essential research technique in its own right. Each of these figures can potentially serve as a credibility check on any estimates we are able to generate. He estimates that criminals in the rich countries around the world are transferring from the poorer countries something like ten times the amount of aid, given by the rich countries to the poor. This, it seems to me, is the most important reason why we need to focus on the economics of crime. International crime prevention strategies should not only benefit the rich countries. From “Capitalism’s Achilles Heel”, Baker 2005. Based on a review of studies of transnational crime

Triangulation: the Economics of the Global Illicit Drugs Trades By 2005 UNODC researchers were convinced they had sufficient data in their Annual Reports Questionnaires to develop a global model of the illicit drugs market. ARQs received from most countries around the world – all continents; rich/poor; developed/less developed countries. We developed mechanisms for testing the credibility of ARQ data from different countries by comparing them with other ARQ data and other studies. We developed mechanisms for filling the gaps in the data, by classifying different countries and “interpolating”. We identified the economic logic of the illicit drugs trades. We identified ways to deduce the “trade routes” of the illicit drugs trades, by comparing “mentions”, and developed this into a “tracking model” that can explain corruption levels in transit countries. I next got my chance to examine the economics of the global illicit drugs trades. By 2004 UNODC researchers were convinced they had sufficient data in their Annual Reports Questionnaires to develop a global model of the illicit drugs market – regardless of being repeated told that it “can’t be done”. I received a phone call from Vienna in late 2004 inviting me to demonstrate how I believed the economics of the global illicit drugs trades could be modelled. ARQs received from most countries around the world – all continents; rich/poor; developed/less developed countries. We developed mechanisms for testing the credibility of ARQ data from different countries by comparing them with other ARQ data and other studies. We developed mechanisms for filling the gaps in the data, by classifying different countries and “interpolating”. We identified the economic logic of the illicit drugs trades. We identified ways to deduce the “trade routes” of the illicit drugs trades, by comparing “mentions”, and developed this into a “tracking model” that can explain corruption levels in transit countries.

The Economics of the Illicit Drugs Trades Finally, we can aggregate the economic models of all the drug types, to see the incomes and profits of entire global illicit drugs trades. Again, it is very significant that prohibition makes traffickers in the rich countries very rich, and only gives relatively modest rewards to the producers, who are mostly in the poorer regions. Note the bottom line gross income of $311 USbillion – well below the Baker figures for total frauds and business crime, and not much more than Zdanowicz’s estimates of transfer pricing frauds in the USA alone.

General Conclusions from the Model Global money laundering may be as much as $US3 trillion per annum Business Fraud exceeds illicit drugs as a source of laundered money Attacking the economics of crime can be an effective transnational crime prevention strategy. Economists can play a valuable role in monitoring and combating transnational crime and money laundering. Does AML reduce crime? – Probably not by much. Does AML reduce ML? – Probably not much, but it diverts it from the finance sectors to more costly avenues. Does AML help catch criminals? – Probably only a few, but sometimes very important ones. Does AML protect the economy? – Probably a massive boost to the economy by ensuring that the finance sector is seen as honest, wary and supervised.

Model Estimates of ML Flows from Developing Countries Region……………………………………………………………………………………. Total US$ Million Caribbean 6,452 Central America 2,525 Central Asia and Transcaucasian countries 15,201 East Africa 3,559 East and South-East Asia (Excl. Brunei, Japan, Singapore, Rep of Korea) 444,536 East Europe 176,963 Near and Middle East /South-West Asia (Excl Israel) 12,213 North Africa 4,178 Oceania (Exc Australia, NZ) 209 South America 30,361 South Asia 3,465 Southeast Europe 129,512 Southern Africa 14,321 West and Central Africa 3,278 Total Developing Countries 846,773

Model Estimates of ML Flows from Developing Countries E Europe 176963 SE Europe 129512 C Asia/Transcaucasus 15201 E & SE Asia 444536 Near & Middle East/SW Asia 12213 Caribbean 6452 N & W Africa 4178 South Asia 3465 Central America 2525 E Africa 3559 Oceania 209 W & C Africa 3278 South America 30361 Southern Africa 14321 C & S America total $39.3 bn; Africa total $25.3 bn; Europe total $306.5bn; ME & Asia total $475.6bn Global total $846.8 bn