1. Are Estimates of the Money Laundering Volume either useful or attainable? Comments on John Walker’s model Peter Reuter Utrecht, Nov. 2, 2007

2. A6116c-2 04/05 Overview Persistent demand for estimates of volume of money laundering –Infamous Camdessus guess of 2-5% global GDP Demand generates a supply –At least a small one However the number serves no important policy purpose That is fortunate: there is no plausible methodology for estimating John Walker’s model illustrates the frailties of estimates –Neither experts nor surveys offer much promise

3. A6116c-3 04/05 How harmful is money laundering? [At least] four mechanisms for harms –Threat to integrity of financial system –Increased fiscal instability –Distortion of investment patterns –Loss of reputation via AML sanctions Few instances of macro-problems –Latvia in 1990s –Dominican Republic ca. 2002 Conjecture: direct harms of ML are modest

4. A6116c-4 04/05 Laundered Money May Be a Point of Vulnerability for Both Crime and Terrorism from crime $ …for use in legitimate activities $ $ $ …laundered in the legitimate financial system… from legal sources $ …for use in terrorist activity $ $ $ …laundered in the legitimate financial system…

5. A6116c-5 04/05 Anti-Money Laundering Controls Are a Means to Diverse Ends Primary Goals –Reduce predicate crimes –Combat global “public bads” Terrorism Kleptocracy/corruption Secondary Goals –Protect the integrity of core financial system –Sanction major felons –Administer “just desserts” –Inconvenience felons

6. A6116c-6 04/05 Does ML volume match these goals? Define ML as specific actions designed to avoid detection of criminal origins Some legal codes include all use of funds from predicate offenses –e.g., drug dealer purchasing cell phone –ML loses its analytic and policy content if defined so broadly ML volume may decline if AML forces offenders to less desired pattern of expenditures –Only secondary objective Primary goals relate to deterrence of crime, corruption –Prevention of terrorism Note that agencies do not offer ML volumes as performance measure

7. A6116c-7 04/05 Money Laundering Is a Highly Differentiated Activity CrimeCash Scale of Operations Severity of harm Drug dealingExclusivelyVery largeSevere Other blue- collar Mostly Small to medium Low to modest White collar Mix of cash & other options Mix of small & large Low to modest Bribery and corruption SometimesLargeSevere TerrorismMixSmallMost Severe

8. A6116c-8 04/05 Current Estimates of Money Laundering Are Not Credible Estimates range from $ hundreds of billions to $trillions Both traditional approaches are problematic There is little prospect for strengthening these estimates Builds on estimates of the underground economy Produces implausibly high estimates Micro approach Builds on estimates of income from criminal activity Produces estimates that lack a systematic empirical base Macro approach

9. A6116c-9 04/05 Fraud estimates are, well, fraudulent Fraud and illegal drugs contend for first place in list of revenue-generating crime Survey of Association of Certified Fraud Examiners members –10% response rate Question; estimate percentage of revenues lost in 2002 as a result of occupational fraud and abuse –What is foundation for individual corporate estimates? –What is base to which figure should be applied Estimate: $600 billion in 2002

10. A6116c-10 04/05 Problems of estimating drug revenues Frequent users of cocaine, heroin and methamphetamine lead unstable lives –Hard to reach through population surveys –Arrestee surveys helpful but don’t know what they miss Can only report expenditures, not quantity of drugs consumed –Triangulation difficult Results highly sensitive to form of question –Among arrestees “How much did you spend at the last transaction?” generates twice as high a figure as “How much did you spend last time you purchased?” Estimates unstable in repeated studies

11. A6116c-11 04/05

12. A6116c-12 04/05

13. A6116c-13 04/05 Australian drug income and ML estimates implausible Annual consumption per user very high –Cannabis 200 grams U.S. survey data estimates 85 grams –Heroin 56 grams U.S. arrestee survey estimates 15 grams –U.K. estimates also much lower per user Estimated ML percentage asserted to be 83% –Yet low level drug dealers account for most of revenues

14. A6116c-14 04/05

15. A6116c-15 04/05 Walker relies on expert judgment Small numbers of experts in this field –Response rate 40% How do experts estimate aggregates? –Intelligence is transaction/person/organization specific –Rewards for agency and agents are for case making, not assessment –Enforcement agencies don’t publish market estimates Experts systematically over-estimate confidence of judgments –I conjecture that police experts even more likely to do so

16. A6116c-16 04/05 Percent revenues laundered also indeterminable What is basis for expert assessment? –How representative a sample of cases is generated by investigations? –Might be more or less likely to involve ML than undetected cases Recent Australian study of 213 tax offenders and 110 serious fraud cases –Only 2 charged with money laundering –Interviews found that most money simply spent Frequently ML is integral part of offense itself –E.g. Enron offshore vehicles

17. A6116c-17 04/05 Conclusions Estimates of volumes of revenues laundered have little policy value –Hard to distinguish from proceeds of crime estimates Existing estimates lack credible base –Expert judgment is particularly suspect Focus should be on identifying measures that are policy relevant –E.g. intensity of AML enforcement for which denominator is ML transactions or participants