John Walker Crime Trends Analysis Measuring Global Moneylaundering Presented by John Walker at the...

John Walker Crime Trends Analysis

Measuring Global Measuring Global MoneylaunderingMoneylaundering

Presented by John WalkerPresented by John Walkerat the Utrecht School of Economicsat the Utrecht School of Economics

November 2007November 2007


Being naïve enough not to know it can’t be Being naïve enough not to know it can’t be donedone

Querying the dominant paradigm

““In the field of observation, chance favours In the field of observation, chance favours only the prepared mind” (Louis Pasteur )only the prepared mind” (Louis Pasteur )

Evidence based actionEvidence based action

Three Important Things in LifeThree Important Things in Life

NaïvetéNaïveté

Iconoclasm

SerendipitySerendipity

Looking before Leaping


An Attraction to MathematicsAn Attraction to Mathematics

Using “matrix algebra”, we can add, Using “matrix algebra”, we can add, multiply subtract or divide tables like this multiply subtract or divide tables like this one.one.

This particular matrix is very special, This particular matrix is very special, because if you multiply it because if you multiply it by itselfby itself, it , it gives the number of routes between gives the number of routes between pairs of countries pairs of countries VIA A THIRDVIA A THIRD country. country.

AA BB CC DD EE FF GG HH II JJ

AA 00 11 00 11 00 11 00 00 11 11

BB 11 00 11 11 00 00 00 00 00 11

CC 00 11 00 11 00 00 00 00 00 00

DD 11 11 11 00 11 11 00 00 00 00

EE 00 00 00 11 00 11 00 11 00 00

FF 11 00 00 11 11 00 11 11 11 00

GG 00 00 00 00 00 11 00 00 00 00

HH 00 00 00 00 11 11 00 00 11 00

II 11 00 00 00 00 11 00 11 00 00

JJ 11 11 00 00 00 00 00 00 00 00

AA BB CC DD EE FF GG HH II JJ

AA 55 22 22 22 22 22 11 22 11 11

BB 22 44 11 22 11 22 00 00 11 11

CC 22 11 22 11 11 11 00 00 00 11

DD 22 22 11 55 11 22 11 22 22 22

EE 22 11 11 11 33 22 11 11 22 00

FF 22 22 11 22 22 66 00 22 22 11

GG 11 00 00 11 11 00 11 11 11 00

HH 22 00 00 22 11 22 11 33 11 00

II 11 11 00 22 22 22 11 11 33 11

JJ 11 11 11 22 00 11 00 00 11 22


A Fascination with EconomicsA Fascination with Economics

1965 – Mathematics Scholarship to LSE

In economics, In economics, supply and demandsupply and demand describe describe

market relations between prospective sellers and market relations between prospective sellers and

buyers of a good. The buyers of a good. The supply and demand modelsupply and demand model

determines price and quantity sold in the market. determines price and quantity sold in the market.

The model is fundamental in microeconomic The model is fundamental in microeconomic

analysis of buyers and sellers and of their analysis of buyers and sellers and of their

interactions in a market. It is also used as a point of interactions in a market. It is also used as a point of

departure for other economic models and theories. departure for other economic models and theories.

The price P is determined by a balance between production at each price (supply S) and the desires of those with purchasing power at each price (demand D). From WikipediaFrom Wikipedia


Discovering MacroeconomicsDiscovering Macroeconomics

1965 – Mathematics Scholarship to LSE 1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.

Destination Country---Destination Country--->>

Origin CountryOrigin Country

\/\/

CC

oo

uu

nn

tt

rr

yy

11

CC

oo

uu

nn

tt

rr

yy

22

CC

oo

uu

nn

tt

rr

yy

33

CC

oo

uu

nn

tt

rr

yy

44

CC

oo

uu

nn

tt

rr

yy

55

CC

oo

uu

nn

tt

rr

yy

66

CC

oo

uu

nn

tt

rr

yy

77

EE

TT

CC

TT

OO

TT

AA

LL

SS

Country 1Country 1 AA BB CC DD A+B+C+DA+B+C+D

Country 2Country 2 EE FF E+FE+F

Country 3Country 3 GG GG

Country 4Country 4 HH HH

Country 5Country 5 II JJ I+JI+J

Country 6Country 6 KK LL K+LK+L

Country 7Country 7 MM MM

EtcEtc NN NN

TOTALSTOTALS AA EE B+F+G+I+KB+F+G+I+K HH C+JC+J LL D+MD+M NN Sum(A:N)Sum(A:N)


Real-World ApplicationsReal-World Applications

1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje. 1970 – Transportation modelling/forecasting

The The gravity model of tradegravity model of trade in international economics predicts bilateral trade flows based on the in international economics predicts bilateral trade flows based on the economic sizes of and distance between two units. The model was first used by Jan Tinbergen in economic sizes of and distance between two units. The model was first used by Jan Tinbergen in 1962. The basic theoretical model for trade between two countries (i and j) takes the form of:1962. The basic theoretical model for trade between two countries (i and j) takes the form of:

Where F is the trade flow, M is the economic mass of each country, D is the distance and G is a Where F is the trade flow, M is the economic mass of each country, D is the distance and G is a

constant. Using logarithms, the equation can be converted to a linear form for econometric constant. Using logarithms, the equation can be converted to a linear form for econometric analysis. The basic model for such a test results in the following equation (note:constant G analysis. The basic model for such a test results in the following equation (note:constant G becomes part of α):becomes part of α):

ln(Bilateral Trade Flow) = α+βln(GDPCountry1)+βln(GDPCountry2)-βln(Distance)+ε ln(Bilateral Trade Flow) = α+βln(GDPCountry1)+βln(GDPCountry2)-βln(Distance)+ε

The model often includes variables to account for income level (GDP per capita), price levels, The model often includes variables to account for income level (GDP per capita), price levels, language relationships, tariffs, contiguity, and colonial history (whether Country1 ever colonized language relationships, tariffs, contiguity, and colonial history (whether Country1 ever colonized Country2 or vice versa). The model has also been used in international relations to evaluate the Country2 or vice versa). The model has also been used in international relations to evaluate the impact of treaties and alliances on trade, and it has been used to test the effectiveness of trade impact of treaties and alliances on trade, and it has been used to test the effectiveness of trade agreements and organizations such as NAFTA and the WTO.agreements and organizations such as NAFTA and the WTO.


Measuring CrimeMeasuring Crime 1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.

1975 – Use of regional crime data to measure well-being 1975 – Use of regional crime data to measure well-being

1970 – Transportation modelling/forecasting


Applications in CriminologyApplications in Criminology 1965 – Mathematics Scholarship to LSE


1988 – Chance meeting re Victimisation Survey

1992 – Opportunistic Proposal for Business Victimisation Survey

1992 – First Costs of Crime Estimates



1978 - Accidental criminology apprenticeship


Applications in CriminologyApplications in Criminology 1965 – Mathematics Scholarship to LSE




1978 - Accidental criminology apprenticeship1978 - Accidental criminology apprenticeship


1992 – Opportunistic Proposal for Business Victimisation Survey1992 – Opportunistic Proposal for Business Victimisation Survey


1995 – First National Estimates of Moneylaundering1995 – First National Estimates of Moneylaundering


A Fundamental Insight about MLA Fundamental Insight about ML

How Much Crime is there

?

HowMuchProfit

is therein theCrime

?

WhatProportion

of the Profits

isLaundered

?

How does it impact

on Society?

Where does it go for

Laundering?


TE = Total

Economy

TC = Total Costs of Crime

TP = Total Proceeds of Crime

KP = Known Proceeds of Crime TM = Total

Money Laundering

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]

Economic Model of Crime, ML & TFEconomic Model of Crime, ML & TF

KM = Known Money Laundering

TT = Total Terrorist Financing

KT = Known Terrorist FinancingIncoming Money Laundering


Estimates of ML in and through Australia (1996)Estimates of ML in and through Australia (1996)

Crime Category/ Implied ML Estimates for Australia ($mill)

Est’d Proceeds of CrimeMin Mid1 Mid2 Max

Homicide Max $2.75m <1 <1 <1 <1

Robbery & Extortion $74.4m <1 22 22 45

Other Violence Min $3.31m <1 <1 <1 <1

Breaking and Entering $714.4m 14 71 71 500Insurance Fraud $1530m 38 77 153 306

Business Fraud $375 - $900m 56 225 540 900Other Fraud $750m 38 113 188 600Motor Vehicle Thefts $533.6m 27 53 187 480Other Thefts $462- 8 82 116 347Environmental Crime $5.21- <1 <1 2 8Illicit Drugs $1500m 300 750 1050 1350Total $5951 - $7661m 402 1394 2328 4536

• Estimates based on Costs of Crime and Expert Survey


• 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

Estimates of ML in and through Australia Estimates of ML in and through Australia (1996)(1996)

• Estimates based on Costs of Crime and Expert Survey


ML

P.o.C.

Costs of Crime

Overseas Economy

Australian Money laundered in Australia

The Australian Economy$380 billion

Costs of Crime$11-21 billion

ML

$1-4.5 billion

Australian Money laundered overseas$5.5

billion?

Overseas Money laundered in Australia

$7.7 billion?

Proceeds of Crime$6-8 billion

$US20,000 billion

Overseas Money laundered overseas

$US100-500 billion?

Estimates of ML in and through Australia (1996)Estimates of ML in and through Australia (1996)


Going GlobalGoing Global 1965 – Mathematics Scholarship to LSE




1978 - Accidental criminology apprenticeship1978 - Accidental criminology apprenticeship


1992 – Opportunistic Proposal for Business Victimisation Survey1992 – Opportunistic Proposal for Business Victimisation Survey


1995 – First National Estimates of Moneylaundering1995 – First National Estimates of Moneylaundering

1996 – First Prototype Global Model of Moneylaundering


Official efforts to measure Global CrimeOfficial efforts to measure Global Crimeup to the 1990sup to the 1990s

• Most countries compile crime statistics. Only measures recorded crime. Accuracy doubts; rigging Most countries compile crime statistics. Only measures recorded crime. Accuracy doubts; rigging

by police, politicians; counting rules.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); common set of definitions; Crime Victims Surveys developed in the 1970s (USA, UK); common set of definitions;

unrecorded crime. Limited crime types, costly, political risk.unrecorded crime. Limited crime types, costly, political risk.

• From 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 Transnational crime analysis mostly country-specific, offence specific or confined to studies of

mafia, yakuza, drug gangs etcmafia, yakuza, drug gangs etc

• Transparency International experimenting with corruption and bribery indexes.Transparency International experimenting with corruption and bribery indexes.


Assumptions about Criminal IncomeAssumptions about Criminal Income

Crime generates income in all countriesCrime generates income in all countries

Income from crime depends on the prevalence of different types of crime and the Income from crime depends on the prevalence of different types of crime and the

average proceeds per crimeaverage proceeds per crime

Sophisticated and organised crimes generate more income per crime than simpler Sophisticated and organised crimes generate more income per crime than simpler

and individual crimesand individual crimes

In general, richer countries generate more income per crime than poor onesIn 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 Income inequality or corruption may support a rich criminal class even in a poor

countrycountry

Not all criminal income is laundered - Even criminals have to eat, sleep, drive fast Not all criminal income is laundered - Even criminals have to eat, sleep, drive fast

cars, and pay accountants and lawyerscars, and pay accountants and lawyers

If you like algebra........If you like algebra........

Total Criminal Profits to be Laundered = Total Criminal Profits to be Laundered = Total Population times GNP/Capita times:Total Population times GNP/Capita times: 700*(TI Corruption Index)*(Bribery+Embezzlement+Fraud rates) + 700*(TI Corruption Index)*(Bribery+Embezzlement+Fraud rates) + 500*Drug Trafficking rate + 100*Theft rate + 65*Burglary rate +500*Drug Trafficking rate + 100*Theft rate + 65*Burglary rate + 50* Drug Possession rate + 20*Robbery rate + 0.2*Homicide rate +50* Drug Possession rate + 20*Robbery rate + 0.2*Homicide rate + 0.1*(Assault rate + Sex Assault rate)0.1*(Assault rate + Sex Assault rate)


Assumptions about Laundering ProcessesAssumptions about Laundering Processes Not all laundered money leaves the countryNot all laundered money leaves the country

Some countries' finance sectors provide perfect cover for local launderersSome countries' finance sectors provide perfect cover for local launderers

Countries where official corruption is common provide benign environments for Countries where official corruption is common provide benign environments for laundererslaunderers

Laundered money seeks countries with attractive banking regimesLaundered money seeks countries with attractive banking regimes

Tax HavensTax Havens

"No questions asked" banking"No questions asked" banking

Countries with stable economies and low riskCountries with stable economies and low risk

Trading, ethnic and linguistic links will determine launderers' preferred destinationsTrading, 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 Other things being equal, "hot" money will be attracted to havens with trading, ethnic, linguistic or geographic links to the generating countrygeographic links to the generating country


Attractiveness to money launderers =Attractiveness to money launderers = [GNP per capita] *[3*BankSecrecy+GovAttitude+SWIFTmember-3*Conflict-Corruption +15][GNP per capita] *[3*BankSecrecy+GovAttitude+SWIFTmember-3*Conflict-Corruption +15]

Where: GNP per capita is measured in US$, BankSecrecy is a scale from 0 (no secrecy laws) to 5 (bank secrecy laws enforced),Where: GNP per capita is measured in US$, 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), 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,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), 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),Corruption is the modified Transparency International index (1=low, 5=high corruption),And the constant '15' ensures that all scores are greater than zero.And the constant '15' ensures that all scores are greater than zero.


Assumptions about Laundering RoutesAssumptions about Laundering Routes

The money may stay in the country of origin..The money may stay in the country of origin..

the more corrupt the country, the more it will tolerate internal money launderingthe more corrupt the country, the more it will tolerate internal money laundering

or it may go to another country:or it may go to another country:

The money to be laundered outside the country is distributed between all other countries in The money to be laundered outside the country is distributed between all other countries in proportion to:proportion to:

their Attractiveness Index, and their Attractiveness Index, and

the distance between the origin and destination countries. The distance between countries is a proxy the distance between the origin and destination countries. The distance between countries is a proxy for the geographic, linguistic, ethnic and trade barriers between countriesfor the geographic, linguistic, ethnic and trade barriers between countries


Percentage Laundered 'at home' = 20%*(T.I. Index Score)Percentage Laundered 'at home' = 20%*(T.I. Index Score)

Percentage generated in country X and laundered in country Y is proportional to:Percentage generated in country X and laundered in country Y is proportional to:

(100-Percentage Laundered 'at home') * Attractiveness Score of Y- (Distance between countries X and Y)(100-Percentage Laundered 'at home') * Attractiveness Score of Y- (Distance between countries X and Y) 2


Model results compared to Press reportsModel 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

After late 1999, it became apparent that most published estimates were based on my model


Triangulation:Triangulation:Shadow Shadow

Economy, Crime Economy, Crime and Money and Money LaunderingLaundering

““Excess” shadow economy Excess” shadow economy might be an indicator of the might be an indicator of the proceeds of crime.proceeds of crime.

GDP per capita vs. Shadow Economy

42.142.141.6

38.9

35.432.2

2116.8

UAE

Italy

Ireland

NZ

JapanUK

Austria

Switzerland

USA

BoliviaGeorgia

PanamaPeru

Colombia

Morocco

Pakistan

Thailand

Mexico

Vietnam

Uruguay

Belarus

Estonia

Russia

Lao PDRYemen

Iran

China

Mongolia

Australia

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 20 40 60 80

Shadow Economy as % of GDP

GD

P/c

apita

($U

S) 2

001

Source: F. Schneider and J. Walker.Source: F. Schneider and J. Walker.

Poor countries with low % shadow Poor countries with low % shadow economies are economies are mostly “command economies”mostly “command economies”

All rich countries have All rich countries have low % shadow low % shadow

economieseconomies

Many of the richest Many of the richest countries with high % countries with high %

shadow economies have shadow economies have significant transnational significant transnational

crime, illicit drug crime, illicit drug production and corrupt production and corrupt

business practices.business practices.

On this basis, the shadow On this basis, the shadow economy in Australia economy in Australia would produce around would produce around AU$20 billion per year, AU$20 billion per year, some of which laundered.some of which laundered.


Attractiveness to ML: - Service Exports and Attractiveness to ML: - Service Exports and Incoming Money LaunderingIncoming Money Laundering

Seychelles Grenada

St Vincent

BelizeDominica

AlgeriaIran

Egypt

China

India

Sudan

MyanmarLibya

Albania

Sw itzerlandAustria

Vanuatu

Maldives

St Kitts

St LuciaAntigua/Barbuda

Aruba

Ethiopia

C. Afr. Rep.

Luxembourg

Tonga

Barbados

Singapore

0.00

0.00

0.01

0.10

1.00

10.00

100.00

0.00 0.01 1.00 100.00Model Estimate of Incoming ML as Percentage of GDP

Serv

ices E

xport

s/G

NP


Attractiveness to ML: Banking Risk AnalysisAttractiveness to ML: Banking Risk Analysis TRANSCRIME “Euroshore” projectTRANSCRIME “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

Old Attractiveness Index Versus New

Bermuda

Ireland

Hong Kong

U.K.Netherlands

LiechtensteinAustria

Singapore

CaymansSwitzerland

Luxembourg

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0

Walker 1999 Model Attractiveness Score

Tra

nsC

rim

e B

ase

d S

core

Transcrime & Walker Attractiveness Indices


Triangulation: Transfer Pricing FraudTriangulation: Transfer Pricing Fraud

USD156.2 bn moved out of the United States by way of USD156.2 bn moved out of the United States by way of

pricing discrepancies during 2001pricing discrepancies during 2001

USD4.27 bn involved countries which are listed on the USD4.27 bn involved countries which are listed on the

United States State Department ‘Al Qaeda watch-list’.United States State Department ‘Al Qaeda watch-list’.

USD 213 bn moved into the United States.USD 213 bn moved into the United States. Fraud or laundering?Fraud or laundering?

Overseas beneficiaries or American?Overseas beneficiaries or American?

from Zdanowicz, J (2004)from Zdanowicz, J (2004)


Triangulation: Cross-border flow AnalysisTriangulation: Cross-border flow Analysis (Raymond Baker, 2005)(Raymond Baker, 2005)

Global FlowsGlobal Flows Low ($US bn)Low ($US bn) High ($US bn)High ($US bn)

DrugsDrugs $120$120 $200$200

Counterfeit goodsCounterfeit goods $80$80 $120$120

Counterfeit currencyCounterfeit currency $3$3 $3$3

Human traffickingHuman trafficking $12$12 $15$15

Illegal arms tradeIllegal arms trade $6$6 $10$10

SmugglingSmuggling $60$60 $100$100

RacketeeringRacketeering $50$50 $100$100

Crime SubtotalCrime Subtotal $331$331 $549$549

MispricingMispricing $200$200 $250$250

Abusive transfer pricingAbusive transfer pricing $300$300 $500$500

Fake transactionsFake transactions $200$200 $250$250

Commercial SubtotalCommercial Subtotal $700$700 $1000$1000

CorruptionCorruption $30$30 $50$50

Grand TotalGrand Total $1061$1061 $1599$1599

From “Capitalism’s Achilles Heel”, Baker 2005. Based on a review of studies of transnational crime


The Economics of the Global Illicit The Economics of the Global Illicit Drugs TradesDrugs Trades

By 2005 UNODC researchers were convinced they had sufficient data in By 2005 UNODC researchers were convinced they had sufficient data in their Annual Reports Questionnaires to develop a global model of the illicit their Annual Reports Questionnaires to develop a global model of the illicit drugs market – regardless of Rand Corp’s “can’t be done” conclusion.drugs market – regardless of Rand Corp’s “can’t be done” conclusion.

ARQs received from most countries around the world – all continents; ARQs received from most countries around the world – all continents; rich/poor; developed/less developed countries.rich/poor; developed/less developed countries.

We developed mechanisms for testing the credibility of ARQ data from We developed mechanisms for testing the credibility of ARQ data from different countries by comparing them with other ARQ data and other different countries by comparing them with other ARQ data and other studies.studies.

We developed mechanisms for filling the gaps in the data, by classifying We developed mechanisms for filling the gaps in the data, by classifying different countries and “interpolating”.different countries and “interpolating”.

We identified the economic logic of the illicit drugs trades.We identified the economic logic of the illicit drugs trades.

We identified ways to deduce the “trade routes” of the illicit drugs trades, by 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 comparing “mentions”, and developed this into a “tracking model” that can explain corruption levels in transit countries.explain corruption levels in transit countries.


UNODC Illicit Drugs model………UNODC Illicit Drugs model………

Table 1. Production and Distribution from Source Countries to Destination Countries

Consumer Regions

Producer Regions

Total Production in

Source Country

(Kg Heroin Equiv)

Total Seized/Lost in Source Country

(Kg Heroin Equiv)

Total Available for Sale

(Kg Heroin Equiv)

Transferred to Markets

(Kg Heroin Equiv)

Total Seized/ lost in Transit

(Kg Heroin Equiv)

East Africa 0 0 0 2,293 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0North Africa 714 0 714 3,410 35 0 714 0 0 0 0 0 0 0 0 0 0 0 0 0 0 714Southern Africa 0 0 0 1,099 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0West and Central Africa 0 0 0 9,954 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Caribbean 0 0 0 364 80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Central America 0 0 0 783 180 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0North America 8,400 209 8,191 28,735 6,621 0 0 0 0 0 0 8,191 0 0 0 0 0 0 0 0 0 8,191South America 5,268 440 4,829 3,255 399 0 0 0 0 0 0 1,574 3,255 0 0 0 0 0 0 0 0 4,829C. Asia & Transcaucasus 770 0 770 12,076 2,748 0 0 0 0 0 0 0 0 770 0 0 0 0 0 0 0 770East and South-East Asia 94,050 582 93,468 59,928 6,885 240 267 119 1,076 39 85 2,050 0 1,222 59,928 0 6,663 9,483 10,508 1,028 762 93,468Nr & M.East /SW Asia 365,150 9,551 355,599 78,352 27,723 2,053 2,429 980 8,878 325 698 16,920 0 10,084 0 78,352 55,003 78,283 86,740 9,180 5,673 355,599South Asia 0 0 0 61,666 252 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Eastern Europe 2,147 0 2,147 89,913 923 0 0 0 0 0 0 0 0 0 0 0 0 2,147 0 0 0 2,147Western & Central Europe 0 0 0 97,248 3,994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0South East Europe 0 0 0 10,208 4,713 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Oceania 0 0 0 6,436 198 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0All Countries 476,500 10,781 465,719 465,719 54,856 2,293 3,410 1,099 9,954 364 783 28,735 3,255 12,076 59,928 78,352 61,666 89,913 97,248 10,208 6,436 465,719

N Africa

S America

E & SE Asia

C Asia &

Transcaucasus

C America

N America

E Africa

S Africa

W & C Africa

E Europe

W & C Europe

SE Europe

Oceania

Caribbean

Near & Middle

East /SW Asia

S Asia

All Countries


UNODC Illicit Drugs model………UNODC Illicit Drugs model………

Table 2. Supply and Demand in Destination Countries

RegionsE Africa

N Africa

S Africa

W & C Africa

Caribbean

C America

N America

S America

C Asia &

Transcaucasus

E & SE Asia

Near & Middle

East /SW Asia

S Asia

E Europe

W & C Europe

SE Europe

Oceania

All Countries

0 714 0 0 0 0 8400 5268 770 94050 365150 0 2147 0 0 0 4765000 0 0 0 0 0 209 440 0 582 9551 0 0 0 0 0 107810 714 0 0 0 0 8191 4829 770 93468 355599 0 2147 0 0 0 4657190 0 0 0 0 0 0 0 0 1339 2830 0 0 0 0 0 2520

0 2 0 0 0 0 21 12 2 125 1006 0 5 0 0 0 1173

2293 3410 1099 9954 364 783 28735 3255 12076 59928 78352 61666 89913 97248 10208 6436 46571947 35 7 51 80 180 6621 399 2748 6885 27723 252 923 3994 4713 198 54856

2246 3375 1092 9903 285 603 22113 2856 9328 53042 50629 61413 88990 93254 5496 6237 41086328 42 28 15 38 73 98 25 21 38 7 71 36 73 26 140 5063 142 30 153 11 44 2175 71 194 2022 379 4355 3198 6779 145 874 20635

84 108 85 524 21 24 1301 286 370 2105 2009 3102 2406 1450 180 99 14154.32021 3038 983 8913 285 603 22113 2856 9328 53042 50629 61413 80091 83929 5496 5614 39035324.2 28.0 11.5 17.0 13.5 25.2 17.0 10.0 25.2 25.2 25.2 19.8 33.3 57.9 30.6 56.5 27.6

40 124 81 57 41 83 402 101 51 108 12 123 133 302 183 561 16682 377 80 508 12 50 8886 288 480 5725 596 7567 10681 25340 1006 3148 64825

0 2 0 0 0 0 21 12 2 125 1006 0 5 0 0 0 1173- 30 - - - - 620 71 12 1,942 157 - 76 - - - 2908

38 84 18 153 7 44 1311 0 60 0 0 4355 2175 5729 37 803 1481319 236 49 355 1 6 6711 217 286 3703 217 3212 7483 18560 861 2274 4419157 351 68 508 8 50 8663 300 360 5770 1381 7566 9739 24290 898 3076 63085

International Wholesalers' Income less Purchase CostsNet Retailer Profit (US$mill)

Gross Value of Regional Trade

Retailer Income (US$mill)

Regional Net Values:Source Country Producers' Income

Source Country Wholesalers' Income less Purchase Costs

Estimated User Population (Thousands)Estimated Actual Consumption per year (Kg Heroin Equiv)

Implied Consumption per user (gms Heroin Equiv)Average Retail Price US$ /gm

Wholesale price at Destination US$ /gmWholesaler Income (US$mill)

Demand:

Producer Income (US$mill)

Supply:Total Intended for Consumption (Kg Heroin Equiv)

Total Production in Source Country (Kg Heroin Equiv)Total Seized/Lost in Source Country (Kg Heroin Equiv)

Total Available for Sale (Kg Heroin Equiv)Farmgate Price at Origin (US$/Kg Heroin Equiv)

Total Seized/Lost at Destination (Kg Heroin Equiv)Total Available for Consumption (Kg Heroin Equiv)

Production:


The Economics of the Illicit Drugs TradesThe Economics of the Illicit Drugs Trades


ConclusionsConclusions

Global money laundering may be as much as $US3 trillion per annumGlobal money laundering may be as much as $US3 trillion per annum

Business Fraud exceeds illicit drugs as a source of laundered moneyBusiness Fraud exceeds illicit drugs as a source of laundered money

Attacking the economics of crime can be an effective transnational crime Attacking the economics of crime can be an effective transnational crime

prevention strategy.prevention strategy.

Economists can play a valuable role in monitoring and combating Economists can play a valuable role in monitoring and combating

transnational crime and money laundering.transnational crime and money laundering. Does AML reduce crime? – Probably not by much.Does AML reduce crime? – Probably not by much. Does AML reduce ML? – Probably not much, but it diverts it from the Does AML reduce ML? – Probably not much, but it diverts it from the

finance sectors to more costly avenues. finance sectors to more costly avenues. Does AML help catch criminals? – Probably only a few, but sometimes very Does AML help catch criminals? – Probably only a few, but sometimes very

important ones.important ones. Does AML protect the economy? – Probably a massive boost to the Does AML protect the economy? – Probably a massive boost to the

economy by ensuring that the finance sector is seen as honest, wary and economy by ensuring that the finance sector is seen as honest, wary and supervised.supervised.


Blending Dodgy Data and Heroic Assumptions …

Dilbert cartoons used with permission. COPYRIGHT: UNITED FEATURES SYNDICATE INC. Distributed by Auspac Media

… and turning it into something surprisingly useful.

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