Transcript Measuring Global Moneylaundering

Measuring Global
Moneylaundering
Presented by John Walker
at the Utrecht School of Economics
November 2007
John Walker Crime Trends Analysis
Three Important Things in Life

Naïveté


Iconoclasm


Querying the dominant paradigm
Serendipity


Being naïve enough not to know it
can’t be done
“In the field of observation, chance
favours only the prepared mind”
(Louis Pasteur )
Looking before Leaping

Evidence based action
John Walker Crime Trends Analysis
An Attraction to Mathematics


Using “matrix algebra”, we can add,
multiply subtract or divide tables like this
one.
This particular matrix is very special,
because if you multiply it by itself, it
gives the number of routes between
pairs of countries VIA A THIRD country.
A
B
C D E
F
G H I
J
A
0
5
1
2
0
2
1
2
0
2
1
2
0
1
0
2
1
1
B
1
2
0
4
1
1
2
0
1
0
2
0
0
0
1
1
C 2
0
1
0
2
1
0
1
0
1
0
0
0
0
1
D 2
1
1
2
1
0
5
1
1
2
0
1
0
2
0
2
0
2
E
0
2
0
1
0
1
1
0
3
1
2
0
1
1
0
2
0
F
1
2
0
2
0
1
1
2
1
2
0
6
1
0
1
2
1
2
0
1
G 1
0
0
0
0
1
0
1
1
0
0
1
0
1
0
1
0
H 2
0
0
0
0
2
1
1
2
0
1
0
3
1
0
I
1
0
1
0
0
2
0
2
1
2
0
1
1
0
3
0
1
J
1
1
0
1
0
2
0
0
1
0
0
0
1
0
2
John Walker Crime Trends Analysis
A Fascination with Economics

1965 – Mathematics Scholarship to LSE
In economics, supply and demand describe
market relations between prospective sellers and
buyers of a good. The supply and demand model
determines price and quantity sold in the market.
The model is fundamental in microeconomic
analysis of buyers and sellers and of their
interactions in a market. It is also used as a point of
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 Wikipedia
John Walker Crime Trends Analysis
Discovering Macroeconomics


1965 – Mathematics Scholarship to LSE
1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.
Destination Country--->
Origin Country
\/
Country 1
C
o
u
n
t
r
y
1
C
o
u
n
t
r
y
2
A
Country 2
C
o
u
n
t
r
y
3
C
o
u
n
t
r
y
4
B
E
Country 3
C
o
u
n
t
r
y
5
C
o
u
n
t
r
y
6
C
C
o
u
n
t
r
y
7
D
A+B+C+D
E+F
G
G
H
Country 5
I
Country 6
K
H
J
I+J
L
Country 7
K+L
M
Etc
A
E
T
O
T
A
L
S
F
Country 4
TOTALS
E
T
C
B+F+G+I+K
H
C+J
L
John Walker Crime Trends Analysis
D+M
M
N
N
N
Sum(A:N)
Real-World Applications

1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.
1970 – Transportation modelling/forecasting


The gravity model of trade 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
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
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
becomes part of α):


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,
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
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.
John Walker Crime Trends Analysis
Measuring Crime

1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.

1970 – Transportation modelling/forecasting

1975 – Use of regional crime data to measure well-being
John Walker Crime Trends Analysis
Applications in Criminology

1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.

1970 – Transportation modelling/forecasting

1975 – Use of regional crime data to measure well-being

1978 - Accidental criminology apprenticeship

1988 – Chance meeting re Victimisation Survey

1992 – Opportunistic Proposal for Business Victimisation Survey

1992 – First Costs of Crime Estimates
John Walker Crime Trends Analysis
Applications in Criminology

1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.

1970 – Transportation modelling/forecasting

1975 – Use of regional crime data to measure well-being

1978 - Accidental criminology apprenticeship

1988 – Chance meeting re Victimisation Survey

1992 – Opportunistic Proposal for Business Victimisation Survey

1992 – First Costs of Crime Estimates

1995 – First National Estimates of Moneylaundering
John Walker Crime Trends Analysis
A Fundamental Insight about ML
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
?
John Walker Crime Trends Analysis
How does
it impact
on Society
?
Economic Model of Crime, ML & TF
TC = Total Costs of Crime
TP = Total Proceeds of Crime
KP = Known Proceeds
of Crime
TE = Total
Economy
TM = Total
Money Laundering
TT = Total
Terrorist Financing
KM = Known
Money Laundering
Incoming Money Laundering
KT = Known Terrorist Financing
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]
John Walker Crime Trends Analysis
Estimates of ML in and through Australia (1996)
• Estimates based on Costs of Crime and Expert Survey
Crime Category/
Implied ML Estimates for Australia ($mill)
Est’d Proceeds of Crime
Min
Mid1
Mid2
Max
Homicide Max $2.75m
Robbery & Extortion $74.4m
<1
<1
<1
22
<1
22
<1
45
Other Violence Min $3.31m
Breaking and Entering $714.4m
Insurance Fraud $1530m
<1
14
38
<1
71
77
<1
71
153
<1
500
306
Business Fraud $375 - $900m
Other Fraud $750m
Motor Vehicle Thefts $533.6m
Other Thefts $462Environmental Crime $5.21Illicit Drugs $1500m
56
38
27
8
<1
300
225
113
53
82
<1
750
540
188
187
116
2
1050
900
600
480
347
8
1350
402
1394
2328
4536
Total $5951 - $7661m
John Walker Crime Trends Analysis
Estimates of ML in and through Australia
(1996)
• 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
John Walker Crime Trends Analysis
Estimates of ML in and through Australia (1996)
Overseas
Economy
Overseas Money laundered
overseas
$US20,000
billion
$US100-500 billion?
Costs of
Crime
The Australian Economy
$380 billion
P.o.C.
Costs of Crime
$11-21 billion
ML
Overseas Money
laundered in Australia
$7.7 billion?
Australian Money laundered
overseas
$5.5 billion?
John Walker Crime Trends Analysis
Proceeds of Crime
$6-8 billion
ML
$1-4.5 billion
Australian Money laundered in
Australia
Going Global

1965 – Mathematics Scholarship to LSE

1967 – AIESEC, Univerzita Karlova v Praze, Kancelářské Stroje.

1970 – Transportation modelling/forecasting

1975 – Use of regional crime data to measure well-being

1978 - Accidental criminology apprenticeship

1988 – Chance meeting re Victimisation Survey

1992 – Opportunistic Proposal for Business Victimisation Survey

1992 – First Costs of Crime Estimates

1995 – First National Estimates of Moneylaundering

1996 – First Prototype Global Model of Moneylaundering
John Walker Crime Trends Analysis
Official efforts to measure Global Crime
up to the 1990s
•
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); common set of definitions; 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
mafia, yakuza, drug gangs etc
•
Transparency International experimenting with corruption and bribery indexes.
John Walker Crime Trends Analysis
Assumptions 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

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)
John Walker Crime Trends Analysis
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.
John Walker Crime Trends Analysis
Assumptions about Laundering Routes

The money may stay in the country of origin..


the more corrupt the country, the more it will tolerate internal money laundering
or it may go to another country:

The money to be laundered outside the country is distributed between all other countries in
proportion to:

their Attractiveness Index, and

the distance between the origin and destination countries. The distance between countries is a proxy
for the geographic, linguistic, ethnic and trade barriers between countries
If you like algebra........

Percentage Laundered 'at home' = 20%*(T.I. Index Score)

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)2
John Walker Crime Trends Analysis
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
After late 1999, it became apparent that most published estimates were based on my model
John Walker Crime Trends Analysis


“Excess” shadow economy
might be an indicator of
the proceeds of crime.
On this basis, the shadow
economy in Australia
would produce around
AU$20 billion per year,
some of which laundered.
GDP per capita vs. Shadow Economy
40,000
All rich countries have
low % shadow
economies
Ireland
35,000
USA
30,000
GDP/capita ($US) 2001
Triangulation:
Shadow
Economy, Crime
and Money
Laundering
Switzerland
Austria
25,000
Japan
UK
Australia
16.821
Italy
Many of the richest
countries with high %
shadow economies have
significant transnational
crime, illicit drug
production and corrupt
business practices.
UAE
20,000
NZ
15,000
Estonia
10,000
35.4
Mexico
32.2
Iran
Uruguay
RussiaBelarus
Colombia Thailand
Panama
Peru
5,000
China
Vietnam
Mongolia
Poor countries
with low % shadow
0
economies are
0
20
mostly “command economies”
38.9
Morocco
Yemen
41.6
42.1
Pakistan
42.1
40
Shadow Economy as % of GDP
Source: F. Schneider and J. Walker.John Walker Crime Trends Analysis
Georgia
Bolivia
Lao PDR
60
80
Attractiveness to ML: - Service Exports and
Incoming Money Laundering
John Walker Crime Trends Analysis
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?
O ld A ttra
tiv e n e s Attractiveness
s In d e x V e rs u s Indices
N ew
Transcrime
& cWalker
CRIMINAL LAW
there an anti-ML law in the jurisdiction?
2. Banks covered by the anti-ML law?
ADMINISTRATIVE
3. Other financial institutions covered by the anti-ML law?
L u x e m b o u rg
REGULATIONS
600
4.. 0Non-financial institutions covered by the anti-ML law?
Cay m ans
5. Other professions carrying out a financial activity covered by the anti-ML law?
S w it z e rla n d
6. ID requirements for the institutions covered by the anti-money law?
500.0
7. Suspicious transactions reporting?
S in g a p o re
8. Central authority (for instance, an FIU) for the collection
of suspicious transactions reports?
A u s t ria
9.. 0Co-operation between banks or other financial institutions
and police authorities?
400
L ie c h t e n s t e in
BANKING LAW
Tra nsCrim e Ba se d Score
1.. 0Is
700
COMPANY LAW
Hong K ong
N e t h e rla n d s
1. Prohibition to open a bank account without UID
. Kof
. the beneficial owner?
300.0
2. Limits to bank secrecy in case of criminal
investigation
and prosecution?
B e rm u d a
1.. 0Minimum
200
Ire la
nd
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?
100
4.. 0Registered 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?
0.0
INTERNATIONAL
CO-OPERATION
PROVISIONS
0.0
100.0
.0
300.0
00.0
500.0
600.0
700.0
800.0
1. Extradition
(at least 2of0 0foreigners)
for ML 4offences?
2. Assistance to foreign law
agencies
W a lenforcement
ke r 1999 M o d
e l A ttr a cin
ti vinvestigation
e n e ss S c o r e 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?
John Walker Crime Trends Analysis
Triangulation: Transfer Pricing Fraud
from Zdanowicz, J (2004)

USD156.2 bn moved out of the United States by way of
pricing discrepancies during 2001

USD4.27 bn involved countries which are listed on the
United States State Department ‘Al Qaeda watch-list’.

USD 213 bn moved into the United States.

Fraud or laundering?

Overseas beneficiaries or American?
John Walker Crime Trends Analysis
Triangulation: Cross-border flow Analysis
(Raymond Baker, 2005)
Global Flows
Low ($US bn)
High ($US bn)
Drugs
$120
$200
Counterfeit goods
$80
$120
Counterfeit currency
$3
$3
Human trafficking
$12
$15
Illegal arms trade
$6
$10
Smuggling
$60
$100
Racketeering
$50
$100
$331
$549
Mispricing
$200
$250
Abusive transfer pricing
$300
$500
Fake transactions
$200
$250
Commercial Subtotal
$700
$1000
Corruption
$30
$50
Grand Total
$1061
$1599
Crime Subtotal
From “Capitalism’s Achilles Heel”, Baker 2005. Based on a review of studies of transnational crime
John Walker Crime Trends Analysis
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 – regardless of Rand Corp’s “can’t be done” conclusion.

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.
John Walker Crime Trends Analysis
UNODC Illicit Drugs model………
Table 1. Production and Distribution from Source Countries to Destination Countries
Consumer Regions
Producer Regions
East Africa
North Africa
Southern Africa
West and Central Africa
Caribbean
Central America
North America
South America
C. Asia & Transcaucasus
East and South-East Asia
Nr & M.East /SW Asia
South Asia
Eastern Europe
Western & Central Europe
South East Europe
Oceania
All Countries
Total
Production in
Source
Country
(Kg Heroin
Equiv)
0
714
0
0
0
0
8,400
5,268
770
94,050
365,150
0
2,147
0
0
0
476,500
Total
Seized/Lost Total Available Transferred to
in Source
for Sale
Markets
Country
(Kg Heroin
(Kg Heroin
(Kg Heroin
Equiv)
Equiv)
Equiv)
0
0
0
0
0
0
209
440
0
582
9,551
0
0
0
0
0
10,781
0
714
0
0
0
0
8,191
4,829
770
93,468
355,599
0
2,147
0
0
0
465,719
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
E
N
S
W
A
f
r
i
c
a
A
f
r
i
c
a
A
f
r
i
c
a
&
Total Seized/
lost in Transit
(Kg Heroin
Equiv)
47
35
7
51
80
180
6,621
399
2,748
6,885
27,723
252
923
3,994
4,713
198
54,856
0
0
0
0
0
0
0
0
0
240
2,053
0
0
0
0
0
2,293
0
714
0
0
0
0
0
0
0
267
2,429
0
0
0
0
0
3,410
0
0
0
0
0
0
0
0
0
119
980
0
0
0
0
0
1,099
C
A
f
r
i
c
a
0
0
0
0
0
0
0
0
0
1,076
8,878
0
0
0
0
0
9,954
C
a
r
i
b
b
e
a
n
0
0
0
0
0
0
0
0
0
39
325
0
0
0
0
0
364
C
N
S
A
m
e
r
i
c
a
A
m
e
r
i
c
a
A
m
e
r
i
c
a
0
0
0
0
0
0
8,191
1,574
0
2,050
16,920
0
0
0
0
0
28,735
0
0
0
0
0
0
0
3,255
0
0
0
0
0
0
0
0
3,255
0
0
0
0
0
0
0
0
0
85
698
0
0
0
0
0
783
John Walker Crime Trends Analysis
C T
r
A a
s n
i s
a c
a
u
& c
a
s
u
s
0
0
0
0
0
0
0
0
770
1,222
10,084
0
0
0
0
0
12,076
E
&
S
E
A
s
i
a
0
0
0
0
0
0
0
0
0
59,928
0
0
0
0
0
0
59,928
N
e
a
r
E
a
s
t
& /
S
MW
i
d A
d s
l i
e a
0
0
0
0
0
0
0
0
0
0
78,352
0
0
0
0
0
78,352
S
E
W
A
s
i
a
E
u
r
o
p
e
&
0
0
0
0
0
0
0
0
0
6,663
55,003
0
0
0
0
0
61,666
0
0
0
0
0
0
0
0
0
9,483
78,283
0
2,147
0
0
0
89,913
C
E
u
r
o
p
e
0
0
0
0
0
0
0
0
0
10,508
86,740
0
0
0
0
0
97,248
S
E
E
u
r
o
p
e
0
0
0
0
0
0
0
0
0
1,028
9,180
0
0
0
0
0
10,208
O
c
e
a
n
i
a
0
0
0
0
0
0
0
0
0
762
5,673
0
0
0
0
0
6,436
A
l
l
C
o
u
n
t
r
i
e
s
0
714
0
0
0
0
8,191
4,829
770
93,468
355,599
0
2,147
0
0
0
465,719
UNODC Illicit Drugs model………
Table 2. Supply and Demand in Destination Countries
Regions
E
N
S
W
A
f
r
i
c
a
A
f
r
i
c
a
A
f
r
i
c
a
&
C
a
r
i
b
b
e
a
n
C
A
f
r
i
c
a
C
N
S
A
m
e
r
i
c
a
A
m
e
r
i
c
a
A
m
e
r
i
c
a
C T
r
A a
s n
i s
a c
a
u
& c
a
s
u
s
E
&
S
E
A
s
i
a
N
e
a
r
E
a
s
t
& /
S
MW
i
d A
d s
l i
e a
S
E
W
A
s
i
a
E
u
r
o
p
e
&
S
E
E
u
r
o
p
e
C
E
u
r
o
p
e
A
l
l
O
c
e
a
n
i
a
C
o
u
n
t
r
i
e
s
Production:
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)
0
0
0
0
714
0
714
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8400
209
8191
0
5268
440
4829
0
770
0
770
0
94050
582
93468
1339
365150
9551
355599
2830
0
0
0
0
2147
0
2147
0
0
0
0
0
0
0
0
0
0
0
0
0
476500
10781
465719
2520
Producer Income (US$mill)
0
2
0
0
0
0
21
12
2
125
1006
0
5
0
0
0
1173
Supply:
Total Intended for Consumption (Kg Heroin Equiv)
Total Seized/Lost at Destination (Kg Heroin Equiv)
Total Available for Consumption (Kg Heroin Equiv)
Wholesale price at Destination US$ /gm
Wholesaler Income (US$mill)
2293
47
2246
28
63
3410
35
3375
42
142
1099
7
1092
28
30
9954
51
9903
15
153
364
80
285
38
11
783
180
603
73
44
28735
6621
22113
98
2175
3255
399
2856
25
71
12076
2748
9328
21
194
59928
6885
53042
38
2022
78352
27723
50629
7
379
61666
252
61413
71
4355
89913
923
88990
36
3198
97248
3994
93254
73
6779
10208
4713
5496
26
145
6436
198
6237
140
874
465719
54856
410863
50
20635
Demand:
Estimated User Population (Thousands)
Estimated Actual Consumption per year (Kg Heroin Equiv)
Implied Consumption per user (gms Heroin Equiv)
Average Retail Price US$ /gm
Retailer Income (US$mill)
84
2021
24.2
40
82
108
3038
28.0
124
377
85
983
11.5
81
80
524
8913
17.0
57
508
21
285
13.5
41
12
24
603
25.2
83
50
1301
22113
17.0
402
8886
286
2856
10.0
101
288
370
9328
25.2
51
480
2105
53042
25.2
108
5725
2009
50629
25.2
12
596
3102
61413
19.8
123
7567
2406
80091
33.3
133
10681
1450
83929
57.9
302
25340
180
5496
30.6
183
1006
99
5614
56.5
561
3148
14154.3
390353
27.6
166
64825
0
2
30
84
236
351
0
0
153
355
508
0
21
620
1311
6711
8663
12
71
0
217
300
2
12
60
286
360
125
1,942
0
3703
5770
1006
157
0
217
1381
0
4355
3212
7566
5
76
2175
7483
9739
0
5729
18560
24290
0
0
803
2274
3076
1173
2908
14813
44191
63085
Regional Net Values:
Source Country Producers' Income
Source Country Wholesalers' Income less Purchase Costs
International Wholesalers' Income less Purchase Costs
Net Retailer Profit (US$mill)
Gross Value of Regional Trade
38
19
57
18
49
68
0
-
7
1
8
44
6
50
John Walker Crime Trends Analysis
37
861
898
The Economics of the Illicit Drugs Trades
John Walker Crime Trends Analysis
Conclusions

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.
John Walker Crime Trends Analysis
Key Skills
Blending Dodgy
Data and Heroic
Assumptions …
Getting the
Message Across..
… and turning it
into something
surprisingly useful.
Dilbert cartoons used with permission.
COPYRIGHT: UNITED FEATURES
SYNDICATE INC. Distributed by Auspac Media
John Walker Crime Trends Analysis