Transcript Money Laundering & Online-Poker

Prof. Dr. Friedrich Schneider
MoneyLaundering_OnlinePoker_E_2013_Sep.ppt
Department of Economics
Johannes Kepler University Linz
Altenbergerstraße 69 , A-4040 Linz-Auhof , AUSTRIA
Phone: 0043-732-2468-8210, Fax:-8209
E-mail: [email protected]
http://www.econ.jku.at/schneider
Is Online Poker a Valid Platform
for Money Laundering in the EU?
(Revised version)
Conference:
International Masters of Gaming Law
October 1-4, 2013, Oslo
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
1 / 13
1. Introduction
Proceeds from organized crime are quite large; often billions of
US-Dollars are “earned”.
Hence money laundering of the proceeds is essential, if the
criminals want to spend this money.
Goal of this lecture:
(1) Show some facts / figures of worldwide and national
criminal proceeds.
(2) How relevant is online-gambling / online-poker for money
laundering?
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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Ta b l e o f C o n t e n t
1. Introduction
2. Proceeds from TOC (Transnational Organized
Crime): Some Facts
3. The German Market for Gambling & Betting
4. Online-Poker used for Money Laundering?
5. Summary & Conclusions
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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2. Proceeds from TOC (Transnational Organized Crime): Some
Facts – Global / Regional / National Figures
(1) The most widely quoted figure for the extent of money
laundered has been the IMF ‘consensus range’ of 2 % to 5 %
of global GDP, made public by the IMF in 1998.
A more recent analysis of the results from various studies
suggests that all criminal proceeds are likely to amount to
some 3.6% of global GDP (2.3 % - 5.5 %), equivalent to
about USD 2.1 trillion in 2009.
(2) Another reliable OECD estimate for the amount available
for laundering through the financial system would be
equivalent to 2.7 % of global GDP (2.1 % - 4 %) or USD 1.6
trillion in 2009.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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2. Proceeds from TOC (Transnational Organized Crime): Some
Facts – Global / Regional / National Figures
Table 2.1: IMF Estimates of Proceeds from TOC and/or
Money Laundered, worldwide, period 1996 to 2009.
Estimation
Minimum
Maximum
Midpoint
Increase
(in %)
Average (1996 to 2009) IMF
estimates of money laundered (as a
percentage of global GDP)
2%
5%
3.5 %
---
Estimate for 1996 (in billion USD)
600
1,500
1,050
---
Estimate for 2005 (in billion USD)
900
2,300
1,600
52 %
Estimate for 2009 (in billion USD)
1,200
2,900
2,050
28 %
Source: OECD Observer, Paris, various years.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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2. Proceeds from TOC (Transnational Organized Crime): Some Facts –
Global / Regional / National Figures: Table 2.2: Proceeds of transnational
crime (time range 2003-2009).
Kind of Crime
(2003-2009)
Billion USD
In % of total
proceeds
Drugs
320
50 %
UNODC, World Drug Report 2005 (data refer to 2003)
Counterfeiting
250
39 %
OECD, Magnitude of Counterfeiting and Piracy of
Tangible Products, 2009
Human
trafficking
31.6
5%
P. Belser (ILO), Forced Labor and Human Trafficking:
Estimating the Profits, 2005
Oil
10.8
2%
GFI estimate based on Baker 2005 (quantities) and US
Energy Information Administration (prices: 2003- 2010)
3.4 - 6.3
0.8 %
GFI estimate based on Interpol, International Scientific
and Professional Advisory Council of the United Nations
Crime Prevention and Criminal Justice Programme
In % of global
GDP in 2009
1.1 %
---
---
In % of average
global GDP, 20002009
1.5 %
---
---
Art and cultural
property
Sources
Source: Global Financial Integrity, Transnational crime in the Developing World, February 2011
and World Bank, Indicators (for current GDP).
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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3. The German Market for Gambling & Betting.
Table 3.1: Breakdown Total Annual Gross Gaming Revenues (GGR) 1) , in Mio. € (2009).
Segment
GGR 2009 (in million EUR)
Total share 2009 (in %)
Slot machines
3.340
32.3%
Lottery “6 aus 49”
2.250
21.7%
Other lottery products
1.080
10.4%
“Super 6”, “Spiel 77”
810
7.8%
Casinos
770
7.4%
Stationary betting (offices)
480
4.6%
Online-poker
340
3.3%
Online-betting
300
2.9%
Black market betting
230
2.2%
Online-casinos
210
2.0%
“PS-Sparen”, “Gewinnsparen”
210
2.0%
Online-lottery
140
1.4%
“Oddset” / football pools
100
1.0%
Betting on horses
60
0.6%
Online-games
30
0.3%
10.3
100.0 %
Total
1) Gross gaming revenues (GGR) = game stakes less the winnings paid out.
Oslo, October 1-4, 2013
Source: Goldmedia (May 2010); Own calculations.
© Prof. Dr. Friedrich Schneider
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4. Online-Poker and Money-Laundering?
Table 4.1: Gambling & Betting in Germany (2009): Total Annual Gross Gaming Revenues (GGR).
Gambling & Betting
GGR 2009 (in billion EUR)
Total share 2009 (in %)
Regulated
8.55
83 %
Unregulated *
1.75
17 %
Sum
10.3
100 %
* Unregulated market subsumes all those in Germany privately offered gambling & betting products, which are
either illicit by German regulation or have an ambiguous legal status (e.g. legal license in another EU-country).
Table 4.2: Online-Gambling & Betting in Germany (2009): Relation Annual Gross Gaming
Revenues (GGR) to online- & unregulated & total market, in billion EUR & per cent.
GGR 2009
(billion EUR)
Share
Online
Share unregulated
[100% = 1.75 bn EUR]
Total share 2009
[100% = 10.3 bn EUR]
Online-poker
0.34
38.8 %
19.4 %
3.3 %
Online-betting
0.30
33.7 %
16. 9 %
2.9 %
Online-casinos
0.21
24.2 %
12.1 %
2.1 %
Online-games
0.03
3.3 %
1.6 %
0.9 %
Sum Online
0.88
100 %
50.0 %
8.5 %
Online-segment
Source both tables: Goldmedia (May 2010); Own calculations.
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© Prof. Dr. Friedrich Schneider
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4. Online-Poker and Money-Laundering?
Table 4.3: Online-Gambling & Betting in Germany (2009): hypothetical
simulation annual online gross gaming revenues (GGR) in per cent
of national criminal money flows (assumption: 100% of the
revenues are laundered money).
GGR 2009
(in billion EUR)
Total share 2009
[100% = 9.9 bn EUR]1)
Online-poker
0.34
3.4 %
Online-betting
0.30
3.0 %
Online-casinos
0.21
2.1 %
Online-games
0.03
0.3 %
Sum Online
0.88
8.8 %
Online-segment
Figure of 9.9 bn € of national German criminal flows are from Schneider (2013).
Source: Own calculations.
1)
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4. Online-Poker and Money-Laundering?
Table 4.4: Online-Poker in Germany: hypothetical simulation online-poker
annual gross gaming revenues (GGR) in per cent of annual
national criminal money flows, period 2005 to 2011
(assumption: 100% of the revenues are laundered money).
Year
National criminal
money flows
(in million EUR)
GGR
online-poker
(in million EUR)
Total share
(in %)
2005
7,239.0
103.3
1.4%
2006
7,903.0
201.6
2.6%
2007
8,645.0
263.4
3.0%
2008
9,243.0
322.6
3.5%
2009
9,897.0
339.4
3.4%
2010
10,450.0
361.0
(p)
3.5%
2011
11,432.0
364.0
(p)
3.2%
(p) = Prognosis Goldmedia.
Source: Goldmedia (May 2010); Own calculations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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5. Summary & Conclusions
(1) The necessity of money laundering is obvious as a great
number of illegal (criminal) transactions are done by
cash.
This amount of cash from criminal activities must be
white washed in order to have a “legal” profit and to be
able to invest or consume these profits.
(2) Tax fraud and/or illegal cross-border capital flows are
by far the biggest/highest share of all illegal transactions
(quite often 66% of all illegal capital flows/proceeds!).
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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5. Summary & Conclusions
(3) In 2009 online-poker, -games, -betting and -casinos did not
play a substantial role in money laundering in Germany, as
only 8.8 % (or 880 million EUR) of the total share of
criminal proceeds of 9.9 billion EUR could hypothetically be
laundered.
Extreme assumption: all revenues (100%) from onlinepoker, -betting and -casinos come from criminal proceeds.
Realistic assumption: 8-12%.
For other countries and for the whole EU, the figures will be
similar!
(4) Laundering via online-gambling and -betting induces high
transaction costs (up to 30 % of the amount) and high risks
of detection; hence other methods will be chosen.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
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5. Summary & Conclusions
(5) Final conclusion and answering the question in the
headline: online-poker is by no means relevant for
money laundering in the EU!
(6) Moreover, there do not exist serious (and
published) scientific studies which would
demonstrate that online-poker is extensively used
for money laundering.
THANK YOU
F O R Y O U R AT T E N T I O N !
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
13 / 13
6. APPENDIX
 Appendix Part A1: Methods & Stages of Money Laundering
 Appendix Part A2: Further Facts & Figures:
Global / Regional / National
 Appendix Part B: References
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Figure A.1: MIMIC estimation of the turnover of transnational crime for 20 highly developed
OECD countries over the periods 1994/95, 1997/98, 2000/01, 2002/03, 2003/04, 2004/05 & 2006/07.
Source: Own calculations.
Functioning of the legal System
Index: 1=worst, and 9=best
-0.038*
(2.09)
Amount of criminal activities
of illegal weapon selling
+0.214**
(3.02)
Amount of criminal activities
of illegal drug selling
+0.361**
(4.11)
Amount of criminal activities of
illegal trade with human beings
+0.245*
(2.59)
Amount of criminal activities
of faked products
+0.142*
(2.59)
Amount of criminal activities
of fraud, computer crime, etc.
+0.084
(1.41)
Amount of domestic
crime activities
+0.104
(1.59)
Real policy expenditures
per capita per country
-0.245*
(-2.51)
Per capita income in USD
+0.193*
(1.74)
Oslo, October 1-4, 2013
Turnover of
Transnational
criminal
activities
Confiscated
money
Cash per
capita
+0.402**
(2.85)
+1.00
(Residuum)
Prosecuted
persons
-0.154 (*)
(-1.49)
(number of persons per
100.000 inhabitants)
Test-Statistics:
RMSEA a) = 0.008 (p-value 0.910)
Chi-squared b) = 24.93 (p-value 0.930)
TMCV c) = 0.041
AGFI d) = 0.752
D.F. e) = 62
a) Steigers Root Mean Square Error of Approximation (RMSEA) for
the test of a close fit; RMSEA < 0.05; the RMSEA-value varies between
0.0 and 1.0.
b) If the structural equation model is asymptotically correct, then the
matrix S (sample covariance matrix) will be equal to Σ (θ) (model
implied covariance matrix). This test has a statistical validity with a
large sample (N ≥ 100) and multinomial distributions; both is given for
this equation using a test of multi normal distributions.
c) Test of Multivariate Normality for Continuous Variables (TMNCV);
p-values of skewness and kurtosis.
d) Test of Adjusted Goodness of Fit Index (AGFI), varying between 0
and 1; 1 = perfect fit.
e) The degrees of freedom are determined by 0.5 (p + q) (p + q + 1) – t;
with p = number of indicators; q = number of causes; t = the number
for free parameters.
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Table A.1:
Calculations of the Turnover of Transnational Crime of 20 OECD
countries using the MIMIC estimations (1995-2006).
Year
Volume of money laundering
(billion USD, 20 OECD countries)
Volume of money laundering
in % of GDP
1995
273
1.33 %
1996
294
1.37 %
1997
315
1.40 %
1998
332
1.42 %
1999
359
1.46 %
2000
384
1.47 %
2001
412
1.52 %
2002
436
1.56 %
2003
475
1.63 %
2004
512
1.66 %
2005
561
1.72 %
2006
603
1.74 %
Source: Own calculations, calibrated figures from the MIMIC estimations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
20 OECD countries
Australia, Austria,
Belgium, Canada,
Denmark, Germany,
Finland, France,
Greece, Great Britain,
Ireland, Italy, Japan,
Netherlands, New
Zealand, Norway,
Portugal, Switzerland,
Spain and USA.
6. Appendix Part A1: Methods & Stages of Money Laundering
Figure A.2: Framework for analysing the costs of cybercrime.
Source: Anderson, et al. (2012, p. 5).
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Table A.2: An estimation of the cost components (partly proceeds) of cyber crime.
Type of cybercrime
1. Cost of genuine cybercrime
Online banking fraud
phishing
malware (consumer)
malware (businesses)
bank tech. countermeasures
Fake antivirus
Copyright-infringing software
Copyright-infringing music etc.
Patent-infringing pharma
Stranded traveler scam
Fake escrow scam
Advance-fee fraud
SUM of 1.
2. Cost of transitional cybercrime
Online payment card fraud
Offline payment card fraud
domestic
international
bank / merchant defence costs
Indirect costs of payment fraud
loss of confidence (consumers)
loss of confidence (merchants)
PABX fraud
SUM of 2.
Oslo, October 1-4, 2013
UK estimates
Global estimates
$ 16 m
$ 4m
$ 6m
$ 50 m
$ 5m
$ 1m
$ 7m
$ 14 m
$ 1m
$ 10 m
$ 50 m
$ 164 m (0.9%)
$ 320 m
$
70 m
$ 200 m
$ 1.000 m
$
97 m
$
22 m
$ 150 m
$ 288 m
$
10 m
$ 200 m
$ 1.000 m
$ 3.457 m (1.6%)
$ 210 m
$ 4.200 m
$ 106 m
$ 147 m
$ 120 m
$ 2.100 m
$ 2.940 m
$ 2.400 m
$ 700 m
$ 1.600 m
$ 185 m
$ 3.068 m (6.7 %)
© Prof. Dr. Friedrich Schneider
$ 10.000 m
$ 20.000 m
$ 4.960 m
$ 44.200 m (19.8 %)
6. Appendix Part A1: Methods & Stages of Money Laundering
Table A.2: An estimation of the cost components (partly proceeds) of cyber crime (cont.).
Type of cybercrime
UK estimates
3. Cost of cybercriminal infrastructure
Expenditure on antivirus
Cost to industry of patching
ISP clean-up expenditures
Cost to users of clean-up
Defense costs of firms generally
Expenditure on law enforcement
SUM of 3.
$
$
$
$
$
$
Global estimates
170 m
50 m
2m
500 m
500 m
15 m
$ 3.400 m
$ 1.000 m
$
40 m
$ 10.000 m
$ 10.000 m
$
400 m
$ 1.237 m (16.7%)
4. Fraud against public institutions
Welfare fraud
Tax fraud
Tax filing fraud
$ 1.900 m
$ 12.000 m
--
$ 24.840 m (11.9%)
$
$
$
20.000m
125.000m
5.200 m
SUM of 4.
$ 13.900 m (75.7%)
$ 150.200m (67.5%)
SUM of 1. – 4.
$ 18.369 m (100%)
$ 222.697m (100%)
Source: Anderson, et al. (2012, p. 24).
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Figure A.3: Goal-model.
Goals of
money
laundering
Options
to act
Support
factors
Integration
Investment
Home country
Foreign country
Offshore
Front companies
Major companies
Securities
Savings accounts
Tangible assets
Tax: circumvention,
evasion, fraud
Not submitting
Counterfeiting
Financing
of crime
Financing more
criminal acts
Corruption
Banking secrecy, International factor, Factor of the inadequate financial market
supervision and of the lacking coordination in fighting domestic money laundering,
Protection factor of secrets, Offshore-factor, Factor of the envelope function of legal
persons, Layering-factor, mixing-factor, counterfeiting-factor, Factor of cashless
payment transactions.
Source: Ackermann (1992, p. 11) and Schneider, Dreer, Riegler (2006, p. 39).
.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Table A.3: Summary evaluation of estimation methods and their studies.
Study [St.]
Method
Result
Evaluation
DIRECT ESTIMATION METHODS
[A]
-----
St. for the
[B] Netherlands (van
Duyne, 1994)
Discrepancy analysis of
internat. balance of
payments & world bal.
of current account
World balance of current
account deficit of around 100
billion USD (due to nonregistered interest income)
Basically interesting approach. BUT: too
unreliable data for offshore banks; lack of
differentiation between legal & illicit source.
Money circulation
method
Return of Dutch guilder in the
amount of 3.7 billion HFL
(according to estimates by van
Duyne 1 billion of that with
illegal origin)
Method can be used as an indication for existence
of money laundering & for plausibility check.
BUT: Assumption of cash dependency, other
reasons for transfer payment very obvious;
dependency on method; very little relevance of
currency in neighboring countries (abroad)
Good approach for detection of money laundering
Case from the USA
Change in cash holdings Transfer of drug money to the centers. BUT: no reliable statements to volume
[C] after change in
of national banks
U.S. in the billions
(distinction legal & illicit funds; significant change
fight against drugs
in anti-money laundering measures required)
St. to measure
annually exported
amount of money
[D]
from the USA to
offshore centres
(Blum, 1981)
[E]
-----
100 billion USD funds from
Estimates based on the illegal sources; 20-25 billion
inflows into offshore USD (according to Gutmann's
financial centers
study) annually leaving USA
in direction offshore centers
Calculation based on
confiscated assets or
individual money
laundering cases
Oslo, October 1-4, 2013
No data on total amount of
actually confiscated assets;
money laundering in the
millions
Highlights importance of offshore centers for
money laundering. BUT: lack of distinction
between legal & illicit funds; in calculations only
limited comprehendible approach from Blum
Too vague, since it can be assumed that the
confiscated assets represent only a fraction of true
extent
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Table A.3: Summary evaluation of estimation methods and their studies (cont.).
Study [St.]
Method
Result
Evaluation
INDIRECT ESTIMATION METHODS
St. for Vienna
(Siska, 1999);
St. for Western
[F] Europe (BND,
1993),
St. for the USA
(ONDCP, 2000)
Around 700 million EUR sales
revenues from hashish &
heroin trade in Vienna;
around 40 billion EUR sales
Quantification based on
revenues from hashish &
the estimated drug use
heroin trade in Western
Europe; around 12 billion
USD sales revenues from
heroin trade in the USA
[G]
Quantification based on
the estimated drug
production
-----
St. for the USA
[H]
(Preston, 1989)
-----
Amount of laundered money
Quantification based on
from drug trafficking for the
confiscated illegal drugs
U.S. 50 - 65 billion USD
Regional application of this method meaningful.
BUT: prices diverge nationally / internationally
very heavily; consumption individually different
Heavy price differences; estimations for
production volume very different
Heavy differences in success rates of prosecution
authorities; very uncertain extrapolation from
confiscated quantity to actual quantity
Source: Own depiction.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. Appendix Part A1: Methods & Stages of Money Laundering
Figure A.4: Infiltration of the Legal Economy by Transnational Organized Crime (TOC).
Means / Instruments of Infiltration of the TOC in the “Official” Economy
With threat of
violence
International
control and
purchase of
companies
Payments to
firms
sympathizing
with
terrorism
Bribery of employees
or functionaries:
corruption
Use of financial
resources
Donations
via
informal
bank
circuits
Commercial
criminal
activity:
Stone and
metals, oil
Classical
criminal
activities
(drug-, arms-,
human
trafficking
Source: Yepes (2008); Own remarks.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Infiltration in
the internat.
financial
markets
through
corporate
vehicles
Appendix A2: Further Facts & Figures: Global / Regional / National
(1) If only flows related to drug trafficking of transnational
organized crime activities were considered, the proceeds
would be on average USD 650 billion per year over 2001 to
2010, and for 2009 equivalent to 1.5 % of global GDP or
USD 870 billion.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.4: Cross-border flows of global ‘dirty money’ (including financial
and tax fraud), in trillion USD.
2000-2005
Variable
Overall amounts
laundered
Of which criminal
component
in % of overall
extrapolated to 2009
low
high
in % of GDP
2000-2005
low
high
midpoint
1.1
1.6
2.9 - 4.3 %
1.7
2.5
2.1
0.3
(27%)
0.5
(31%)
0.9 - 1.5 %
0.5
(29%)
0.9
(36%)
0.7
(33%)
Source: R. W. Baker, Capitalism’s Achilles Heel: Dirty Money and How to Renew the Free-Market System,
New Jersey, 2005, p. 172 and World Bank, Indicators (for GDP).
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.5: Amount & Top 20 Destinations of Laundered Money (2005). Source: Unger (2007, p. 80).
Rank
Destination
% of worldwide
money laundering
Walker estimate 2.85 trillion USD
Amount in billion USD
IMF estimate of 1.5 trillion worldwide
Amount in billion USD
1
United States
18.9 %
538.145
283.50
2
Cayman Islands
4.9 %
138.329
73.50
3
Russia
4.2 %
120.493
63.00
4
Italy
3.7 %
105.688
55.50
5
China
3.3 %
94.726
49.50
6
Romania
3.1 %
89.595
46.50
7
Canada
3.0 %
85.444
45.00
8
Vatican City
2.8 %
80.596
42.00
9
Luxembourg
2.8 %
78.468
42.00
10
France
2.4 %
68.471
36.00
11
Bahamas
2.3 %
66.398
34.50
12
Germany
2.2 %
61.315
33.00
13
Switzerland
2.1 %
58.993
31.50
14
Bermuda
1.9 %
52.887
28.50
15
Netherlands
1.7 %
49.591
25.50
16
Liechtenstein
1.7 %
48.949
25.50
17
Austria
1.7 %
48.376
25.50
18
Hong Kong
1.6 %
44.519
24.00
19
United Kingdom
1.6 %
44.478
24.00
20
Spain
1.2 %
35.461
18.00
SUM of 20 countries
67.1 %
1,910.922
1,006.50
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.6: Annual money-laundering by region, period 2000 to 2005*,
in billion USD.
Region / Year
2000
2002
2005*
America
313
37.8%
328
38.3%
350
37.7%
Asia-Pacific
246
29.7%
254
29.7%
292
31.5%
Europe
230
27.8%
234
27.3%
241
26.0%
Middle East / Africa
38
4.6%
40
4.7%
44
4.7%
Total
827
In % of GDP
100%
2.7 %
856
100%
2.6 %
927
100%
2.0 %
* projection
Source: Celent, Anti-Money Laundering: A Brave New World for Financial Institutions, September 2002.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.7: Estimates of worldwide turnover of organized crime.
Year
Volume (worldwide)
in trillion USD
As a percentage
of global GDP
M. Schuster
1994
0.5-0.8 trillion
0.9 - 3.0 %
International Monetary Fund & Interpol
1996
0.5 trillion
1.6 %
1994/98
0.7-1 trillion
2.4 - 3.4 %
S. Kerry
1997
0.42-1 trillion
1.4 - 3.3 %
J. Walker
1998
2.85 trillion
9.5 %
1998
1.3 trillion
4.3 %
2001
1.9 trillion
5.9 %
2003
2.1 trillion
5.6 %
I. Takats (2007)
2005
0.6-1.5 trillion
1.3 - 3.3 %
J.D. Agarwal and A. Agarwal (2006)
2005
2.0-2.5 trillion
4.4 -5.5 %
Global Financial Integrity (2011)
(estimate for transnational crime)
2000-2009
0.65 trillion
1.5 %
J. Walker (based on J. Walker &
B. Unger) (2009)
2001
1 trillion
3.4 %
Origin / study
UN estimates
National Criminal Intelligence Service
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.7: Estimates of worldwide turnover of organized crime (cont.).
Year
Volume (worldwide)
in trillion USD
As a percentage
of global GDP
2001
0.8 trillion
2.5 %
2002
0.96 trillion
2.9 %
2003
1.2 trillion
3.2 %
2004
1.4 trillion
3.3 %
2005
1.5 trillion
3.3 %
2006
1.7 trillion
3.4 %
Tentative estimate*
2009*
2.0 trillion
3.4 %
Median of all estimates
2009**
1.9 trillion
3.3 %
Inter-quartile range of all estimates
2009**
1.5-2.4 trillion
2.6 - 4.1 %
Average of all estimates
2009**
2.1 trillion
3.6 %
Confidence interval of mean (95%)
2009**
1.6-2.6 trillion
2.7 - 4.4 %
Origin / study
F. Schneider
(University of Linz)
Tentative estimate, assuming that Schneider’s proportion of turnover of organized crime expressed as a percentage of GDP
remained unchanged over 2006-2009 period.
** Extrapolated to global GDP in 2009.
Source: See appendix.
*
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.8: FATF estimates of global amounts of laundered money from 1988 to 2009.
Estimate of drug sales in key markets (1988)
As a percentage of global GDP (1988)
Assumed proportion that is laundered (1988)
USD 124 billion
0.8 %
66 – 70 %
Estimate of amounts laundered related to drugs
USD 85 billion
Proportion in % of global GDP (1988)
0.5 % of GDP
Estimated proportion of drugs in total amounts laundered
Estimated total amounts (all crimes) laundered in 1988
As a percentage of global GDP
25 %
USD 340 billion
2.0 % of GDP
Extrapolated to global GDP in 2000
USD 0.6 trillion
Extrapolated to global GDP in 2009
USD 1.2 trillion
Source:
Organization for Economic Co-operation and Development, Financial Action Task Force on Money Laundering, Paris,
1990, p. 6. quoted in UNDCP, Economic and Social Consequences of Drug Abuse and Illicit Trafficking, UNDCP
Technical Series No. 6, Vienna 1998, p, 26; International Monetary Fund, Financial System Abuse, Financial Crime
and Money Laundering- Background Paper, February 2010.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.9: FATF Estimate of World-Wide Money
Laundering, period 1988 to 2005.
Year
Amounts estimated
to have been laundered
(in billion USD)
As a percentage
of global GDP
Increase
(in %)
1988
340.0
2.0 %
---
1996
1,100.0
3.5 %
223.5 %
2005
2,300.0
3.0 %
109.1 %
Source: International Monetary Fund, Financial System Abuse, Financial Crime and Money
Laundering-Background Paper, February 12, 2001, and FATF, 2007.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.10: Updated FATF model of global amounts laundered.
Estimate of drug sales in key markets (UNODC estimate for 2003)
As a percentage of World GDP
USD 322 bn
0.9 % of GDP
Assumed proportion that is laundered (initial FATF estimate)
Estimate of amounts laundered related to drugs
Proportion in % of global GDP (2003)
66 - 70 %
USD 220 bn
0.6 % of GDP
Estimated proportion of drugs in total amounts laundered
(initial FATF estimate)
25 %
Estimated total amounts (of all crimes) laundered in 2003
USD 880 bn
As a percentage of GDP in 2003
2.4 % of GDP
Extrapolated to global GDP in 2009
USD 1.4 trillion
Source: International Monetary Fund, Financial System Abuse, Financial Crime and Money LaunderingBackground Paper, Feb. 2010; UNODC, 2005 World Drug Report, Volume 1, Analysis, Vienna, p. 127.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Million
EUR
Figure A.5: Sum of “national” criminal money flows in Austria, in million EUR (1994-2011).
Source: Own calculations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.11: Comparison three Scenarios (I, II & III) of Development Gross Gaming
Revenues (GGR) Online-Market in Germany (four online-segments: casino,
gambling, poker, sports betting; Scenario I = monopoly, Scenario II = partial
(stationary) liberalization, Sc. III = full liberalization), in million € (2009-2015).
Year
Online-GGR
Scenario I
(million EUR)
Online-GGR
Scenario II
(million EUR)
Online-GGR
Scenario III
(million EUR)
Delta
Scenario III-I
(million EUR)
2009
875.2
875.2
875.2
0.0
2010
973.8
977.2
978.7
+4.9
2011
1,020.2
1,030.6
1,031.5
+11.3
2012
1,058.2
1,094.3
1,169.2
+111.0
2013
1,097.8
1,154.3
1,303.6
+205.8
2014
1,132.0
1,203.9
1,419.3
+287.3
2015
1,164.5
1,251.3
1,507.4
+342.9
Delta 09/15
(Mio. EUR)
+289.3
+376.1
+632.2
+342.9
Delta 09/15 (%)
+33.1%
+43.0%
+72.2%
--
Source: Goldmedia (May 2010); Own calculations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.12:
Development of Annual Gross Gaming Revenues (GGR)
Online-Market in Germany (four online-segments: casino,
gambling, poker, sports betting), in million EUR (2005-2009).
Year
GGR
(in million EUR)
change versus
previous year
(in million EUR)
change versus
previous year
(in %)
2005
315
---
---
2006
534
+219
+69,5%
2007
692
+158
+29,6%
2008
825
+133
+19,2%
2009
875
+50
+6,1%
Delta 05/09
(Mio. EUR)
+560
---
---
Delta 05/09 (%)
+177.8 %
---
---
Source: Goldmedia (May 2010); Own calculations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.13:
Development of Annual Gross Gaming Revenues (GGR)
Online-Poker in Germany, in million EUR (2005-2009).
Year
GGR
(in million EUR)
change versus
previous year
(in million EUR)
change versus
previous year
(in %)
2005
103.3
---
---
2006
201.6
+98.3
+95.2%
2007
263.4
+61.8
+30.7%
2008
322.6
+59.2
+22.5%
2009
339.4
+16.8
+5.2%
Delta 05/09
(Mio. EUR)
+236.1
---
---
Delta 05/09 (%)
+228.6%
---
---
Source: Goldmedia (May 2010); Own calculations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
Appendix A2: Further Facts & Figures: Global / Regional / National
Table A.14:
Comparison of three Scenarios (I, II & III) of Development
Gross
Gaming Revenues (GGR) Online-Poker in Germany (Scenario I
= monopoly, Scenario II = partial (stationary) liberalization,
Scenario III = full liberalization), in million EUR (2009-2015).
Year
Online-GGR
Scenario I
(million EUR)
Online-GGR
Scenario II
(million EUR)
Online-GGR
Scenario III
(million EUR)
Delta
Scenario III-I
(million EUR)
2009
339.4
339.4
339.4
0.0
2010
361.0
361.0
360.3
-0.7
2011
364.0
364.0
364.2
+0.2
2012
362.5
371.2
412.9
+50.4
2013
366.9
377.3
456.1
+89.2
2014
371.6
381.5
491.1
+119.5
2015
376.6
386.5
510.0
+133.4
Delta 09/15
(Mio. EUR)
+37.2
+47.1
+170.6
+133.4
Delta 09/15 (%)
+11.0%
+13.9%
+50.3%
--
Source: Goldmedia (May 2010); Own calculations.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. APPENDIX – Part B: References
Anderson, Ross, et al., “Measuring the Cost of Cybercrime”. Working paper, 2012.
http://weis2012.econinfosec.org/papers/Anderson_WEIS2012.pdf
Schmid, Michael, and Solveig Börnsen. “Glücksspielmarkt Deutschland 2015: Situation und
Prognose des Glücksspielmarktes in Deutschland”. Publisher Dr. Klaus Goldhammer. Berlin:
Goldmedia GmbH Media Consulting & Research, May 2010. [Goldmedia (May 2010)]
Schneider, Friedrich, Dreer, Elisabeth and Wolfgang Riegler, “GELDWÄSCHE – Formen,
Akteure, Größenordnung – und warum die Politik machtlos ist”. Wiesbaden : Gabler, August
2006.
Schneider, Friedrich, and Martin Maurhart. „Volkswirtschaftliche Analyse des legalen/illegalen
Marktes für Glücksspiel in Deutschland“. Linz, December 2009.
Schneider, Friedrich. “The Financial Flows of Transnational Crime and Tax Fraud in OECD
Countries: What Do We (Not) Know?”. Linz, October 2012.
Unger, Brigitte, “The Scale and Impacts of Money Laundering”. UK: Edward Elgar, March
2007.
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider
6. APPENDIX – Part B: References
Sources for figures & tables:
See text box below respective figure / table; exceptions: tables 2.6, 2.7 & A.7.
Sources for Table 2.6: Peter Reuter, “Chasing Dirty Money – the Fight against Money Laundering,” Washington 2004; based
on Office of National Drug Policy (2000 and 2001); Simon and Witte (1982); GAO (1980); Federal Bureau of Investigations’
annual Uniform Crime Reports; Internal Revenue Service; International Organization on Migration; Abt. Smith, and
Christiansen (1985); Kaplan and Matteis (1967), Carlson et al. (1984) and Key (1979).
Sources for Table 2.7:
Brigitte Unger, The Scale and Impacts of Money Laundering, Cheltenham (UK), Edward Elgar
Publishing Company, 2007, p. 66, based on studies by Smekens and Verbruggen (2004), Business criminality: Criminaliteit en
rechtshandhaving (2001), WODC (2003, p. 60) and NIPO (2002).
Sources for Table A.7: UNODC calculations, based on F. Schneider, Turnover of Organized Crime and Money Laundering:
Some Preliminary Findings, in Public Choice, Vol. 144, 2010, pp. 473-486; J. Walker, ‘How Big is Global Money Laundering?’
Journal of Money Laundering Control, 1999, Vol. 3, No. 1; I. Takats, A theory of “crying wolf”: the economics of money
laundering enforcement. Paper presented at the conference “Tackling Money Laundering”, University of Utrecht, Utrecht, The
Netherlands, November 2–3, 2007; J.D. Agarwal and A. Agarwal, “Globalization and international capital flows,” Finance India,
19, 2004, pp. 65–99; J.D. Agarwal and A. Agarwal, “ Money laundering: new forms of crime, and victimization”, paper presented
at the National Workshop on New Forms of Crime, and Victimization, with reference to Money Laundering. University of
Madras, Indian Society of Victimology, Department of Criminology, 2006; Global Financial Integrity, Transnational Crime in the
Developing World, February 2011; J. Walker and B. Unger, “Measuring Global Money Laundering: The Walker Gravity Model,”
Review of Law & Economics, vol. 5, issue 2, the Berkeley Electronic Press; F. Schneider, “Money Laundering: some preliminary
empirical findings”, Linz, Nov. 2007, Paper presented at the Conference ‘Tackling Money Laundering’, University of Utrecht, the
Netherlands, November 2–3, 2007 and World Bank, Indicators (current GDP).
Oslo, October 1-4, 2013
© Prof. Dr. Friedrich Schneider