The Financial Flows of Transnational Crime and Tax Fraud in OECD Countries

Abstract

This article has two goals. First, it presents a preliminary literature review of the empirical findings of the worldwide proceeds of transnational organized crime (TOC) and for some Organisation for Economic Co-operation and Development countries as well as a breakdown of the different types of crime proceeds. Also the illegal cross-border flows of global dirty money (including tax fraud figures) are shown, which are by far the biggest share (66 percent) of all illegal transactions. Second, some remarks are made about the infiltration of the TOC into the “official” economic system, and the functioning of the Hawala (or informal) banking is described. There are four main conclusions. First, the revenues of transnational crime are extremely difficult to estimate. Second, fighting transnational crime is very difficult, as there are no efficient and powerful international organizations that can effectively do this job. Third, tax fraud and/or other illegal cross-border capital flows should be the prime target for governments to reduce them. Fourth, this article should be seen as a first start/attempt in order to shed some light on the gray area of the magnitude of proceeds of tax fraud and of transnational crime.

Keywords

transnational organized crime (TOC)money laundering illegal cross-border flows tax fraud figures Hawala banking infiltration of transnational crime

Over the last decade, the growth of the world economy has been quite high, and has improved the economic well-being all over the globe. This development was also accompanied by some risks. Two of them are proceeds from transnational organized crime (TOC) and from financial and tax fraud, which both rose remarkably in the last twenty years. This raises three questions.¹ First, from where does transnational crime get its proceeds and what do we know about their size and development? Second, how large are the proceeds from financial and tax fraud? Third, what economic implications does transnational crime have?

In this article, I attempt to give preliminary answers to the first two questions, on the origin of both types of criminal proceeds. A detailed analysis of the financial proceeds and their sources is crucial in order to reduce their possibilities, so that the basis of their operations is at least limited. Hence, the literature review meets two objectives: to widen the knowledge of this subject and to focus on the literature closely related to the research topic. The body of literature on transnational crime financing is huge and diverse and quite often merely descriptive; hence, in my article only those contributions are summarized that contain the latest and (hopefully) the most reliable figures. Also, I briefly answer the third question, but my main focus lies on providing more detailed answers on the size and development of the proceeds of transnational crime and the ones from financial and tax fraud.

My article is structured as follows. The second section provides a literature review of the empirical findings and on the kinds of transnational crime proceeds. The third section shows the infiltration of transnational crime into the economic system and makes some remarks about the Hawala banking system. In the fourth section, some conclusions and policy recommendations are drawn.

Transnational Crime Proceeds and Money Laundering²

Worldwide Figures

Dirty money from crime is earned through various underground activities, like drug, weapons, and human trafficking. How much illicit crime money in all its forms can be observed?³ The most widely quoted figure for the extent of money laundering criminal proceeds has been the International Monetary Fund (IMF, 2001a, 2001b, 2003) consensus range of 2.0 percent to 5.0 percent of global gross domestic product (GDP).

Tables 1 and 2 show the Financial Action Task Force (FATF) estimates and the IMF estimates of worldwide money laundered, for a similar period (FATF 1988–2005; IMF 1996–2009). Considering first the FATF estimate, the amount of worldwide money laundering is 2.0 percent in 1988, increased to 3.5 percent in 1996 and decreased again to 3.0 percent in 2005. The IMF estimate has a range of between 2.0 percent and 5.0 percent over the period 1996–2009. In absolute terms, the worldwide money laundered increased by 36.0 percent from 1996 to 2005 and by 33.0 percent from 2005 to 2009, which is quite a dramatic increase. These FATF or IMF figures are more or less in a similar range.

Table 1.

FATF Estimate of Worldwide Money Laundering, 1988–2005.

Amounts estimated to have been laundered
Year	In billion USD	As percentage of global GDP
1988	340.00	2.0
1996	1,100.00	3.5
2005	2,300.00	3.0

Source: IMF (2001a, 2001b, 2003) and FATF (2007).

Note. FATF = Financial Action Task Force; GDP = gross domestic product; USD = United States dollar.

Table 2.

IMF Estimates of Money Laundered Worldwide, 1996–2009.

Estimation	Minimum	Maximum	Midpoint	Increase (%)
IMF estimates of money laundered as percentage of global GDP	2%	5%	3.5%	–
Estimate for 1996 (billion USD)	600	1,500	1,100	–
Estimate for 2005 (billion USD)	900	2,300	1,500	36
Estimate for 2009 (billion USD)	1,200	2,900	2,000	33

Source: OECD, Observer, Paris, various years (1999–2010).

Note. GDP = gross domestic product; IMF = International Monetary Fund; USD = United States dollar.

Table 3 shows the FATF estimates of global amounts of laundered money up to the year 2009. Here the key focus lies on drugs and for this a calculation of the total amounts laundered from all criminal proceeds. In the year 2000, it is estimated that laundered money is 0.6 trillion United States dollar (USD) and doubles up to the year 2009 to 1.2 trillion USD.

Table 3.

FATF Estimates of Global Amounts of Laundered Money, 1988–2009.

Estimate of drug sales in key markets (1988)	USD 124 billion
As percentage of global GDP (1988)	0.8%
Assumed proportion that is laundered	2/3–70%
Estimate of amounts laundered related to drugs	USD 85 billion
Proportion as percentage of global GDP (1988)	0.5% of GDP
Estimated proportion of drugs in total amounts laundered	25%
Estimated total amounts laundered in 1988	USD 340 billion
As a percentage of global GDP	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: OECD (1990); IMF (2010).

Note. FATF = Financial Action Task Force; GDP = gross domestic product; USD = United States dollar.

Table 4 shows some newer FATF data, again for the drug market for the year 2003. For the year 2003, the FATF estimates of the total amounts laundered (from all criminal proceeds) is 880 billion USD, or 2.4 percent of world GDP. Extrapolated to the year 2009, the calculation reaches 1.4 trillion USD.

Table 4.

Updated FATF Model of Global Amounts Laundered.

Estimate of drug sales in key markets (UNODC estimate for 2003)	USD 322 billion
As a percentage of world GDP	0.9% of GDP
Assumed proportion that is laundered (initial FATF estimate)	66%–70%
Estimate of amounts laundered related to drugs	USD 220 billion
Proportion as percentage of global GDP (2003)	0.6% of GDP
Estimated proportion of drugs in total amounts laundered (initial FATF estimate)	25%
Estimated total amounts laundered in 2003	USD 880 billion
As percentage of GDP	2.4% of GDP
Extrapolated to global GDP in 2009	USD 1.4 trillion

Source: IMF (2010) and United Nations Office on Drugs and Crime (UNODC) (2005a, 2005b).

Note. FATF = Financial Action Task Force; GDP = gross domestic product; USD = United States dollar.

Table 5 indicates money laundering by region over the period 2000–2005. North and South America have by far the biggest share with 37.8 percent in 2000, which remains more or less constant up to 2005 with 37.7 percent. This is followed by Asia Pacific with a modest increase from 29.7 percent in 2000 to 31.5 percent in 2005. Europe slightly decreased over this period, from 27.8 percent in 2000 (of all money laundered proceeds) and to 26.0 percent in 2005.

Table 5.

Annual Money Laundering by Region (Billion USD), 2000–2005.

Region	2000		2002		2005^a
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	100%	856	100%	927	100%
In % of GDP	2.7%		2.6%		2.0%

Source: Celent (2002, 2006).

Note. GDP = gross domestic product; USD = United States dollar.

^aThe 2005 estimates are projections.

In Table 6, the cross-border flows of global “dirty money” in trillion USD are shown over the period 2000–2005 on a worldwide basis. This includes tax fraud money and all money that leaves a country due to some criminal reason. One clearly sees from Table 6 that the overall amounts of dirty money laundered is between 1.1 and 1.6 trillion USD and increases to 1.7 to 2.5 trillion USD in the year 2009. This is quite a large sum, which amounts to between 2.9 and 4.3 percent of the world GDP. The classic criminal component lies only between 27.0 percent and 31.0 percent of the total dirty money. Hence, one clearly sees that capital flight and tax fraud money are by far the biggest proportions of dirty money.⁴

Table 6.

Cross-border Flows of Global Dirty Money Including Tax Evasion (Trillion USD), 2000–2005.

Variable	2000–2005			Extrapolated to 2009
Variable	Low	High	Percentage of GDP 2000–2005	Low	High	Midpoint
Overall amount laundered	1.1	1.6	2.9–4.3%	1.7	2.5	2.1
Criminal component	0.3	0.5	0.9–1.5%	0.5	0.9	0.7

Source: Baker (2005); World Bank Indicators (for GDP).

Note. GDP = gross domestic product; USD = United States dollar.

Table 7 shows the proceeds of transnational crime (time range 2003–2009). Here we have a clear-cut result. Drugs are the biggest business with 50.0 percent, followed by counterfeiting with 39.0 percent, human trafficking with 5.0 percent, and oil with 2.0 percent. The proceeds from all other crime are much lower. In total, we have here a sum of 650 billion USD, which amounts to 1.1 percent of global GDP.

Table 7.

Proceeds of Transnational Crime, 2003–2009.

Kind of crime	In billion USD	As percentage of total proceeds	Sources
Drugs	320	50	UNODC (2005a, 2005b; 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)
Wildlife	7.8–10	1.4	GFI estimate based on Francesco Colombo, “Animal Trafficking—A Cruel Billion-Dollar Business,” Inter Press Service, September 6, 2003; Coalition Against Wildlife Trafficking, World Wildlife Fund
Timber	7.0	1.1	GFI estimate for 2009 based on Seneca Creek and Wood Resources International, OECD
Fish	4.2–9.5	1.1	GFI estimate for 2010, based on Norwegian national advisory group against organized IUU-fishing (FFA) and United Nations Food and Agriculture Organization
Art and cultural property	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
Gold	2.3	0.4	GFI estimate based on estimates from UNODC (2010) and other sources on illegal gold trade in DRC, South Africa, and Peru
Human organs	0.6–1.2	0.1	GFI estimate based on WHO, Council of Europe, United Nations
Small arms and light weapons	0.3–1.0	0.1	GFI estimate based on Small Arms Survey and UNODC
Diamonds and colored gemstones	0.9	0.1	GFI estimate for 2009 based on UN, Kimberley Process: Rough Diamond Statistics and US Geological Survey
Total (midpoint estimates)	645	100.0
Total rounded	650
In % of global GDP in 2009	1.1%
In % of average global GDP, 2000–2009	1.5%

Source: Global Financial Integrity (2011), and World Bank Indicators (for current GDP).

Note. DRC = Democratic Republic of Congo; FFA = Forum Fisheries Agency; GDP = gross domestic product; GFI = Global Financial Integrity; ILO = International Labor Office; IUU = Illegal, Unreported, and Unregulated; OECD = Organisation for Economic Co-operation and Development; WHO = World Health Organization; UN = United Nations; UNODC = United Nations Office on Drugs and Crime; USD = United States dollar.

Finally, table 8 presents estimates of worldwide turnover of organized crime in trillion USD. These estimates clearly show a huge range, and it is left to the reader to make his or her own evaluation and judgment of plausibility. A median of all estimates is 1.9 trillion USD for the year 2009, and the average is 2.1 trillion USD in 2009 or 3.6 percent of world GDP. The confidence interval lies between 1.6 and 2.6 trillion USD, or 2.7 percent to 4.4 percent. In general, we see that we have a huge variation, and all figures should be handled with great care.

Table 8.

Estimates of Worldwide Turnover of Organized Crime.

Origin/study	Year	Volume worldwide in trillion USD	As percentage of global GDP
Schuster	1994	USD 0.5–0.8 trillion	0.9–3.0%
International Monetary Fund and Interpol	1996	USD 0.5 trillion	1.6%
UN estimates	1994/1998	USD 0.7–1 trillion	2.4–3.4%
Kerry	1997	USD 0.42–1 trillion	1.4–3.3%
Walker	1998	USD 2.85 trillion	9.5%
National Criminal Intelligence Service	1998	USD 1.3 trillion	4.3%
	2001	USD 1.9 trillion	5.9%
	2003	USD 2.1 trillion	5.6%
Takats (2007)	2005	USD 0.6–1.5 trillion	1.3–3.3%
Agarwal and Agarwal (2004, 2006)	2005	USD 2.0–2.5 trillion	4.4–5.5%
Global Financial Integrity (2011; estimate for transnational crime)	2000–2009	USD 0.65 trillion	1.5%
Walker (based on Walker and Unger, 2009)	2001	USD 1 trillion	3.4%
Schneider	2001	USD 0.8 trillion	2.5%
	2002	USD 0.96 trillion	2.9%
	2003	USD 1.2 trillion	3.2%
	2004	USD 1.4 trillion	3.3%
	2005	USD 1.5 trillion	3.3%
	2006	USD 1.7 trillion	3.4%
Tentative estimate	2009^a	USD 2.0 trillion	3.4%
Median of all estimates	2009^b	USD 1.9 trillion	3.3%
Interquartile range of all estimates	2009^b	USD 1.5–2.4 trillion	2.6–4.1%
Average of all estimates	2009^b	USD 2.1 trillion	3.6%
Confidence interval of mean (95%)	2009^b	USD 1.6–2.6 trillion	2.7–4.4%

Note. GDP = gross domestic product; UN = United nations; USD = United States dollar.

^aThis is a tentative estimate, assuming that Schneider’s proportion of turnover of organized crime expressed as a percentage of GDP remained unchanged over 2006–2009 period. ^bThis estimate is extrapolated to global GDP in 2009.

National Crime Proceeds and Money Laundering

In table 9, the estimated earnings from criminal activities are shown for the United States over the period 1965–2010. We have here two series: estimated criminal income including financial and tax fraud proceeds and estimated criminal income excluding financial and tax fraud proceeds. In absolute figures, there is a strong increase from 49 billion USD in 1965 to 1,043 billion USD in 2010. If we standardize these figures in percentage of GDP, we have a modest increase up to the year 2000; it was 6.8 percent in 1965 and 8.0 percent in 2000, then it decreased to 7.0 percent in 2010. If we consider the ratio of criminal income in percentage of total illicit income (criminal plus financial and tax fraud income), we see that classical criminal income ranges between 29.0 percent in 2000 and a maximum of 49.0 percent in 1985. This clearly shows that financial and tax fraud is again by far the largest crime figure in the United States.⁵

Table 9.

Estimated Earnings from Criminal Activity in the United States, 1965–2010.^a

	Tax evasion included		Criminal income (tax evasion excluded)
Year	Estimated criminal income (billions of USD)	As percentage of GDP	Estimated criminal income (billions of USD)	As percentage of GDP
1965	49	6.8	18	2.5
1970	74	7.1	26	2.5
1975	118	7.2	45	2.7
1980	196	7.0	78	2.8
1985	342	8.1	166	4.0
1990	471	8.1	209	3.6
1995	595	8.0	206	2.8
2000	779	8.0	224	2.3
2010^b	1,043	7.0	300 (235–350)	2.0 (1.6–2.3)

Note. GDP = gross domestic product; USD = United States dollar.

^aCriminal activities include trafficking in illicit drugs, human trafficking, burglary, larceny-theft, motor vehicle theft, robbery, fraud, arson, nonarson fraud, counterfeiting, illegal gambling, loan sharking, and prostitution. Tax evasion crimes included federal income, federal profits, and excise tax evasion. ^bThis is a tentative UNODC (2004, 2005a, 2005b, 2010) estimate based on previous estimates and trends derived from new drug and crime data.

In table 10, figures for Australia are shown. Here, we clearly see that fraud, drugs, and shop lifting are the three biggest types of crime. In total, the criminal proceeds in Australia reached 10.9 billion Australian dollars or 7.1 billion USD, and they are in a range between 1.5 percent and 2.8 percent of Australian GDP. Table 11 shows the crime proceeds for the Netherlands. Again, we have the remarkable result that 73.0 percent of all crime proceeds come from financial, social security, and tax fraud, followed by drugs with 12.4 percent and illegal workers with 3.1 percent. In the Netherlands, between 11.0 and 19.0 billion euros are the range of the crime proceeds, which amounts to between 2.6 percent and 4.3 percent of official GDP.

Table 10.

Estimated Criminal Proceeds in Australia, 1998 and 2003.

Illegal activities	2003 (revised estimates; millions of SD)		Midpoint estimates (percentage of GDP)
Illegal activities	Minimum	Maximum	1998	2003 (revised)
Fraud	3,000	3,500	1.8%	0.4%
Drugs	2,000		0.2%	0.3%
Theft			0.3%
Shoplifting	1,020	2,460		0.2%
Car theft	654			0.1%
Stealing from persons	545			0.1%
Other theft	659			0.1%
Burglaries	1,193		0.2%	0.2%
Assaults	979		0.1%	0.1%
Homicide	323		0.0%	0.0%
Property damage	510		0.2%	0.1%
Robbery and extortion	37		0.0%	0.0%
Total in million $A	10,920	12,860	2.8%	1.5%
Total in billion USD	7.1	8.3
As percentage of GDP	1.4%	1.6%

Source: Walker (1998, 2003), as quoted in Unger (2007); Walker (2004, 2007).

Note. GDP = gross domestic product; USD = United States dollar.

Table 11.

Estimated Unlawful Earnings in the Netherlands, 2003.

Type of crime	Proceeds of crime, million €	Proceeds of crime midpoint estimates in percentage of total
Financial, social security, and tax fraud^a	7,735–15,450	73.3
Drugs	1,960	12.4
Illegal workers	490	3.1
Prostitution	460	2.9
Theft	345	2.2
Burglary	340	2.1
Fencing	190	1.2
Illegal gambling	130	0.8
Illegal copying	90	0.6
Computer crime	26	0.2
Violent offenses	6	0
Other offenses	187	1.2
Total in million €	11,959–19,674
Total in billion €	13,5–22,3
As a percentage of GDP	2.6–4.3%

Source: Unger (2007), based on studies by Smekens and Verbruggen (2004), Business criminality: Criminaliteit en rechtshandhaving (2001); WODC (2003); and NIPO (2002).

Note. GDP = gross domestic product.

^aThis estimate is based on the assumption that between 5% and 10% of the total amounts were discovered and reported.

Finally, table 12 presents the crime proceeds of Italy. Crime proceeds from drugs are by far the largest with 60.0 billion euros, followed by ecomafia/agromafia with 16.0 billion euros and loan sharking with 15.0 billion euros. Total income of crime proceeds is 135.0 billion euros or 8.9 percent of the Italian GDP.

Table 12.

Estimates of the Income and Profits of Organized Crime in Italy, 2009.

Income	In billion €
Trafficking drugs	60.00
Trafficking in human beings	0.87
Arms trafficking	5.80
Smuggling	1.20
Subtotal trafficking		67.87
Protection racket	9.00
Loan sharking (usury)	15.00
Subtotal and predatory activities		24.00
Theft and robbery	1.00	1.00
Procurement	6.50
Agro-crime	7.50
Games and gambling	2.50
Counterfeiting	6.50
Illegal construction	2.00
Subtotal—illegal economic activities		25.00
Ecomafia/agromafia	16.00	16.00
Prostitution	0.60	0.60
Financial gains	0.75	0.75
Total income in billion €	135.22	135.22
Total income in billion USD		188.58
Total income in % of GDP		8.9%

Source: SOS Impresa (2010).

Note. GDP = gross domestic product; USD = United States dollar.

Money Laundering—Some Methodical Remarks

Laundering “crime” money has the purpose to make dirty money appear legal (Walker 1999, 2000, 2004, 2007).⁶ There are many methods of money laundering. Table 13 presents the twelve most common methods (Unger 2007; Walker 2007). Which of these methods is chosen depends on the type of crime activity and on the specific institutional arrangements in a country where the criminal money is “earned.” For example, in the drug business, method 8 (business ownership) is quite often used.⁷ In the drug business and in big cities, smaller amounts of cash are earned by drug dealers in many different cash-intensive places such as restaurants, which are especially well suited for money laundering purposes.

Table 13.

The Methods of Money Launderin.^a

(1)	Wire transfers or electronic banking	The primary tool of money launderers to move funds around in the banking system. These moves can conceal the illicit origins of the funds or just place the money where the launderers need them. Often the funds go through several banks and even different jurisdictions.
(2)	Cash deposits	Money launderers need to deposit cash advances to bank accounts prior to wire transfers. Due to antimoney-laundering regulations they often “structure” the payments, that is, break down large to smaller amounts. This is also called smurfing.
(3)	Informal value transfer systems (IVTSs)	Money launderers need not rely on the banking sector, other transfer providers, such as the Hawala or Hindi are readily available to undertake fund transfers. These systems consist of shops (mainly selling groceries, phone cards, or other similar items), which are also involved in transfer services. IVTSs enable international fund transfers, as these shops are present in several jurisdictions.
(4)	Cash smuggling	Money launderers might mail, FedEx, or simply carry cash with them from one region to another, or even to different jurisdictions.
(5)	Gambling	Casinos, horse races, and lotteries are ways of legalizing funds. The money launderer can buy (for “dirty” cash) winning tickets—or in the case of casinos chips—and redeem the tickets or the chips in a “clean” bank check. Afterward, the check can be easily deposited in the banking sector.
(6)	Insurance policies	Money launderers purchase single premium insurance (with dirty cash), redeem early (and pay some penalty) in order to receive clean checks to deposit. Longer-term premium payments might make laundering even harder to detect.
(7)	Securities	Usually used to facilitate fund transfers, where underlying security deals provide cover (and legitimate looking reason) for transfers.
(8)	Business ownership	Money might be laundered through legitimate businesses, where laundering funds can be added to legitimate revenues. Cash-intensive operations, such as restaurants, are especially well suited for laundering
(9)	Shell corporations	Money launderers might create companies exclusively to provide cover for fund moves without legitimate business activities.
(10)	Purchases	Real estate or any durable good purchases can be used to launder monies. Typically, the item is bought for cash and resold for clean monies, like bank checks.
(11)	Credit card advance payment	Money launderers pay money in advance with dirty money, and receive clean checks on the balance from the bank.
(12)	ATM operations	Banks might allow other firms to operate their ATMs, that is, to maintain and fill them with cash. Money launderers fill ATMs with dirty cash, and receive clean checks (for the cash withdrawn) from the bank.

Source: Unger (2007).

With the help of the Multiple Indicators Multiple Causes estimation procedure, Schneider (2008a, 2008b) and Buehn and Schneider (2013, in press) estimate that money laundering and/or financial turnover from transnational crime has increased from 1995 USD 273 billion (1.33 percent of the total official GDP) to USD 603 billion (or 1.74 percent of the official GDP) in 2006 for twenty Organisation for Economic Co-operation and Development countries (Australia, Austria, Belgium, Canada, Denmark, Germany, Finland, France, Greece, Great Britain, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Switzerland, Spain, and the United States). These figures show a steady increase in the volume of laundered money over 1995–2006.⁸

Unger (2007) estimates the amount of laundered money and its top twenty destination countries (table 15). Two estimates for the year 2005 are presented, one by Walker (1999, 2007) and one by the IMF. The Walker figure of 2.850 billion USD is much larger than the IMF figure of 1.500 billion USD. Walker’s figures have been criticized as much too high, which was one reason why the IMF estimates are also shown. Table 15 clearly demonstrates that two-thirds of worldwide money laundering was sent to the top twenty countries listed. One should realize that most of these countries are highly developed and have quite sizable legal/official economies. What is also amazing is that there are only a few small countries and/or offshore countries and tax heavens among them (Cayman Islands, Vatican City, Bermuda, and Liechtenstein).⁹ The majority of countries that attract money laundering flows are economically “big” players. The United States has the largest worldwide share of money laundering (almost 19 percent), followed by the Cayman Islands (4.9 percent), Russia (4.2 percent), and Italy (3.7 percent); smaller countries like Switzerland (2.1 percent of worldwide money laundering), Liechtenstein (1.7 percent), and Austria (1.7 percent) are attractive. If one takes the lower IMF value for Austria, Switzerland, and the United Kingdom, roughly 5.5 percent of the total amount is laundered, which comes close to roughly 10 percent of official GDP of these three countries. However, it needs to be emphasized that it is not clear whether this money is “only” laundered in these countries or remains in these countries; it may well leave these countries after the laundering process. In general, table 15 demonstrates how substantial the amount of laundered money is and that two-thirds of these funds are concentrated in only twenty countries.

Table 14.

The Amount of Laundered Money and Top Twenty Destinations of Laundered Money, 2005.^a

Rank	Destination	Percentage 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	283,500
2	Cayman Islands	4.9	138,329	73,500
3	Russia	4.2	120,493	63,000
4	Italy	3.7	105,688	55,500
5	China	3.3	94,726	49,500
6	Romania	3.1	89,595	46,500
7	Canada	3.0	85,444	45,000
8	Vatican City	2.8	80,596	42,000
9	Luxembourg	2.8	78,468	42,000
10	France	2.4	68,471	36,000
11	Bahamas	2.3	66,398	34,500
12	Germany	2.2	61,315	33,000
13	Switzerland	2.1	58,993	31,500
14	Bermuda	1.9	52,887	28,500
15	Netherlands	1.7	49,591	25,500
16	Liechtenstein	1.7	48,949	25,500
17	Austria	1.7	48,376	25,500
18	Hong Kong	1.6	44,519	24,000
19	United Kingdom	1.6	44,478	24,000
20	Spain	1.2	35,461	18,000
	Total	67.1	1,910,922	1,006,500

Source: Unger (2007).

Note. IMF: International Monetary Fund; USD = United States dollar.

Table 15.

Estimates of the Various Cost Components (Partly Proceeds) of Cybercrime.

Types of cybercrime	UK estimate	Global estimate	Reference period	Criminal revenue	Direct losses	Indirect losses	Defense cost
1. Cost of genuine cybercrime
Online banking fraud
Phishing	$16 m	$320 m	2007	x^?	x^?
Malware (consumer)	$4 m	$70 m	2010	x↓	x↓
Malware (businesses)	$6 m	$200 m		x↓	x↓
Bank tech. countermeasures	$50 m	$1.000 m	2010				x^?
Fake antivirus	$5 m	$97 m	2008–2010	x	x
Copyright-infringing software	$1 m	$22 m	2010	x↓	x
Copyright-infringing music, etc.	$7 m	$150 m	2011	x
Patent-infringing pharma	$14 m	$288 m	2010	x↓
Stranded traveler scam	$1 m	$10 m	2011	x
Fake escrow scam	$10 m	$200 m	2011	x↓
Advance-fee fraud	$50 m	$1.000 m	2011	x↓
SUM of 1	$164 (0.9%)	$3.457 (1.6%)
2. Cost of transitional cybercrime
Online payment card fraud	$210 m	$4.200 m	2010		(x)
Offline payment card fraud
Domestic	$106 m	$2.100 m	2010		x↓
International	$147 m	$2.940 m	2010		x↓
Bank/merchant defense costs	$120 m	$2.400 m	2010				x↓
Indirect costs of payment fraud
Loss of confidence (consumers)	$700 m	$10.000 m	2010			x^?
Loss of confidence (merchants)	$1.600 m	$20.000 m	2009			x^?
PABX fraud	$185 m	$4.960 m	2011	x	x
SUM of 2	$3.068 (6.7%)	$44.200 m (19.8%)
3. Cost of cybercriminal infrastructure
Expenditure on antivirus	$170 m	$3.400 m	2012				x
Cost to industry of patching	$50 m	$1.000 m	2010				x?
ISP cleanup expenditures	$2 m	$40 m	2010			x?
Cost to users of cleanup	$500 m	$10.000 m	2012			x?
Defense costs of firms generally	$500 m	$10.000 m	2010				x?
Expenditure on law enforcement	$15 m	$400 m	2010				x
SUM of 3	$1.237 m	$24.840 m (11.9%)
4. Cost of cybercrime from fraud against public inst.
Welfare	$1.900 m	$20.000 m	2011	x	(x)
Tax fraud	$12.000 m	$125.000 m	2011	x?	(x)
Tax filing fraud	–	$5.200 m	2010	x	(x)
SUM of 4	$13.900 m (75.7%)	$150.200 m (67.5%)
SUM of 1–4	$18.369 m (100%)	$222.697 m (100%)

Source: Anderson et al. (2012, 24).

Note. Estimating costs and scaling: figures in boldface are estimates based on data or assumption for the reference area. Unless both figures in a row are boldface, the nonboldface figure has been scaled using the UK share of world GDP unless otherwise stated in the main text. Extrapolations from UK numbers to the global scale should be interpreted with utmost caution. A threshold to enter this table is defined at $10 m for the global estimates.

Legend: x: included; (x): partly converted; with qualifiers x for likely overestimated, x for likely underestimated, and x^? for high uncertainty.

Bagella, Busato, and Argentiero (2009) use a two-sector dynamic general equilibrium model to measure money laundering for the United States and the EU-15 macro areas over the sample 2000:01–2007:01 on a quarterly data basis. Their series is generated through a fully microfounded dynamic model, which is appropriately calibrated to replicate selected stochastic properties of the two economies. Their model (and the analysis) has a short-run perspective. Bagella, Busato, and Argentiero (2009) find several main results. First, money laundering accounts for approximately 19 percent of the GDP measured for the EU-15, while it accounts for 13 percent in the US economy, over the sample 2000:01–2007:04. Second, the simulated money laundering appears less volatile than the corresponding GDP. Third, as regards the EU-15 macro area, the simulated statistics suggest that money laundering volatility is one-third of the GDP volatility. Fourth, for the US economy, the same statistics produce a figure of two-fifths. Considering these estimates, I must admit that they are quite high, and I have some doubts about their plausibility.

In their latest study, Walker and Unger (2009) again undertake an attempt to measure global money laundering and/or the proceeds from transnational crime that are pumped through the financial system worldwide. They criticize methods such as case studies, proxy variables, or models for measuring the crime economy, arguing that they all tend to overestimate money laundering. They present a gravity model that makes it possible to estimate the flows of illicit funds from and to each jurisdiction in the world. This “Walker Model” was first developed in 1994 and was recently updated. Walker and Unger (2009) show that their model belongs to the group of gravity models that have recently become popular in international trade theory. They demonstrate that the original Walker Model estimates are compatible with recent findings on money laundering.

Once the scale of money laundering is known, its macroeconomic effects and the impact of crime prevention, regulation and law enforcement effects on money laundering, and transnational crime can also be measured. Walker and Unger (2009) conclude that their model still seems to be the most reliable and robust method to estimate global money laundering, and thereby the important effects of transnational crime on economic, social, and political institutions. Rightly they argue that the attractiveness of the distance indicator in the Walker Model is a first approximation, but is still not theoretically satisfactory. A better micro foundation for the Walker Model will be needed. A micro foundation means that the behavior of money launderers is analyzed, in particular what makes them send their money to a specific country. Hence, Walker and Unger (2009) argue that an economics of crime micro foundation for the Walker Model would mean that, similar to international trade theory, behavioral assumptions about money launderers have to be made. Their gravity model can be seen as a reduced form or outcome of a rational calculus of sending the money to a certain country and potentially making large profits.

Using their model in table 14, the amount of laundered money and the top (“most” attractive) twenty destinations of laundered money are shown for 2005. The United States ranks number one, followed by the Cayman Islands and Russia. With 18.9 percent of worldwide money laundering, the United States has by far the biggest share, followed by the Cayman Islands with 4.9 percent.

Cost and Proceeds of Cybercrime—The Latest Development in TOC

According to Anderson et al. (2012), in the last ten to fifteen years cybercrime arose from white-collar crimes. In the year 2007, the European Commission defined cybercrime as including traditional forms of crime such as fraud or forgery, though committed over electronic communication, networks, and information systems; the publication of illegal content over electronic media; and crimes unique to electronic networks.¹⁰

Today, cybercrime takes many forms, like online banking fraud (phishing), fake antivirus, computer programs, fake errors cam, and other variants. In a first systematic article, Anderson et al. (2012) try to give a survey in measuring the cost of cybercrime and/or the criminal proceeds from some types of cybercrime.¹¹ Cybercrime is a rather new development and is certainly becoming more and more important.

What types of cybercrime costs can one observe? Anderson et al. (2012) state the following four types:

Costs in anticipation of cybercrime, such as antivirus software, insurance, and compliance;

Costs as a consequence of cybercrime, such as direct losses and indirect costs, such as weakened competitiveness as a result of intellectual property compromise;

Costs in response to cybercrime, such as compensation payments to victims and fines paid to regulatory bodies; and

Indirect costs such as reputational damage to firms, loss of confidence in cyber transactions by individuals and businesses, reduced public-sector revenues, and the growth of the underground economy.

These types of costs are shown in figure 1, where Anderson et al. (2012) try to analyze the costs of cybercrime and also some criminal revenues. From Figure 1, one clearly sees that criminal revenues or criminal proceeds, my main topic in this article, can be derived from the direct losses of cybercrime, where we have also defense costs and indirect costs. Direct losses (or proceeds of national or transnational criminal activities) include money withdrawn from victim accounts, stolen software, and faked financial transactions.

Figure 1.

Framework for analyzing the costs of cybercrime.

What do we know about the costs (and partly proceeds of criminal activities) in the cybercrime area? Anderson et al. (2012) provide some first estimates of these costs (and partly proceeds) of the category of cybercrime.¹²

Considering the four cost components (cost of genuine cybercrime, cost of transitional cybercrime, cost of cybercriminal infrastructure, and cost of traditional crimes becoming “cyber”), one clearly realizes that number 4, “Cost of crime against public institutions (welfare tax fraud)” becoming “cyber,” is by far the largest part, covering 67.5 percent of all costs of cybercrime, which amount to a sum of 222.697 billion USD on a global estimate. If we further turn to the global estimate, we see the cost of “genuine cybercrime” on a worldwide basis is 3.457 billion USD or 1.6 percent of the total cost of cybercrime. The 3.457 billion USD can also be seen as the large part of the proceeds of genuine cybercrime activities. If we consider number 2, “Cost of transitional cybercrime,” we see that it amounts to 44.2 billion USD or 20.8 percent of the total cost of cybercrime. With 24.8 billion USD, the cost of cybercriminal infrastructure is quite sizable, and amounts to 11.2 percent of the total cost. As already said, the costs of traditional crimes becoming cyber are with 150.200 billion USD, the largest part of the cost of cybercrime. In general, table 16 clearly shows that the cost and proceeds of cybercrime activities are sizable. In the future, they will certainly rise because the use of electronic networks for crime activities is becoming more and more attractive.

Table 16.

Estimates of the Amount of Informal Money Flows through Hawala Banking.

Author/source	Country/area	Year/period	Estimated amount of informal money flows
Schneider and Caruso (2011)	India	2000–2005	5–10 billion USD per year
Fischer (2002)	Saudi Arabia	Unknown	40 billion USD per year
Fletcher and Baldrin (2002)	Pakistan	2001	2.5 billion USD
Viles (2008)	Somalia		0.5–1 billion, USD
Page and Plaza (2006)	Global	2004	57.53 billion USD
Omer (2002)	Somalia
Omer and El Koury (2004)	Somalia	2004	0.7–1 billion USD per year
Zaidi (2010)	Pakistan	Unknown	2.5–3 billion USD per year
IMF (2001a, 2001b, 2003)	Pakistan and Afghanistan	Unknown	2–5 billion USD per year
Jessee (2006)	Pakistan	Unknown	2–3 billions USD per year

Note. IMF: International Monetary Fund; USD = United States dollar.

The Infiltration of Transnational Crime Organizations and the Informal Money Banking (Hawala) System

Obviously, transnational crime organizations prefer to use an informal banking system, in order to make it more difficult for the state authorities to detect these activities. The most famous and oldest informal banking system is the Hawala banking system, which is analyzed in the The Informal Money Banking (Hawala) System subsection after a short introduction of the kinds of infiltration in which organized crime organizations are involved.

The Infiltration of Transnational Crime

In figure 2, the various channels of the infiltration of the transnational crime groups are summarized. Figure 2 concentrates on the use of financial resources and clearly demonstrates that the financial means/flows stand on six pillars ranking from legal investments to classical criminal activities.

Figure 2.

Infiltration of the legal economy by transnational organized crime.

The Informal Money Banking (Hawala) System

During the 1990s, international concern grew over the so-called underground banking and its abuse by criminal offenders. Some studies (e.g., Williams 2007; Savona and ISPAC [Organization], 1997; El-Quorchi, Munzele Maimbo, and Wilson 2003) have explained how informal systems operate, including their risks. The Informal Value Transfer Systems change from region to region (Hawala or door-to-door banking). Williams (2007) and El-Quorchi, Munzele Maimbo, and Wilson (2003) argue that Hawala is vulnerable to criminal abuse, and like the other financial institutions, there is evidence that money derived from drug trafficking, illegal arms sales, body part trade, corruption, tax evasion, and all kinds of fraud have indeed moved through Hawala networks.

Hawala banking still takes place, and some authors analyze (Perkel 2004; Bunt 2007). These authors point to the need for a regulation of the Hawala banking system, because it can be another way to transfer criminal financial flows. According to Bunt (2007), Hawala bankers are financial service providers who carry out financial transactions without a license and therefore without any government control.^13,14 They accept cash, checks, or other valuable goods (diamonds and gold) at one location and pay a corresponding sum in cash or other remuneration at another location. Unlike official banks, Hawala bankers disregard the legal obligations concerning the identification of clients, record keeping, and the disclosure of unusual transactions, to which these official financial institutions are subject.

To repeat, through this system, Hawala bankers ensure the transfer of money without having to move it physically or electronically. When a payment needs to be made overseas, the underground banker will get in touch with a courier (or more recently using e-mail, fax, or phone) in that country informing him of the details of making the payment. If the recipient of the payment wishes to personally obtain the money, a code referring to the underground banker in the country of payment is given to the recipient. Such a system is almost untraceable since it leaves little if any paper trail. Transaction records are being kept only until the money is delivered, at which time they are destroyed. Even when there is a paper or an electronic record of the transaction it is often in dialects and languages that serve as a de facto encryption system.¹⁵

In table 16, some guess estimates or estimates of Hawala banking turnover or proceeds are shown. On a global scale, Page and Plaza (2006) estimate 57.53 billion USD as the amount of informal money flows used by Hawala banking for the year 2004. The country sums are much smaller with the exception of the study by Fischer (2002), where he comes up with an estimated amount of informal money flows of Hawala banking for Saudi Arabia of 40 billion USD per year. Countries like Pakistan or Afghanistan range between 2.5 and 3.0 billion USD per year. Table 16 should be seen as a first attempt to come up with some ideas of the relative size of the estimated amount of informal money flows via Hawala banking.

According to Bunt (2007), there are two different views about Hawala banking. In the first point of view, Hawala banking is regarded as a centuries-old institution that has not yet outlived its usefulness. Low-income workers and migrant workers in particular supposedly put more trust in Hawala bankers than in formal banks. This viewpoint emphasizes the problem associated with subjecting Hawala banking to the same rules as formal banks. Regulation either through registration or licensing is seen as ineffective because it will simply push the system further into the underground, further complicating the already problematic task of controlling Hawala transactions (Razavy 2005; Perkel 2004). Hence, Hawala banking might be the closest thing of a free market banking, without government regulation and it functioned well for centuries. One should clearly emphasize these advantages of Hawala banking when criticizing it.

From the second and opposite point of view, Bunt (2007) argues that Hawala banking is described as “underground banking,” a system that flies under the radar of modern supervision of financial transactions. Underground banking can be considered a threat to the effectiveness of antimoney laundering measures and the fight against terrorist financing. To prevent underground bankers from becoming a safe haven for criminals and terrorists, they should be subject to the standard regulations regarding record keeping, disclosure of unusual transactions, and identification of clients.¹⁶

Summary and Conclusions

This article reviews the literature of the finances of TOC with a focus on estimations of the volume of the finances of transnational crime. I come to the following preliminary findings.

The necessity of money laundering is obvious as a great number of illegal (criminal) transactions are done by cash. Hence, this amount of cash from criminal activities must be laundered in order to have some “legal” profit, to do some investment or consumption in the legal world. Further, to get an estimate of the extent and development of the amount of the financial means of transnational crime over time is even more difficult. On a worldwide basis, in 2009, 1.4 trillion USD (or 2.5 percent of world GDP) are estimated to be laundered coming from all types of crime (IMF 2010). These figures are very preliminary with a quite large margin of error but give a clear indication how important money laundering and the turnover of transnational crime nowadays is. Finally, tax fraud and/or illegal cross-border capital flows are by far the biggest/highest share of all illegal transactions; quite often 66 percent of all illegal capital flows/proceeds.

From these preliminary results, I draw four conclusions. First, the revenues of transnational crime are extremely difficult to estimate. They are defined differently in almost every country, the measures taken against them are different and vary from country to country, and it is not at all clear which part of these revenues from transnational crime stays in a country with the consequence of a severe double counting problem.¹⁷ Second, fighting transnational crime is very difficult, as there are no efficient and powerful international organizations that can effectively do this job.¹⁸ Third, tax fraud and/or other illegal cross-border capital flows should be the prime target for governments to reduce them; that is, a rigorous fight against tax heavens should have the highest priority. Fourth, this article should be seen as a first start/attempt in order to shed some light on the gray area of the magnitude of proceeds of tax fraud and of transnational crime.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Friedrich Schneider is a professor of Economics of the Johannes Kepler University of Linz, Austria, since 1986. He obtained his PhD in economics from the University of Konstanz in 1976 and has held numerous visiting and honorary positions at a number of universities. He was the European editor of Public Choice from 1991 to 2004. To date, he has published sixty-seven books, 196 articles in leading scientific journals, and 171 articles in edited volumes.

References

Agarwal

J. D.

Agarwal

Aman

. 2004. “Globalization and International Capital Flows.” Finance India 19:65–99.

Agarwal

J. D.

Agarwal

Aman

. 2006. “Money Laudering: New Forms of Crime, and Victimization.” Paper presented at the National Workshop on New Forms of Crime, Victimization, Department of Criminology, University of Madras, India.

Anderson

Ross

Barton

Chris

Böhme

Rainer

Clayton

Richard

Eeten

Michael J. G. Van

Levi

Michael

Moore

Tyler

Savage

Stefan

. 2012. “Measuring the Cost of Cybercrime.” http://weis2012.econinfosec.org/papers/Anderson_WEIS2012.pdf.

Bagella

Michele

Busato

Francesco

Argentiero

Amedeo

. 2009. “Money Laundering in a Microfounded Dynamic Model: Simulations for the U.S. and the EU-15 Economies.” Review of Law and Economics 5:879–902.

Baker

Raymond W.

2005. Capitalism's Achilles Heel—Dirty Money and How to Renew the Free-market System. Hoboken, NJ: Wiley.

Buehn

Andreas

Schneider

Friedrich

. 2013, in press. “A Preliminary Attempt to Estimate the financial Flows of Transnational Crime Using the MIMIC Method.” In Research Handbook on Money Laundering, edited by Unger

Brigitte

, Cheltenham, UK: Edward Elgar.

Bunt

Henk van D.

2007. “The Relation between Organized Crime and Informal Markets: The Role of Hawala Bankers in the Transfer of Proceeds from Organized Crime.” CRIMPREV Discussion Paper, Gant University, Gant, Belgium.

Celent

2002. “Anti-money Laundering: A Brave New World for Financial Institutions.” Accessed September 27, 2002. www.celent.com/PressReleases/20020927/AntiMoneyLaundering.htm.

Celent

2006. “Anti-money Laundering: A Brave New World for Financial Institutions.” Study, Celent Communications, Washington DC, September 2006.

10.

D’Souza

Jayesh

. 2012. Terrorist Financing, Money Laundering and Tax Evasion: Examining the Performance of Financial Intelligence Units. Boca Raton, FL: CRC Press.

11.

Detica and Office of Cyber Security and Information Assurance. 2011. “The Cost of Cyber-crime.” http://www.cabinetoffice.gov.uk/resource-library/cost-of-cyber-crime.

12.

El-Quorchi

Munzele Maimbo

Wilson

J. F

. 2003. Informal Funds Transfer Systems: An Analysis of the Informal Hawala System, 1–53. Washington, DC: International Monetary Fund.

13.

European Commission. 2007. “Towards a General Policy on the Fight Against Cybercrime.” COM (2007) 267 Final. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2007:0267:FIN:EN:PDF.

14.

FATF (Financial Action Task Force). 2004. Report on Money Laundering and Terrorist Financing. Typologies (2003–2004). Paris: Financial Action Task Force.

15.

FATF. 2005. Report on Money Laundering and Terrorist Financing. Typologies (2004–2005). Paris: Financial Action Task Force.

16.

FATF. 2007. Financial System Abuse. Report. Paris: Financial Action Task Force.

17.

FATF-GAFI (Financial Action Task Force-Groupe d’action financière). 2005. Third Mutual Evaluation Report on Anti-money Laundering and Competing the Financing of Terrorism. Norway, 1-160. Paris: Financial Action Task Force-Groupe d’action financière.

18.

FATF-GAFI. 2006. Third Mutual Evaluation Report on Anti-money Laundering and Competing the Financing of Terrorism. Portugal, 1-165. Paris: Financial Action Task Force-Groupe d’action financière.

19.

Fischer

Howard S.

2002. Hawala—What Is it and How to Emasculate it. Study. Beverly Hills, CA: Clinco & Fisher, LLP.

20.

Fletcher

Baldrin

. 2002. “Money Laundry Counter Measures with Primary Focus upon Terrorism and the U.S. Patriot Act 2001.” Journal of Money Laundering Control 6:105–36.

21.

Florencio

Dinei

Herley

Cormac

. 2011. “Sex, Lies and Cyber-crime Surveys.” In Proceedings (online) of the Workshop on Economics of Information Security. http://research.microsoft.com/pubs/149886/SexLiesandCybercrimeSurveys.pdf.

22.

Global Financial Integrity. 2011. Transnational Crime in the Developing World. Washington, DC: Global Financial Integrity.

23.

IMF (International Monetary Fund). 2001a. “Financial System Abuse, Financial Crime and Money Laundering.” Report, Monetary and Exchange Affairs and Policy Development, Washington, DC.

24.

IMF. 2001b. Financial System Abuse. Financial Crime and Money Laundering—Background Paper. Washington, DC: Monetary and Exchange Affairs and Policy Development.

25.

IMF. 2003. Money Laundering: New Facts. Study. Washington, DC: Monetary and Exchange Affairs and Policy Development.

26.

IMF. 2010. Financial System Abuse. Financial Crime and Money Laundering—Background Paper. Washington, DC: Monetary and Exchange Affairs and Policy Development.

27.

Jessee

D. D

. 2006. “Tactical Means, Strategic Ends: Al Qaeda’s Use of Denial and Deception.” Terrorism and Political Violence 18:367–88.

28.

Johnson

2001. “In Pursuit of Dirty Money: Identifying Weaknesses in the Global Financial System.” Journal of Money Laundering Control 5:122–33.

29.

Kanich

Chris

Weaver

Nicholas

McCoy

Damon

Halvorson

Tristan

Kreibich

Christian

Levchenko

Kirill

Paxson

Vern

Voelker

Geoffrey M.

Savage

Stefan

. 2011. “Show Me the Money: Characterizing Spam-advertised Revenue.” In Proceedings of the USENIX Security Symposium, San Francisco, CA.

30.

Kar

Cartwright-Smith

. 2009. Illicit Financial Flows from Developing Countries: 2002-2006. Washington, DC: Global Financial Integrity Study.

31.

Koh

J.-M

. 2006. Suppressing Terrorist Financing and Money Laundering. Berlin: Springer.

32.

Levi

2009a. Money Laundering Risks and E-gaming: A European Overview and Assessment. Final Report, Wales, UK: Cardiff School of Social Sciences, Cardiff University.

33.

Levi

2009b. “Fear of Fraud and Fear of Crime: A Review.” In The Criminology of White-collar Crime, edited by Simpson

Weisburd

, 84–109. New York: Springer.

34.

Levi

2011. “Social Reactions to White-collar Crimes and their Relationship to Economic Crises.” In Economic Crisis and Crime, edited by Deflem

Mathieu

, 87–105. Bingley, UK: Emerald.

35.

Levi

Burrows

John

. 2008. “Measuring the Impact of Fraud in the UK: A Conceptual and Empirical Journey.” British Journal of Criminology 48:293–318.

36.

Levi

Gilmore

. 2002. “Terrorist Finance, Money Laundering and the Rise and Rise of Mutual Evaluation: A New Paradigm for Crime Control.” In Financing Terrorism, 87–115. Dordrecht, the Netherlands: Kluwer Academic Publishers.

37.

Levi

Suddle

. 1989. “White Collar Crime, Shamelessness and Disintegration: The Control of Tax Evasion in Pakistan.” Journal of Law and Society 16:489–505.

38.

Makarenko

2002. “A Model of Terrorist-criminal Relations.” Jane’s Intelligence Review 11:101–18.

39.

Makarenko

2003a. “Tracing the Dynamics of the Illicit Arms Trade.” Jane’s Intelligence Review 12: 223–41.

40.

Makarenko

2003b. “A Model of Terrorist-criminal Relations.” Jane’s Intelligence Review 12: 341–49.

41.

Makarenko

2003c. “Transnational Terror and Organised Crime: Blurring the Lines.” Sais Review 24:49–61.

42.

Masciandaro

2004. Global Financial Crime: Terrorism, Money Laundering and Off Shore Centres. Aldershot, UK: Ashgate.

43.

Masciandaro

2005. “Financial Supervisory Unification and Financial Intelligence Units.” Journal of Money Laundering Control 8:354–71.

44.

Masciandaro

2006. “Offshore Financial Centres and International Soft Laws: Explaining the Regulation Gap.” Second Annual Conference: Società Italiana di Diritto ed Economia, Roma, 1–49.

45.

Moore

Tyler

Clayton

Richard

Anderson

Ross

. 2009. “The Economics of Online Crime.” Journal of Economic Perspectives 23:3–20.

46.

Napoleoni

2005. Terror Incorporated: Tracing the Dollars behind the Terror Networks. New York: Seven Stories Press.

47.

OECD (Organisation for Economic Co-operation and Development). 1990. “Financial Action Task Force on Money Laundering, p. 6. Paris.” UNDCP, Economic and Social Consequences of Drug Abuse and Illicit Trafficking, UNDCP Technical Series No. 6, United Nations Office of Drug Control and Crime Prevention, Vienna, Austria, 1998.

48.

OECD. various years. Observer, Paris.

49.

Omer

2002. A Report on Supporting Systems and Procedures for the Effective Regulation and Monitoring of Somali Remittance Companies (Hawala). UNDP Report. New York: United Nations Development Programme.

50.

Omer

El Koury

. 2004. “Regulation and Supervision in a Vacuum: The Story of the Somali Remittance Sector.” Small Enterprise Development 15:44–52.

51.

Page

Plaza

. 2006. “Migration Remittances and Development: A Review of Global Evidence.” Journal of African Economies 15:245–336.

52.

Perkel

William

. 2004. “Money Laundering and Terrorism: Informal Value Transfer Systems.” American Criminal Law Review 41:183–211.

53.

Pickhardt

Michael

Prinz

Aloys

. ed. 2012. Tax Evasion and the Shadow Economy. Cheltenham, UK: Edward Elgar.

54.

Razavy

Mark

. 2005. “Hawala: An Underground Haven for Terrorist of Social Phenomenon?” Crime, Law and Social Change 44:277–99.

55.

Reuter

Peter

. 2004. Chasing Dirty Money—The Fight against Money Laundering, Report. Washington, DC: Institute for International Economics.

56.

Richard

A. C

. 2005. Fighting Terrorist Financing: Transatlantic Cooperation and International Institutions. Baltimore: Center for Transatlantic Relations, Johns Hopkins University.

57.

Rider

2004. “The War on Terror and Crime and the Offshore Centres.” In Global Financial Crime: Terrorism, Money Laundering and Offshore Centres, 61–95. Aldershot, UK: Ashgate.

58.

Savona

E. U.

, and ISPAC (Organization). 1997. Responding to Money Laundering: International Perspectives. Amsterdam, the Netherlands: Harwood Academic Publishers.

59.

Schneider

Friedrich

. 2004. “The Financial Flows of Islamic Terrorism.” In Global Financial Crime: Terrorism, Money Laundering and Offshore Centres, edited by Masciandaro

Donato

, 97–126. Aldershot, UK: Ashgate.

60.

Schneider

Friedrich

. 2005. “Shadow Economies Around the World: What Do We Really Know?” European Journal of Political Economy 21:598–642.

61.

Schneider

Friedrich

. 2008a. Turnover of Organised Crime and Money Laundering: Some Preliminary Empirical Findings. Discussion Paper, Linz, Austria: Department of Economics, University of Linz.

62.

Schneider

Friedrich

. 2008b. “Money Laundering from Revenues from Organised Crime: Some Preliminary Empirical Findings.” Revised version of a paper which was presented at the Conference “Illicit Trade and Globalization.” CES-ifo Venice Summer Workshop, Venice National University, San Servolo, July 14–15, 2008.

63.

Schneider

Friedrich

. 2009. “Die Finanzströme von Organisierter Kriminalität und Terrorismus: Was wissen wir (nicht)?” Vierteljahreshefte zur Wirtschaftsforschung 78:73–87.

64.

Schneider

Friedrich

. 2010. “The (Hidden) Financial Flows of Terrorist and Organized Crime: A Literature Review and Some Preliminary Empirical Results.” Paper presented at the EUSECON Conference, Athens, Greece, April 12–13, 2010.

65.

Schneider

Friedrich

. 2011. “The Financial Flows of Transnational Crime: Some Preliminary Empirical Results.” In Usual and Unusual Organizing Criminals in Europe and Beyond: Profitable Crimes from Underworld To Upperworld, edited by Antonopoulos

Georgios

Groenhuijsen

Marc

Harvey

Jackie

Kooijmans

Tijs

Maljevic

Almir

Lampe

Klaus von

, 215–32. Festschrift for Petrus Van Duyne. Apeldoorn, the Netherlands: Maklu.

66.

Schneider

Friedrich

Caruso

Raul

. 2011. “The (Hidden) Financial Flows of Terrorist and Transnational Crime Organizations: A Literature Review and Preliminary Empirical Results.” Paper presented at the AEA/ASSA Conference 2011, Denver, CO.

67.

Schneider

Friedrich

Dreer

Elisabeth

Riegler

Wolfgang

. 2006. Geldwäsche: Formen, Akteure, Größenordnung—Warum die Politik machtlos ist. Wiesbaden, Germany: Gabler-Verlag.

68.

Schneider

Friedich

Enste

Dominik

. 2000. “Shadow Economy: Causes, Size and Consequences.” Journal of Economic Literature 38:77–114.

69.

Schneider

Friedrich

Windischbauer

Ursula

. 2008. “Money Laundering: Some Facts.” European Journal of Law and Economics 26:387–404.

70.

Smith

David

. 2011. “Black Money: The Business of Money Laundering.” Economy Watch.com. www.economywatch.com.

71.

SOS Impresa. 2010. XII Rapporto—Le mani della criminalità sulle imprese. Rome: SOS Impresa.

72.

Takats

Ilöd

. 2007. “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.

73.

Taylor

Jerome

. 2011. “Overseas Cyber-Crimewave Taking Pound 600m a Year from the Taxman.” The Independent.

74.

Truman

E. M.

Reuter

Peter

. 2004. Chasing Dirty Money: Progress on Anti-money Laundering. Washington, DC: Institute for International Economics.

75.

Unger

Brigitte

. 2007. The Scale and Impacts of Money Laundering. Cheltenham, UK: Edward Elgar.

76.

UNODC (United Nations Office on Drugs and Crime). 2004. “World Drug Report. United Nations Office for Drug and Crime Prevention (UDCCP).” Report, United Nations Office for Drug and Crime Prevention, New York.

77.

UNODC. 2005a. “World Drug Report. United Nations Office for Drug and Crime Prevention (UDCCP).” Report, United Nations Office for Drug and Crime Prevention, New York.

78.

UNODC. 2005b. World Drug Report Vol. 1 Analysis. Vienna: United Nations Office for Drug and Crime Prevention.

79.

UNODC. 2010. “The Globalization of Crime: A Transnational Organized Crime Threat Assessment.” Documentation, United Nations Office for Drug and Crime Prevention, New York.

80.

van Eeten

Michael

Bauer

Johannes M.

. 2008. “Economics of Malware: Security Decisions, Incentives and Externalities.” Technical Report OECD STI Working Paper 2008/1, OECD, Paris. Accessed May 29, 2008. http://www.oecd.org/dataoecd/53/17/40722462.pdf.

81.

Viles

2008. “Hawala, Hysteria and Hegemony.” Journal of Money Laundering Control 11:25–33.

82.

Walker

John

. 1999. “How Big Is Global Money Laundering?” Journal of Money Laundering Control 3:64–85.

83.

Walker

John

. 2000. “Money Laundering: Quantifying International Patterns.” Australian Social Monitor 2:139–47.

84.

Walker

John

. 2004. A Very Temptative Exploration of the Relationship between Shadow Economy and the Production and Transit of Illicit Drugs. New York: UNODC.

85.

Walker

John

. 2007. “Measuring Global Money Laundering.” Paper presented at the Conference “Tackling Money Laundering,” University of Utrecht, Utrecht, the Netherlands.

86.

Walker

John

Unger

Brigitte

. 2009 “Measuring Global Money Laundering: The Walker Gravity Model.” Review of Law and Economics 5:821–53. doi:10.2202/1555-5879.1418.

87.

Wilkinson

2005. “International Terrorism: The Changing Threat and the EU’s Response.” Cahier de Chaillot 84:81–89.

88.

Williams

2007. Warming Indicators and Terrorist Finances, Terrorism Financing and State Responses: A Comparative Perspective. Stanford, CA: Stanford University Press.

89.

Yepes

Verdugo

. 2008. “International Cooperation in the Fight against Terrorist Financing.” Dissertation, Universitat de Barcelona, Barcelona, Spain.

90.

Zaidi

Manzar Abbas S.

2010. “Understanding the Appeal of Taliban in Pakistan.” Journal of Strategic Security 3:1–14.

91.

Zdanowicz

John

. 2009. “Trade-Based Money Laundering and Terrorist Financing.” Review of Law and Economics 5:854–78.

The Financial Flows of Transnational Crime and Tax Fraud in OECD Countries

Abstract

Keywords

Transnational Crime Proceeds and Money Laundering 2

Worldwide Figures

National Crime Proceeds and Money Laundering

Money Laundering—Some Methodical Remarks

Cost and Proceeds of Cybercrime—The Latest Development in TOC

The Infiltration of Transnational Crime Organizations and the Informal Money Banking (Hawala) System

The Infiltration of Transnational Crime

The Informal Money Banking (Hawala) System

Summary and Conclusions

Footnotes

Declaration of Conflicting Interests

Funding

Notes

Author Biography

References

Transnational Crime Proceeds and Money Laundering²