Illicit Financial Flows From

Developing Countries: 2001-2010

Dev Kar and Sarah Freitas
December 2012
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Illicit Financial Flows From
Developing Countries: 2001-2010
Dev Kar and Sarah Freitas
1

December 2012

Global Financial Integrity Wishes to Thank
The Ford Foundation for Supporting this Project
1
Dev Kar, formerly a Senior Economist at the International Monetary Fund (IMF), is Lead Economist at Global Financial Integrity (GFI) and
Sarah Freitas is an Economist at GFI. The authors would like to thank Simn Ramrez Amaya, an intern at GFI, for assistance with data
research as well as Raymond Baker and other staff at GFI for helpful comments. Any errors that remain are the authors responsibility.
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c Illicit Financial Flows from Developing Countries: 2001-2010
We are pleased to present here our analysis of Illicit Financial Flows From Developing Countries:
2001-2010.
In our previous annual reports we have utilized the World Bank Residual model adjusted for trade
mispricing, presented in both gross non-normalized and in fltered normalized calculations. In this
years report we are adding a second form of analysis, the Hot Money Narrow model adjusted for
trade mispricing, again presented in non-normalized and normalized calculations. The results for
2010 are summarized as follows:
World Bank Residual Plus Trade Mispricing, Non-Normalized US$ 1,138 billion
World Bank Residual Plus Trade Mispricing, Normalized US$ 892 billion
Hot Money Narrow Plus Trade Mispricing, Non-Normalized US$ 859 billion
Hot Money Narrow Plus Trade Mispricing, Normalized US$ 783 billion
The consideration which led us to include a second type of measure of illicit fows has to do with
the potential for some level of licit fnancial fows to appear in the gap between the source of funds
and use of funds. This will bear further examination, as we continue to augment our analytical
methodologies.
What is perhaps most important to appreciate is that none of our estimates include several major
components of illicit fows, such as smuggling, cross-border movements of cash, trade mispricing
that occurs in the same invoice exchanged between importers and exporters, and the mispricing
of all services and intangibles which are not covered in IMF Direction of Trade Statistics. If we had
reliable fgures or estimates on these exclusions, without question our estimates of illicit fows from
emerging market and developing countries would be much higher.
Our preceding 2009 analysis utilizing the World Bank Residual model produced a range of estimates
of illicit fows from US$775 billion to US$903 billion for the year. The 2010 estimates summarized
above in four calculations and depicted in charts in the text indicate a growing order of magnitude,
suggesting that the slightly improving global economy afforded rising levels of unrecorded fows.
Whatever strengthened fnancial regulations may be in place or may be contemplated cannot yet
be seen to have an effect on the continued passage of funds out of poorer countries, through
the global shadow fnancial system, and ultimately into richer western economies. The somewhat
more conservative analysis produced by the Hot Money Narrow methodology suggests that trade
mispricing is rising in importance in the shift of illicit funds abroad.
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We have added to this years report a special section on Sovereign Wealth Funds (SWF),
recognizing their growing importance. The lack of clarity and consistency with which SWF-related
transactions are handled in balance of payments compilations of some countries calls for remedial
action by regulatory and statistical agencies with such large assets under management. Libyas
SWF appears to have been used for political as much as investment purposes, whereas Norways
SWF has made every citizen of the country a comfortable kroner millionaire. With now more than 60
SWFs around the globe an enormous pool of capital exists, and standards of accounting for such
funds need to be regularized through the auspices of the International Monetary Fund.
Six years ago when Global Financial Integrity was formed, the term illicit fnancial fows was non-
existent or insignifcant in the global political-economy lexicon. Today this term and its surrounding
concepts are used and addressed by the G20, UN, World Bank, IMF, OECD, European Union,
and national governments across the planet. A UN offcial recently commented that GFIs job is to
unpack the opaque. And this will continue to be our role in years to come.
We thank Dev Kar and Sarah Freitas for their excellent work in producing this analysis. The ongoing
support of the Ford Foundation is most gratefully acknowledged and appreciated.

Raymond W. Baker
Director, Global Financial Integrity
December 12, 2012
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e Illicit Financial Flows from Developing Countries: 2001-2010
Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . g
Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
II. Coverage of fows in the World Bank Residual method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
a. Normalization through use of flters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
III. Trends in Illicit Financial Flows from Developing Countries and Regions. . . . . . . . . . . . . . . . 9
IV. Special Issues: Sovereign Wealth Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
V. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Appendix Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Charts and Tables within Report
Table A. The United States and China: Balance of Payments Components, 1991 . . . . . . . . . . . . . 4
Chart 1. Volume of Illicit Financial Flows in
Nominal Terms from All Developing Countries 2001-2010. . . . . . . . . . . . . . . . . . . . . . . . . 5
Table B. Four Estimates of Capital Flight, All Developing Countries, 2001-2010. . . . . . . . . . . . . . . 6
Chart 2. Illicit Financial Flows by Region, 2001-2010. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Chart 3. Normalized vs. Non-normalized GER, 2001-2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Table C. Illicit Financial Flows Broken Down by Region in Nominal Terms . . . . . . . . . . . . . . . . . . 10
Table D. Illicit Financial Flows Broken Down by Region in Real Terms . . . . . . . . . . . . . . . . . . . . . 12
Chart 4. Illicit Flows in Real Terms 2001-2010; Regional Shares in Developing World Total . . . . . 14
Chart 5. Real Rates of Growth of IFFs from 2001-2010 by Region . . . . . . . . . . . . . . . . . . . . . . . . 15
Chart 6. Regional Illicit Flows in Nominal Terms 2001-2010;
Shares Related to HMN and GER Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Chart 7. Top 20 Countries Cumulative Illicit Flows,
Nominal HMN+GER Non-normalized, 2001-2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Table E. Total Illicit Financial Flows from the Top Ten Developing Countries . . . . . . . . . . . . . . . . 17
Table F. Changes in Cumulative Non-Normalized Illicit Outfow Rankings in Nominal Terms . . . 18
Chart 8. Top 10 Countries of 2010 Tracking Nominal Illicit Financial Flows . . . . . . . . . . . . . . . . . 19
Chart 9. Oil Prices and Illicit Flows Out of Five Major Countries . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Chart 10. Heat Map of Cumulative Illicit Financial Flows
from Developing Countries, 2001-2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
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g Illicit Financial Flows from Developing Countries: 2001-2010
Abstract
This 2012 report on illicit fnancial fows (IFFs) from developing countries and regions updates
estimates provided in Illicit Financial Flows from Developing Countries Over the Decade Ending
2009 published by Global Financial Integrity in December 2011. The report presents an additional
method of estimating fows based on the Hot Money Narrow measure adjusted for trade
misinvoicing. The measure results in estimates of capital fows that are more likely to be illicit
by nature. These conservative estimates of illicit fows are then compared against the previous
estimates based on the World Bank Residual method adjusted for trade misinvoicing (the CED+GER
method). The gap, representing fows of licit capital, has narrowed since the onset of the global
economic crisis in 2008. We conclude by pointing out that estimates of illicit fnancial fows from
certain countries with large sovereign wealth funds (SWFs) may be subject to signifcant margins of
error due to incomplete or incorrect recording of SWF-related transactions.

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i Illicit Financial Flows from Developing Countries: 2001-2010
Executive Summary
Two main issues, which arose in the past year, encouraged us to supplement our standard
methodology used to estimate illicit fows based on the World Bank Residual method adjusted for
trade misinvoicing.
First, we investigated the net measurement of inward from outward capital fight traditionally used
by economists in academic journals. We reaffrm our commitment to a gross outfow approach,
rather than a net approach, because only a return of licit capital that is recorded can offset loss of
capital. The return of unrecorded and illicit capital cannot be used for productive purposes. In other
words, the gross/net issue is linked to the nature of the capital.
Second, we explored the effect of the global fnancial crisis on both illicit and licit fows, determining
that the residual method of estimating illicit fows adjusted for trade misinvoicing may include some
licit capital as well as illicit. Moreover, if the CED+GER method includes licit capital, the support for
a gross outfows approach is strengthened, as one cannot be sure whether the inward capital fight
is licit or illicit in nature. Therefore, we present estimates of illicit fows using both the CED+GER
method and the conservatively focused Hot Money Narrow method adjusted for trade misinvoicing
(HMN+GER).
A frm judgment as to which method provides a more accurate method for estimating illicit fows
is somewhat premature at this stage. While the HMN+GER method provides more conservative
estimates of illicit outfows, it may exclude certain illicit transactions such as round-tripped FDI
which could be erroneously recorded as private sector fows. We invite readers to comment on the
appropriateness of the two methodologies for estimating illicit fows including reasons why one
should be preferred over the other.
Using robust (non-normalized) estimates for both measures, we found that in 2010 developing
countries lost between US$858.8 billion to US$1,138 billion, implying that as much as US$279 billion
of the higher fgure could be licit capital fows of the private sectoroutfows that took place as a
result of normal portfolio maximizing considerations. While the two estimates were quite close in
the early 2000s, capital market liberalization in many large emerging markets may have encouraged
more licit or normal capital fight over the years. The gap between the HMN+GER and CED+GER
estimates widened, reaching a peak in 2008 at the onset of the global economic crisis. In the
following year, outfows of legal capital fight dropped more sharply than illicit outfows. The latter
showed a steady upward trend for all developing countries more or less immune to macroeconomic
shocks and adjustments.
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We then further analyzed the gap between the two non-normalized (or robust) estimates in order
to shed light on possible legal capital fight from the various regions of the developing world during
the 10-year period studied. We observed that in the case of developing Europe, the MENA region,
and Western Hemisphere, the gap tends to widen over time, reaching a peak in 2008 although it has
closed in the following two years. The widening gap is perhaps the result of more normal capital
fight due to a relaxation of capital controls. In all three regions, licit outfows plunged in 2009 due
to the effects of the crisis on domestic and foreign capital markets noted above. In the case of Asia,
the gap, which was almost nonexistent in the early 2000s, began to widen in 2005 and reached a
peak in 2008 at the onset of the crisis. But the gap closed almost completely in 2009 as both licit
and illicit outfows from Asia fell in tandem.
A fnding that is worrisome is that the HMN+GER measure of illicit fows increased at a faster
pace than the CED+GER measure (13.3 percent vs. 12.6 percent). The adverse implication is that
increasing illicit fows are likely to result from a worsening of governance-related drivers given the
scant evidence of a systematic increase in measurement errors.
In order to avoid overlap and to focus more sharply on fows that are likely to be purely illicit, we
analyze trends, shares, and country rankings based on the HMN+GER method. According to this
measure, illicit fows from developing countries in the robust calculation increased by over US$500
billion since 2001 implying a real growth rate of 8.6 percent per annum on average, which exceeded
their average rate of economic growth (6.3 percent per annum). We established that about 80
percent of illicit outfows were channeled through the deliberate misinvoicing of trade, although the
shares of outfows from trade misinvoicing and the balance of payments have fuctuated.

We found that Asia, accounting for 61.2 percent of cumulative outfows, was still the main driver of
such fows from developing countries. Indeed, fve of the ten countries with the largest illicit outfows
(China, Malaysia, the Philippines, India, and Indonesia) are in Asia. The Western Hemisphere, led
by Mexico, follows at 15.6 percent, with the Middle East and North Africa (MENA) at 9.9 percent.
Developing Europe follows MENA in share size, making up 7.0 percent of illicit fows, with the
balance fowing out of Africa (6.3 percent).
MENA had the highest growth rate of illicit capital in real terms (26.3 percent per annum on average),
followed by Africa (23.8 percent), Asia (7.8 percent), Europe (3.6 percent), and Western Hemisphere
(2.7 percent). The rapid growth of outfows from the MENA region was due mainly to the increase
in crude oil prices, which drove the regions current account surplus. It seems that rising oil prices
provide more incentive for unrecorded fows. The fnding is consistent with Almounsor (2005) who
also found a signifcant positive link between illicit outfows and crude oil prices.
Trade misinvoicing continued to be the preferred method of transferring illicit capital from all
regions except the MENA region where it only accounted for 37 percent of total outfows over the
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k Illicit Financial Flows from Developing Countries: 2001-2010
decade ending 2010. At one extreme, Asia preferred trade misinvoicing over balance of payments
leakages by 94 percent to 6 percent. Trade misinvoicing was also the dominant channel of illicit
outfows from the Western Hemisphere (84 percent), Africa (65 percent), and developing Europe
(53 percent).
According to the HMN+GER method, the ten countries with the largest outfows of illicit capital (in
declining order of magnitude) were China, Mexico, Malaysia, Saudi Arabia, the Russian Federation,
the Philippines, Nigeria, India, Indonesia, and the United Arab Emirates. Total outfows from China
over the decade ending 2010 (US$2,742 billion) exceeded total cumulative outfows from all other
nine countries on the list (US$1,728 billion). The new rankings imply that illicit fows impact more
people more adversely than what the previous IFF reports indicated. This is because the CED+GER
rankings included Kuwait, Venezuela, Qatar, and Poland among the top ten countries with the
largest outfows. However, these countries have relatively much higher income and fewer people
living on less than US$2 a day, compared to the Philippines, Nigeria, India, and Indonesia which
are ranked among the top ten countries under the HMN+GER methodology. Hence, the revised
rankings do a much better job of refecting the adverse impact of illicit fows on poverty compared
to the CED+GER method.
Finally, we explored the signifcant statistical issues related to the recording of sovereign wealth
funds (SWFs) in the balance of payments and how incomplete or incorrect recording of SWF-
related transactions can lead to errors in estimating illicit fows (due to errors in recording balance
of payments variables). If, for instance, there is a drawdown of reserve assets to invest in SWFs
and the drawdown is fully recorded, while an SWF-related drawdown to pay off external debt is not
recorded then the increased use of funds is not offset by a decline in external debt which would be
refected in an increase in unrecorded capital outfow. Had the subsequent debt repayment been
correctly recorded, there would have been no change in unrecorded outfows. Errors could also be
introduced in the appropriate recording of reserves due to SWF-related deposits. We conclude that
the criteria as to whether specifc SWF funds are to be considered part of reserve assets should not
be based on mechanical rules but should be based on judgments regarding encumbrance, control,
and ease of availability.
We looked at the net errors and omissions (NEO) in the balance of payments for a group of ten
countries with the largest SWFs. While NEOs are driven by many factors, the purpose was to see
whether there is a simple casual link between SWFs and NEOs given the statistical capacity of the
SWF country. Normally we would expect countries with strong statistical systems to do a better
job of capturing SWF transactions. In general, we found that there is little correlation between the
balance of payments of certain countries with large SWFs and the relative strength or weakness
of their statistical systems. This led us to believe that SWF transactions do not seem to adversely
impact the NEO, although there are a few notable exceptions. The fnding that the NEO in the
balance of payments data reported by United Arab Emirates, Saudi Arabia, and Qatar to the IMF
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are relatively high imply that estimates of illicit fows from these countries must be interpreted with
caution due to the risk of signifcant measurement errors.
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1 Illicit Financial Flows from Developing Countries: 2001-2010
I. Introduction
1. Studies at Global Financial Integrity (GFI) on illicit fnancial fows from developing countries
have been based on the World Bank Residual method (using the change in external debt
or CED version) adjusted for trade misinvoicing. Economists such as Claessens and Naud
(1993), Cumby and Levich (1989), Epstein (2005), Gunter (2004), Ndikumana and Boyce
(2008), Schneider (1997), and others have used this method for many years to estimate the
volume of capital fight from developing countries and entire regions. The methodology used
in GFI studies has been consistent with this overall approach, except for the fact that the
traditional approach netted out fows in both directions, while GFIs methodology is based
on gross outfows. In this report, we revisit our methodology, reaffrming the gross outfow
approach and fne-tuning our balance of payments estimates to provide the reader with
alternative estimates of illicit fnancial fows.
2. The need to broaden the methodology was based on two reasons. First, we looked more
closely at the rationale for preferring the gross outfow approach in contrast to the traditional
net approach. Some economists, such as Fuest and Riedel (2012) and Nitsch (2012), imply
that our gross approach may signifcantly overstate the problem of capital fight.
2
However,
the rationale for netting capital fows rests on the premise that net infows of legitimate capital
(i.e. reversal of capital fight) represent a beneft to a country. Legitimate infows need to
offset the original loss of capital through other channels either within the same year or across
previous years in order to arrive at a net cumulative position over a given period. However,
if we are concerned with estimating illicit fnancial fows or illegal capital fight, the netting
out procedure makes little sense. This is because there is no such concept as net crime
fows in both directions are illicit. Hence, illicit infows which cannot be used productively
and are much more likely to end up in the underground economy provide little or no beneft
to governments. The rationale of netting fows is reasonable in analyses of legal or normal
capital fight. We will show that the method traditionally used by economists may well
capture both normal, or legal, and abnormal, or illegal, capital fight. The gross versus net
issue is therefore linked to the nature of capital (i.e. whether it is licit or illicit) which required
us to examine, more closely, the types of capital included in the traditional versus GFI
methodologies.
3. Second, during the course of our study on illicit fows in connection with the report Illicit
Financial Flows from Developing Countries Over the Decade Ending 2009 (henceforth the
2011 IFF Report), we noticed a sharp decline in total outfows of illicit capital from developing
countries and regions in 2009. However, the 2011 IFF Report found no evidence that major
2
See, for example, Tax Evasion and Tax Avoidance: The Role of International Proft Shifting, Clemens Fuest and Nadine Riedel and Trade
Mispricing and Illicit Flows, Volker Nitsch, in Draining Development? Controlling Flows of Illicit Funds from Developing Countries, edited by
Peter Reuter, The World Bank, 2012, Washington DC.
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2 Global Financial Integrity
developing countries adopted macroeconomic, structural, or governance-related policy
measures which could account for this decline. We attributed the sharp fall in illicit fows to
the slowdown in recorded source of funds (such as new loans and foreign direct investment)
relative to use of funds. This can also be thought of as an increase in the latter relative to the
former. Hence, the need to explain the fall in illicit outfows as a result of the global economic
crisis became apparent. The question was if illicit fows reacted so strongly to an economic
crisis, what is the response of licit or normal capital fight?
4. This report is organized as follows. Section II discusses the rationale for adding a second
methodology to focus more sharply on illicit fows and minimize the risk of including legitimate
capital fows. We will compare estimates of illicit fows using the new approach against the
previous method based on change in external debt (CED) adjusted for trade misinvoicing
based on the gross excluding reversals (GER) method. To maintain a sharp focus, section
III presents our analysis of the trends in illicit outfows using the new non-normalized
methodology from developing countries and regions over the period 2001-2010. Section IV
discusses the impact of sovereign wealth funds (SWFs) on the reliability of estimates of illicit
fows from developing countries that maintain large SWFs. The fnal section will draw the main
conclusions of this study.
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3 Illicit Financial Flows from Developing Countries: 2001-2010
II. Coverage of Flows in the
World Bank Residual Method
5. The World Bank Residual method estimates the gap between recorded source of funds
and use of funds. The equation is quite straightforward. If a countrys source of funds total
US$100 million and its use of funds only amounts to US$75 million, then the Residual method
indicates that US$25 million in unrecorded capital must have leaked out of the balance
of payments. The approach we adopted thus far in our studies assumes that if fows are
unrecorded then they must be illicit, because there is no logical reason why legitimate capital
transactions should go unrecorded.
6. In economic literature, the World Bank Residual measure is typically used in isolation
without consideration of the balance of payments identity from which it is derived, as shown
by Claessens and Naud (1993). The conclusion that the gap between recorded fows is
unrecorded (and therefore illicit) follows from this isolation. However, full balance of payments
accounting reveals that the gap between the source of funds and use of funds may include
some licit as well as illicit fows. The following analysis shows why licit fows may be included.
7. As Claessens and Naud (1993) demonstrate, the equation for the World Bank Residual
method can be derived directly from the balance of payments identity. Using their
nomenclature, let A be the current account balance, B represent net equity fows (including
net foreign direct investment and portfolio investment), C the other short-term capital of other
sectors, D the portfolio investments involving other bonds, E the change in deposit-money-
banks foreign assets, F the change in reserves of the central bank, G the net errors and
omissions (NEO), and H the change in external debt. Then, equation (1) demonstrates the
balance of payments identity:
A + B + C+ D + E + F + G + H = 0 (1)
Or, C + D + E + G = - (A + B + F + H) (2)
Equation (2) implies that recorded (and therefore legal) private capital fows (C + D + E) plus
net errors and omissions (G) must equal the negative of the sum of the current account
balance (A), net equity fows (B), change in reserves (F), and the change in external debt (H).
The right hand side of the above equation is the World Bank Residual equation.

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4 Global Financial Integrity
8. One could estimate capital fight using either the left- or right-hand side of the above equation
the result will be equivalent. Table A demonstrates that the World Bank Residual estimates
of capital fight can be derived using the 1991 balance of payments data reported by China
and the United States to the IMF. Because the classifcation of the balance of payments items
is consistent with those used by Claessens and Naud in accordance with the Balance of
Payments Manual in effect at the time, we had to use the data published in the 1992 Balance of
Payments Yearbook. The reported data show that, in fact, the right hand side of the equation
(private sector capital fows plus the NEO) is equal to the World Bank Residual estimate based
on change in external debt (with sign reversed). This implies that the illicit component of the CED
method (i.e., the NEO) is simply the difference between the CED estimates and private sector licit
fows. Hence, more conservative estimates of illicit fows are based on the illicit component of the
CED plus trade misinvoicing based on the gross excluding reversals (GER) method.
Balance of Payments Components
Scale
United States
(US$ billions)
China
(US$ millions)
A. Current Account -3.69 13,765.00
Capital Account (B+C+D+E+F+H) 4.81 -14,298.00
B. Net Equity Flows -36.64 4,038.00
FDI abroad -27.15 -913.00
FDI in the country 11.50 4,366.00
Portfolio invest. (corporate equities) -20.99 585.00
C. Other short-term capital 6.40 -196.00
Other sectors -6.17 -196.00
Resident offcial sector 12.57 0.00
D. Portfolio investment
Other bonds 31.64 -8,143.00
E. Change in DMB foreign assets -8.80 1,655.00
Short-term capital -15.50 558.00
Long-term capital 6.70 1,097.00
F. Reserves 5.76 -14,537.00
H. Other long-term capital 6.45 2,885.00
Resident offcial sector 6.45 2,236.00
Other sectors 0.00 649.00
G. Net errors and omissions -1.12 533.00
Memoranda Items
Balance of payments check 2/ 0.00 0.00
Private sector fows +NEO (C+D+E+G) 28.12 -6,151.00
World Bank Residual (A+B+F+H) -28.12 6,151.00
1/ Corresponds to the format and fgures published in the Balance of Payments Yearbook, Part 1, 1992, IMF.
The position of items H and G are not in the order that they appear in Recent Estimates of Capital Flight, Stijn
Claessens and David Naude (1993). As item H is classifed under the capital account, the order was switched with
item G.
2/ The balance of payments check consists of the fact that the current account plus the capital account and the net
errors and omissions must sum to zero.
3/ According to the BOP identity, as pointed out by Claessens and Naude, the BOP equation implies that C+D+E+G
= -(A+B+F+H). The last two line items verify this for the United States and China.
Table A. The United States and China: Balance of Payments
Components, 1991 1/
(in U.S. dollars)
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5 Illicit Financial Flows from Developing Countries: 2001-2010
9. According to the above formulation, a narrower version of illicit fows can be derived simply by
adding the NEO to the GER estimates of trade misinvoicing. The NEO has been traditionally
used by economists as the Hot Money Narrow (HMN) method. As we pointed out in our 2008
study, there are three versions of the Hot Money method, starting with the Narrow version and
progressively including more types of private sector fows.
3
The broader Hot Money measures
yield larger estimates of capital fight. However, the broader Hot Money method suffers
from the same drawback as the World Bank Residual method: both methodologies produce
estimates that could include both licit and illicit fows. This goes against the purpose of GFI
studies, which is to look solely at illicit fows.


10. Licit capital fight can be simply estimated as the difference between the World Bank Residual
estimates and the HMN. Chart 1 plots normalized and non-normalized estimates based
on the CED+GER and the HMN+GER methods. The green and the purple lines represent
non-normalized CED+GER and HMN+GER estimates which are also presented in Table
B.
4
We can see that in the early 2000s, the two lines were quite close. The gap between
the two lines, representing licit capital fight, increased as capital controls were eased in
many large emerging markets such as in Brazil, China, India, Mexico, and Russia. The gap
is widest in 2008, at the onset of the global economic crisis. Then in the following year, as
outfows of legal capital fight dropped much more sharply than did illicit outfows, the gap
Chart 1. Volume of Illicit Financial Flows in Nominal Terms
from All Developing Countries 2001-2010 1/
(in millions of U.S. dollars)
3
Reference, Illicit Financial Flows from Developing Countries: 2002-2006, Dev Kar and Devon Cartwright-Smith,
Global Financial Integrity, December 2008, pp. 4-5.
4
See Kar, Dev, and Sarah Freitas, Illicit Financial Flows from Developing Countries Over the Decade Ending 2009,
Global Financial Integrity, 2011 for details on the process of normalization.
!"
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Chart 1. Vo|ume of I|||c|t I|nanc|a| I|ows |n Nom|na| 1erms from A||
Deve|op|ng Countr|es 2001-2010 1]
(|n m||||ons of U.S. do||ars)
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6 Global Financial Integrity
narrowed followed by some widening in 2010 as licit outfows picked up along with the pace
of economic activity. Outfows of licit capital fell in 2009 because economic agents retained
more capital domestically due to the fnancial squeeze resulting from the crisis and the fact
that major capital markets in the United States and Europe were in turmoil.
Table B. Four Estimates of Capital Flight, All Developing Countries, 2001-2010
(in billions of U.S. dollars)

Non-normalized IFFs (CED+GER) 1/
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average Logarithmic
Africa 24.1 25.0 33.2 41.6 37.4 53.7 85.3 101.9 76.3 86.1 564.6 56.5 18.08
Asia 225.5 216.1 273.4 345.6 425.0 497.6 535.6 608.9 423.1 584.0 4,134.9 413.5 12.03
Developing Europe 72.3 64.4 104.1 128.7 104.9 141.0 253.0 329.2 111.7 126.3 1,435.6 143.6 10.83
MENA 55.8 37.8 89.6 133.0 157.7 218.3 283.4 305.9 186.6 178.4 1,646.4 164.6 20.21
Western Hemisphere 99.5 98.7 116.7 120.0 128.7 135.7 189.6 213.8 138.5 163.1 1,404.2 140.4 7.07
All Developing Countries 477.1 441.9 617.0 768.9 853.7 1,046.2 1,346.9 1,559.8 936.1 1,138.0 9,185.7 918.6 12.61
Normalized IFFs (CED+GER) 1/
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average Logarithmic
Africa 9.6 16.0 28.1 33.3 31.7 48.3 77.5 93.9 72.3 63.0 473.6 47.4 24.73
Asia 221.4 192.5 253.7 331.5 395.0 383.5 424.4 513.6 388.8 490.6 3,595.0 359.5 10.40
Developing Europe 67.3 56.0 92.3 109.5 91.0 134.4 242.2 314.5 80.4 43.3 1,230.9 123.1 4.63
MENA 49.9 32.6 84.7 128.6 151.5 210.1 218.1 288.8 175.0 158.0 1,497.3 149.7 20.07
Western Hemisphere 81.1 93.9 108.7 97.3 110.8 125.1 154.8 149.9 127.1 137.0 1,185.7 118.6 6.22
All Developing Countries 429.3 391.0 567.5 700.3 780.0 901.3 1,117.1 1,360.7 843.6 891.9 7,982.5 798.3 11.45
Revised IFFs (HMN+GER Non-normalized) 2/
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average Logarithmic
Africa 11.1 8.8 9.9 17.9 35.5 46.5 59.1 74.4 70.5 51.1 384.8 38.5 29.15
Asia 177.8 193.0 244.2 332.9 387.9 384.6 418.8 478.3 415.5 535.7 3,568.8 356.9 12.43
Developing Europe 41.1 23.5 32.4 39.4 29.4 19.4 44.2 56.7 46.5 73.7 406.3 40.6 8.03
MENA 33.2 8.0 7.3 22.1 63.7 55.6 41.1 140.7 141.3 89.2 602.3 60.2 31.74
Western Hemisphere 67.2 66.4 65.3 77.6 98.5 82.7 106.7 121.1 102.1 109.3 896.9 89.7 7.06
All Developing Countries 330.5 299.8 359.0 490.0 615.1 588.7 669.9 871.3 776.0 858.8 5,859.2 585.9 13.28
Revised IFFs (HMN+GER Normalized) 2/
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average Logarithmic
Africa 7.5 7.1 9.3 11.7 30.0 44.0 51.9 70.1 69.2 41.5 342.3 34.2 32.38
Asia 175.2 189.9 241.9 328.8 379.1 368.0 409.2 461.9 398.2 523.1 3,475.4 347.5 12.13
Developing Europe 39.2 18.9 25.7 23.5 27.1 18.5 36.6 49.7 28.6 30.2 297.8 29.8 2.94
MENA 28.1 6.7 5.5 20.4 61.1 53.0 38.5 129.5 134.6 82.2 559.6 56.0 34.01
Western Hemisphere 62.3 62.0 62.4 71.0 91.6 78.8 95.9 97.7 91.6 106.1 819.4 81.9 6.59
All Developing Countries 312.3 284.5 344.7 455.5 588.9 562.3 632.0 809.0 722.1 783.2 5,494.6 549.5 12.88
1/ Estimates include both licit as well as illicit fnancial fows. Estimates updated from 2011 IFF Update.
2/ Estimates pertain to illicit fnancial fows.
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7 Illicit Financial Flows from Developing Countries: 2001-2010
11. Charts 2a-2e show the gap between two estimates representing possible legal capital fight
from the various regions of the developing world. We observe that in the case of developing
Europe, the MENA region, and Western Hemisphere (charts 2c -2e)the gap tends to widen
over time, reaching a peak in 2008. In all three regions, licit outfows plunged in 2009 due
to the effects of the crisis in domestic and foreign capital markets noted above. In contrast,
Chart 2b indicates that the gap in Asia, which was almost nonexistent in the early 2000s,
1/ Estimates of GER in the CED+GER and the HMN+GER lines are non-normalized. All tables and charts in section III of this report and
Tables 1, 2, and 14 of the Appendix use non-normalized estimates, as discussed in the following section on normalization.
Chart 2. Illicit Financial Flows by Region, 2001-2010 1/
(in millions of U.S. dollars)
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Chart 2a. Vo|ume of I|||c|t I|nanc|a| I|ows |n
Nom|na| 1erms from Afr|ca, 2001-2010
(|n m||||ons of U.S. do||ars)
-./01.2"345654789:;<=>" ?@301.2"345654789:;<=>"
Chart 2a. Volume of Illicit Financial Flows in
Nominal Terms from Africa, 2001-2010
(in millions of U.S. dollars)
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Chart 2b. Vo|ume of I|||c|t I|nanc|a| I|ows |n
Nom|na| 1erms from As|a, 2001-2010
(|n m||||ons of U.S. do||ars)
-./01.2"345654789:;<=>" ?@301.2"345654789:;<=>"
Chart 2b. Volume of Illicit Financial Flows in
Nominal Terms from Asia, 2001-2010
(in millions of U.S. dollars)
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Chart 2c. Vo|ume of I|||c|t I|nanc|a| I|ows |n Nom|na|
1erms from Deve|op|ng Lurope, 2001-2010
(|n m||||ons of U.S. do||ars)
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Chart 2c. Volume of Illicit Financial Flows in
Nominal Terms from Developing Europe, 2001-2010
(in millions of U.S. dollars)
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Chart 2e. Vo|ume of I|||c|t I|nanc|a| I|ows |n Nom|na|
1erms from Western nem|sphere, 2001-2010
(|n m||||ons of U.S. do||ars)
-./01.2"345654789:;<=>" ?@301.2"345654789:;<=>"
Chart 2e. Volume of Illicit Financial Flows in Nominal
Terms from Western Hemisphere, 2001-2010
(in millions of U.S. dollars)
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Chart 2d. Vo|ume of I|||c|t I|nanc|a| I|ows |n
Nom|na| 1erms from MLNA, 2001-2010
(|n m||||ons of U.S. do||ars)
-./01.2"345654789:;<=>" ?@301.2"345654789:;<=>"
Chart 2d. Volume of Illicit Financial Flows
in Nominal Terms from MENA, 2001-2010
(in millions of U.S. dollars)
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8 Global Financial Integrity
began to widen in 2005 and reached a peak in 2008 at the onset of the crisis, closing almost
completely in 2009 as both licit and illicit outfows fell in tandem. In the last year, both types
of fows recovered along with increase in global economic activity. China, Malaysia, the
Philippines, and India led Asia as the major drivers of licit and illicit fows. The relaxation of
capital controls by these countries over the years perhaps encouraged more legal or normal
capital fight accounting for the widening regional gap between the CED+GER and HMN+GER
measures through 2008. Further research is needed in order to analyze the factors driving licit
capital fows from the various regions. For instance, legal capital fight seems to be driving
the widening gap between the two measures in the case of developing Europe. In fact, the
Central Bank of Russia as well as the IMF corroborates the existence of large capital fight
from the country, which are predominantly recorded private sector outfows.
a. Normalization through use of flters
12. As Chart 1 showed, the HMN+GER approach to estimating illicit fows is more conservative
than the CED+GER approach, which may include some legitimate private sector capital fows.


Moreover, Chart 3 shows that the conservative or normalized GER estimates are so close
to the non-normalized estimates that not much would be gained by generating a range.
Therefore, non-normalized GER estimates will be used throughout the remainder of the report
in generating both the HMN+GER and CED+GER estimates.
Chart 3. Normalized vs. Non-normalized GER, 2001-2010
(in millions of U.S. dollars)
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Chart 3. Norma||zed vs. Non-norma||zed GLk, 2001-2010
(|n m||||ons of U.S. do||ars)
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9 Illicit Financial Flows from Developing Countries: 2001-2010
III. Trends in Illicit Financial Flows from
Developing Countries and Regions
13. Table B shows estimates of illicit fnancial fows from developing countries based on the
HMN+GER and CED+GER methods. Using non-normalized estimates, the data indicate
that on average, developing countries lost between US$585.9 billion to US$918.6 billion per
annum over the period 2001-2010. In 2010, they lost a minimum of US$858.8 billion and as
much as US$1,138.0 billion.
14. We also observe that the HMN+GER measure has increased at a faster pace than the
CED+GER measure (13.3 percent vs. 12.6 percent). The relatively faster rate of increase
in purely illicit outfows is worrisome given that there is no reason for human statistical
errors (included in both the CED and the HMN measures) to have increased in a systematic
manner throughout the decade. In fact, with the increasing availability and adoption of new
technologies, and the provision of technical assistance by the IMF to developing countries in
order to build their statistical capacities, one would expect the proportion of statistical errors
to decline over the past decade.
5
The implication is that the signifcant increase in illicit fows
is likely to result from a worsening of governance-related drivers.
15. It is somewhat premature to make a defnitive judgment as to which method provides a
more accurate method for estimating illicit fows. While the HMN+GER method is the most
conservative measure, it may exclude some illicit transactions (such as round-tripped FDI)
which show up as recorded private sector fows. We invite our readers to provide comments
on the two alternative methodologies and the reasons why one of them should be preferred
over the other.
16. For the sake of brevity and sharper focus on illicit fows, we shall henceforth confne
the discussion of trends, shares, and country rankings in terms of the HMN+GER
estimates. Going by that measure, the increase in illicit fows of over US$500 billion since
2001 implies a nominal growth rate of 13.3 percent per annum (Table C). In infation-adjusted
or real terms, illicit fows grew by 8.6 percent per annum on average (Table D), which
exceeded their average rate of economic growth (6.3 percent per annum). About 20.0 percent
of total outfows were channeled through balance of payments leakages while the bulk
(approximately 80.0 percent) was transferred through the deliberate misinvoicing of external
trade. Over the decade, the shares of outfows from trade misinvoicing and the balance of
payments have fuctuated. In 2004, trade misinvoicing reached a peak of 86.1 percent of total
IFFs, dropping to a low of 62.3 percent in 2009. However, in 2010 outfows through trade
misinvoicing picked up again to reach 64.2 percent of the total (Table C).

5
There is no evidence that net errors and omissions have a clear increasing pattern to them; reference IMF Committee on Balance of
Payments Statistics, Annual Report 2011, IMF, Table 1, pp. 17-18.
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10 Global Financial Integrity
Table C. Illicit Financial Flows by Region in Nominal Terms 1/
(millions of U.S. dollars, unless otherwise indicated)

HMN (Hot Money Narrow, Balance of Payments component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total
Share of Region in
Total (in %) 1/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 2,597.64 3,221.96 4,147.09 1,705.94 20,296.96 17,871.45 17,825.18 23,488.48 30,941.93 20,755.05 142,851.68 12.23 -49.08 35.50
Asia 11,425.56 6,430.32 6,696.46 6,656.05 15,306.33 16,869.06 17,679.33 22,225.26 66,647.09 91,726.77 261,662.22 22.41 27.34 30.67
Developing Europe 12,932.53 11,479.23 15,647.52 10,397.39 16,150.18 9,754.15 27,928.90 43,109.26 18,469.56 23,145.48 189,014.21 16.19 20.20 10.25
MENA 12,844.44 4,248.36 4,243.83 2,841.25 51,940.31 44,103.30 31,300.12 114,750.53 100,655.75 67,021.29 433,949.18 37.17 -50.18 44.33
Western Hemisphere 13,725.55 13,030.16 10,447.70 12,129.15 22,021.10 7,101.79 8,805.86 1,804.47 22,249.87 28,820.58 140,136.22 12.00 22.80 -0.27
All Developing Countries 53,525.72 38,410.03 41,182.60 33,729.78 125,714.88 95,699.75 103,539.39 205,378.00 238,964.21 231,469.16 1,167,613.50 100.00 -3.24 25.21
GER (Gross Excluding Reversals, Trade Mispricing component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 1/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 8,500.58 5,615.80 5,752.05 16,223.58 15,223.01 28,613.35 41,239.88 50,915.87 39,584.24 30,301.94 241,970.30 5.16 -30.63 27.00
Asia 166,408.68 186,549.71 237,461.31 326,277.67 372,635.85 367,754.60 401,161.88 456,103.67 348,866.43 443,945.48 3,307,165.27 70.49 21.42 10.91
Developing Europe 28,196.78 12,030.67 16,711.71 29,026.61 13,298.00 9,606.50 16,245.02 13,624.66 28,039.81 50,525.16 217,304.91 4.63 44.50 5.02
MENA 20,385.49 3,796.51 3,025.28 19,278.57 11,775.39 11,467.04 9,841.22 25,978.67 40,684.36 22,167.25 168,399.77 3.59 -83.53 17.11
Western Hemisphere 53,459.84 53,411.79 54,841.52 65,463.52 76,459.43 75,583.04 97,921.87 119,301.74 79,868.07 80,433.72 756,744.54 16.13 0.70 7.27
All Developing Countries 258,803.00 246,137.54 303,518.10 421,816.06 463,176.89 466,563.29 528,479.34 603,580.03 483,156.50 551,710.80 4,691,584.79 100 12.43 9.95
Total HMN + GER
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 1/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 11,098.22 8,837.76 9,899.14 17,929.52 35,519.97 46,484.80 59,065.06 74,404.35 70,526.16 51,056.99 384,821.97 6.57 -38.13 29.15
Asia 177,834.24 192,980.03 244,157.77 332,933.72 387,942.18 384,623.66 418,841.21 478,328.93 415,513.52 535,672.25 3,568,827.49 60.91 22.43 12.43
Developing Europe 41,129.31 23,509.90 32,359.23 39,424.00 29,448.18 19,360.65 44,173.92 56,733.92 46,509.37 73,670.64 406,319.12 6.93 36.87 8.03
MENA 33,229.92 8,044.87 7,269.11 22,119.82 63,715.70 55,570.33 41,141.34 140,729.20 141,340.11 89,188.53 602,348.95 10.28 -58.47 31.74
Western Hemisphere 67,185.39 66,441.95 65,289.21 77,592.67 98,480.53 82,684.82 106,727.73 121,106.21 102,117.94 109,254.30 896,880.76 15.31 6.53 7.06
All Developing Countries 330,477.08 299,814.50 358,974.46 489,999.73 615,106.56 588,724.27 669,949.26 871,302.61 776,007.11 858,842.70 5,859,198.29 100.00 9.65 13.28
HMN Percent of Total 16.2 12.8 11.5 6.9 20.4 16.3 15.5 23.6 30.8 27.0 19.9 Ave. HMN % (2001-2010) 18.1
GER Percent of Total 78.3 82.1 84.6 86.1 75.3 79.2 78.9 69.3 62.3 64.2 80.1 Ave. GER % (2001-2010) 76.0
Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries.
1/ Based on cumulative outfows from the region in total outfows from developing countries over the period 2001-2010
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11 Illicit Financial Flows from Developing Countries: 2001-2010
Table C. Illicit Financial Flows by Region in Nominal Terms 1/
(millions of U.S. dollars, unless otherwise indicated)

HMN (Hot Money Narrow, Balance of Payments component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total
Share of Region in
Total (in %) 1/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 2,597.64 3,221.96 4,147.09 1,705.94 20,296.96 17,871.45 17,825.18 23,488.48 30,941.93 20,755.05 142,851.68 12.23 -49.08 35.50
Asia 11,425.56 6,430.32 6,696.46 6,656.05 15,306.33 16,869.06 17,679.33 22,225.26 66,647.09 91,726.77 261,662.22 22.41 27.34 30.67
Developing Europe 12,932.53 11,479.23 15,647.52 10,397.39 16,150.18 9,754.15 27,928.90 43,109.26 18,469.56 23,145.48 189,014.21 16.19 20.20 10.25
MENA 12,844.44 4,248.36 4,243.83 2,841.25 51,940.31 44,103.30 31,300.12 114,750.53 100,655.75 67,021.29 433,949.18 37.17 -50.18 44.33
Western Hemisphere 13,725.55 13,030.16 10,447.70 12,129.15 22,021.10 7,101.79 8,805.86 1,804.47 22,249.87 28,820.58 140,136.22 12.00 22.80 -0.27
All Developing Countries 53,525.72 38,410.03 41,182.60 33,729.78 125,714.88 95,699.75 103,539.39 205,378.00 238,964.21 231,469.16 1,167,613.50 100.00 -3.24 25.21
GER (Gross Excluding Reversals, Trade Mispricing component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 1/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 8,500.58 5,615.80 5,752.05 16,223.58 15,223.01 28,613.35 41,239.88 50,915.87 39,584.24 30,301.94 241,970.30 5.16 -30.63 27.00
Asia 166,408.68 186,549.71 237,461.31 326,277.67 372,635.85 367,754.60 401,161.88 456,103.67 348,866.43 443,945.48 3,307,165.27 70.49 21.42 10.91
Developing Europe 28,196.78 12,030.67 16,711.71 29,026.61 13,298.00 9,606.50 16,245.02 13,624.66 28,039.81 50,525.16 217,304.91 4.63 44.50 5.02
MENA 20,385.49 3,796.51 3,025.28 19,278.57 11,775.39 11,467.04 9,841.22 25,978.67 40,684.36 22,167.25 168,399.77 3.59 -83.53 17.11
Western Hemisphere 53,459.84 53,411.79 54,841.52 65,463.52 76,459.43 75,583.04 97,921.87 119,301.74 79,868.07 80,433.72 756,744.54 16.13 0.70 7.27
All Developing Countries 258,803.00 246,137.54 303,518.10 421,816.06 463,176.89 466,563.29 528,479.34 603,580.03 483,156.50 551,710.80 4,691,584.79 100 12.43 9.95
Total HMN + GER
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 1/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 11,098.22 8,837.76 9,899.14 17,929.52 35,519.97 46,484.80 59,065.06 74,404.35 70,526.16 51,056.99 384,821.97 6.57 -38.13 29.15
Asia 177,834.24 192,980.03 244,157.77 332,933.72 387,942.18 384,623.66 418,841.21 478,328.93 415,513.52 535,672.25 3,568,827.49 60.91 22.43 12.43
Developing Europe 41,129.31 23,509.90 32,359.23 39,424.00 29,448.18 19,360.65 44,173.92 56,733.92 46,509.37 73,670.64 406,319.12 6.93 36.87 8.03
MENA 33,229.92 8,044.87 7,269.11 22,119.82 63,715.70 55,570.33 41,141.34 140,729.20 141,340.11 89,188.53 602,348.95 10.28 -58.47 31.74
Western Hemisphere 67,185.39 66,441.95 65,289.21 77,592.67 98,480.53 82,684.82 106,727.73 121,106.21 102,117.94 109,254.30 896,880.76 15.31 6.53 7.06
All Developing Countries 330,477.08 299,814.50 358,974.46 489,999.73 615,106.56 588,724.27 669,949.26 871,302.61 776,007.11 858,842.70 5,859,198.29 100.00 9.65 13.28
HMN Percent of Total 16.2 12.8 11.5 6.9 20.4 16.3 15.5 23.6 30.8 27.0 19.9 Ave. HMN % (2001-2010) 18.1
GER Percent of Total 78.3 82.1 84.6 86.1 75.3 79.2 78.9 69.3 62.3 64.2 80.1 Ave. GER % (2001-2010) 76.0
Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries.
1/ Based on cumulative outfows from the region in total outfows from developing countries over the period 2001-2010
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12 Global Financial Integrity
Table D. Illicit Financial Flows by Region in Real Terms 1/
(millions of 2005 U.S. dollars, unless otherwise indicated)

HMN (Hot Money Narrow, Balance of Payments component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 2/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 3,046.90 3,867.83 4,725.81 1,830.78 20,296.96 17,074.15 16,249.92 19,500.26 28,166.71 17,684.04 132,443.35 12.16 -59.28 29.92
Asia 13,401.60 7,719.32 7,630.93 7,143.15 15,306.33 16,116.48 16,116.95 18,451.53 60,669.44 78,154.46 240,710.20 22.11 22.37 25.30
Developing Europe 15,169.20 13,780.33 17,831.09 11,158.29 16,150.18 9,318.99 25,460.74 35,789.54 16,813.01 19,720.77 181,192.13 16.64 14.74 5.71
MENA 15,065.87 5,099.98 4,836.05 3,049.18 51,940.31 42,135.72 28,534.03 95,266.49 91,627.83 57,104.52 394,659.98 36.25 -60.46 38.39
Western Hemisphere 16,099.37 15,642.15 11,905.65 13,016.79 22,021.10 6,784.96 8,027.66 1,498.08 20,254.26 24,556.16 139,806.16 12.84 17.52 -4.38
All Developing Countries 62,782.94 46,109.61 46,929.53 36,198.18 125,714.88 91,430.29 94,389.30 170,505.90 217,531.25 197,219.94 1,088,811.83 100.00 -10.30 20.06
GER (Gross Excluding Reversals, Trade Mispricing component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 2/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 9,970.75 6,741.53 6,554.74 17,410.85 15,223.01 27,336.82 37,595.39 42,270.63 36,033.88 25,818.43 224,956.03 4.95 -39.57 21.78
Asia 195,188.91 223,945.02 270,598.45 350,155.26 372,635.85 351,347.94 365,710.00 378,659.67 317,576.23 378,258.85 3,204,076.19 70.52 16.04 6.35
Developing Europe 33,073.39 14,442.30 19,043.78 31,150.83 13,298.00 9,177.93 14,809.40 11,311.26 25,524.89 43,049.40 214,881.19 4.73 40.71 0.70
MENA 23,911.14 4,557.54 3,447.45 20,689.41 11,775.39 10,955.46 8,971.52 21,567.63 37,035.34 18,887.36 161,798.24 3.56 -96.09 12.29
Western Hemisphere 62,705.67 64,118.59 62,494.51 70,254.26 76,459.43 72,211.05 89,268.22 99,044.93 72,704.62 68,532.67 737,793.96 16.24 -6.09 2.85
All Developing Countries 324,849.86 313,804.99 362,138.93 489,660.62 489,391.68 471,029.19 516,354.53 552,854.12 488,874.95 534,546.71 4,543,505.60 100.00 8.54 6.13
Total HMN + GER
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 2/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 13,017.64 10,609.36 11,280.54 19,241.63 35,519.97 44,410.97 53,845.31 61,770.88 64,200.60 43,502.47 357,399.38 6.35 -47.58 23.83
Asia 208,590.52 231,664.35 278,229.38 357,298.41 387,942.18 367,464.42 381,826.96 397,111.19 378,245.67 456,413.32 3,444,786.39 61.16 17.13 7.81
Developing Europe 48,242.59 28,222.64 36,874.88 42,309.12 29,448.18 18,496.92 40,270.14 47,100.80 42,337.90 62,770.17 396,073.32 7.03 32.55 3.59
MENA 38,977.01 9,657.52 8,283.50 23,738.59 63,715.70 53,091.17 37,505.56 116,834.12 128,663.17 75,991.87 556,458.22 9.88 -69.31 26.32
Western Hemisphere 78,805.05 79,760.74 74,400.16 83,271.04 98,480.53 78,996.00 97,295.88 100,543.02 92,958.88 93,088.82 877,600.12 15.58 0.14 2.65
All Developing Countries 387,632.81 359,914.60 409,068.46 525,858.80 615,106.56 562,459.49 610,743.84 723,360.02 706,406.21 731,766.65 5,632,317.42 100.00 3.47 8.62
HMN Percent of Total 16.2 12.8 11.5 6.9 20.4 16.3 15.5 23.6 30.8 27.0 19.3 Ave. HMN % (2001-2010) 18.1
GER Percent of Total 83.8 87.2 88.5 93.1 79.6 83.7 84.5 76.4 69.2 73.0 80.7 Ave. GER % (2001-2010) 81.9
Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries.
1/ Current dollar estimates are defated by the U.S. Producer Price Index base 2005 (from IMF IFS online database).
2/ Based on cumulative outfows from the region in total outfows from developing countries over the period 2001-2010.
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13 Illicit Financial Flows from Developing Countries: 2001-2010
Table D. Illicit Financial Flows by Region in Real Terms 1/
(millions of 2005 U.S. dollars, unless otherwise indicated)

HMN (Hot Money Narrow, Balance of Payments component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 2/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 3,046.90 3,867.83 4,725.81 1,830.78 20,296.96 17,074.15 16,249.92 19,500.26 28,166.71 17,684.04 132,443.35 12.16 -59.28 29.92
Asia 13,401.60 7,719.32 7,630.93 7,143.15 15,306.33 16,116.48 16,116.95 18,451.53 60,669.44 78,154.46 240,710.20 22.11 22.37 25.30
Developing Europe 15,169.20 13,780.33 17,831.09 11,158.29 16,150.18 9,318.99 25,460.74 35,789.54 16,813.01 19,720.77 181,192.13 16.64 14.74 5.71
MENA 15,065.87 5,099.98 4,836.05 3,049.18 51,940.31 42,135.72 28,534.03 95,266.49 91,627.83 57,104.52 394,659.98 36.25 -60.46 38.39
Western Hemisphere 16,099.37 15,642.15 11,905.65 13,016.79 22,021.10 6,784.96 8,027.66 1,498.08 20,254.26 24,556.16 139,806.16 12.84 17.52 -4.38
All Developing Countries 62,782.94 46,109.61 46,929.53 36,198.18 125,714.88 91,430.29 94,389.30 170,505.90 217,531.25 197,219.94 1,088,811.83 100.00 -10.30 20.06
GER (Gross Excluding Reversals, Trade Mispricing component)
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 2/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 9,970.75 6,741.53 6,554.74 17,410.85 15,223.01 27,336.82 37,595.39 42,270.63 36,033.88 25,818.43 224,956.03 4.95 -39.57 21.78
Asia 195,188.91 223,945.02 270,598.45 350,155.26 372,635.85 351,347.94 365,710.00 378,659.67 317,576.23 378,258.85 3,204,076.19 70.52 16.04 6.35
Developing Europe 33,073.39 14,442.30 19,043.78 31,150.83 13,298.00 9,177.93 14,809.40 11,311.26 25,524.89 43,049.40 214,881.19 4.73 40.71 0.70
MENA 23,911.14 4,557.54 3,447.45 20,689.41 11,775.39 10,955.46 8,971.52 21,567.63 37,035.34 18,887.36 161,798.24 3.56 -96.09 12.29
Western Hemisphere 62,705.67 64,118.59 62,494.51 70,254.26 76,459.43 72,211.05 89,268.22 99,044.93 72,704.62 68,532.67 737,793.96 16.24 -6.09 2.85
All Developing Countries 324,849.86 313,804.99 362,138.93 489,660.62 489,391.68 471,029.19 516,354.53 552,854.12 488,874.95 534,546.71 4,543,505.60 100.00 8.54 6.13
Total HMN + GER
Region/Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Totals
Share of Region in
Total (in %) 2/
Percent Change
2009-2010
Logarithmic Growth
2001-2010
Africa 13,017.64 10,609.36 11,280.54 19,241.63 35,519.97 44,410.97 53,845.31 61,770.88 64,200.60 43,502.47 357,399.38 6.35 -47.58 23.83
Asia 208,590.52 231,664.35 278,229.38 357,298.41 387,942.18 367,464.42 381,826.96 397,111.19 378,245.67 456,413.32 3,444,786.39 61.16 17.13 7.81
Developing Europe 48,242.59 28,222.64 36,874.88 42,309.12 29,448.18 18,496.92 40,270.14 47,100.80 42,337.90 62,770.17 396,073.32 7.03 32.55 3.59
MENA 38,977.01 9,657.52 8,283.50 23,738.59 63,715.70 53,091.17 37,505.56 116,834.12 128,663.17 75,991.87 556,458.22 9.88 -69.31 26.32
Western Hemisphere 78,805.05 79,760.74 74,400.16 83,271.04 98,480.53 78,996.00 97,295.88 100,543.02 92,958.88 93,088.82 877,600.12 15.58 0.14 2.65
All Developing Countries 387,632.81 359,914.60 409,068.46 525,858.80 615,106.56 562,459.49 610,743.84 723,360.02 706,406.21 731,766.65 5,632,317.42 100.00 3.47 8.62
HMN Percent of Total 16.2 12.8 11.5 6.9 20.4 16.3 15.5 23.6 30.8 27.0 19.3 Ave. HMN % (2001-2010) 18.1
GER Percent of Total 83.8 87.2 88.5 93.1 79.6 83.7 84.5 76.4 69.2 73.0 80.7 Ave. GER % (2001-2010) 81.9
Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries.
1/ Current dollar estimates are defated by the U.S. Producer Price Index base 2005 (from IMF IFS online database).
2/ Based on cumulative outfows from the region in total outfows from developing countries over the period 2001-2010.
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14 Global Financial Integrity
17. Adjusting the outfow estimates for infation or for private sector fows marginally alters the
regional shares found previously. Chart 4 shows the shares of cumulative illicit outfows from
the various regions of the developing world over the decade ending 2010 under the HMN+GER
method. We note that Asia still remains the main driver of illicit outfows from developing
countries regardless of the method of estimation. On a cumulative basis, the region
accounted for 61.2 percent of total outfows, mostly due to massive outfows from mainland
China and India. The Western Hemisphere follows at 15.6 percent, with the Middle East and
North Africa (MENA) at 9.9 percent. The MENA region has a smaller share than the Western
Hemisphere in this study compared to the 2011 Update due to the fact that Algeria, Iran,
and Iraq have not fully reported the balance of payments data necessary for the HMN+GER
method. Developing Europe follows MENA in share size, making up 7.0 percent of illicit fows,
with the balance fowing out of Africa (6.3 percent). The relative increase in outfows from
Africa can be mainly attributed to a larger number of countries for which we were able to
collect basic data on the balance of payments and bilateral trade fows. The relative shares are
subject to the caveat that restricted data availability on important countries of certain regions
may understate the regional shares and overstate others.

18. In real or infation-adjusted terms, outfows from MENA grew the fastest at 26.3 percent per
annum on average followed by Africa (23.8 percent), Asia (7.8 percent), Europe (3.6 percent),
and Western Hemisphere (2.7 percent). We note that the rapid growth of outfows from the
MENA region was due mainly to the increase in crude oil prices, which drove MENAs current
account surpluses. The signifcant positive link between illicit outfows and crude oil prices
was noted in a recent study by Almounsor (2005). The real rates of growth in illicit outfows
from the various regions is presented in Chart 5.

Chart 4. Illicit Flows in Real Terms 2001-2010; Regional Shares
in Developing World Total 1/
(in percent)
1/ Based on cumulative outfows from the region as a share of total illicit outfows from developing countries, where illicit outfows
are based estimated on the HMN+GER non-normalized methodology.
6.3
61.2
7.0
9.9
1S.6
Chart 4. I|||c|t I|ows |n kea| 1erms 2001-2010, keg|ona|
Shares |n Deve|op|ng Wor|d 1ota| 1]
Afrlca
Asla
ueveloplng Lurope
MLnA
WesLern Pemlsphere
1/ 8ased on cumulauve ouulows from Lhe reglon as a share of LoLal llllclL ouulows from developlng c
where llllclL ouulows are based on Lhe PMn+CL8 non-normallzed meLhodology.
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15 Illicit Financial Flows from Developing Countries: 2001-2010
19. As noted above, the massive outfows from Asia are mainly driven by China, where the heavily
preferred method of transferring illicit capital is through the misinvoicing of trade (see Chart 6).
20. Trade misinvoicing is the preferred method of transferring illicit capital from all regions except
the MENA region where it accounts for only 37 percent of total outfows for the decade
ending 2010 (Chart 6). In declining order of dominance, the share of trade misinvoicing in total
outfows by region is Asia (94.0 percent), Western Hemisphere (84.0 percent), Africa (65.0
percent), and developing Europe (53.0 percent). Large current account surpluses of countries
in the MENA region driven by crude oil exports entail larger outfows through the balance of
payments. In the case of Europe, the relatively large unrecorded outfows from the Russias
balance of payments dominate regional outfows.
Chart 5. Real Rates of Growth of IFFs from 2001-2010 by Region 1/
1/ Real rates of growth are calculated as the slope of the logarithmic trend over the observed period 2001-2010. Illicit fnancial fows
are calculated under the HMN+GER methodology.
0.00 3.00 10.00 13.00 20.00 23.00 30.00
Afrlca
Asla
ueveloplng Lurope
MLnA
WesLern Pemlsphere
ercent Growth
k
e
g
|
o
n

Chart S. kea| kates of Growth of IIIs from 2001-2010 by keg|on 1]
1/ 8eal raLes of growLh are calculaLed as Lhe slope of Lhe logarlLhmlc Lrend over Lhe observed perlod
2001-2010. llllclL nanclal ows are calculaLed under Lhe PMn+CL8 meLhodology.
Chart 6. Regional Illicit Flows in Nominal Terms 2001-2010;
Shares Related to HMN and GER Components
(average percent shares over 10 years)
6.49
16.0S
3S.31
47.06
62.76
93.S1
83.9S
64.69
S2.94
37.24
!"# $!"# %!"# &!"# '!"# (!!"#
)*+,#
-.*/.01#2.3+*45.0.#
)60+7,#
8.9.:;4+1<#=>0;4.#
?=@)#
Chart 6. keg|ona| I|||c|t I|ows |n Nom|na| 1erms 2001-2010,
Shares ke|ated to nMN and GLk Components (average
percent shares over 10 years)
2?@#AB,:,17.#;6#C,D3.1/*E#F5,0.#;6#G;/,:#
H=I#AH0;**#G0,J.#?+*+19;+7+1<#K>L:;ME#F5,0.#;6#G;/,:#
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16 Global Financial Integrity
21. The countries with the ten highest illicit outfows are China, Mexico, Malaysia, Saudi Arabia,
the Russian Federation, the Philippines, Nigeria, India, Indonesia, and the United Arab
Emirates, in declining order of magnitude (see Chart 7). Cumulative illicit outfows from China
(US$2,742 billion) exceed total cumulative outfows from all other nine countries on the list
(US$1,728 billion).
22. Chart 7 shows the cumulative illicit outfows from the top 20 countries over the period 2001-
2010. Together, the top 10 exporters of illicit capital account for 76 percent of cumulative illicit
outfows from developing countries over the period (Table E). The groups share in total illicit
outfows from developing countries, which was 73 percent in 2001, grew to 80 percent in
2006, and averaged 75.2 percent in 2009-2010.
23. Given that illicit fows are mainly driven by macroeconomic issues captured by corruption
perceptions indicators, the size of the underground economy, and weak regulatory institutions,
we can expect governance related drivers to take a back seat in explaining such outfows.
Rather, to the extent that the HMN+GER method only captures illicit fows, if the HMN+GER
method did in fact capture only illicit fows, we would expect to see a stronger correlation
between such outfows and governance-related indicators, noted above. We plan to study this
issue in more detail in the context a case study on Russia (to be published in early 2013).
Chart 7. Top 20 Countries Cumulative Illicit Flows,
Nominal HMN+GER Non-normalized, 2001-2010
(in billions of U.S. dollars)
2,742
476
283
210
132
138
129
123
109
107
84
64
64
64
36
31
41
40
38
37
0 300 1,000 1,300 2,000 2,300 3,000
Chlna, .8.: Malnland
Mexlco
Malaysla
Saudl Arabla
8usslan lederauon
hlllpplnes
nlgerla
lndla
lndonesla
unlLed Arab LmlraLes
SouLh Afrlca
1halland
CosLa 8lca
lraq
CaLar
Serbla, 8epubllc of
oland
anama
venezuela, 8epubllca 8ollvarlana de
8runel uarussalam
Chart 7. 1op 20 Countr|es' Cumu|anve I|||c|t I|ows, Nom|na|
nMN+GLk Non-norma||zed, 2001-2010
(|n b||||ons of U.S. do||ars)
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17 Illicit Financial Flows from Developing Countries: 2001-2010
Table E. Total Illicit Financial Flows from the Top Ten Developing Countries 1/
(in billions of U.S.dollars)

Country/Region 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Total
Illicit
Outfows
Average
of
Outfows
(where
data is
available)
China, Mainland 142.20 153.80 183.53 251.06 283.48 296.08 326.66 348.42 336.11 420.36 2,741.70 274.17
Non-normalized HMN 4.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 41.38 52.94 99.05 9.91
Non-normalized GER 137.47 153.80 183.53 251.06 283.48 296.08 326.66 348.42 294.73 367.43 2,642.65 264.26
China's Percent of all country IFF 43% 51% 51% 51% 46% 50% 49% 40% 43% 49% 47%
Mexico 36.30 36.71 38.43 41.21 48.10 49.51 61.97 61.13 51.07 51.17 475.61 47.56
Non-normalized HMN 3.30 1.90 4.41 4.82 3.87 1.13 2.46 0.00 16.50 17.74 56.13 5.61
Non-normalized GER 33.00 34.81 34.02 36.39 44.23 48.38 59.51 61.13 34.58 33.43 419.48 41.95
Mexico's Percent of all country IFF 11% 12% 11% 8% 8% 8% 9% 7% 7% 6% 8%
Malaysia 13.07 12.54 17.73 19.58 28.08 29.62 32.48 37.35 30.41 64.38 285.24 28.52
Non-normalized HMN 2.39 0.39 0.00 0.00 6.63 7.46 5.20 8.59 5.24 21.66 57.58 5.76
Non-normalized GER 10.67 12.15 17.73 19.58 21.45 22.16 27.28 28.76 25.17 42.72 227.66 22.77
Malaysia's Percent of all country IFF 4% 4% 5% 4% 5% 5% 5% 4% 4% 7% 5%
Saudi Arabia 0.00 0.00 0.00 0.00 36.44 21.02 16.34 32.97 64.89 38.30 209.96 21.00
Non-normalized HMN 0.00 0.00 0.00 0.00 34.75 20.56 15.63 30.03 60.75 34.38 196.10 19.61
Non-normalized GER 0.00 0.00 0.00 0.00 1.69 0.46 0.72 2.94 4.13 3.92 13.86 1.39
Saudi Arabia's Percent of all country IFF 0% 0% 0% 0% 6% 4% 2% 4% 8% 4% 4%
Russian Federation 28.70 6.08 11.68 20.36 7.91 0.00 13.35 11.28 8.60 43.64 151.59 15.16
Non-normalized HMN 9.56 6.08 9.18 5.87 7.91 0.00 13.35 11.28 1.73 8.29 73.23 7.32
Non-normalized GER 19.14 0.00 2.50 14.49 0.00 0.00 0.00 0.00 6.88 35.35 78.35 7.84
Russian Federation's Percent of all country IFF 9% 2% 3% 4% 1% 0% 2% 1% 1% 5% 3%
Philippines 6.54 7.09 11.18 12.24 17.48 17.80 22.46 18.11 8.29 16.62 137.82 13.78
Non-normalized HMN 0.00 0.00 0.90 0.27 1.80 1.59 2.08 1.12 0.00 1.99 9.75 0.98
Non-normalized GER 6.54 7.09 10.29 11.97 15.68 16.21 20.38 16.99 8.29 14.63 128.07 12.81
Philippines' Percent of all country IFF 2% 2% 3% 2% 3% 3% 3% 2% 1% 2% 2%
Nigeria 0.91 0.00 0.00 1.70 17.83 19.14 19.30 24.18 26.33 19.65 129.04 12.90
Non-normalized HMN 0.00 0.00 0.00 0.00 17.34 17.15 14.40 20.74 26.33 15.35 111.31 11.13
Non-normalized GER 0.91 0.00 0.00 1.70 0.48 1.98 4.90 3.44 0.00 4.30 17.73 1.77
Nigeria's Percent of all country IFF 0% 0% 0% 0% 3% 3% 3% 3% 3% 2% 2%
India 7.88 8.29 9.45 22.61 30.94 10.51 4.92 26.82 0.28 1.61 123.32 12.33
Non-normalized HMN 0.71 0.19 0.00 0.00 0.54 0.00 0.00 0.00 0.28 1.61 3.33 0.33
Non-normalized GER 7.17 8.10 9.45 22.61 30.40 10.51 4.92 26.82 0.00 0.00 119.99 12.00
India's Percent of all country IFF 2% 3% 3% 5% 5% 2% 1% 3% 0% 0% 2%
Indonesia 0.32 2.87 15.12 17.64 11.38 12.72 15.49 16.54 11.56 5.21 108.86 10.89
Non-normalized HMN 0.00 1.76 3.51 3.09 0.18 0.00 1.37 0.24 2.97 1.48 14.61 1.46
Non-normalized GER 0.32 1.11 11.61 14.54 11.20 12.72 14.12 16.30 8.59 3.73 94.25 9.43
Indonesia's Percent of all country IFF 0% 1% 4% 4% 2% 2% 2% 2% 1% 1% 2%
United Arab Emirates 4.60 0.00 0.80 1.00 5.50 11.80 0.00 51.70 23.50 7.60 106.50 10.65
Non-normalized HMN 4.60 0.00 0.80 1.00 5.50 11.80 0.00 51.70 23.50 7.60 106.50 10.65
Non-normalized GER 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
U.A.E.'s percent of All Country IFF 1% 0% 0% 0% 1% 2% 0% 6% 3% 1% 2%
Total of top 10 Countries 240.52 227.39 287.92 387.39 487.16 468.19 512.98 628.50 561.05 668.55 4,469.65 446.96
Top 10 Countries as percent
of all country IFFs
73% 76% 80% 79% 79% 80% 77% 72% 72% 78% 76%
Developing World total 330.48 299.81 358.97 490.00 615.11 588.72 669.95 871.30 776.01 858.84 5,859.20 585.92
1/ Top 10 country rankings based on cumulative illicit outfows (non-normalized HMN+GER) over the period 2001-2010.
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18 Global Financial Integrity
24. Table F shows the change in country rankings between the 2011 IFF Report utilizing the
CED+GER method and this 2012 IFF Report, adding the HMN+GER calculation. China
retains the top spot by far, while Mexico and Saudi Arabia retain the second and fourth ranks
respectively. However, Malaysia moves up two ranks and several countries move from the
11-20 rankings up to the top ten, including the Philippines, Nigeria, India, and Indonesia. The
United Arab Emirates slips from the 7
th
place in the CED+GER rankings to the 10
th
place while
Qatar comes in at the 15
th
place.
25. The new rankings imply that corruption and the resulting illicit fows impacts more people
more adversely than what the IFF previous reports indicated. Specifcally, if we consider
that Kuwait, Venezuela, Qatar, and Poland, which are countries with lower populations and
a smaller proportion of people living on less than US$2 a day, compared to the Philippines,
Nigeria, India, and Indonesia which have much larger populations with a far greater proportion
of people who are considered poor by that measure, it is apparent that the revised rankings do
a better job of measuring the adverse impact of illicit fows on poverty. The issue of entrenched
poverty among the previous list of countries with the ten highest illicit outfows pales in
comparison to the ten highest exporters of illicit capital indicated by the HMN+GER method.

26. The role of specifc countries in driving overall illicit fows from developing countries has
varied over time. If we compare the share of each country at the beginning of the period
(2001) with the average share for the period, then among the top ten countries, the share of
illicit outfows from the country in total outfows have increased for all countries except the
Russian Federation (see Table F). Chinas role in driving illicit fows from developing countries
remained strong, accounting for an average of 47 percent of all illicit outfows over the
decade. Though Chinas share has fuctuated over the past ten years, in 2010 it increased to
49 percent, a high that has not been reached since 2007. Chart 8 shows that Saudi Arabia
Rank
2011 Update
(CED+GER)
Population Under
US$2 per Day (%)
2012 Update
(HMN+GER)
Population Under
US$2 per Day (%)
1 China, Mainland 29.79 (2008) China, Mainland 29.79 (2008)
2 Mexico 5.19 (2008) Mexico 5.19 (2008)
3 Russian Federation 0.05 (2009) Malaysia 2.27 (2009)
4 Saudi Arabia . Saudi Arabia .
5 Malaysia 2.27 (2009) Russian Federation 0.05 (2009)
6 Kuwait 21.69 (2009) Philippines 41.53 (2009)
7 United Arab Emirates . Nigeria 84.49 (2010)
8 Venezuela, Rep. Bol. de 12.91 (2006) India 68.72 (2010)
9 Qatar . Indonesia 46.12 (2010)
10 Poland 0.2 (2009) United Arab Emirates .
Table F. Changes in Cumulative Non-Normalized Illicit Outfow
Rankings in Nominal Terms
1/ 2011 Update refers to the report, Illicit Financial Flows from Developing Countries Over the Decade Ending 2009, Global
Financial Integrity, December 2011. 2012 Update refers to the report Illicit Financial Flows from Developing Countries: 2001-
2010, Global Financial Integrity, December 2012.
2/ Source, GFI staff estimates and World Bank Development Indicators database. Years are in paraentheses and represent the
latest period for which data was available.
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19 Illicit Financial Flows from Developing Countries: 2001-2010
and Nigeria, which are exporters of oil, are now becoming more important sources of illicit
capital. As shown in Chart 9, oil prices and illicit outfows seem to be signifcantly correlated.
Russia is an oil exporter where this link does not hold. The reason behind the link between oil
prices and illicit outfows will be explored in a forthcoming case study on Russia.
27. The private sector in developing countries transfers illicit capital into the global shadow
fnancial system through different channels, depending on the country of origin. For instance,
while trade mispricing is the preferred method of sending illicit funds out of China, the
balance of payments (captured by the HMN method) is the major channel for transferring
unrecorded capital from oil exporters such as Nigeria, the Russian Federation, Saudi Arabia,
and Indonesia. Mexico and Malaysia are the only oil exporters where trade mispricing is the
preferred method of transferring illicit capital abroad.
28. The accompanying heat map of the distribution of illicit fows from developing countries
(Chart 10) provides a birds eye view of the problem in terms of the severity of the issue in
relation to the absolute volume of outfows. It should be noted that a heat map based on the
distribution of illicit fows to GDP or illicit fows to population will present quite a different
picture from the one presented here based on level outfows only (see Chart 10). For instance,
China, which is the largest exporter of illicit capital, would, by virtue of its enormous economy,
has an outfow to GDP ratio that will be lower than many developing countries. At the same
time, countries with a relatively small economy would likely register much higher ratios than
the largest exporters of such capital.
Chart 8. Top 10 Countries of 2010 Tracking Nominal Illicit Financial Flows
(as percent of Developing World Total)
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20 Global Financial Integrity
1/ These countries, among the top ten exporters of illicit capital, are also some of the worlds leading oil exporters.
Chart 9. Oil Prices and Illicit Flows Out of Five Major Countries 1/
(in millions of U.S dollars or or oil prices in U.S. dollars per barrel)
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Chart 9a. Mexico
Chart 9b. Saudi Arabia Chart 9c. Russian Federation
Chart 9e. United Arab Emirates Chart 9d. Nigeria
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21 Illicit Financial Flows from Developing Countries: 2001-2010
Chart 10. Heat Map of Cumulative Illicit Financial Flows from Developing Countries, 2001-2010
(in millions of U.S dollars)
Chart design by E.J. Fagan.
< 999
6,000 - 10,999
11,000 - 15,999
16,000 - 20,999
21,000 - 26,999
27,000 - 32,999
33,000 - 37,999
38,000 - 99,999
No Data or Advanced Economy
100,000 - 499,999
> 500,000
1,000 - 5,999
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22 Global Financial Integrity

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23 Illicit Financial Flows from Developing Countries: 2001-2010
IV. Special Issues: Sovereign Wealth Funds
29. This section presents a discussion of whether and how the large sovereign wealth funds
(SWFs) of certain countries can impact estimates of illicit fnancial fows from them. The
purpose of this discussion is to shed light on how estimates of unrecorded capital fows can
be subject to signifcant errors in measurement for certain countries that maintain large SWFs.
30. SWFs are government-owned long-term investment funds that are typically funded by the
transfer of foreign exchange assets. While the SWFs of some countries are relatively new,
others have existed for decades. There have always been different reasons for governments
to establish SWFs. Kiribati, a small Pacifc island country, set up the Revenue Equalization
Reserve Fund in 1956 to stabilize mining receipts. Today, however, the objective of Kiribatis
SWF is to save for a rainy day, i.e. to ride out global economic downturns that can have
particularly adverse impacts on small open economies. Norway, on the other hand, views
its SWF as a pension fund. Oil exporting countries like Saudi Arabia, Kuwait, Qatar, and the
United Arab Emirates set up SWFs in order to make provisions for the day when their primary
resource fnally runs out. So the SWFs in the case of countries mainly dependent on oil
exports tend to work as an insurance fund in the long run and as a stabilization fund in the
medium term to smooth out fuctuations in revenues from oil exports.
31. Appendix Table 14 presents basic information on the major SWFs by country of ownership,
name of investment funds, the size of assets under management, date of inception, origin of
funds, and the average illicit fows from the country in question. This is not to suggest that
illicit outfows have anything to do with SWFsthe intention of placing the data in the same
table is to facilitate the discussion of statistical issues. Concerns about the transparency of
operations and transactions of SWFs have increased in recent years along with the rising
importance of SWFs in the international fnancial system. Assets under management in SWFs
are expected to grow further as the reserve assets of countries in current account surplus
grow. The IMF, in a recent staff study, has noted this concern and suggested some steps to
improve transparency.
6

32. The lack of transparency and of an agreed accounting framework that is consistent across
SWFs militates against the achievement of high-quality balance of payments statistics,
which in turn detracts from the reliability of illicit fow estimates. Note that only the portion
attributable to the CED component (based on the World Bank Residual method) is impacted
by the incomplete or incorrect recording of SWF-related transactions in the balance of
payments. SWF-related statistical issues do not affect estimates of trade misinvoicing.
6
Reference, Sovereign Wealth FundsA Work Agenda, Prepared by the Monetary and Capital Markets and Policy Development and
Review Departments, International Monetary Fund, February 29, 2008.
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24 Global Financial Integrity
However, as the overwhelming portion of illicit fows from most oil exporting countries from
the Middle East (Saudi Arabia, Kuwait, United Arab Emirates, Qatar) are driven by balance
of payments leakages, outfow estimates involving these countries may be over- or under-
stated depending upon the nature of recording errors related to SWF transactions. That said,
users of balance of payments data cannot correct for these recording errors in SWF-related
transactions. In fact, SWF-related transactions may not be included at all in the balance of
payments if opacity related to SWF transactions and operations is high.
33. Sovereign Wealth FundsA Work Agenda
7
notes that .it is important that suffcient data
on SWFs activities are captured in the relevant macroeconomic datasets, meaning that the
quality of fscal, national accounts, and other datasets may be affected apart from balance
of payments statistics. The IMF report notes that, as of the date of publication, in a number
of cases SWF-related transactions and operations are not included in a member countrys
balance of payments or its international investment position (IIP) data.
34. There are also signifcant statistical issues related to the recording of SWF-related
transactions in the balance of payments. Incomplete or incorrect recording of SWF-related
transactions can lead to errors in the recording of specifc balance of payments variables,
such as reserve assets. For instance, say there is a drawdown of reserve assets to invest in
SWFs and the drawdown is fully recorded, while an SWF-related drawdown to pay off external
debt is not. Then the increased use of funds (which is not offset by a decline in the change in
external debt) would show up as an increase in unrecorded capital outfow. However, the debt
repayment, if correctly recorded, would imply no change in unrecorded outfows. Errors could
also be introduced in the appropriate recording of reserves due to SWF-related deposits. For
instance, if the government and the central bank both assume control over an SWF-related
addition to reserves due to a misunderstanding of the nature of control over the assets, then
the central bank would record the addition, thereby understating the unrecorded portion.
There is no information on whether such errors in recording are systemic.
35. Errors due to misclassifcation of SWF-related reserve assets are noted in the Sixth Edition
of the IMFs Balance of Payments Manual (BPM6), which notes that Some governments
create special purpose government funds, usually called sovereign wealth funds (SWFs). The
establishment of a special purpose government fund raises the issue of whether or not the
external assets held in the fund should be included in reserve assets. (paragraph 6.93). If
legal or administrative guidance encumbers the assets, then they are not readily available to
the central bank and as such should not be counted as reserve assets. So the criteria as to
whether specifc SWF funds are a part of reserve assets or not are not straightforward and
7
Reference, op.cit.
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25 Illicit Financial Flows from Developing Countries: 2001-2010
involve some judgment involving encumbrance, control, ready availability, etc. Specifcally,
BPM6 notes that:
If the special purpose government funds external assets are on the books of the
central bank, or an agency of the central government, that allows the monetary
authorities control over the disposition of funds, then the presumption is that the
assets are reserve assets (provided all other criteria for being a reserve asset are
met). On the other hand, if the funds are held in a long-term fund with a separate
legal identity, the presumption is that they should not be included in reserve
assets, not least because the ready availability criterion is less likely to be met.
(paragraph 6.95).
36. Furthermore, the Manual notes that in some cases where the assets are invested in a
separate investment corporation, there may be an agreement that such assets can be readily
called back if needed. In other cases, funds could be withdrawn during the annual budgetary
process.
37. The following guidance is reproduced verbatim for the beneft of BOP compilers involved in
the accounting of SWF transactions and operations:
Any fnal determination of whether an asset can be classifed as a reserve asset or
not, depends on an examination of the circumstances: namely, is the asset readily
available to the monetary authorities and is there a liquid claim of a resident entity
on a nonresident in foreign currency? But in the absence of legal or administrative
impediments, and given the fungibility of assets, even assets that had been
earmarked as part of a special purpose government fundbut that could be used to
meet balance of payments fnancing needs and other related purposesare reserve
assets (subject to the other criteria being met, including, importantly, the control of
the monetary authorities over the disposition of the funds).
Assets held in special purpose government funds that meet the defnition of reserve
assets are classifed within reserve assets depending on their nature. So, if the special
purpose government funds hold deposits, securities, and other reserve assets, these
are classifed as such within reserve assets. Assets held in a resident special purpose
government fund that are claims on nonresidents but do not meet the criteria to
be classifed as reserve assets are classifed in the fnancial account and IIP under
the appropriate instrument and functional category. If special purpose government
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26 Global Financial Integrity
funds own direct investment equity and debt securities that could be classifed in
either direct investment or reserves assets, as general guidance, in the hierarchy of
the balance of payments and IIP between direct investment and reserve assets, the
equity securities should be classifed as direct investment ahead of reserve assets,
and debt securities should be classifed as reserve assets ahead of direct investment.
(paragraphs 6.97-6.98).
38. It should be noted that if SWF transactions are not recorded properly, or are recorded
differently by two agencies that have not coordinated their data compilation systems, then the
errors in classifcation and/or recording will show up as larger net errors and omissions (NEO)
in the balance of payments.
39. Note also that there are many reasons for NEOs, including statistical errors in recording,
valuation changes, illicit fows, SWFs, etc. One cannot say that larger NEOs are only caused
by the incomplete recording of SWF-related transactions. Nevertheless, the information
presented in the following table provides an indication of whether countries with sizeable
SWFs tend to have larger NEOs than those that do not or those that have much stronger
statistical systems.
40. Appendix Table 15 presents data on net errors and omissions (NEOs) and the fnancial
account balance for ten countries with the largest SWFs. The purpose is to explore whether
there is a link between SWFs and NEOs given statistical capacity. Normally, we would expect
countries with strong statistical systems to do a better job of capturing SWF transactions.
Caution must be exercised in interpreting the data in that table. We must bear in mind the
fact that the NEO is impacted by many factors not just the accuracy and coverage of SWF
transactions. Nevertheless, keeping this caveat in mind, the balance of payments of certain
countries with large SWFs and comparatively weaker statistical systems such as the United
Arab Emirates, Saudi Arabia, and Qatar show relatively high NEOs which range from 40-120
percent of the fnancial account balance on average over the period 2001 to 2010. On the
other hand, there are other oil producing countries among the ten highest exporters of illicit
capital, such as Kuwait and Russia, where the impact of SWF accounting seems to be more
limited (ranging from 16 to 24 percent). Norway, an oil-exporting country with the third largest
SWF but a strong statistical system has a relatively high NEO, averaging nearly 33 percent of
its fnancial account balance. Singapore and the United States, with strong statistical systems
in place and signifcantly large SWFs, record NEOs that range from 9-13 percent on average.
China, with the largest SWF, records an NEO that is not unreasonably large. This leads us to
believe that its SWF transactions do not seem to adversely impact the NEO.
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27 Illicit Financial Flows from Developing Countries: 2001-2010
V. Conclusions
41. This report adds to the World Bank Residual methodology a second, more narrowly focused,
methodology for estimating illicit fnancial fows. The methodology is based on the Hot Money
Narrow method adjusted for the misinvoicing of trade (HMN+GER) using non-normalized and
normalized estimates for the GER component. In accordance with the rationale for excluding
illicit infows, both the HMN+GER and CED+GER methods consider illicit outfows only and
set infows to zero. We then showed that normalization to arrive at a conservative estimate of
illicit fows through the HMN+GER method would not produce an estimate substantially lower
than the non-normalized one. The main reason for the close proximity of the range is that trade
misinvoicing from most countries including China and India is larger than 10 percent of exports
in almost all years, so that the normalized and non-normalized GER estimates are nearly equal.
42. A comparison of illicit fows from developing countries under the CED+GER and HMN+GER
methods showed them to be reasonably correlated. According to the non-normalized
CED+GER method, illicit outfows over the period 2001-2010 averaged US$918 billion per
annum, while under the non-normalized HMN+GER method they averaged US$586 billion per
annum. The gap between the estimates comprising of licit outfows widened during the three
years prior to the global economic crisis (2006-2008), but fell sharply in the last two years as
legal capital fight plunged in the wake of the crisis as a result of lower growth and the tighter
squeeze on the availability of capital domestically. In 2010, the CED+GER approach shows
that developing countries lost US$1.13 trillion through illicit fnancial fows while the HMN+GER
shows that they lost US$859 billion. The following trends and patterns are based on the
HMN+GER method.
43. Asia still remains the primary driver of illicit fows from developing countries led by China. In
fact, fve of the ten countries with the highest illicit outfows are in Asia (China, Malaysia, the
Philippines, India, and Indonesia). The Western Hemisphere follows at 15.6 percent of total
outfows while the MENA region contributed 9.9 percent of total outfows. In infation-adjusted
terms, outfows from MENA grew the fastest at 26.3 percent followed by Africa (23.8 percent),
Asia (7.8 percent), developing Europe (3.6 percent), and Western Hemisphere (2.7 percent). The
rapid growth in illicit outfows from the MENA region can be traced to the increase in crude oil
prices.
44. According to our estimates, trade misinvoicing is the preferred method of transferring illicit
capital from all regions except the MENA region, where it accounted for just 37 percent of total
outfows over the decade ending 2010. Large current account surpluses are the main drivers of
illicit outfows from the balance of payments of countries in that region.
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28 Global Financial Integrity
45. The countries with the ten highest outfows are China, Mexico, Malaysia, Saudi Arabia,
Russia, the Philippines, Nigeria, India, Indonesia, and the United Arab Emirates in declining
order of magnitude. Cumulative outfows from China (US$2,742 billion) exceed total
cumulative outfows from all other nine countries (US$1,728 billion). The top ten countries
account for 76.2 percent of cumulative illicit fows from all developing countries over this
period. The Philippines, India, Indonesia, and Nigeria in the current list of the top ten
exporters of illicit capital displace Kuwait, Venezuela, Qatar, and Poland respectively. In
doing so, the Hot Money Narrow list accentuates the impact of illicit fows on poverty
because the four new entrants (the Philippines, India, Indonesia, and Nigeria) have a much
higher combined population with a signifcantly larger share of people living on less than
US$2 a day than the ones they displaced (Kuwait, Venezuela, Qatar, and Poland).
46. The special issues section of the report highlighted the problem of estimating illicit fows
from some developing countries with large SWFs. The discussion pointed out that if SWF
transactions are not properly recorded or are recorded in an inconsistent manner by
different government agencies, then the errors in classifcation or recording will show up
as larger net errors and omissions (NEOs) in the balance of payments (in relation to their
fnancial account balance. Based on data presented in Appendix Table 15, we show that
the balance of payments of certain countries with large SWFs and comparatively weaker
statistical systems such as the United Arab Emirates, Saudi Arabia, and Qatar show
relatively high NEOs, which range from 40-120 percent of the fnancial account balance
on average over the period 2001 to 2010. On the other hand, there are other oil producing
countries among the ten highest exporters of illicit capital, such as Kuwait and Russia,
where the impact of SWF accounting seems to be more limited (ranging from 16 to 24
percent). Norway, an oil-exporting country with the third largest SWF but a strong statistical
system has a surprisingly high NEO which averaged nearly 33 percent of its fnancial
account balance. Singapore and the United States with the ffth and tenth largest SWFs
respectively show much smaller SWFs ranging from 9-13 percent on average. China with
the largest SWF records an NEO that is not unreasonably large which lead us to believe that
its SWF transactions are, for the most part, properly refected in its balance of payments.
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29 Illicit Financial Flows from Developing Countries: 2001-2010
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31 Illicit Financial Flows from Developing Countries: 2001-2010
Glossary
Glossary of Databases
IMF Balance of Payments (BOP): IMF database that provides international transactions data and
International Investment Position (IIP) data. BOP data begin as early as 1960 for some countries but
IIP data are only available starting 2002. For the purposes of calculating illicit fnancial fows, the
following time series can be found in this database: current account, foreign direct investment, and
change in reserves.
IMF Direction of Trade Statistics (DOTS): IMF database containing data on exports and imports
of goods on a bilateral basis, beginning in 1980. No bilateral trade data are available for services or
for specifc commodities.
IMF International Financial Statistics (IFS): IMF database containing macroeconomic data,
including monetary, fscal, prices, and national accounts, starting in 1948. For the purposes of
calculating illicit fnancial fows, supplementary trade data are used if the country does not report
Direction of Trade Statistics.
World Bank Global Development Finance (GDF): World Bank database that provides external
debt and fnancial fows statistics for countries that report public and publicly-guaranteed debt
under the World Banks Debtor Reporting System (DRS). Data typically begins in 1960.
Glossary of Terms
Balance of Payments: is a statistical statement that systematically summarizes, for a specifc time
period, the economic transactions of an economy with the rest of the world. Transactions, for the
most part between residents and nonresidents, consist of those involving goods, services, and
income; those involving fnancial claims on, and liabilities to, the rest of the world; and those (such
as gifts) classifed as transfers. While the current account mainly consists of exports and imports of
goods and services and worker remittances, the fnancial account includes transactions involving
foreign direct investment, portfolio capital fows, changes in reserve holdings of the central bank
line items that are necessary to estimate illicit fows based on the World Bank Residual method.
Change in External Debt (CED): is a version of the World Bank Residual method that includes
change in external debt as an indicator of new loans (i.e., a source of funds for a country). The World
Bank Residual method estimates unrecorded (defned to be illicit) outfows from the balance of
payments by estimating the gap between source and use of funds. Note that the CED measure only
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32 Global Financial Integrity
includes gross illicit outfows from a country, occurring when source of funds is greater than use of
funds (in other words, calculations have a positive sign). Thus, when the use of funds exceeds the
source of funds, that is, when there are inward transfers of illicit capital (calculations have a negative
sign), the CED method sets illicit fows to zero for that year. In contrast, economists have typically
netted out illicit infows from outfows under the traditional World Bank Residual method.
Current Account Balance: Covered in the current account are all transactions (other than those in
fnancial items) that involve economic values and occur between resident and nonresident entities.
Also covered are offsets to current economic values provided or acquired without a quid pro quo.
Specifcally, the major classifcations are goods and services, income, and current transfers.
Export Under-invoicing: A countrys exports to the world are compared to world imports from
that country, adjusted for cost of insurance and freight. Illicit outfows from a country are indicated
whenever exports of goods from that country are understated relative to the reporting of world
imports from that country adjusted for the cost of insurance and freight.
External Debt: (World Bank defnition) measure of debt owed to nonresidents repayable in foreign
currency, goods, or services. Total external public and publicly guaranteed debt includes long-term
debt, use of IMF credit, and short-term debt. While private non-guaranteed debt is also included in
total debt, the data are not comprehensive for some developing countries.
Foreign Direct Investment: measure of all net transactions between a direct investor in one
economy and a direct investment enterprise (recipient) in another economy.
Gross Excluding Reversals (GER): method of calculating gross illicit outfows defned as export
under-invoicing and import over-invoicing. In other words, GER calculations are based on the
sum of discrepancies between (i) a countrys exports and world imports from that country and
(ii) a countrys imports and world exports to that country. The absolute value of the export under-
invoicing, which is a negative estimate under (i), is added to import over-invoicing to arrive at a GER
estimate.
Hot Money Narrow (HMN): more conservative measure of illicit fnancial fows from the balance of
payments than the CED.
Illicit Financial Flows: funds that are illegally earned, transferred, or utilized and cover all
unrecorded private fnancial outfows that drive the accumulation of foreign assets by residents in
contravention of applicable laws and regulatory frameworks.
Import Over-invoicing: A countrys imports from the world (adjusted for cost of insurance and
freight) are compared to world exports to that country. Illicit outfows from a country will be
indicated if the countrys imports are overstated with respect to world exports to that country.
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33 Illicit Financial Flows from Developing Countries: 2001-2010
Non-normalized: Change in External Debt (CED) or Gross Excluding Reversals (GER) calculations
which have not been subjected to the normalization process. Non-normalized estimates represent
the upper bound (robust estimate) of the possible range of illicit fows.
Normalized: Under the CED+GER method, the normalization process subjects both the Change
in External Debt (CED) calculations and the Gross Excluding Reversals (GER) calculations for the
entire list of developing countries, for which data are available, to two flters: (i) estimates must have
the right sign (indicating outfow, rather than infow) in the majority of the years covering the sample
period and (ii) exceed the threshold (10 percent) with respect to exports valued at free-on-board (or
f.o.b.) basis. Normalized estimates represent a lower bound (conservative estimate) of the possible
range of illicit fows. Normalization is not required under the HMN+GER method, because it is
already much more conservative than the normalized CED+GER method.
Change in Reserves: According to the IMF, net transactions in assets that are considered by the
monetary authorities of an economy to be available for use in funding payments imbalances, and, in
some instances, meeting other fnancial needs.
Trade Misinvoicing: Traditional method in which a countrys exports (respectively, imports) to the
world are compared to world imports (respectively, exports) from that country to determine export
or import under- and over-statement. Export under-invoicing and Import over-invoicing refect illicit
outfows, while export-over-invoicing and import under-invoicing refect illicit infows. Traditionally,
economists have netted out illicit infows from outfows thereby understating the adverse impact of
illicit fows on developing countries. As illicit infows are also unrecorded, they cannot be taxed by
the government and are generally unusable for legitimate productive purposes. Hence, only gross
outfows through trade mispricing as considered in the GER method (see defnition of GER).
World Bank Residual Method: measures a countrys source of funds (infows of capital) vis--
vis its recorded use of funds (outfows and/or expenditures of capital). Source of funds includes
increases in net external indebtedness and the net infow of foreign direct investment. Use of funds
includes the current account defcit that is fnanced by the capital account fows and additions to
central bank reserves. Illicit outfows (infows) exist when the source of funds exceeds (falls short
of) the uses of funds. Traditionally, economists have netted out illicit infows from outfows thereby
understating the adverse impact of illicit fows on developing countries. As illicit infows are also
unrecorded, they cannot be taxed by the government and are generally unusable for legitimate
productive purposes. Hence, only gross outfows are considered in the Change in External Debt
(CED) method (see defnition of CED).
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34 Global Financial Integrity
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35 Illicit Financial Flows from Developing Countries: 2001-2010
Appendix Tables
Table 1. HMN and GER Components in Total Illicit Flows from
Developing Countries and Regions, 2001-2010
(in millions of 2005 U.S. dollars and in percent)

Region 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Average
Africa-HMN 3,047 3,868 4,726 1,831 20,297 17,074 16,250 19,500 28,167 17,684 13,244
Africa-GER 9,971 6,742 6,555 17,411 15,223 27,337 37,595 42,271 36,034 25,818 22,496
HMN Percent of Total 23.4% 36.5% 41.9% 9.5% 57.1% 38.4% 30.2% 31.6% 43.9% 40.7% 35%
GER Percent of Total 76.6% 63.5% 58.1% 90.5% 42.9% 61.6% 69.8% 68.4% 56.1% 59.3% 65%
Asia-HMN 13,402 7,719 7,631 7,143 15,306 16,116 16,117 18,452 60,669 78,154 24,071
Asia-GER 195,189 223,945 270,598 350,155 372,636 351,348 365,710 378,660 317,576 378,257 320,407
HMN Percent of Total 6.4% 3.3% 2.7% 2.0% 3.9% 4.4% 4.2% 4.6% 16.0% 17.1% 6%
GER Percent of Total 93.6% 96.7% 97.3% 98.0% 96.1% 95.6% 95.8% 95.4% 84.0% 82.9% 94%
Developing Europe-HMN 15,169 13,780 17,831 11,158 16,150 9,319 25,461 35,790 16,813 19,721 18,119
Developing Europe-GER 33,073 14,442 19,044 31,151 13,298 9,178 14,809 11,311 25,525 43,049 21,488
HMN Percent of Total 31.4% 48.8% 48.4% 26.4% 54.8% 50.4% 63.2% 76.0% 39.7% 31.4% 47%
GER Percent of Total 68.6% 51.2% 51.6% 73.6% 45.2% 49.6% 36.8% 24.0% 60.3% 68.6% 53%
MENA-HMN 15,066 5,100 4,836 3,049 51,940 42,136 28,534 95,266 91,628 57,105 39,466
MENA-GER 23,911 4,558 3,447 20,689 11,775 10,955 8,972 21,568 37,035 18,887 16,180
HMN Percent of Total 38.7% 52.8% 58.4% 12.8% 81.5% 79.4% 76.1% 81.5% 71.2% 75.1% 63%
GER Percent of Total 61.3% 47.2% 41.6% 87.2% 18.5% 20.6% 23.9% 18.5% 28.8% 24.9% 37%
Western Hemisphere-HMN 16,099 15,642 11,906 13,017 22,021 6,785 8,028 1,498 20,254 24,556 13,981
Western Hemisphere-GER 62,706 64,119 62,495 70,254 76,459 72,211 89,268 99,045 72,705 68,532 73,779
HMN Percent of Total 20.4% 19.6% 16.0% 15.6% 22.4% 8.6% 8.3% 1.5% 21.8% 26.4% 16%
GER Percent of Total 79.6% 80.4% 84.0% 84.4% 77.6% 91.4% 91.7% 98.5% 78.2% 73.6% 84%
All Developing Countries-HMN 62,783 46,110 46,930 36,198 125,715 91,430 94,389 170,506 217,531 197,220 108,881
All Developing Countries-GER 324,850 313,805 362,139 489,661 489,392 471,029 516,355 552,854 488,875 534,545 454,350
HMN Percent of Total 16.2% 12.8% 11.5% 6.9% 20.4% 16.3% 15.5% 23.6% 30.8% 27.0% 18%
GER Percent of Total 83.8% 87.2% 88.5% 93.1% 79.6% 83.7% 84.5% 76.4% 69.2% 73.0% 82%
Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported
to the IMF by member countries.
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36 Global Financial Integrity
Table 2. Country Rankings: by Largest Average IFF Estimates, 2001-2010
(in millions of U.S. dollars)
Rank Country
Average of
all years
(where data
is available)
1 China, P.R.: Mainland 274,170
2 Mexico 47,561
3 Malaysia 28,524
4 Saudi Arabia 20,996
5 Russian Federation 15,159
6 Philippines 13,782
7 Nigeria 12,904
8 India 12,332
9 Indonesia 10,886
10 United Arab Emirates 10,650
11 Iraq 10,597
12 South Africa 8,390
13 Thailand 6,426
14 Costa Rica 6,370
15 Qatar 5,611
16 Serbia, Republic of 5,144
17 Poland 4,077
18 Panama 3,987
19 Venezuela, Republica Bolivariana de 3,791
20 Brunei Darussalam 3,704
21 Brazil 3,510
22 Syrian Arab Republic 3,260
23 Egypt 3,099
24 Honduras 3,081
25 Turkey 2,896
26 Sudan 2,637
27 Kuwait 2,419
28 Chile 2,417
29 Aruba 2,354
30 Lebanon 2,105
31 Kazakhstan 1,916
32 Trinidad and Tobago 1,884
33 Vietnam 1,753
34 Dominican Republic 1,695
35 Ethiopia 1,685
Source: Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by
member countries and external debt data reported by those countries to the World Bank.
Note: Countries below rank 143 either had missing data (see Table 9) or have received only illicit infows over the period 2001-2010.
Note: IFF estimates calculated using the HMN+GER non-normalized methodology.
Rank Country
Average of
all years
(where data
is available)
36 Argentina 1,670
37 Guatemala 1,622
38 Bulgaria 1,585
39 Croatia 1,525
40 Congo, Republic of 1,503
41 Algeria 1,471
42 Hungary 1,442
43 Bahamas, The 1,408
44 Bangladesh 1,406
45 Morocco 1,283
46 Colombia 1,254
47 Ecuador 1,135
48 Nicaragua 1,093
49 Liberia 1,083
50 Montenegro 1,042
51 Equatorial Guinea 1,003
52 Bahrain, Kingdom of 971
53 Peru 952
54 Libya 902
55 Romania 884
56 El Salvador 867
57 Bosnia and Herzegovina 836
58 Nepal 801
59 Paraguay 754
60 Oman 741
61 Uruguay 736
62 Myanmar 728
63 Lithuania 692
64 Cote d'Ivoire 688
65 Latvia 680
66 Uganda 680
67 Cameroon 674
68 Turkmenistan 659
69 Botswana 604
70 Zambia 548
71 Armenia, Republic of 526
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37 Illicit Financial Flows from Developing Countries: 2001-2010
Rank Country
Average of
all years
(where data
is available)
72 Cambodia 498
73 Angola 483
74 Georgia 477
75 Malawi 469
76 Macedonia, FYR 461
77 Ukraine 460
78 Congo, Democratic Republic of 438
79 Azerbaijan, Republic of 429
80 Madagascar 423
81 Namibia 420
82 Jamaica 415
83 Bolivia 386
84 Zimbabwe 365
85 Lao People's Democratic Republic 342
86 Tanzania 333
87 Swaziland 308
88 Guinea 306
89 Djibouti 292
90 Gabon 289
91 Mali 289
92 Fiji 270
93 Moldova 252
94 Pakistan 251
95 Yemen, Republic of 249
96 Burkina Faso 242
97 Afghanistan, Islamic Republic of 240
98 Ghana 218
99 Papua New Guinea 203
100 Barbados 198
101 Togo 196
102 Guyana 188
103 Lesotho 179
104 Rwanda 158
105 Sri Lanka 153
106 Albania 136
107 Samoa 125
Rank Country
Average of
all years
(where data
is available)
108 Niger 122
109 Mauritius 114
110 Kenya 112
111 Mongolia 112
112 Belize 107
113 Suriname 104
114 Solomon Islands 91
115 Tajikistan 91
116 Jordan 88
117 Mozambique 85
118 Belarus 78
119 Kyrgyz Republic 71
120 Dominica 64
121 Seychelles 62
122 Haiti 57
123 Sierra Leone 53
124 Maldives 52
125 Burundi 49
126 Gambia, The 47
127 Guinea-Bissau 46
128 Tunisia 31
129 Bhutan 27
130 Tonga 27
131 Cape Verde 27
132 Benin 23
133 Central African Republic 18
134 Comoros 16
135 Vanuatu 13
136 Antigua and Barbuda 12
137 St. Vincent and the Grenadines 7
138 Sao Tome and Principe 6
139 Timor-Leste, Dem. Rep. of 5
140 St. Lucia 4
141 Grenada 4
142 St. Kitts and Nevis 3
143 Senegal 1
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38 Global Financial Integrity
Table 3. HMN ( Balance of Payments)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . . . . . . . . . . . .
Albania 0 0 0 0 0 0 0 0 0 0 0 0
Algeria . . . . 203 1,962 500 3,358 2,673 1,265 9,961 1,660
Angola 309 68 822 0 574 0 1,641 1,236 0 181 4,830 483
Antigua and Barbuda 16 40 5 19 11 11 7 4 7 0 120 12
Argentina 2,810 1,890 1,428 0 0 0 0 0 0 1,036 7,164 716
Armenia, Republic of 0 4 2 6 0 16 2 0 0 0 29 3
Aruba 5 0 0 0 0 0 0 12 14 0 31 3
Azerbaijan, Republic of 1 87 112 50 126 256 361 845 1,461 990 4,288 429
Bahamas, The 0 0 0 0 150 0 0 0 53 283 485 49
Bahrain, Kingdom of 0 0 700 0 0 0 0 30 66 0 796 80
Bangladesh 106 349 0 25 629 623 905 120 649 56 3,462 346
Barbados 0 0 0 0 0 0 0 7 0 65 71 7
Belarus 1 289 13 0 0 286 0 194 0 0 784 78
Belize 0 9 35 4 8 8 39 12 5 0 120 12
Benin 0 0 0 10 0 0 0 0 6 0 16 2
Bhutan . . . . . 0 137 0 0 0 137 27
Bolivia 203 640 174 625 372 105 112 0 454 802 3,485 349
Bosnia and Herzegovina 0 0 0 0 0 0 68 74 0 0 142 14
Botswana 744 0 161 293 0 0 0 0 0 0 1,198 120
Brazil 498 154 933 2,145 201 0 3,152 0 347 3,292 10,722 1,072
Brunei Darussalam 2,205 2,329 1,838 1,190 3,969 5,786 5,860 8,232 5,420 . 36,829 4,092
Bulgaria 0 716 889 0 1,218 986 3,052 4,229 0 0 11,089 1,109
Burkina Faso 0 4 4 0 2 9 0 0 0 . 20 2
Burundi 31 0 14 19 75 0 37 0 12 0 189 19
Cambodia 0 0 40 46 14 72 45 45 8 29 300 30
Cameroon 162 177 0 0 48 0 0 0 0 0 388 39
Cape Verde 24 8 12 0 0 9 0 108 40 68 268 27
Central African Republic . . . . . . . . . . . .
Chad . . . . . . . . . . . .
Chile 861 952 724 270 1,324 1,526 450 0 0 558 6,665 666
China, Mainland 4,732 0 0 0 0 0 0 0 41,383 52,936 99,051 9,905
Colombia 151 0 0 0 0 0 0 127 0 0 278 28
Comoros . . . . . . . . . . . .
Congo, Democratic Republic of 0 236 0 0 46 17 170 0 0 0 468 47
Congo, Republic of 12 220 116 93 0 0 199 . . . 639 91
Costa Rica 0 51 0 0 0 0 0 48 0 197 296 30
Cote d'Ivoire 0 26 888 0 38 38 0 44 37 25 1,096 110
Croatia 474 638 1,355 1,305 1,288 1,722 1,659 2,263 1,637 1,010 13,352 1,335
Djibouti 0 0 0 16 45 54 0 55 0 117 286 29
Dominica 0 0 0 0 0 0 0 0 0 6 6 1
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39 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 452 139 1,568 981 458 123 0 16 129 961 4,827 483
Ecuador 514 317 0 0 0 0 0 1 131 0 963 96
Egypt 1,146 0 0 45 2,431 0 0 2,896 0 2,145 8,663 866
El Salvador 457 615 143 0 449 485 0 0 0 0 2,149 215
Equatorial Guinea . . . . . . . . . . . .
Eritrea . . . . . . . . . . . .
Ethiopia 229 915 390 354 0 0 158 0 501 3,075 5,623 562
Fiji 0 135 47 0 101 153 0 0 72 0 508 51
Gabon 104 125 260 357 439 . . . . . 1,285 257
Gambia, The . . 0 3 34 7 42 31 0 87 203 25
Georgia 0 0 6 0 0 62 33 44 0 20 166 17
Ghana 51 0 0 0 0 0 37 374 1,259 0 1,721 172
Grenada 0 0 10 1 25 0 0 0 0 0 36 4
Guatemala 0 65 61 0 0 0 0 0 0 359 485 48
Guinea 2 0 157 0 0 2 0 0 0 0 161 16
Guinea-Bissau 0 3 0 4 5 1 0 5 9 4 32 3
Guyana 45 1 20 43 68 53 82 56 0 177 545 54
Haiti 0 0 0 0 0 0 0 0 46 0 46 5
Honduras 43 0 0 0 204 332 353 0 346 0 1,278 128
Hungary 0 0 0 2,100 2,580 2,744 349 3,373 771 2,187 14,104 1,410
India 711 190 0 0 541 0 0 0 279 1,613 3,335 333
Indonesia 0 1,763 3,510 3,094 179 0 1,368 238 2,974 1,480 14,605 1,461
Iran, Islamic Republic of . . . . . . . . . . . .
Iraq . . . . 0 0 3,660 9,245 6,116 7,951 26,971 4,495
Jamaica 14 61 0 22 0 0 0 350 0 0 447 45
Jordan 154 130 0 0 0 206 0 0 0 0 490 49
Kazakhstan 654 0 932 1,016 1,800 3,128 2,966 5,746 783 0 17,025 1,703
Kenya 0 0 277 67 234 0 258 0 0 0 835 83
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . 0 0 0 0 0 0 0 0
Kuwait 2,869 1,869 574 0 0 0 4,732 10,049 0 0 20,092 2,009
Kyrgyz Republic 0 22 0 19 0 0 356 0 241 71 711 71
Lao PDR 51 130 82 0 0 403 735 409 523 402 2,735 273
Latvia 0 71 13 0 296 0 212 577 0 0 1,169 117
Lebanon . 0 0 734 610 2,818 5,997 1,746 3,042 0 14,946 1,661
Lesotho 0 179 71 0 0 0 0 0 117 0 368 37
Liberia . . . . 39 98 76 43 288 106 651 108
Libya 1,206 0 0 0 1,450 0 0 1,753 0 2,137 6,545 655
Lithuania 0 0 0 0 49 289 54 0 0 0 392 39
Macedonia, FYR 1 10 33 0 6 0 52 31 0 0 133 13
Madagascar 57 0 0 35 0 . . . . . 92 18
Malawi 221 0 27 0 24 40 0 153 55 0 520 52
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40 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 2,394 391 4 0 6,633 7,460 5,201 8,592 5,242 21,663 57,580 5,758
Maldives 4 0 0 0 0 0 0 0 0 0 4 0
Mali 0 6 0 26 24 37 0 0 74 0 168 17
Mauritania . . . . . . . . . . . .
Mauritius 86 0 0 0 0 0 0 0 0 0 86 9
Mexico 3,304 1,903 4,411 4,816 3,870 1,128 2,458 0 16,496 17,739 56,126 5,613
Moldova 0 24 0 0 0 0 0 0 0 0 24 2
Mongolia 32 0 6 0 81 14 212 775 0 0 1,120 112
Montenegro . . . . . . 0 0 0 0 0 0
Morocco 0 182 297 282 404 521 0 412 521 160 2,778 278
Mozambique 60 60 0 0 0 0 0 0 0 0 120 12
Myanmar 14 19 78 141 604 626 336 1,362 1,010 2,132 6,322 632
Namibia 18 0 89 0 0 0 0 0 0 317 424 42
Nepal 0 67 0 0 0 0 0 107 0 181 355 36
Nicaragua 0 327 100 405 63 401 34 314 79 300 2,024 202
Niger 0 9 15 0 0 0 18 57 5 . 104 12
Nigeria 0 0 0 0 17,345 17,151 14,399 20,740 26,330 15,350 111,315 11,131
Oman 555 842 565 396 859 9 0 0 1,031 0 4,258 426
Pakistan 0 0 44 0 202 0 0 51 0 729 1,026 103
Panama 499 0 0 0 359 0 474 0 0 0 1,332 133
Papua New Guinea 2 0 0 0 59 15 0 73 0 91 239 24
Paraguay 0 263 41 0 214 0 212 0 0 0 729 73
Peru 0 0 0 0 0 407 138 123 596 0 1,265 126
Philippines 0 0 898 274 1,799 1,592 2,082 1,119 0 1,988 9,752 975
Poland 0 981 1,961 0 798 0 3,302 12,161 10,045 10,462 39,710 3,971
Qatar 1,031 1,260 0 5,568 4,703 0 2,310 2,206 11,384 0 28,462 2,846
Romania 0 856 289 0 0 0 1,320 2,065 1,729 119 6,378 638
Russian Federation 9,558 6,078 9,179 5,870 7,913 0 13,347 11,277 1,726 8,285 73,234 7,323
Rwanda 0 0 0 9 0 0 0 6 0 6 20 2
Samoa . . . 3 0 0 2 39 0 0 44 6
Sao Tome and Principe 0 0 0 0 0 6 10 32 6 10 64 6
Saudi Arabia 0 0 0 0 34,751 20,560 15,629 30,026 60,754 34,380 196,100 19,610
Senegal 0 0 0 0 0 0 0 0 0 0 0 0
Serbia, Republic of . . . . . . 0 212 76 0 288 72
Seychelles 0 10 5 0 1 0 0 0 0 0 15 2
Sierra Leone 0 16 50 54 62 28 15 32 7 4 267 27
Solomon Islands 0 0 0 6 0 0 0 2 0 12 21 2
Somalia . . . . . . . . . . . .
South Africa 0 485 0 0 0 0 0 0 804 0 1,289 129
Sri Lanka 0 0 114 189 72 106 165 0 0 881 1,527 153
St. Kitts and Nevis 6 0 0 8 0 1 0 19 0 0 34 3
St. Lucia 0 2 0 0 15 0 1 10 9 0 37 4
Table 3. HMN ( Balance of Payments) (cont)
(in millions of U.S. dollars)
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41 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 11 0 0 17 23 16 0 0 0 0 67 7
Sudan 24 0 14 0 0 88 0 131 733 861 1,851 185
Suriname 0 0 0 0 0 0 0 100 19 168 287 29
Swaziland 0 0 92 0 41 238 701 0 55 0 1,127 113
Syrian Arab Republic 0 160 0 256 137 1,488 746 1,226 747 0 4,761 476
Tajikistan . 56 30 32 77 265 337 18 0 0 815 91
Tanzania 297 551 340 96 704 0 0 443 380 480 3,290 329
Thailand 327 0 0 710 0 0 0 0 0 3,837 4,874 487
Timor-Leste, Dem. Rep. of . . . . . 3 9 7 0 5 25 5
Togo 5 0 10 0 0 0 0 0 0 0 16 2
Tonga 0 0 14 38 12 12 39 9 27 . 151 17
Trinidad and Tobago 235 425 0 269 444 143 75 0 0 0 1,591 159
Tunisia 0 34 47 128 27 37 37 0 0 0 311 31
Turkey 2,091 759 0 0 0 0 0 0 0 0 2,849 285
Turkmenistan . . . . . . . . . . . .
Uganda 7 124 164 270 454 9 7 0 161 0 1,196 120
Ukraine 152 889 834 0 0 0 458 0 0 0 2,334 233
United Arab Emirates 0 800 1,000 5,500 11,800 0 51,700 23,500 7,600 0 101,900 10,190
Uruguay 0 2,394 0 0 174 152 279 0 0 565 3,564 356
Uzbekistan . . . . . . . . . . . .
Vanuatu 0 21 22 25 17 4 5 0 37 0 130 13
Venezuela, Rep. Bolivariana de 3,601 2,781 795 2,503 13,589 2,211 939 608 3,520 2,313 32,861 3,286
Vietnam 847 1,038 0 915 396 0 578 1,045 9,022 3,690 17,530 1,753
Yemen, Republic of 110 0 0 0 0 0 0 0 0 0 110 11
Zambia 154 0 169 0 64 40 58 0 62 65 611 61
Zimbabwe 0 0 0 0 . . 0 0 0 0 0 0

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and external debt data reported to the IMF and
World Bank respectively by member countries.
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42 Global Financial Integrity
Table 4. CED Normalized (Change in External Debt - Balance of Payments)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . . 0 0 13,934 0 761 0 535 0 15,230 1,904
Albania 0 0 0 0 0 0 0 0 0 0 0 0
Algeria 0 1,942 2,699 0 0 0 0 0 0 0 4,642 464
Angola 0 2,155 2,455 1,982 4,269 0 7,777 0 0 0 18,637 1,864
Antigua and Barbuda 63 53 98 0 16 101 0 0 0 0 332 33
Argentina 17,984 12,366 20,898 3,479 0 0 0 16,666 11,678 12,331 95,401 9,540
Armenia, Republic of 225 182 155 306 0 129 459 284 235 517 2,491 249
Aruba 0 42 224 260 74 532 724 0 88 0 1,944 194
Azerbaijan, Republic of 0 462 482 0 0 1,664 3,269 14,215 11,573 16,456 48,122 4,812
Bahamas, The 0 0 0 0 0 0 0 0 0 0 0 0
Bahrain, Kingdom of 0 738 909 1,101 1,883 4,574 2,168 4,534 3,103 3,268 22,280 2,228
Bangladesh 0 2,081 1,257 787 0 2,535 1,402 2,289 0 3,254 13,605 1,361
Barbados 0 0 0 0 0 0 0 0 0 0 0 0
Belarus 0 0 0 0 0 0 0 0 0 0 0 0
Belize 0 0 48 0 37 58 78 0 0 0 222 22
Benin 0 0 0 0 0 0 0 0 0 0 0 0
Bhutan . . . . 0 0 174 0 0 0 174 29
Bolivia 0 938 914 663 604 0 0 0 1,123 0 4,242 424
Bosnia and Herzegovina 0 0 0 0 0 0 0 0 0 0 0 0
Botswana 868 0 876 1,238 559 824 901 790 2,059 1,360 9,476 948
Brazil 0 8,136 9,582 0 0 0 0 0 0 0 17,718 1,772
Brunei Darussalam 2,019 1,945 2,585 2,925 . 5,261 4,958 7,116 4,468 6,048 37,325 4,147
Bulgaria 0 953 1,991 1,676 0 5,085 9,344 11,471 4,961 0 35,481 3,548
Burkina Faso 0 0 0 0 0 0 0 0 0 0 0 0
Burundi 0 0 0 0 0 0 0 0 0 0 0 0
Cambodia 0 0 0 0 0 0 0 0 0 0 0 0
Cameroon 0 0 0 0 0 0 0 0 0 0 0 0
Cape Verde 3 0 3 0 0 38 0 25 0 20 89 9
Central African Republic 0 0 0 0 0 0 0 0 0 0 0 0
Chad 0 0 0 0 384 1,222 2,020 2,366 637 0 6,628 663
Chile 3,452 4,009 3,884 8,862 6,209 15,044 26,629 0 15,251 19,590 102,930 10,293
China, Mainland 45,733 0 0 0 0 0 0 0 0 0 45,733 4,573
Colombia 2,555 0 3,794 0 0 2,848 3,136 0 3,333 0 15,667 1,567
Comoros 6 17 14 8 . . . . . . 46 12
Congo, Democratic Republic of 170 0 1,744 528 0 928 3,440 1,370 0 0 8,181 818
Congo, Republic of 0 1,033 1,205 1,726 0 1,056 0 0 0 0 5,019 502
Costa Rica 0 0 0 0 0 0 0 0 0 0 0 0
Cote d'Ivoire 0 591 1,696 1,298 0 1,364 923 0 0 0 5,871 587
Croatia 509 2,054 0 0 0 0 0 2,562 2,058 2,721 9,903 990
Djibouti 17 94 89 77 35 125 217 0 38 70 762 76
Dominica 0 0 0 0 0 0 0 0 0 0 0 0
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43 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 422 1,777 2,409 1,859 0 934 407 0 0 0 7,808 781
Ecuador 847 1,737 785 967 0 2,115 0 0 0 0 6,451 645
Egypt 0 2,501 4,511 4,989 0 9,294 9,304 3,740 2,913 0 37,252 3,725
El Salvador 1,131 639 1,443 0 715 358 0 0 0 720 5,006 501
Equatorial Guinea 398 0 410 0 . . . . . . 808 202
Eritrea . . . . . . . . . . . .
Ethiopia 0 1,141 687 0 0 0 275 0 1,402 3,113 6,617 662
Fiji 0 0 0 0 0 0 0 0 0 0 0 0
Gabon 0 375 1,159 1,370 1,667 1,652 885 2,652 1,409 1,688 12,857 1,286
Gambia, The 36 69 71 32 0 48 13 0 126 114 509 51
Georgia 0 0 62 80 0 0 0 3,219 0 246 3,607 361
Ghana 0 362 0 0 0 0 0 855 0 0 1,218 122
Grenada 0 0 0 0 0 0 0 0 0 0 0 0
Guatemala 0 0 0 3,121 0 0 980 0 0 0 4,100 410
Guinea 0 0 0 0 0 0 0 0 0 0 0 0
Guinea-Bissau 0 55 52 34 0 8 11 0 0 . 160 18
Guyana 0 0 0 0 0 0 0 0 0 0 0 0
Haiti 0 0 0 0 0 0 0 0 0 0 0 0
Honduras 0 0 0 0 0 0 0 0 0 0 0 0
Hungary 3,841 0 9,715 8,296 0 12,000 32,333 18,506 0 0 84,692 8,469
India 0 0 0 0 0 0 0 0 0 0 0 0
Indonesia 0 0 8,909 0 0 0 0 19,263 15,979 0 44,151 4,415
Iran, Islamic Republic of 3,320 2,654 6,992 0 . . . . . . 12,965 3,241
Iraq . . . . . . . 0 0 0 0 0
Jamaica 0 0 392 189 0 912 1,516 0 0 1,672 4,680 468
Jordan 663 393 802 605 0 961 0 0 0 0 3,424 342
Kazakhstan 3,541 3,698 5,193 11,821 13,667 22,554 25,772 28,991 7,344 8,229 130,811 13,081
Kenya 0 0 0 0 0 0 0 0 0 0 0 0
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . . . . . . . . .
Kuwait 7,517 7,320 16,110 15,595 29,297 44,241 55,232 53,405 0 19,878 248,595 24,860
Kyrgyz Republic 0 66 112 403 0 0 0 306 308 0 1,194 119
Lao PDR 0 624 0 152 93 893 1,674 826 647 312 5,221 522
Latvia 0 1,139 1,259 1,967 0 3,248 11,082 0 1,035 0 19,729 1,973
Lebanon 1,956 866 0 1,062 0 2,222 2,034 0 0 0 8,140 814
Lesotho 0 0 0 0 0 0 0 0 0 0 0 0
Liberia . 0 0 0 0 0 0 0 0 0 0 0
Libya 1,875 0 0 0 0 4,291 9,157 21,015 4,383 11,326 52,047 5,205
Lithuania 0 0 0 1,926 0 3,964 5,373 0 0 0 11,262 1,126
Macedonia, FYR 151 0 0 790 0 336 838 0 341 0 2,456 246
Madagascar 0 0 0 0 0 0 0 0 0 0 0 0
Malawi 0 0 0 0 0 0 0 0 0 0 0 0
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44 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 9,792 0 0 0 17,272 22,426 20,426 39,206 21,506 38,512 169,140 16,914
Maldives 0 0 0 0 0 0 0 0 0 0 0 0
Mali 0 0 0 0 0 0 0 0 0 0 0 0
Mauritania . . . . . . . . . . . .
Mauritius 0 0 0 0 0 0 0 0 0 0 0 0
Mexico 0 0 0 0 0 0 0 0 0 0 0 0
Moldova 0 201 0 0 0 118 0 0 0 498 817 82
Mongolia 0 66 566 168 0 290 485 537 0 0 2,111 211
Montenegro . . . . . . 0 0 988 0 988 247
Morocco 0 0 0 0 0 0 0 0 0 0 0 0
Mozambique 0 0 0 0 0 0 0 0 0 0 0 0
Myanmar 0 1,166 973 0 0 901 1,831 1,278 1,140 0 7,290 729
Namibia 365 296 1,269 1,149 524 1,719 916 0 522 1,431 8,190 819
Nepal 0 532 149 116 0 0 220 0 0 0 1,017 102
Nicaragua 0 0 0 0 0 0 0 0 0 0 0 0
Niger 0 0 0 0 0 0 0 0 0 . 0 0
Nigeria 2,846 5,135 9,751 12,333 14,454 12,791 24,690 37,403 27,732 28,573 175,709 17,571
Oman 0 0 0 0 3,822 5,569 0 7,378 0 5,300 22,069 2,207
Pakistan 0 2,026 3,522 1,852 0 0 0 3,384 0 0 10,783 1,078
Panama 876 0 875 557 0 2,773 0 0 2,170 0 7,251 725
Papua New Guinea 0 0 0 0 0 0 0 0 0 0 0 0
Paraguay 0 379 0 376 0 0 0 0 0 0 754 75
Peru 0 894 1,567 0 0 2,910 0 0 5,189 0 10,560 1,056
Philippines 0 0 0 0 0 0 0 0 0 0 0 0
Poland 0 10,609 20,005 20,201 0 31,781 42,867 0 14,180 0 139,643 13,964
Qatar 4,923 4,108 4,537 9,771 15,382 23,255 26,026 44,960 23,623 40,994 197,579 19,758
Romania 0 1,851 3,291 0 0 6,825 10,620 8,057 11,933 0 42,577 4,258
Russian Federation 18,443 12,546 35,579 37,046 66,388 0 48,593 203,251 0 0 421,845 42,185
Rwanda 0 0 0 0 0 0 0 0 0 0 0 0
Samoa 0 0 0 0 0 0 0 0 0 0 0 0
Sao Tome and Principe 0 0 0 0 0 0 0 0 0 0 0 0
Saudi Arabia 7,740 0 27,627 50,755 47,657 52,217 58,963 39,726 81,186 61,987 427,857 42,786
Senegal 0 0 0 0 0 0 0 0 0 . 0 0
Serbia, Republic of . . . . . . 0 0 0 0 0 0
Seychelles 0 0 83 0 109 368 297 0 123 0 981 98
Sierra Leone 0 0 0 0 0 0 0 0 0 0 0 0
Solomon Islands 0 0 0 0 0 0 0 0 0 0 0 0
Somalia . . . . . . . . . . . .
South Africa 0 0 0 0 0 0 0 0 0 0 0 0
Sri Lanka 0 524 0 0 0 0 958 0 0 0 1,482 148
St. Kitts and Nevis 0 0 0 0 0 0 0 0 0 0 0 0
St. Lucia 0 0 0 0 0 0 0 0 0 0 0 0
Table 4. CED Normalized (Change in External Debt - Balance of Payments) (cont)
(in millions of U.S. dollars)
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45 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 0 0 0 0 0 0 0 0 0 0 0 0
Sudan 0 218 1,140 1,018 0 0 0 1,814 0 3,374 7,565 756
Suriname 0 0 0 0 0 0 0 0 0 0 0 0
Swaziland 0 250 0 0 0 0 304 0 0 172 726 73
Syrian Arab Republic 0 0 0 0 0 2,404 1,213 1,558 1,088 0 6,263 626
Tajikistan 0 104 0 0 0 254 148 1,761 0 0 2,268 227
Tanzania 0 0 0 0 0 0 0 0 0 0 0 0
Thailand 0 0 0 0 0 0 0 0 0 0 0 0
Timor-Leste, Dem. Rep. of . . . . . . . . . . . .
Togo 0 0 0 0 0 0 0 0 0 0 0 0
Tonga . . 525 3,327 0 0 33 0 0 8 3,893 487
Trinidad and Tobago 558 883 1,316 2,564 2,721 6,499 5,445 8,205 3,014 4,423 35,629 3,563
Tunisia 917 2,476 2,327 0 0 1,296 1,716 0 0 0 8,732 873
Turkey 5,482 10,715 0 0 0 16,869 21,636 0 0 0 54,701 5,470
Turkmenistan . . . . . . . . . . . .
Uganda 0 0 0 0 0 0 0 0 0 0 0 0
Ukraine 8,885 3,975 4,479 11,866 0 20,852 21,247 15,256 15,311 7,657 109,527 10,953
United Arab Emirates 5,700 7,208 16,966 27,131 44,290 50,825 45,068 97,739 24,792 0 319,718 31,972
Uruguay 328 3,810 0 633 0 0 0 0 963 2,062 7,795 780
Uzbekistan . . . . . . . . . . . .
Vanuatu 18 20 0 5 0 0 6 0 26 0 75 7
Venezuela, Rep. Bolivariana de 4,300 9,329 8,527 14,862 30,789 18,386 28,866 29,122 14,944 18,885 178,009 17,801
Vietnam 0 0 0 0 0 0 0 0 12,968 11,049 24,017 2,402
Yemen, Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Zambia 0 0 0 538 0 0 750 0 0 2,289 3,578 358
Zimbabwe 0 337 197 0 . . . . . . 534 133

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and external debt data reported to the IMF and
World Bank respectively by member countries.
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46 Global Financial Integrity
Table 5. CED Non-normalized (Change in External Debt - Balance of Payments)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . . 0 0 13,934 0 761 0 535 0 15,230 1,904
Albania 0 0 38 0 34 0 0 0 0 0 72 7
Algeria 0 1,942 2,699 1,132 667 1,800 2,635 0 0 0 10,875 1,088
Angola 207 2,155 2,455 1,982 4,269 2,699 7,777 3,239 773 0 25,555 2,556
Antigua and Barbuda 63 53 98 0 16 101 0 0 1 0 332 33
Argentina 17,984 12,366 20,898 3,479 0 0 679 16,666 11,678 12,331 96,080 9,608
Armenia, Republic of 225 182 155 306 0 129 459 284 235 517 2,491 249
Aruba 0 42 224 260 74 532 724 0 88 0 1,944 194
Azerbaijan, Republic of 68 462 482 0 589 1,664 3,269 14,215 11,573 16,456 48,779 4,878
Bahamas, The 0 0 0 0 0 0 0 0 0 0 0 0
Bahrain, Kingdom of 145 738 909 1,101 1,883 4,574 2,168 4,534 3,103 3,268 22,425 2,242
Bangladesh 0 2,081 1,257 787 0 2,535 1,402 2,289 874 3,254 14,479 1,448
Barbados 0 0 81 0 0 137 589 0 185 0 993 99
Belarus 0 581 92 0 931 0 991 127 0 2,107 4,829 483
Belize 0 8 48 0 37 58 78 0 5 20 255 26
Benin 0 83 0 29 0 0 0 0 0 0 112 11
Bhutan . . . . 0 0 174 0 0 0 174 29
Bolivia 0 938 914 663 604 0 0 664 1,123 124 5,030 503
Bosnia and Herzegovina 0 0 0 0 0 0 1,099 0 520 0 1,619 162
Botswana 868 0 876 1,238 559 824 901 790 2,059 1,360 9,476 948
Brazil 0 8,136 9,582 2,975 0 0 0 17,986 0 10,564 49,244 4,924
Brunei Darussalam 2,019 1,945 2,585 2,925 4,234 5,261 4,958 7,116 4,468 6,048 41,559 4,156
Bulgaria 0 953 1,991 1,676 276 5,085 9,344 11,471 4,961 0 35,758 3,576
Burkina Faso 0 0 0 0 0 0 0 0 0 0 0 0
Burundi 0 87 81 28 0 0 0 0 0 0 196 20
Cambodia 59 146 86 123 66 90 184 0 0 32 787 79
Cameroon 0 322 873 0 0 0 0 0 0 0 1,195 119
Cape Verde 3 0 3 0 0 38 0 25 0 20 89 9
Central African Republic 0 285 10 41 0 0 0 0 0 0 337 34
Chad 0 0 0 0 384 1,222 2,020 2,366 637 0 6,628 663
Chile 3,452 4,009 3,884 8,862 6,209 15,044 26,629 6,586 15,251 19,590 109,515 10,952
China, Mainland 45,733 8,108 8,094 0 9,521 87,023 81,895 62,539 0 68,175 371,087 37,109
Colombia 2,555 0 3,794 413 1,814 2,848 3,136 1,843 3,333 0 19,737 1,974
Comoros 6 17 14 8 . . . . . . 46 12
Congo, Democratic Republic of 170 0 1,744 528 0 928 3,440 1,370 0 0 8,181 818
Congo, Republic of 0 1,033 1,205 1,726 126 1,056 0 336 478 0 5,959 596
Costa Rica 28 0 0 0 302 0 250 423 0 469 1,472 147
Cote d'Ivoire 0 591 1,696 1,298 0 1,364 923 0 647 0 6,518 652
Croatia 509 2,054 595 0 291 1,011 82 2,562 2,058 2,721 11,883 1,188
Djibouti 17 94 89 77 35 125 217 0 38 70 762 76
Dominica 11 0 34 0 0 0 0 0 0 0 45 5
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47 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 422 1,777 2,409 1,859 0 934 407 0 123 0 7,930 793
Ecuador 847 1,737 785 967 124 2,115 1,135 1,284 0 400 9,394 939
Egypt 0 2,501 4,511 4,989 217 9,294 9,304 3,740 2,913 966 38,435 3,844
El Salvador 1,131 639 1,443 262 715 358 0 0 0 720 5,268 527
Equatorial Guinea 398 118 410 0 . . . . . . 926 231
Eritrea . . . . . . . . . . 0 .
Ethiopia 0 1,141 687 0 0 0 275 0 1,402 3,113 6,617 662
Fiji 0 91 0 0 0 161 0 0 0 0 251 25
Gabon 115 375 1,159 1,370 1,667 1,652 885 2,652 1,409 1,688 12,972 1,297
Gambia, The 36 69 71 32 0 48 13 0 126 114 509 51
Georgia 0 16 62 80 0 0 0 3,219 0 246 3,623 362
Ghana 0 362 37 0 0 0 126 855 284 682 2,347 235
Grenada 0 24 0 8 0 0 0 0 0 0 33 3
Guatemala 0 0 0 3,121 255 102 980 0 0 130 4,588 459
Guinea 0 73 103 0 0 67 56 0 0 0 299 30
Guinea-Bissau 0 55 52 34 0 8 11 0 0 . 160 18
Guyana 0 30 37 0 0 0 0 10 0 380 458 46
Haiti 0 0 84 0 27 144 0 0 0 0 256 26
Honduras 0 215 130 0 0 0 0 0 529 0 875 87
Hungary 3,841 2,696 9,715 8,296 6,102 12,000 32,333 18,506 2,496 0 95,987 9,599
India 0 0 0 0 0 5,597 0 11,633 5,698 0 22,928 2,293
Indonesia 0 0 8,909 4,429 3,442 0 8,535 19,263 15,979 2,179 62,736 6,274
Iran, Islamic Republic of 3,320 2,654 6,992 0 . . . . . . 12,965 3,241
Iraq . . . . 0 0 53,545 2,718 0 0 56,263 9,377
Jamaica 0 0 392 189 0 912 1,516 0 52 1,672 4,732 473
Jordan 663 393 802 605 0 961 0 0 0 0 3,424 342
Kazakhstan 3,541 3,698 5,193 11,821 13,667 22,554 25,772 28,991 7,344 8,229 130,811 13,081
Kenya 0 508 540 0 0 0 0 0 0 0 1,048 105
Kiribati . . . . . . . . . . 0 .
Kosovo, Republic of . . . . . . . . . 0 0 0
Kuwait 7,517 7,320 16,110 15,595 29,297 44,241 55,232 53,405 0 19,878 248,595 24,860
Kyrgyz Republic 0 66 112 403 0 61 0 306 308 0 1,255 126
Lao PDR 0 624 0 152 93 893 1,674 826 647 312 5,221 522
Latvia 0 1,139 1,259 1,967 0 3,248 11,082 0 1,035 0 19,729 1,973
Lebanon 1,956 866 0 1,062 0 2,222 2,034 0 0 0 8,140 814
Lesotho 0 264 205 212 0 0 0 0 194 0 876 88
Liberia 0 157 187 137 0 113 0 0 0 0 593 59
Libya 1,875 0 0 0 1,968 4,291 9,157 21,015 4,383 11,326 54,015 5,401
Lithuania 110 433 0 1,926 0 3,964 5,373 0 1,643 0 13,448 1,345
Macedonia, FYR 151 64 74 790 0 336 838 0 341 142 2,736 274
Madagascar 0 0 93 0 0 0 0 0 0 0 93 9
Malawi 0 2 66 50 0 0 0 0 0 0 119 12
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48 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 9,792 8,003 4,576 0 17,272 22,426 20,426 39,206 21,506 38,512 181,719 18,172
Maldives 42 0 0 0 0 0 0 0 0 0 42 4
Mali 0 0 0 63 0 0 0 0 0 0 63 6
Mauritania 0 20 133 0 . . . . . . 153 38
Mauritius 199 0 10 0 0 0 0 0 0 12,886 13,095 1,309
Mexico 13,424 0 4,618 11,723 8,457 6,180 20,967 11,318 0 10,131 86,819 8,682
Moldova 15 201 66 0 0 118 60 0 0 498 958 96
Mongolia 0 66 566 168 29 290 485 537 0 153 2,294 229
Montenegro . . . . . . 0 0 988 0 988 247
Morocco 0 478 2,451 0 0 2,373 2,772 0 0 0 8,074 807
Mozambique 0 0 0 83 0 0 0 0 0 0 83 8
Myanmar 0 1,166 973 202 206 901 1,831 1,278 1,140 19 7,716 772
Namibia 365 296 1,269 1,149 524 1,719 916 0 522 1,431 8,190 819
Nepal 0 532 149 116 0 27 220 0 67 0 1,111 111
Nicaragua 0 0 0 0 0 0 0 0 0 0 0 0
Niger 0 60 105 0 0 0 0 0 0 . 165 18
Nigeria 2,846 5,135 9,751 12,333 14,454 12,791 24,690 37,403 27,732 28,573 175,709 17,571
Oman 530 428 22 0 3,822 5,569 722 7,378 0 5,300 23,771 2,377
Pakistan 0 2,026 3,522 1,852 0 0 0 3,384 0 482 11,265 1,127
Panama 876 14 875 557 0 2,773 0 0 2,170 0 7,265 726
Papua New Guinea 0 0 58 0 572 0 0 886 0 3,253 4,769 477
Paraguay 0 379 76 376 0 101 0 149 0 321 1,401 140
Peru 0 894 1,567 751 45 2,910 0 0 5,189 0 11,356 1,136
Philippines 0 3,101 3,512 1,749 2,714 3,979 3,570 1,500 2,464 4,518 27,108 2,711
Poland 2,786 10,609 20,005 20,201 0 31,781 42,867 0 14,180 812 143,241 14,324
Qatar 4,923 4,108 4,537 9,771 15,382 23,255 26,026 44,960 23,623 40,994 197,579 19,758
Romania 0 1,851 3,291 920 519 6,825 10,620 8,057 11,933 0 44,016 4,402
Russian Federation 18,443 12,546 35,579 37,046 66,388 4,603 48,593 203,251 8,756 36,415 471,619 47,162
Rwanda 0 4 21 0 0 0 0 0 0 0 25 2
Samoa 0 27 33 0 0 0 0 5 0 0 65 7
Sao Tome and Principe 0 0 0 0 0 0 0 0 0 0 0 0
Saudi Arabia 7,740 2,680 27,627 50,755 47,657 52,217 58,963 39,726 81,186 61,987 430,537 43,054
Senegal 0 54 0 0 0 0 0 0 0 . 54 6
Serbia, Republic of . . . . . . 899 0 0 0 899 225
Seychelles 0 0 83 12 109 368 297 0 123 0 993 99
Sierra Leone 0 55 115 24 51 0 0 0 0 0 246 25
Solomon Islands 0 0 0 27 0 0 0 0 0 0 27 3
Somalia . . . . . . . . . . 0 .
South Africa 10,339 4,138 1,148 0 0 0 0 0 0 0 15,624 1,562
Sri Lanka 0 524 218 355 0 0 958 0 0 513 2,568 257
St. Kitts and Nevis 31 0 9 0 12 4 0 0 0 0 56 6
St. Lucia 0 0 0 0 25 0 14 190 0 0 229 23
Table 5. CED Non-normalized (Change in External Debt - Balance of Payments) (cont)
(in millions of U.S. dollars)
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49 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 0 0 0 0 0 0 0 0 0 0 0 0
Sudan 0 218 1,140 1,018 0 0 495 1,814 0 3,374 8,060 806
Suriname 0 0 0 0 0 0 0 89 0 429 518 52
Swaziland 0 250 154 164 0 48 304 0 0 172 1,092 109
Syrian Arab Republic 0 0 0 448 0 2,404 1,213 1,558 1,088 0 6,711 671
Tajikistan 0 104 0 58 0 254 148 1,761 0 0 2,327 233
Tanzania 0 607 0 956 0 0 0 0 0 71 1,635 163
Thailand 0 0 887 1,254 0 0 6,320 0 6,488 0 14,949 1,495
Timor-Leste, Dem. Rep. of . . . . . 619 1,039 2,081 1,335 1,588 6,663 1,333
Togo 0 68 29 0 0 0 0 0 0 0 97 10
Tonga . . 525 3,327 0 0 33 0 0 8 3,893 487
Trinidad and Tobago 558 883 1,316 2,564 2,721 6,499 5,445 8,205 3,014 4,423 35,629 3,563
Tunisia 917 2,476 2,327 334 0 1,296 1,716 0 0 0 9,066 907
Turkey 5,482 10,715 4,242 2,339 0 16,869 21,636 7,618 0 0 68,901 6,890
Turkmenistan . . . . . . . . . . 0 .
Uganda 0 305 575 260 0 0 0 57 0 0 1,197 120
Ukraine 8,885 3,975 4,479 11,866 2,779 20,852 21,247 15,256 15,311 7,657 112,306 11,231
United Arab Emirates 5,700 7,208 16,966 27,131 44,290 50,825 45,068 97,739 24,792 8,691 328,409 32,841
Uruguay 328 3,810 0 633 0 0 241 0 963 2,062 8,036 804
Uzbekistan . . 606 792 728 1,356 2,988 3,134 4,787 3,825 18,216 2,277
Vanuatu 18 20 1 5 0 0 6 0 26 0 75 8
Venezuela, Rep. Bolivariana de 4,300 9,329 8,527 14,862 30,789 18,386 28,866 29,122 14,944 18,885 178,009 17,801
Vietnam 1,471 1,085 25 1,876 305 0 0 264 12,968 11,049 29,044 2,904
Yemen, Republic of 98 233 0 0 0 127 0 0 0 0 458 46
Zambia 0 0 52 538 0 0 750 96 244 2,289 3,971 397
Zimbabwe 0 337 197 0 . . . . . . 534 133

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and external debt data reported to the IMF and
World Bank respectively by member countries.
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50 Global Financial Integrity
Table 6. GER Normalized (Gross Excluding Reversals - Trade Misinvoicing)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . 1,289 513 203 0 0 0 0 41 110 2,157 240
Albania 0 0 0 0 91 103 315 405 233 164 1,312 131
Algeria 0 0 0 0 0 0 0 0 0 0 0 0
Angola . . . . . . . . . . . .
Antigua and Barbuda . . . . . . . . . . . .
Argentina 0 0 0 0 0 0 0 0 0 0 0 0
Armenia, Republic of 273 170 248 97 316 204 445 1,210 1,071 1,192 5,226 523
Aruba 1,259 805 1,224 2,188 3,537 3,708 3,933 4,907 1,829 123 23,513 2,351
Azerbaijan, Republic of . . . . . . . . . . . .
Bahamas, The 484 772 1,001 1,063 1,694 1,331 1,622 2,086 1,616 1,925 13,595 1,360
Bahrain, Kingdom of 0 609 0 1,468 2,215 2,256 1,629 0 0 0 8,178 818
Bangladesh 758 0 967 920 0 2,154 1,833 0 0 2,311 8,943 894
Barbados 0 300 329 572 532 64 54 0 0 0 1,852 185
Belarus . . . . . . . . . . . .
Belize 109 66 86 88 94 80 134 122 101 75 954 95
Benin 0 0 0 0 0 0 0 0 0 0 0 0
Bhutan . . . . . . . . . . . .
Bolivia 0 0 0 0 0 0 0 0 0 0 0 0
Bosnia and Herzegovina 459 591 748 903 1,232 371 919 1,162 939 900 8,222 822
Botswana 0 0 0 0 0 611 1,157 1,146 1,082 0 3,996 400
Brazil 0 0 0 0 0 0 0 0 0 0 0 0
Brunei Darussalam 0 0 0 0 . 0 0 0 0 0 0 0
Bulgaria 539 0 0 0 0 0 0 0 0 0 539 54
Burkina Faso 166 132 111 295 196 174 321 408 214 380 2,397 240
Burundi 0 6 0 0 30 119 28 32 47 37 300 30
Cambodia 254 280 327 403 412 511 0 742 729 1,027 4,685 469
Cameroon 325 0 279 896 473 980 1,221 1,480 0 0 5,653 565
Cape Verde . . . . . . . . . . . .
Central African Republic 33 78 14 0 14 0 0 0 35 0 174 17
Chad . . . . . . . . . . . .
Chile 0 0 0 0 0 0 0 0 0 0 0 0
China, Mainland 137,470 153,797 183,530 251,058 283,483 296,078 326,661 348,421 294,726 367,426 2,642,648 264,265
Colombia 1,350 0 1,316 1,808 0 0 0 0 0 0 4,474 447
Comoros 21 9 6 16 16 26 20 21 11 11 158 16
Congo, Democratic Republic of 346 345 412 598 922 547 0 0 0 0 3,170 317
Congo, Republic of 1,142 0 918 2,961 672 2,149 1,529 2,636 620 1,761 14,388 1,439
Costa Rica 1,913 2,375 3,423 4,690 5,757 5,831 6,258 6,992 8,854 17,310 63,403 6,340
Cote d'Ivoire 0 0 612 916 1,508 934 0 0 0 0 3,971 397
Croatia 484 0 0 0 0 0 0 0 0 0 484 48
Djibouti 205 172 212 214 233 304 279 345 301 364 2,629 263
Dominica 17 12 18 26 41 46 75 150 127 121 632 63
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51 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 1,097 536 345 0 0 746 371 1,125 3,834 4,064 12,118 1,212
Ecuador 972 0 0 860 1,317 0 0 4,738 0 0 7,887 789
Egypt 1,124 1,492 1,217 3,073 2,533 4,200 4,515 3,103 0 0 21,256 2,126
El Salvador 0 515 552 730 587 371 956 827 944 908 6,390 639
Equatorial Guinea 450 0 0 0 0 0 0 1,968 2,876 2,904 8,198 820
Eritrea . . . . . . . . . . . .
Ethiopia 672 321 97 0 787 1,148 1,288 1,755 2,543 2,567 11,177 1,118
Fiji 147 70 206 249 158 254 239 390 203 280 2,197 220
Gabon 0 0 0 0 0 0 0 0 0 0 0 0
Gambia, The 17 19 11 28 20 24 31 33 41 47 271 27
Georgia 244 248 380 450 403 612 411 854 517 488 4,607 461
Ghana 0 0 0 0 0 0 0 0 0 0 0 0
Grenada . . . . . . . . . . . .
Guatemala 1,998 1,339 1,301 1,350 1,489 808 950 878 3,366 2,257 15,736 1,574
Guinea 304 116 161 451 295 291 644 261 0 378 2,901 290
Guinea-Bissau 15 42 0 34 19 13 194 0 32 69 419 42
Guyana 81 59 60 84 140 90 209 202 182 230 1,336 134
Haiti 0 0 0 40 0 88 95 121 0 62 405 41
Honduras 2,524 2,679 2,722 2,920 2,985 3,031 3,048 3,297 2,857 3,465 29,527 2,953
Hungary 0 0 0 0 0 0 0 0 0 0 0 0
India 7,172 8,102 9,447 22,609 30,404 0 0 26,820 0 0 104,554 10,455
Indonesia 0 0 11,610 14,542 11,204 12,724 14,122 16,302 0 0 80,504 8,050
Iran, Islamic Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Iraq . . . . . . . 10,404 11,944 14,263 36,611 12,204
Jamaica 235 303 428 413 823 225 212 582 457 0 3,679 368
Jordan 0 0 0 0 0 0 0 0 0 0 0 0
Kazakhstan 0 0 0 0 0 0 0 0 0 0 0 0
Kenya 0 0 0 0 0 0 0 0 0 0 0 0
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . . . . . . . . .
Kuwait 0 0 0 0 0 0 0 0 0 0 0 0
Kyrgyz Republic . . . . . . . . . . . .
Lao PDR 0 0 0 0 0 114 115 182 182 0 594 59
Latvia 836 649 761 1,197 791 665 0 0 0 0 4,899 490
Lebanon 1,034 309 0 280 0 0 1,070 1,255 1,068 960 5,976 598
Lesotho 0 0 0 0 74 176 314 330 358 0 1,253 125
Liberia . 897 815 851 948 1,480 1,828 608 1,040 631 9,098 1,011
Libya 0 0 0 0 0 0 0 0 0 0 0 0
Lithuania 0 0 0 0 0 0 0 0 0 0 0 0
Macedonia, FYR 0 168 247 381 477 287 713 994 582 630 4,480 448
Madagascar 0 128 0 797 460 1,643 0 682 185 0 3,896 390
Malawi 135 112 183 159 470 418 453 860 696 684 4,171 417
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52 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 10,674 12,153 17,727 19,579 21,447 22,156 27,277 28,756 25,172 42,719 227,659 22,766
Maldives 109 148 103 68 38 44 0 0 0 0 510 51
Mali 0 0 274 102 145 187 186 967 393 373 2,626 263
Mauritania . . . . . . . . . . . .
Mauritius 0 0 0 0 0 0 0 0 301 423 724 72
Mexico 32,999 34,811 34,022 36,392 44,231 48,380 59,508 61,132 34,578 33,429 419,482 41,948
Moldova 209 108 226 337 241 192 446 507 228 0 2,494 249
Mongolia 0 0 0 0 0 0 0 0 0 0 0 0
Montenegro . . . . 925 860 1,198 1,473 902 896 6,254 1,042
Morocco 0 0 0 0 3,382 0 0 0 1,931 0 5,312 531
Mozambique 0 189 0 0 0 330 0 0 0 0 520 52
Myanmar 0 0 0 0 0 0 0 0 0 0 0 0
Namibia 0 0 0 0 0 444 807 841 1,104 0 3,195 320
Nepal 603 479 356 421 501 683 584 776 1,551 1,703 7,658 766
Nicaragua 436 443 525 650 954 1,120 1,174 1,213 1,130 1,256 8,902 890
Niger 50 0 0 87 100 0 0 0 276 554 1,067 107
Nigeria 0 0 0 0 0 0 0 0 0 0 0 0
Oman 0 0 0 0 0 2,450 0 0 0 0 2,450 245
Pakistan 0 0 0 0 0 0 0 0 0 0 0 0
Panama 1,732 2,223 2,406 2,705 3,571 4,617 5,072 5,786 5,082 5,343 38,536 3,854
Papua New Guinea 0 0 0 0 0 0 0 0 480 899 1,379 138
Paraguay 156 305 158 0 0 1,123 627 1,579 1,447 1,412 6,808 681
Peru 1,147 0 0 0 0 0 0 0 0 0 1,147 115
Philippines 6,540 7,091 10,286 11,968 15,685 16,206 20,380 16,992 8,292 14,631 128,072 12,807
Poland 0 0 0 0 0 0 0 0 0 0 0 0
Qatar 0 0 0 0 0 0 0 0 18,981 0 18,981 1,898
Romania 0 0 0 0 0 0 0 0 0 0 0 0
Russian Federation 19,140 0 0 0 0 0 0 0 0 0 19,140 1,914
Rwanda 50 53 37 221 35 90 126 88 371 490 1,560 156
Samoa 51 59 84 79 322 116 142 137 103 115 1,209 121
Sao Tome and Principe . . . . . . . . . . . .
Saudi Arabia 0 0 0 0 0 0 0 0 0 0 0 0
Senegal 0 0 0 0 0 0 0 0 0 0 0 0
Serbia, Republic of 4,080 5,469 7,409 9,776 6,434 5,407 4,194 0 5,637 2,750 51,156 5,116
Seychelles 82 210 149 82 75 0 0 0 0 0 598 60
Sierra Leone 24 53 45 40 32 0 53 0 0 0 247 25
Solomon Islands 28 25 35 70 88 94 136 171 91 153 891 89
Somalia . . . . . . . . . . . .
South Africa 0 0 0 0 0 10,927 19,838 21,006 18,117 0 69,887 6,989
Sri Lanka 0 0 0 0 0 0 0 0 0 0 0 0
St. Kitts and Nevis . . . . . . . . . . . .
St. Lucia . . . . . . . . . . . .
Table 6. GER Normalized (Gross Excluding Reversals - Trade Misinvoicing) (cont)
(in millions of U.S. dollars)
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http:/ / reali tyvi ews.blogspot.com/
53 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines . . . . . . . . . . . .
Sudan 376 272 0 0 0 0 1,124 9,158 5,663 7,721 24,314 2,431
Suriname 56 106 74 120 115 0 0 185 0 0 657 66
Swaziland 0 0 0 0 0 300 467 389 410 0 1,565 157
Syrian Arab Republic 13,085 0 0 12,761 1,078 0 0 0 0 0 26,924 2,692
Tajikistan . . . . . . . . . . . .
Tanzania 0 0 0 0 0 0 0 0 0 0 0 0
Thailand 0 0 0 0 0 0 0 0 0 0 0 0
Timor-Leste, Dem. Rep. of . . . . . . . . . . . .
Togo 154 61 292 151 168 131 227 438 200 120 1,940 194
Tonga 17 15 13 13 9 13 9 19 6 5 120 12
Trinidad and Tobago 0 1,003 1,576 1,848 1,749 0 2,246 0 2,917 4,326 15,665 1,566
Tunisia 0 0 0 0 0 0 0 0 0 0 0 0
Turkey 0 0 0 0 0 0 0 0 0 0 0 0
Turkmenistan . . . . . . . . . . . .
Uganda 0 0 140 238 362 456 676 1,130 1,309 1,261 5,572 557
Ukraine 0 0 0 0 0 0 0 0 0 0 0 0
United Arab Emirates 0 0 0 0 0 0 0 0 0 0 0 0
Uruguay 0 294 353 346 0 0 522 0 0 1,008 2,523 252
Uzbekistan . . . . . . . . . . . .
Vanuatu . . . . . . . . . . . .
Venezuela, Rep. Bolivariana de 0 0 0 0 0 0 0 0 0 0 0 0
Vietnam 0 0 0 0 0 0 0 0 0 0 0 0
Yemen, Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Zambia 0 0 387 583 1,279 465 1,249 0 0 0 3,962 396
Zimbabwe 342 655 0 297 360 1,787 0 0 0 0 3,442 344

Source: Staff estimates, Global Financial Integrity, based on trade data reported to the IMF by member countries.
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54 Global Financial Integrity
Table 7. GER Non-normalized (Gross Excluding Reversals - Trade Misinvoicing)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . 1,289 513 203 0 0 0 0 41 110 2,157 240
Albania 14 0 14 15 91 103 315 405 233 164 1,356 136
Algeria 1,626 490 497 746 0 298 0 0 1,094 0 4,752 475
Angola . . . . . . . . . . . .
Antigua and Barbuda . . . . . . . . . . . .
Argentina 888 598 0 1,166 760 0 1,380 4,748 0 0 9,539 954
Armenia, Republic of 273 170 248 97 316 204 445 1,210 1,071 1,192 5,226 523
Aruba 1,259 805 1,224 2,188 3,537 3,708 3,933 4,907 1,829 123 23,513 2,351
Azerbaijan, Republic of . . . . . . . . . . . .
Bahamas, The 484 772 1,001 1,063 1,694 1,331 1,622 2,086 1,616 1,925 13,595 1,360
Bahrain, Kingdom of 146 609 588 1,468 2,215 2,256 1,629 0 0 0 8,912 891
Bangladesh 758 391 967 920 533 2,154 1,833 729 0 2,311 10,597 1,060
Barbados 24 300 329 572 532 64 54 0 0 30 1,905 191
Belarus . . . . . . . . . . . .
Belize 109 66 86 88 94 80 134 122 101 75 954 95
Benin 0 0 62 114 35 0 0 0 0 0 212 21
Bhutan . . . . . . . . . . . .
Bolivia 164 215 0 0 0 0 0 0 0 0 379 38
Bosnia and Herzegovina 459 591 748 903 1,232 371 919 1,162 939 900 8,222 822
Botswana 71 79 0 206 277 611 1,157 1,146 1,082 213 4,841 484
Brazil 0 0 1,190 1,464 1,702 0 5,435 7,792 5,795 994 24,373 2,437
Brunei Darussalam 0 0 0 0 . 0 0 0 0 212 212 24
Bulgaria 539 441 657 425 502 0 1,166 510 526 0 4,765 477
Burkina Faso 166 132 111 295 196 174 321 408 214 380 2,397 240
Burundi 0 6 3 0 30 119 28 32 47 37 303 30
Cambodia 254 280 327 403 412 511 0 742 729 1,027 4,685 469
Cameroon 325 113 279 896 473 980 1,221 1,480 227 362 6,355 636
Cape Verde . . . . . . . . . . . .
Central African Republic 33 78 14 7 14 0 2 0 35 0 183 18
Chad . . . . . . . . . . . .
Chile 1,085 1,051 949 1,289 1,522 1,337 2,178 5,750 939 1,410 17,509 1,751
China, Mainland 137,470 153,797 183,530 251,058 283,483 296,078 326,661 348,421 294,726 367,426 2,642,648 264,265
Colombia 1,350 1,079 1,316 1,808 1,379 540 628 2,760 1,355 51 12,266 1,227
Comoros 21 9 6 16 16 26 20 21 11 11 158 16
Congo, Democratic Republic of 346 345 412 598 922 547 207 0 277 260 3,914 391
Congo, Republic of 1,142 0 918 2,961 672 2,149 1,529 2,636 620 1,761 14,388 1,439
Costa Rica 1,913 2,375 3,423 4,690 5,757 5,831 6,258 6,992 8,854 17,310 63,403 6,340
Cote d'Ivoire 45 0 612 916 1,508 934 761 427 506 76 5,786 579
Croatia 484 359 595 308 150 0 0 0 0 0 1,896 190
Djibouti 205 172 212 214 233 304 279 345 301 364 2,629 263
Dominica 17 12 18 26 41 46 75 150 127 121 632 63
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55 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 1,097 536 345 0 0 746 371 1,125 3,834 4,064 12,118 1,212
Ecuador 972 295 23 860 1,317 608 692 4,738 267 611 10,383 1,038
Egypt 1,124 1,492 1,217 3,073 2,533 4,200 4,515 3,103 0 1,066 22,322 2,232
El Salvador 135 515 552 730 587 371 956 827 944 908 6,525 653
Equatorial Guinea 450 59 0 322 175 360 917 1,968 2,876 2,904 10,030 1,003
Eritrea . . . . . . . . . . . .
Ethiopia 672 321 97 52 787 1,148 1,288 1,755 2,543 2,567 11,230 1,123
Fiji 147 70 206 249 158 254 239 390 203 280 2,197 220
Gabon 720 387 243 259 0 0 0 0 0 0 1,609 161
Gambia, The 17 19 11 28 20 24 31 33 41 47 271 27
Georgia 244 248 380 450 403 612 411 854 517 488 4,607 461
Ghana 461 0 0 0 0 0 0 0 0 0 461 46
Grenada . . . . . . . . . . . .
Guatemala 1,998 1,339 1,301 1,350 1,489 808 950 878 3,366 2,257 15,736 1,574
Guinea 304 116 161 451 295 291 644 261 0 378 2,901 290
Guinea-Bissau 15 42 6 34 19 13 194 4 32 69 428 43
Guyana 81 59 60 84 140 90 209 202 182 230 1,336 134
Haiti 2 11 31 40 41 88 95 121 34 62 524 52
Honduras 2,524 2,679 2,722 2,920 2,985 3,031 3,048 3,297 2,857 3,465 29,527 2,953
Hungary 0 0 0 0 0 0 0 0 318 0 318 32
India 7,172 8,102 9,447 22,609 30,404 10,512 4,923 26,820 0 0 119,989 11,999
Indonesia 320 1,110 11,610 14,542 11,204 12,724 14,122 16,302 8,586 3,733 94,253 9,425
Iran, Islamic Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Iraq . . . . . . . 10,404 11,944 14,263 36,611 12,204
Jamaica 235 303 428 413 823 225 212 582 457 26 3,705 371
Jordan 0 0 0 162 0 0 0 97 132 0 391 39
Kazakhstan 411 1,000 135 0 0 0 0 0 0 589 2,136 214
Kenya 72 0 36 136 0 43 0 0 0 0 287 29
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . . . . . . . . .
Kuwait 251 447 0 18 744 884 351 0 1,403 0 4,098 410
Kyrgyz Republic . . . . . . . . . . . .
Lao PDR 0 0 0 8 0 114 115 182 182 86 688 69
Latvia 836 649 761 1,197 791 665 572 161 0 0 5,632 563
Lebanon 1,034 309 0 280 130 0 1,070 1,255 1,068 960 6,106 611
Lesotho 18 19 0 66 74 176 314 330 358 62 1,417 142
Liberia . 897 815 851 948 1,480 1,828 608 1,040 631 9,098 1,011
Libya 2,414 59 0 0 0 0 0 0 0 0 2,473 247
Lithuania 0 0 0 0 0 0 0 2,540 1,239 2,752 6,532 653
Macedonia, FYR 0 168 247 381 477 287 713 994 582 630 4,480 448
Madagascar 24 128 66 797 460 1,643 121 682 185 32 4,139 414
Malawi 135 112 183 159 470 418 453 860 696 684 4,171 417
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56 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 10,674 12,153 17,727 19,579 21,447 22,156 27,277 28,756 25,172 42,719 227,659 22,766
Maldives 109 148 103 68 38 44 4 0 0 0 514 51
Mali 52 45 274 102 145 187 186 967 393 373 2,723 272
Mauritania . . . . . . . . . . . .
Mauritius 16 2 107 40 0 0 0 166 301 423 1,057 106
Mexico 32,999 34,811 34,022 36,392 44,231 48,380 59,508 61,132 34,578 33,429 419,482 41,948
Moldova 209 108 226 337 241 192 446 507 228 0 2,494 249
Mongolia 0 0 0 0 0 0 0 0 0 0 0 0
Montenegro . . . . 925 860 1,198 1,473 902 896 6,254 1,042
Morocco 343 153 300 700 3,382 271 688 1,465 1,931 821 10,054 1,005
Mozambique 9 189 75 0 0 330 126 0 0 0 730 73
Myanmar 366 0 62 533 0 0 0 0 0 0 961 96
Namibia 47 41 0 129 168 444 807 841 1,104 194 3,774 377
Nepal 603 479 356 421 501 683 584 776 1,551 1,703 7,658 766
Nicaragua 436 443 525 650 954 1,120 1,174 1,213 1,130 1,256 8,902 890
Niger 50 0 0 87 100 0 46 0 276 554 1,113 111
Nigeria 906 0 0 1,702 485 1,984 4,905 3,444 0 4,305 17,730 1,773
Oman 28 144 332 70 0 2,450 129 0 0 0 3,152 315
Pakistan 0 0 0 0 0 0 505 677 298 0 1,480 148
Panama 1,732 2,223 2,406 2,705 3,571 4,617 5,072 5,786 5,082 5,343 38,536 3,854
Papua New Guinea 0 52 124 93 0 0 33 111 480 899 1,791 179
Paraguay 156 305 158 0 0 1,123 627 1,579 1,447 1,412 6,808 681
Peru 1,147 718 728 614 972 651 534 1,309 1,577 0 8,250 825
Philippines 6,540 7,091 10,286 11,968 15,685 16,206 20,380 16,992 8,292 14,631 128,072 12,807
Poland 0 322 106 632 0 0 0 0 0 0 1,060 106
Qatar 335 0 0 0 0 175 168 4,807 18,981 978 25,443 2,544
Romania 30 0 0 0 0 0 2,434 0 0 0 2,465 246
Russian Federation 19,140 0 2,497 14,487 0 0 0 0 6,876 35,353 78,352 7,835
Rwanda 50 53 37 221 35 90 126 88 371 490 1,560 156
Samoa 51 59 84 79 322 116 142 137 103 115 1,209 121
Sao Tome and Principe . . . . . . . . . . . .
Saudi Arabia 0 0 0 0 1,694 461 716 2,939 4,131 3,921 13,862 1,386
Senegal 9 0 0 0 6 0 0 0 0 0 15 1
Serbia, Republic of 4,080 5,469 7,409 9,776 6,434 5,407 4,194 0 5,637 2,750 51,156 5,116
Seychelles 82 210 149 82 75 4 0 0 0 0 602 60
Sierra Leone 24 53 45 40 32 0 53 18 0 0 266 27
Solomon Islands 28 25 35 70 88 94 136 171 91 153 891 89
Somalia . . . . . . . . . . . .
South Africa 984 961 0 3,052 4,113 10,927 19,838 21,006 18,117 3,615 82,613 8,261
Sri Lanka 0 0 0 0 0 0 0 0 0 0 0 0
St. Kitts and Nevis . . . . . . . . . . . .
St. Lucia . . . . . . . . . . . .
Table 7. GER Non-normalized (Gross Excluding Reversals - Trade Misinvoicing) (cont)
(in millions of U.S. dollars)
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57 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines . . . . . . . . . . . .
Sudan 376 272 0 0 139 70 1,124 9,158 5,663 7,721 24,523 2,452
Suriname 56 106 74 120 115 31 9 185 53 0 750 75
Swaziland 38 32 0 119 133 300 467 389 410 67 1,954 195
Syrian Arab Republic 13,085 93 91 12,761 1,078 468 108 0 0 158 27,842 2,784
Tajikistan . . . . . . . . . . . .
Tanzania 0 0 0 0 0 0 0 0 40 0 40 4
Thailand 1,898 1,490 2,070 3,461 8,352 6,095 4,198 14,880 8,406 8,534 59,384 5,938
Timor-Leste, Dem. Rep. of . . . . . . . . . . . .
Togo 154 61 292 151 168 131 227 438 200 120 1,940 194
Tonga 17 15 13 13 9 13 9 19 6 5 120 12
Trinidad and Tobago 75 1,003 1,576 1,848 1,749 620 2,246 888 2,917 4,326 17,248 1,725
Tunisia 0 0 0 0 0 0 0 0 0 0 0 0
Turkey 784 1,822 2,088 18 1,736 906 3,432 3,519 8,244 3,558 26,106 2,611
Turkmenistan 694 685 600 . . . . . . . 1,978 659
Uganda 26 6 140 238 362 456 676 1,130 1,309 1,261 5,604 560
Ukraine 0 0 0 0 0 0 0 290 727 1,253 2,271 227
United Arab Emirates 0 0 0 0 0 0 0 0 0 0 0 0
Uruguay 191 294 353 346 286 139 522 132 528 1,008 3,799 380
Uzbekistan . . . . . . . . . . . .
Vanuatu . . . . . . . . . . . .
Venezuela, Rep. Bolivariana de 2,332 496 0 2,039 181 0 0 0 0 0 5,048 505
Vietnam 0 0 0 0 0 0 0 0 0 0 0 0
Yemen, Republic of 0 0 0 0 0 4 468 1,910 0 0 2,382 238
Zambia 95 0 387 583 1,279 465 1,249 260 206 342 4,864 486
Zimbabwe 342 655 0 297 360 1,787 96 14 103 0 3,655 365

Source: Staff estimates, Global Financial Integrity, based on trade data reported to the IMF by member countries.
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58 Global Financial Integrity
Table 8. Illicit Financial Flows (HMN+GER Normalized)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . 1,289 513 203 0 0 0 0 41 110 2,157 240
Albania 0 0 0 0 91 103 315 405 233 164 1,312 131
Algeria 0 0 0 0 203 1,962 500 3,358 2,673 1,265 9,961 996
Angola 309 68 822 0 574 0 1,641 1,236 0 181 4,830 483
Antigua and Barbuda 16 40 5 19 11 11 7 4 7 0 120 12
Argentina 2,810 1,890 1,428 0 0 0 0 0 0 1,036 7,164 716
Armenia, Republic of 273 175 250 103 316 220 447 1,210 1,071 1,192 5,255 526
Aruba 1,264 805 1,224 2,188 3,537 3,708 3,933 4,919 1,842 123 23,544 2,354
Azerbaijan, Republic of 1 87 112 50 126 256 361 845 1,461 990 4,288 429
Bahamas, The 484 772 1,001 1,063 1,843 1,331 1,622 2,086 1,669 2,208 14,080 1,408
Bahrain, Kingdom of 0 609 700 1,468 2,215 2,256 1,629 30 66 0 8,974 897
Bangladesh 864 349 967 945 629 2,778 2,737 120 649 2,367 12,406 1,241
Barbados 0 300 329 572 532 64 54 7 0 65 1,923 192
Belarus 1 289 13 0 0 286 0 194 0 0 784 78
Belize 109 75 121 92 102 88 172 134 106 75 1,074 107
Benin 0 0 0 10 0 0 0 0 6 0 16 2
Bhutan . . . . . 0 137 0 0 0 137 27
Bolivia 203 640 174 625 372 105 112 0 454 802 3,485 349
Bosnia and Herzegovina 459 591 748 903 1,232 371 987 1,235 939 900 8,364 836
Botswana 744 0 161 293 0 611 1,157 1,146 1,082 0 5,194 519
Brazil 498 154 933 2,145 201 0 3,152 0 347 3,292 10,722 1,072
Brunei Darussalam 2,205 2,329 1,838 1,190 3,969 5,786 5,860 8,232 5,420 0 36,829 3,683
Bulgaria 539 716 889 0 1,218 986 3,052 4,229 0 0 11,629 1,163
Burkina Faso 166 136 115 295 198 183 321 408 214 380 2,417 242
Burundi 31 6 14 19 104 119 65 32 60 37 489 49
Cambodia 254 280 367 449 426 583 45 786 737 1,056 4,985 498
Cameroon 487 177 279 896 522 980 1,221 1,480 0 0 6,040 604
Cape Verde 24 8 12 0 0 9 0 108 40 68 268 27
Central African Republic 33 78 14 0 14 0 0 0 35 0 174 17
Chad . . . . . . . . . . . .
Chile 861 952 724 270 1,324 1,526 450 0 0 558 6,665 666
China, Mainland 142,202 153,797 183,530 251,058 283,483 296,078 326,661 348,421 336,109 420,362 2,741,700 274,170
Colombia 1,501 0 1,316 1,808 0 0 0 127 0 0 4,752 475
Comoros 21 9 6 16 16 26 20 21 11 11 158 16
Congo, Democratic Republic of 346 581 412 598 968 565 170 0 0 0 3,639 364
Congo, Republic of 1,154 220 1,034 3,054 672 2,149 1,728 2,636 620 1,761 15,027 1,503
Costa Rica 1,913 2,426 3,423 4,690 5,757 5,831 6,258 7,040 8,854 17,507 63,699 6,370
Cote d'Ivoire 0 26 1,500 916 1,546 972 0 44 37 25 5,066 507
Croatia 958 638 1,355 1,305 1,288 1,722 1,659 2,263 1,637 1,010 13,836 1,384
Djibouti 205 172 212 230 277 357 279 401 301 480 2,915 292
Dominica 17 12 18 26 41 46 75 150 127 127 638 64
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59 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 1,549 676 1,913 981 458 869 371 1,141 3,963 5,025 16,946 1,695
Ecuador 1,486 317 0 860 1,317 0 0 4,739 131 0 8,850 885
Egypt 2,270 1,492 1,217 3,119 4,964 4,200 4,515 5,998 0 2,145 29,919 2,992
El Salvador 457 1,130 695 730 1,036 855 956 827 944 908 8,539 854
Equatorial Guinea 450 0 0 0 0 0 0 1,968 2,876 2,904 8,198 820
Eritrea . . . . . . . . . . . .
Ethiopia 901 1,236 487 354 787 1,148 1,446 1,755 3,044 5,642 16,800 1,680
Fiji 147 205 253 249 259 407 239 390 275 280 2,705 270
Gabon 104 125 260 357 439 0 0 0 0 0 1,285 129
Gambia, The 17 19 11 31 54 31 73 64 41 134 474 47
Georgia 244 248 386 450 403 674 444 898 517 509 4,773 477
Ghana 51 0 0 0 0 0 37 374 1,259 0 1,721 172
Grenada 0 0 10 1 25 0 0 0 0 0 36 4
Guatemala 1,998 1,404 1,362 1,350 1,489 808 950 878 3,366 2,617 16,221 1,622
Guinea 307 116 318 451 295 292 644 261 0 378 3,062 306
Guinea-Bissau 15 45 0 38 25 14 194 5 42 74 451 45
Guyana 126 60 80 127 208 142 290 258 182 407 1,880 188
Haiti 0 0 0 40 0 88 95 121 46 62 451 45
Honduras 2,567 2,679 2,722 2,920 3,189 3,363 3,401 3,297 3,203 3,465 30,805 3,081
Hungary 0 0 0 2,100 2,580 2,744 349 3,373 771 2,187 14,104 1,410
India 7,884 8,292 9,447 22,609 30,945 0 0 26,820 279 1,613 107,889 10,789
Indonesia 0 1,763 15,120 17,636 11,383 12,724 15,490 16,540 2,974 1,480 95,110 9,511
Iran, Islamic Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Iraq . . . . 0 0 3,660 19,648 18,060 22,214 63,582 10,597
Jamaica 249 364 428 435 823 225 212 932 457 0 4,126 413
Jordan 154 130 0 0 0 206 0 0 0 0 490 49
Kazakhstan 654 0 932 1,016 1,800 3,128 2,966 5,746 783 0 17,025 1,703
Kenya 0 0 277 67 234 0 258 0 0 0 835 83
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . 0 0 0 0 0 0 0 0
Kuwait 2,869 1,869 574 0 0 0 4,732 10,049 0 0 20,092 2,009
Kyrgyz Republic 0 22 0 19 0 0 356 0 241 71 711 71
Lao PDR 51 130 82 0 0 516 850 591 705 402 3,328 333
Latvia 837 720 774 1,197 1,087 665 212 577 0 0 6,068 607
Lebanon 1,034 309 0 1,014 610 2,818 7,067 3,001 4,110 960 20,923 2,092
Lesotho 0 179 71 0 74 176 314 330 476 0 1,621 162
Liberia . 897 815 851 987 1,578 1,904 650 1,329 737 9,749 1,083
Libya 1,206 0 0 0 1,450 0 0 1,753 0 2,137 6,545 655
Lithuania 0 0 0 0 49 289 54 0 0 0 392 39
Macedonia, FYR 1 178 280 381 483 287 765 1,024 582 631 4,613 461
Madagascar 57 128 0 832 460 1,643 0 682 185 0 3,987 399
Malawi 357 112 210 159 494 458 453 1,013 751 684 4,691 469
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60 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 13,068 12,543 17,731 19,579 28,079 29,616 32,478 37,348 30,414 64,383 285,239 28,524
Maldives 114 148 103 68 38 44 0 0 0 0 514 51
Mali 0 6 274 128 169 224 186 967 467 373 2,795 279
Mauritania . . . . . . . . . . . .
Mauritius 86 0 0 0 0 0 0 0 301 423 811 81
Mexico 36,303 36,714 38,433 41,208 48,101 49,508 61,966 61,132 51,073 51,168 475,608 47,561
Moldova 209 132 226 337 241 192 446 507 228 0 2,518 252
Mongolia 32 0 6 0 81 14 212 775 0 0 1,120 112
Montenegro . . . . 925 860 1,198 1,473 902 896 6,254 1,042
Morocco 0 182 297 282 3,786 521 0 412 2,452 160 8,091 809
Mozambique 60 249 0 0 0 330 0 0 0 0 639 64
Myanmar 14 19 78 141 604 626 336 1,362 1,010 2,132 6,322 632
Namibia 18 0 89 0 0 444 807 841 1,104 317 3,619 362
Nepal 603 545 356 421 501 683 584 883 1,551 1,884 8,013 801
Nicaragua 436 770 625 1,056 1,017 1,521 1,208 1,527 1,210 1,556 10,926 1,093
Niger 50 9 15 87 100 0 18 57 282 554 1,171 117
Nigeria 0 0 0 0 17,345 17,151 14,399 20,740 26,330 15,350 111,315 11,131
Oman 555 842 565 396 859 2,458 0 0 1,031 0 6,707 671
Pakistan 0 0 44 0 202 0 0 51 0 729 1,026 103
Panama 2,231 2,223 2,406 2,705 3,930 4,617 5,546 5,786 5,082 5,343 39,868 3,987
Papua New Guinea 2 0 0 0 59 15 0 73 480 990 1,618 162
Paraguay 156 568 199 0 214 1,123 839 1,579 1,447 1,412 7,537 754
Peru 1,147 0 0 0 0 407 138 123 596 0 2,412 241
Philippines 6,540 7,091 11,184 12,242 17,484 17,798 22,462 18,111 8,292 16,619 137,824 13,782
Poland 0 981 1,961 0 798 0 3,302 12,161 10,045 10,462 39,710 3,971
Qatar 1,031 1,260 0 5,568 4,703 0 2,310 2,206 30,365 0 47,443 4,744
Romania 0 856 289 0 0 0 1,320 2,065 1,729 119 6,378 638
Russian Federation 28,698 6,078 9,179 5,870 7,913 0 13,347 11,277 1,726 8,285 92,374 9,237
Rwanda 50 53 37 229 35 90 126 94 371 496 1,580 158
Samoa 51 59 84 82 322 116 145 176 103 115 1,253 125
Sao Tome and Principe 0 0 0 0 0 6 10 32 6 10 64 6
Saudi Arabia 0 0 0 0 34,751 20,560 15,629 30,026 60,754 34,380 196,100 19,610
Senegal 0 0 0 0 0 0 0 0 0 0 0 0
Serbia, Republic of 4,080 5,469 7,409 9,776 6,434 5,407 4,194 212 5,713 2,750 51,443 5,144
Seychelles 82 220 153 82 75 0 0 0 0 0 613 61
Sierra Leone 24 70 95 94 94 28 68 32 7 4 515 51
Solomon Islands 28 25 35 77 88 94 136 173 91 166 912 91
Somalia . . . . . . . . . . . .
South Africa 0 485 0 0 0 10,927 19,838 21,006 18,921 0 71,176 7,118
Sri Lanka 0 0 114 189 72 106 165 0 0 881 1,527 153
St. Kitts and Nevis 6 0 0 8 0 1 0 19 0 0 34 3
St. Lucia 0 2 0 0 15 0 1 10 9 0 37 4
Table 8. Illicit Financial Flows (HMN+GER Normalized) (cont)
(in millions of U.S. dollars)
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61 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 11 0 0 17 23 16 0 0 0 0 67 7
Sudan 400 272 14 0 0 88 1,124 9,290 6,396 8,582 26,166 2,617
Suriname 56 106 74 120 115 0 0 285 19 168 944 94
Swaziland 0 0 92 0 41 538 1,168 389 465 0 2,693 269
Syrian Arab Republic 13,085 160 0 13,018 1,215 1,488 746 1,226 747 0 31,684 3,168
Tajikistan . 56 30 32 77 265 337 18 0 0 815 91
Tanzania 297 551 340 96 704 0 0 443 380 480 3,290 329
Thailand 327 0 0 710 0 0 0 0 0 3,837 4,874 487
Timor-Leste, Dem. Rep. of . . . . . 3 9 7 0 5 25 5
Togo 159 61 302 151 168 131 227 438 200 120 1,956 196
Tonga 17 15 27 51 21 25 49 28 33 5 271 27
Trinidad and Tobago 235 1,428 1,576 2,117 2,192 143 2,321 0 2,917 4,326 17,256 1,726
Tunisia 0 34 47 128 27 37 37 0 0 0 311 31
Turkey 2,091 759 0 0 0 0 0 0 0 0 2,849 285
Turkmenistan . . . . . . . . . . . .
Uganda 7 124 304 509 816 465 682 1,130 1,470 1,261 6,767 677
Ukraine 152 889 834 0 0 0 458 0 0 0 2,334 233
United Arab Emirates 0 800 1,000 5,500 11,800 0 51,700 23,500 7,600 0 101,900 10,190
Uruguay 0 2,688 353 346 174 152 801 0 0 1,573 6,087 609
Uzbekistan . . . . . . . . . . . .
Vanuatu 0 21 22 25 17 4 5 0 37 0 130 13
Venezuela, Rep. Bolivariana de 3,601 2,781 795 2,503 13,589 2,211 939 608 3,520 2,313 32,861 3,286
Vietnam 847 1,038 0 915 396 0 578 1,045 9,022 3,690 17,530 1,753
Yemen, Republic of 110 0 0 0 0 0 0 0 0 0 110 11
Zambia 154 0 556 583 1,342 505 1,306 0 62 65 4,573 457
Zimbabwe 342 655 0 297 360 1,787 0 0 0 0 3,442 344

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries.
Note: Illicit fnancial fows calculated using normalized GER estimates.
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62 Global Financial Integrity
Table 9. Illicit Financial Flows (HMN+GER Non-normalized)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . 1,289 513 203 0 0 0 0 41 110 2,157 240
Albania 14 0 14 15 91 103 315 405 233 164 1,356 136
Algeria 1,626 490 497 746 203 2,260 500 3,358 3,767 1,265 14,713 1,471
Angola 309 68 822 0 574 0 1,641 1,236 0 181 4,830 483
Antigua and Barbuda 16 40 5 19 11 11 7 4 7 0 120 12
Argentina 3,698 2,488 1,428 1,166 760 0 1,380 4,748 0 1,036 16,703 1,670
Armenia, Republic of 273 175 250 103 316 220 447 1,210 1,071 1,192 5,255 526
Aruba 1,264 805 1,224 2,188 3,537 3,708 3,933 4,919 1,842 123 23,544 2,354
Azerbaijan, Republic of 1 87 112 50 126 256 361 845 1,461 990 4,288 429
Bahamas, The 484 772 1,001 1,063 1,843 1,331 1,622 2,086 1,669 2,208 14,080 1,408
Bahrain, Kingdom of 146 609 1,288 1,468 2,215 2,256 1,629 30 66 0 9,708 971
Bangladesh 864 740 967 945 1,162 2,778 2,737 848 649 2,367 14,059 1,406
Barbados 24 300 329 572 532 64 54 7 0 94 1,976 198
Belarus 1 289 13 0 0 286 0 194 0 0 784 78
Belize 109 75 121 92 102 88 172 134 106 75 1,074 107
Benin 0 0 62 125 35 0 0 0 6 0 228 23
Bhutan . . . . . 0 137 0 0 0 137 27
Bolivia 366 855 174 625 372 105 112 0 454 802 3,864 386
Bosnia and Herzegovina 459 591 748 903 1,232 371 987 1,235 939 900 8,364 836
Botswana 815 79 161 499 277 611 1,157 1,146 1,082 213 6,039 604
Brazil 498 154 2,122 3,609 1,903 0 8,587 7,792 6,143 4,286 35,095 3,510
Brunei Darussalam 2,205 2,329 1,838 1,190 3,969 5,786 5,860 8,232 5,420 212 37,042 3,704
Bulgaria 539 1,156 1,546 425 1,720 986 4,218 4,738 526 0 15,854 1,585
Burkina Faso 166 136 115 295 198 183 321 408 214 380 2,417 242
Burundi 31 6 17 19 104 119 65 32 60 37 492 49
Cambodia 254 280 367 449 426 583 45 786 737 1,056 4,985 498
Cameroon 487 291 279 896 522 980 1,221 1,480 227 362 6,743 674
Cape Verde 24 8 12 0 0 9 0 108 40 68 268 27
Central African Republic 33 78 14 7 14 0 2 0 35 0 183 18
Chad . . . . . . . . . . . .
Chile 1,946 2,004 1,673 1,559 2,846 2,863 2,628 5,750 939 1,968 24,174 2,417
China, Mainland 142,202 153,797 183,530 251,058 283,483 296,078 326,661 348,421 336,109 420,362 2,741,700 274,170
Colombia 1,501 1,079 1,316 1,808 1,379 540 628 2,886 1,355 51 12,544 1,254
Comoros 21 9 6 16 16 26 20 21 11 11 158 16
Congo, Democratic Republic of 346 581 412 598 968 565 377 0 277 260 4,382 438
Congo, Republic of 1,154 220 1,034 3,054 672 2,149 1,728 2,636 620 1,761 15,027 1,503
Costa Rica 1,913 2,426 3,423 4,690 5,757 5,831 6,258 7,040 8,854 17,507 63,699 6,370
Cote d'Ivoire 45 26 1,500 916 1,546 972 761 470 543 101 6,881 688
Croatia 958 997 1,950 1,612 1,438 1,722 1,659 2,263 1,637 1,010 15,248 1,525
Djibouti 205 172 212 230 277 357 279 401 301 480 2,915 292
Dominica 17 12 18 26 41 46 75 150 127 127 638 64
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63 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 1,549 676 1,913 981 458 869 371 1,141 3,963 5,025 16,946 1,695
Ecuador 1,486 612 23 860 1,317 608 692 4,739 398 611 11,345 1,135
Egypt 2,270 1,492 1,217 3,119 4,964 4,200 4,515 5,998 0 3,212 30,985 3,099
El Salvador 592 1,130 695 730 1,036 855 956 827 944 908 8,674 867
Equatorial Guinea 450 59 0 322 175 360 917 1,968 2,876 2,904 10,030 1,003
Eritrea . . . . . . . . . . . .
Ethiopia 901 1,236 487 407 787 1,148 1,446 1,755 3,044 5,642 16,853 1,685
Fiji 147 205 253 249 259 407 239 390 275 280 2,705 270
Gabon 824 512 503 616 439 0 0 0 0 0 2,895 289
Gambia, The 17 19 11 31 54 31 73 64 41 134 474 47
Georgia 244 248 386 450 403 674 444 898 517 509 4,773 477
Ghana 513 0 0 0 0 0 37 374 1,259 0 2,183 218
Grenada 0 0 10 1 25 0 0 0 0 0 36 4
Guatemala 1,998 1,404 1,362 1,350 1,489 808 950 878 3,366 2,617 16,221 1,622
Guinea 307 116 318 451 295 292 644 261 0 378 3,062 306
Guinea-Bissau 15 45 6 38 25 14 194 9 42 74 460 46
Guyana 126 60 80 127 208 142 290 258 182 407 1,880 188
Haiti 2 11 31 40 41 88 95 121 80 62 570 57
Honduras 2,567 2,679 2,722 2,920 3,189 3,363 3,401 3,297 3,203 3,465 30,805 3,081
Hungary 0 0 0 2,100 2,580 2,744 349 3,373 1,089 2,187 14,422 1,442
India 7,884 8,292 9,447 22,609 30,945 10,512 4,923 26,820 279 1,613 123,324 12,332
Indonesia 320 2,872 15,120 17,636 11,383 12,724 15,490 16,540 11,560 5,212 108,858 10,886
Iran, Islamic Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Iraq . . . . 0 0 3,660 19,648 18,060 22,214 63,582 10,597
Jamaica 249 364 428 435 823 225 212 932 457 26 4,152 415
Jordan 154 130 0 162 0 206 0 97 132 0 881 88
Kazakhstan 1,066 1,000 1,067 1,016 1,800 3,128 2,966 5,746 783 589 19,161 1,916
Kenya 72 0 312 203 234 43 258 0 0 0 1,121 112
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . 0 0 0 0 0 0 0 0
Kuwait 3,120 2,316 574 18 744 884 5,083 10,049 1,403 0 24,190 2,419
Kyrgyz Republic 0 22 0 19 0 0 356 0 241 71 711 71
Lao PDR 51 130 82 8 0 516 850 591 705 489 3,423 342
Latvia 837 720 774 1,197 1,087 665 784 738 0 0 6,800 680
Lebanon 1,034 309 0 1,014 740 2,818 7,067 3,001 4,110 960 21,053 2,105
Lesotho 18 198 71 66 74 176 314 330 476 62 1,785 179
Liberia . 897 815 851 987 1,578 1,904 650 1,329 737 9,749 1,083
Libya 3,620 59 0 0 1,450 0 0 1,753 0 2,137 9,018 902
Lithuania 0 0 0 0 49 289 54 2,540 1,239 2,752 6,924 692
Macedonia, FYR 1 178 280 381 483 287 765 1,024 582 631 4,613 461
Madagascar 81 128 66 832 460 1,643 121 682 185 32 4,231 423
Malawi 357 112 210 159 494 458 453 1,013 751 684 4,691 469
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64 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 13,068 12,543 17,731 19,579 28,079 29,616 32,478 37,348 30,414 64,383 285,239 28,524
Maldives 114 148 103 68 38 44 4 0 0 0 518 52
Mali 52 51 274 128 169 224 186 967 467 373 2,891 289
Mauritania . . . . . . . . . . . .
Mauritius 103 2 107 40 0 0 0 166 301 423 1,143 114
Mexico 36,303 36,714 38,433 41,208 48,101 49,508 61,966 61,132 51,073 51,168 475,608 47,561
Moldova 209 132 226 337 241 192 446 507 228 0 2,518 252
Mongolia 32 0 6 0 81 14 212 775 0 0 1,120 112
Montenegro . . . . 925 860 1,198 1,473 902 896 6,254 1,042
Morocco 343 335 597 982 3,786 792 688 1,877 2,452 980 12,832 1,283
Mozambique 68 249 75 0 0 330 126 0 0 0 850 85
Myanmar 380 19 140 674 604 626 336 1,362 1,010 2,132 7,283 728
Namibia 65 41 89 129 168 444 807 841 1,104 511 4,198 420
Nepal 603 545 356 421 501 683 584 883 1,551 1,884 8,013 801
Nicaragua 436 770 625 1,056 1,017 1,521 1,208 1,527 1,210 1,556 10,926 1,093
Niger 50 9 15 87 100 0 64 57 282 554 1,217 122
Nigeria 906 0 0 1,702 17,829 19,135 19,304 24,184 26,330 19,655 129,045 12,904
Oman 583 985 898 466 859 2,458 129 0 1,031 0 7,410 741
Pakistan 0 0 44 0 202 0 505 728 298 729 2,506 251
Panama 2,231 2,223 2,406 2,705 3,930 4,617 5,546 5,786 5,082 5,343 39,868 3,987
Papua New Guinea 2 52 124 93 59 15 33 184 480 990 2,030 203
Paraguay 156 568 199 0 214 1,123 839 1,579 1,447 1,412 7,537 754
Peru 1,147 718 728 614 972 1,058 673 1,432 2,173 0 9,515 952
Philippines 6,540 7,091 11,184 12,242 17,484 17,798 22,462 18,111 8,292 16,619 137,824 13,782
Poland 0 1,303 2,066 632 798 0 3,302 12,161 10,045 10,462 40,770 4,077
Qatar 2,540 1,031 1,260 0 5,568 4,878 168 7,117 21,187 12,362 56,110 5,611
Romania 30 856 289 0 0 0 3,754 2,065 1,729 119 8,842 884
Russian Federation 28,698 6,078 11,676 20,357 7,913 0 13,347 11,277 8,602 43,638 151,586 15,159
Rwanda 50 53 37 229 35 90 126 94 371 496 1,580 158
Samoa 51 59 84 82 322 116 145 176 103 115 1,253 125
Sao Tome and Principe 0 0 0 0 0 6 10 32 6 10 64 6
Saudi Arabia 0 0 0 0 36,444 21,021 16,345 32,965 64,886 38,301 209,962 20,996
Senegal 9 0 0 0 6 0 0 0 0 0 15 1
Serbia, Republic of 4,080 5,469 7,409 9,776 6,434 5,407 4,194 212 5,713 2,750 51,443 5,144
Seychelles 82 220 153 82 75 4 0 0 0 0 617 62
Sierra Leone 24 70 95 94 94 28 68 51 7 4 533 53
Solomon Islands 28 25 35 77 88 94 136 173 91 166 912 91
Somalia . . . . . . . . . . . .
South Africa 984 1,446 0 3,052 4,113 10,927 19,838 21,006 18,921 3,615 83,901 8,390
Sri Lanka 0 0 114 189 72 106 165 0 0 881 1,527 153
St. Kitts and Nevis 6 0 0 8 0 1 0 19 0 0 34 3
St. Lucia 0 2 0 0 15 0 1 10 9 0 37 4
Table 9. Illicit Financial Flows (HMN+GER Non-normalized) (cont)
(in millions of U.S. dollars)
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65 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 11 0 0 17 23 16 0 0 0 0 67 7
Sudan 400 272 14 0 139 158 1,124 9,290 6,396 8,582 26,374 2,637
Suriname 56 106 74 120 115 31 9 285 72 168 1,037 104
Swaziland 38 32 92 119 174 538 1,168 389 465 67 3,081 308
Syrian Arab Republic 13,085 254 91 13,018 1,215 1,956 854 1,226 747 158 32,603 3,260
Tajikistan . 56 30 32 77 265 337 18 0 0 815 91
Tanzania 297 551 340 96 704 0 0 443 420 480 3,329 333
Thailand 2,225 1,490 2,070 4,171 8,352 6,095 4,198 14,880 8,406 12,371 64,257 6,426
Timor-Leste, Dem. Rep. of . . . . . 3 9 7 0 5 25 5
Togo 159 61 302 151 168 131 227 438 200 120 1,956 196
Tonga 17 15 27 51 21 25 49 28 33 5 271 27
Trinidad and Tobago 311 1,428 1,576 2,117 2,192 763 2,321 888 2,917 4,326 18,839 1,884
Tunisia 0 34 47 128 27 37 37 0 0 0 311 31
Turkey 2,874 2,581 2,088 18 1,736 906 3,432 3,519 8,244 3,558 28,955 2,896
Turkmenistan 694 685 600 . . . . . . . 1,978 659
Uganda 33 130 304 509 816 465 682 1,130 1,470 1,261 6,800 680
Ukraine 152 889 834 0 0 0 458 290 727 1,253 4,604 460
United Arab Emirates 4,600 0 800 1,000 5,500 11,800 0 51,700 23,500 7,600 106,500 10,650
Uruguay 191 2,688 353 346 460 290 801 132 528 1,573 7,363 736
Uzbekistan . . . . . . . . . . . .
Vanuatu 0 21 22 25 17 4 5 0 37 0 130 13
Venezuela, Rep. Bolivariana de 5,933 3,277 795 4,542 13,770 2,211 939 608 3,520 2,313 37,909 3,791
Vietnam 847 1,038 0 915 396 0 578 1,045 9,022 3,690 17,530 1,753
Yemen, Republic of 110 0 0 0 0 4 468 1,910 0 0 2,492 249
Zambia 249 0 556 583 1,342 505 1,306 260 268 407 5,476 548
Zimbabwe 342 655 0 297 360 1,787 96 14 103 0 3,655 365

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries.
Note: Illicit fnancial fows calculated using non-normalized GER estimates.
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66 Global Financial Integrity
Table 10. Normalized Illicit Financial Flows (CED Normalized+GER Normalized)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . 1,289 513 203 13,934 0 761 0 576 110 17,386 1,932
Albania 0 0 0 0 91 103 315 405 233 164 1,312 131
Algeria 0 1,942 2,699 0 0 0 0 0 0 0 4,642 464
Angola 0 2,155 2,455 1,982 4,269 0 7,777 0 0 0 18,637 1,864
Antigua and Barbuda 63 53 98 0 16 101 0 0 0 0 332 33
Argentina 17,984 12,366 20,898 3,479 0 0 0 16,666 11,678 12,331 95,401 9,540
Armenia, Republic of 497 352 403 403 316 333 904 1,494 1,305 1,709 7,718 772
Aruba 1,259 847 1,448 2,448 3,611 4,240 4,657 4,907 1,917 123 25,457 2,546
Azerbaijan, Republic of 0 462 482 0 0 1,664 3,269 14,215 11,573 16,456 48,122 4,812
Bahamas, The 484 772 1,001 1,063 1,694 1,331 1,622 2,086 1,616 1,925 13,595 1,360
Bahrain, Kingdom of 0 1,348 909 2,569 4,099 6,830 3,797 4,534 3,103 3,268 30,457 3,046
Bangladesh 758 2,081 2,224 1,707 0 4,689 3,234 2,289 0 5,565 22,549 2,255
Barbados 0 300 329 572 532 64 54 0 0 0 1,852 185
Belarus 0 0 0 0 0 0 0 0 0 0 0 0
Belize 109 66 134 88 131 138 212 122 101 75 1,176 118
Benin 0 0 0 0 0 0 0 0 0 0 0 0
Bhutan . . . . 0 0 174 0 0 0 174 29
Bolivia 0 938 914 663 604 0 0 0 1,123 0 4,242 424
Bosnia and Herzegovina 459 591 748 903 1,232 371 919 1,162 939 900 8,222 822
Botswana 868 0 876 1,238 559 1,435 2,058 1,936 3,141 1,360 13,472 1,347
Brazil 0 8,136 9,582 0 0 0 0 0 0 0 17,718 1,772
Brunei Darussalam 2,019 1,945 2,585 2,925 . 5,261 4,958 7,116 4,468 6,048 37,325 4,147
Bulgaria 539 953 1,991 1,676 0 5,085 9,344 11,471 4,961 0 36,021 3,602
Burkina Faso 166 132 111 295 196 174 321 408 214 380 2,397 240
Burundi 0 6 0 0 30 119 28 32 47 37 300 30
Cambodia 254 280 327 403 412 511 0 742 729 1,027 4,685 469
Cameroon 325 0 279 896 473 980 1,221 1,480 0 0 5,653 565
Cape Verde 3 0 3 0 0 38 0 25 0 20 89 9
Central African Republic 33 78 14 0 14 0 0 0 35 0 174 17
Chad 0 0 0 0 384 1,222 2,020 2,366 637 0 6,628 663
Chile 3,452 4,009 3,884 8,862 6,209 15,044 26,629 0 15,251 19,590 102,930 10,293
China, Mainland 183,203 153,797 183,530 251,058 283,483 296,078 326,661 348,421 294,726 367,426 2,688,382 268,838
Colombia 3,904 0 5,111 1,808 0 2,848 3,136 0 3,333 0 20,140 2,014
Comoros 27 27 20 24 16 26 20 21 11 11 204 20
Congo, Democratic Republic of 516 345 2,156 1,126 922 1,475 3,440 1,370 0 0 11,351 1,135
Congo, Republic of 1,142 1,033 2,122 4,687 672 3,204 1,529 2,636 620 1,761 19,408 1,941
Costa Rica 1,913 2,375 3,423 4,690 5,757 5,831 6,258 6,992 8,854 17,310 63,403 6,340
Cote d'Ivoire 0 591 2,308 2,214 1,508 2,298 923 0 0 0 9,842 984
Croatia 993 2,054 0 0 0 0 0 2,562 2,058 2,721 10,388 1,039
Djibouti 222 266 302 292 268 428 496 345 339 434 3,392 339
Dominica 17 12 18 26 41 46 75 150 127 121 632 63
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67 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 1,519 2,313 2,754 1,859 0 1,680 778 1,125 3,834 4,064 19,926 1,993
Ecuador 1,819 1,737 785 1,827 1,317 2,115 0 4,738 0 0 14,338 1,434
Egypt 1,124 3,993 5,728 8,062 2,533 13,494 13,818 6,842 2,913 0 58,508 5,851
El Salvador 1,131 1,154 1,995 730 1,302 728 956 827 944 1,629 11,396 1,140
Equatorial Guinea 848 0 410 0 0 0 0 1,968 2,876 2,904 9,005 901
Eritrea . . . . . . . . . . . .
Ethiopia 672 1,462 784 0 787 1,148 1,563 1,755 3,944 5,679 17,795 1,779
Fiji 147 70 206 249 158 254 239 390 203 280 2,197 220
Gabon 0 375 1,159 1,370 1,667 1,652 885 2,652 1,409 1,688 12,857 1,286
Gambia, The 53 87 82 60 20 72 44 33 167 161 781 78
Georgia 244 248 442 531 403 612 411 4,073 517 735 8,214 821
Ghana 0 362 0 0 0 0 0 855 0 0 1,218 122
Grenada 0 0 0 0 0 0 0 0 0 0 0 0
Guatemala 1,998 1,339 1,301 4,471 1,489 808 1,929 878 3,366 2,257 19,836 1,984
Guinea 304 116 161 451 295 291 644 261 0 378 2,901 290
Guinea-Bissau 15 97 52 68 19 21 205 0 32 69 579 58
Guyana 81 59 60 84 140 90 209 202 182 230 1,336 134
Haiti 0 0 0 40 0 88 95 121 0 62 405 41
Honduras 2,524 2,679 2,722 2,920 2,985 3,031 3,048 3,297 2,857 3,465 29,527 2,953
Hungary 3,841 0 9,715 8,296 0 12,000 32,333 18,506 0 0 84,692 8,469
India 7,172 8,102 9,447 22,609 30,404 0 0 26,820 0 0 104,554 10,455
Indonesia 0 0 20,519 14,542 11,204 12,724 14,122 35,565 15,979 0 124,655 12,466
Iran, Islamic Republic of 3,320 2,654 6,992 0 0 0 0 0 0 0 12,965 1,296
Iraq . . . . . . . 10,404 11,944 14,263 36,611 12,204
Jamaica 235 303 820 602 823 1,137 1,728 582 457 1,672 8,359 836
Jordan 663 393 802 605 0 961 0 0 0 0 3,424 342
Kazakhstan 3,541 3,698 5,193 11,821 13,667 22,554 25,772 28,991 7,344 8,229 130,811 13,081
Kenya 0 0 0 0 0 0 0 0 0 0 0 0
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . . . . . . . . .
Kuwait 7,517 7,320 16,110 15,595 29,297 44,241 55,232 53,405 0 19,878 248,595 24,860
Kyrgyz Republic 0 66 112 403 0 0 0 306 308 0 1,194 119
Lao PDR 0 624 0 152 93 1,007 1,789 1,009 829 312 5,814 581
Latvia 836 1,787 2,020 3,164 791 3,912 11,082 0 1,035 0 24,628 2,463
Lebanon 2,990 1,175 0 1,342 0 2,222 3,105 1,255 1,068 960 14,116 1,412
Lesotho 0 0 0 0 74 176 314 330 358 0 1,253 125
Liberia . 897 815 851 948 1,480 1,828 608 1,040 631 9,098 1,011
Libya 1,875 0 0 0 0 4,291 9,157 21,015 4,383 11,326 52,047 5,205
Lithuania 0 0 0 1,926 0 3,964 5,373 0 0 0 11,262 1,126
Macedonia, FYR 151 168 247 1,171 477 623 1,551 994 923 630 6,935 694
Madagascar 0 128 0 797 460 1,643 0 682 185 0 3,896 390
Malawi 135 112 183 159 470 418 453 860 696 684 4,171 417
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68 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 20,466 12,153 17,727 19,579 38,719 44,582 47,702 67,963 46,678 81,231 396,798 39,680
Maldives 109 148 103 68 38 44 0 0 0 0 510 51
Mali 0 0 274 102 145 187 186 967 393 373 2,626 263
Mauritania . . . . . . . . . . . .
Mauritius 0 0 0 0 0 0 0 0 301 423 724 72
Mexico 32,999 34,811 34,022 36,392 44,231 48,380 59,508 61,132 34,578 33,429 419,482 41,948
Moldova 209 308 226 337 241 310 446 507 228 498 3,310 331
Mongolia 0 66 566 168 0 290 485 537 0 0 2,111 211
Montenegro . . . . 925 860 1,198 1,473 1,890 896 7,242 1,207
Morocco 0 0 0 0 3,382 0 0 0 1,931 0 5,312 531
Mozambique 0 189 0 0 0 330 0 0 0 0 520 52
Myanmar 0 1,166 973 0 0 901 1,831 1,278 1,140 0 7,290 729
Namibia 365 296 1,269 1,149 524 2,163 1,723 841 1,626 1,431 11,386 1,139
Nepal 603 1,010 506 537 501 683 804 776 1,551 1,703 8,675 868
Nicaragua 436 443 525 650 954 1,120 1,174 1,213 1,130 1,256 8,902 890
Niger 50 0 0 87 100 0 0 0 276 554 1,067 107
Nigeria 2,846 5,135 9,751 12,333 14,454 12,791 24,690 37,403 27,732 28,573 175,709 17,571
Oman 0 0 0 0 3,822 8,019 0 7,378 0 5,300 24,519 2,452
Pakistan 0 2,026 3,522 1,852 0 0 0 3,384 0 0 10,783 1,078
Panama 2,608 2,223 3,281 3,262 3,571 7,390 5,072 5,786 7,252 5,343 45,787 4,579
Papua New Guinea 0 0 0 0 0 0 0 0 480 899 1,379 138
Paraguay 156 684 158 376 0 1,123 627 1,579 1,447 1,412 7,562 756
Peru 1,147 894 1,567 0 0 2,910 0 0 5,189 0 11,707 1,171
Philippines 6,540 7,091 10,286 11,968 15,685 16,206 20,380 16,992 8,292 14,631 128,072 12,807
Poland 0 10,609 20,005 20,201 0 31,781 42,867 0 14,180 0 139,643 13,964
Qatar 4,923 4,108 4,537 9,771 15,382 23,255 26,026 44,960 42,604 40,994 216,560 21,656
Romania 0 1,851 3,291 0 0 6,825 10,620 8,057 11,933 0 42,577 4,258
Russian Federation 37,583 12,546 35,579 37,046 66,388 0 48,593 203,251 0 0 440,985 44,099
Rwanda 50 53 37 221 35 90 126 88 371 490 1,560 156
Samoa 51 59 84 79 322 116 142 137 103 115 1,209 121
Sao Tome and Principe 0 0 0 0 0 0 0 0 0 0 0 0
Saudi Arabia 7,740 0 27,627 50,755 47,657 52,217 58,963 39,726 81,186 61,987 427,857 42,786
Senegal 0 0 0 0 0 0 0 0 0 0 0 0
Serbia, Republic of 4,080 5,469 7,409 9,776 6,434 5,407 4,194 0 5,637 2,750 51,156 5,116
Seychelles 82 210 232 82 184 368 297 0 123 0 1,579 158
Sierra Leone 24 53 45 40 32 0 53 0 0 0 247 25
Solomon Islands 28 25 35 70 88 94 136 171 91 153 891 89
Somalia . . . . . . . . . . . .
South Africa 0 0 0 0 0 10,927 19,838 21,006 18,117 0 69,887 6,989
Sri Lanka 0 524 0 0 0 0 958 0 0 0 1,482 148
St. Kitts and Nevis 0 0 0 0 0 0 0 0 0 0 0 0
St. Lucia 0 0 0 0 0 0 0 0 0 0 0 0
Table 10. Normalized Illicit Financial Flows (CED Normalized+GER Normalized) (cont)
(in millions of U.S. dollars)
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69 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 0 0 0 0 0 0 0 0 0 0 0 0
Sudan 376 490 1,140 1,018 0 0 1,124 10,973 5,663 11,095 31,879 3,188
Suriname 56 106 74 120 115 0 0 185 0 0 657 66
Swaziland 0 250 0 0 0 300 771 389 410 172 2,291 229
Syrian Arab Republic 13,085 0 0 12,761 1,078 2,404 1,213 1,558 1,088 0 33,187 3,319
Tajikistan 0 104 0 0 0 254 148 1,761 0 0 2,268 227
Tanzania 0 0 0 0 0 0 0 0 0 0 0 0
Thailand 0 0 0 0 0 0 0 0 0 0 0 0
Timor-Leste, Dem. Rep. of . . . . . . . . . . . .
Togo 154 61 292 151 168 131 227 438 200 120 1,940 194
Tonga 17 15 538 3,339 9 13 43 19 6 13 4,013 401
Trinidad and Tobago 558 1,886 2,892 4,412 4,470 6,499 7,691 8,205 5,931 8,748 51,294 5,129
Tunisia 917 2,476 2,327 0 0 1,296 1,716 0 0 0 8,732 873
Turkey 5,482 10,715 0 0 0 16,869 21,636 0 0 0 54,701 5,470
Turkmenistan . . . . . . . . . . . .
Uganda 0 0 140 238 362 456 676 1,130 1,309 1,261 5,572 557
Ukraine 8,885 3,975 4,479 11,866 0 20,852 21,247 15,256 15,311 7,657 109,527 10,953
United Arab Emirates 5,700 7,208 16,966 27,131 44,290 50,825 45,068 97,739 24,792 0 319,718 31,972
Uruguay 328 4,103 353 979 0 0 522 0 963 3,071 10,318 1,032
Uzbekistan . . . . . . . . . . . .
Vanuatu 18 20 0 5 0 0 6 0 26 0 75 7
Venezuela, Rep. Bolivariana de 4,300 9,329 8,527 14,862 30,789 18,386 28,866 29,122 14,944 18,885 178,009 17,801
Vietnam 0 0 0 0 0 0 0 0 12,968 11,049 24,017 2,402
Yemen, Republic of 0 0 0 0 0 0 0 0 0 0 0 0
Zambia 0 0 387 1,121 1,279 465 1,999 0 0 2,289 7,540 754
Zimbabwe 342 992 197 297 360 1,787 0 0 0 0 3,976 398

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries and
external debt data reported to the World Bank by those countries.
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70 Global Financial Integrity
Table 11. Non-normalized Illicit Financial Flows (CED Non-normalized+GER Non-normalized)
(in millions of U.S. dollars)

Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Afghanistan . 1,289 513 203 13,934 0 761 0 576 110 17,386 1,932
Albania 14 0 52 15 125 103 315 405 233 164 1,428 143
Algeria 1,626 2,432 3,197 1,879 667 2,098 2,635 0 1,094 0 15,627 1,563
Angola 207 2,155 2,455 1,982 4,269 2,699 7,777 3,239 773 0 25,555 2,556
Antigua and Barbuda 63 53 98 0 16 101 0 0 1 0 332 33
Argentina 18,872 12,964 20,898 4,645 760 0 2,058 21,414 11,678 12,331 105,619 10,562
Armenia, Republic of 497 352 403 403 316 333 904 1,494 1,305 1,709 7,718 772
Aruba 1,259 847 1,448 2,448 3,611 4,240 4,657 4,907 1,917 123 25,457 2,546
Azerbaijan, Republic of 68 462 482 0 589 1,664 3,269 14,215 11,573 16,456 48,779 4,878
Bahamas, The 484 772 1,001 1,063 1,694 1,331 1,622 2,086 1,616 1,925 13,595 1,360
Bahrain, Kingdom of 291 1,348 1,497 2,569 4,099 6,830 3,797 4,534 3,103 3,268 31,337 3,134
Bangladesh 758 2,472 2,224 1,707 533 4,689 3,234 3,018 874 5,565 25,075 2,508
Barbados 24 300 411 572 532 201 643 0 185 30 2,898 290
Belarus 0 581 92 0 931 0 991 127 0 2,107 4,829 483
Belize 109 74 134 88 131 138 212 122 106 94 1,209 121
Benin 0 83 62 144 35 0 0 0 0 0 324 32
Bhutan . . . . 0 0 174 0 0 0 174 29
Bolivia 164 1,153 914 663 604 0 0 664 1,123 124 5,408 541
Bosnia and Herzegovina 459 591 748 903 1,232 371 2,018 1,162 1,460 900 9,842 984
Botswana 939 79 876 1,444 836 1,435 2,058 1,936 3,141 1,573 14,317 1,432
Brazil 0 8,136 10,772 4,440 1,702 0 5,435 25,778 5,795 11,558 73,616 7,362
Brunei Darussalam 2,019 1,945 2,585 2,925 4,234 5,261 4,958 7,116 4,468 6,260 41,771 4,177
Bulgaria 539 1,394 2,648 2,102 778 5,085 10,510 11,980 5,487 0 40,523 4,052
Burkina Faso 166 132 111 295 196 174 321 408 214 380 2,397 240
Burundi 0 93 84 28 30 119 28 32 47 37 499 50
Cambodia 313 426 414 526 478 602 184 742 729 1,059 5,472 547
Cameroon 325 435 1,152 896 473 980 1,221 1,480 227 362 7,550 755
Cape Verde 3 0 3 0 0 38 0 25 0 20 89 9
Central African Republic 33 363 24 49 14 0 2 0 35 0 520 52
Chad 0 0 0 0 384 1,222 2,020 2,366 637 0 6,628 663
Chile 4,537 5,060 4,833 10,151 7,731 16,381 28,807 12,335 16,189 21,001 127,025 12,702
China, Mainland 183,203 161,905 191,624 251,058 293,004 383,100 408,556 410,959 294,726 435,601 3,013,735 301,374
Colombia 3,904 1,079 5,111 2,221 3,193 3,388 3,764 4,603 4,687 51 32,002 3,200
Comoros 27 27 20 24 16 26 20 21 11 11 204 20
Congo, Democratic Republic of 516 345 2,156 1,126 922 1,475 3,647 1,370 277 260 12,095 1,209
Congo, Republic of 1,142 1,033 2,122 4,687 798 3,204 1,529 2,972 1,098 1,761 20,348 2,035
Costa Rica 1,941 2,375 3,423 4,690 6,059 5,831 6,508 7,415 8,854 17,778 64,875 6,488
Cote d'Ivoire 45 591 2,308 2,214 1,508 2,298 1,684 427 1,153 76 12,304 1,230
Croatia 993 2,413 1,190 308 442 1,011 82 2,562 2,058 2,721 13,779 1,378
Djibouti 222 266 302 292 268 428 496 345 339 434 3,392 339
Dominica 28 12 52 26 41 46 75 150 127 121 678 68
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71 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Dominican Republic 1,519 2,313 2,754 1,859 0 1,680 778 1,125 3,957 4,064 20,049 2,005
Ecuador 1,819 2,032 808 1,827 1,441 2,724 1,827 6,022 267 1,010 19,777 1,978
Egypt 1,124 3,993 5,728 8,062 2,750 13,494 13,818 6,842 2,913 2,033 60,757 6,076
El Salvador 1,266 1,154 1,995 992 1,302 728 956 827 944 1,629 11,794 1,179
Equatorial Guinea 848 177 410 322 175 360 917 1,968 2,876 2,904 10,956 1,096
Eritrea . . . . . . . . . . . .
Ethiopia 672 1,462 784 52 787 1,148 1,563 1,755 3,944 5,679 17,847 1,785
Fiji 147 161 206 249 158 415 239 390 203 280 2,448 245
Gabon 835 762 1,402 1,629 1,667 1,652 885 2,652 1,409 1,688 14,582 1,458
Gambia, The 53 87 82 60 20 72 44 33 167 161 781 78
Georgia 244 264 442 531 403 612 411 4,073 517 735 8,230 823
Ghana 461 362 37 0 0 0 126 855 284 682 2,808 281
Grenada 0 24 0 8 0 0 0 0 0 0 33 3
Guatemala 1,998 1,339 1,301 4,471 1,744 910 1,929 878 3,366 2,387 20,324 2,032
Guinea 304 189 264 451 295 358 700 261 0 378 3,199 320
Guinea-Bissau 15 97 58 68 19 21 205 4 32 69 589 59
Guyana 81 89 98 84 140 90 209 212 182 610 1,794 179
Haiti 2 11 116 40 68 232 95 121 34 62 780 78
Honduras 2,524 2,894 2,852 2,920 2,985 3,031 3,048 3,297 3,386 3,465 30,402 3,040
Hungary 3,841 2,696 9,715 8,296 6,102 12,000 32,333 18,506 2,814 0 96,304 9,630
India 7,172 8,102 9,447 22,609 30,404 16,109 4,923 38,452 5,698 0 142,917 14,292
Indonesia 320 1,110 20,519 18,971 14,645 12,724 22,657 35,565 24,565 5,912 156,989 15,699
Iran, Islamic Republic of 3,320 2,654 6,992 0 0 0 0 0 0 0 12,965 1,296
Iraq . . . . 0 0 53,545 13,121 11,944 14,263 92,873 15,479
Jamaica 235 303 820 602 823 1,137 1,728 582 509 1,698 8,437 844
Jordan 663 393 802 767 0 961 0 97 132 0 3,815 381
Kazakhstan 3,952 4,698 5,329 11,821 13,667 22,554 25,772 28,991 7,344 8,817 132,947 13,295
Kenya 72 508 576 136 0 43 0 0 0 0 1,334 133
Kiribati . . . . . . . . . . . .
Kosovo, Republic of . . . . . . . . . 0 0 0
Kuwait 7,769 7,767 16,110 15,613 30,041 45,124 55,583 53,405 1,403 19,878 252,693 25,269
Kyrgyz Republic 0 66 112 403 0 61 0 306 308 0 1,255 126
Lao PDR 0 624 0 160 93 1,007 1,789 1,009 829 398 5,909 591
Latvia 836 1,787 2,020 3,164 791 3,912 11,654 161 1,035 0 25,361 2,536
Lebanon 2,990 1,175 0 1,342 130 2,222 3,105 1,255 1,068 960 14,246 1,425
Lesotho 18 283 205 278 74 176 314 330 553 62 2,293 229
Liberia 0 1,054 1,001 988 948 1,593 1,828 608 1,040 631 9,691 969
Libya 4,289 59 0 0 1,968 4,291 9,157 21,015 4,383 11,326 56,488 5,649
Lithuania 110 433 0 1,926 0 3,964 5,373 2,540 2,883 2,752 19,980 1,998
Macedonia, FYR 151 232 321 1,171 477 623 1,551 994 923 773 7,215 722
Madagascar 24 128 159 797 460 1,643 121 682 185 32 4,232 423
Malawi 135 115 249 209 470 418 453 860 696 684 4,289 429
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72 Global Financial Integrity
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
Malaysia 20,466 20,156 22,303 19,579 38,719 44,582 47,702 67,963 46,678 81,231 409,378 40,938
Maldives 151 148 103 68 38 44 4 0 0 0 556 56
Mali 52 45 274 165 145 187 186 967 393 373 2,787 279
Mauritania 0 20 133 0 . . . . . . 153 38
Mauritius 215 2 117 40 0 0 0 166 301 13,310 14,152 1,415
Mexico 46,423 34,811 38,640 48,114 52,688 54,560 80,476 72,450 34,578 43,560 506,301 50,630
Moldova 224 308 292 337 241 310 506 507 228 498 3,452 345
Mongolia 0 66 566 168 29 290 485 537 0 153 2,294 229
Montenegro . . . . 925 860 1,198 1,473 1,890 896 7,242 1,207
Morocco 343 632 2,752 700 3,382 2,644 3,460 1,465 1,931 821 18,128 1,813
Mozambique 9 189 75 83 0 330 126 0 0 0 813 81
Myanmar 366 1,166 1,035 735 206 901 1,831 1,278 1,140 19 8,677 868
Namibia 412 337 1,269 1,278 692 2,163 1,723 841 1,626 1,625 11,965 1,196
Nepal 603 1,010 506 537 501 710 804 776 1,618 1,703 8,769 877
Nicaragua 436 443 525 650 954 1,120 1,174 1,213 1,130 1,256 8,902 890
Niger 50 60 105 87 100 0 46 0 276 554 1,278 128
Nigeria 3,753 5,135 9,751 14,035 14,939 14,775 29,595 40,847 27,732 32,877 193,438 19,344
Oman 558 572 354 70 3,822 8,019 850 7,378 0 5,300 26,923 2,692
Pakistan 0 2,026 3,522 1,852 0 0 505 4,060 298 482 12,745 1,274
Panama 2,608 2,238 3,281 3,262 3,571 7,390 5,072 5,786 7,252 5,343 45,801 4,580
Papua New Guinea 0 52 182 93 572 0 33 997 480 4,152 6,560 656
Paraguay 156 684 234 376 0 1,224 627 1,729 1,447 1,732 8,209 821
Peru 1,147 1,612 2,295 1,365 1,016 3,561 534 1,309 6,766 0 19,606 1,961
Philippines 6,540 10,192 13,799 13,717 18,399 20,185 23,950 18,492 10,756 19,149 155,180 15,518
Poland 2,786 10,931 20,110 20,834 0 31,781 42,867 0 14,180 812 144,301 14,430
Qatar 5,258 4,108 4,537 9,771 15,382 23,430 26,194 49,767 42,604 41,972 223,022 22,302
Romania 30 1,851 3,291 920 519 6,825 13,054 8,057 11,933 0 46,481 4,648
Russian Federation 37,583 12,546 38,075 51,533 66,388 4,603 48,593 203,251 15,632 71,768 549,972 54,997
Rwanda 50 56 57 221 35 90 126 88 371 490 1,584 158
Samoa 51 86 118 79 322 116 142 143 103 115 1,274 127
Sao Tome and Principe 0 0 0 0 0 0 0 0 0 0 0 0
Saudi Arabia 7,740 2,680 27,627 50,755 49,351 52,677 59,678 42,665 85,317 65,908 444,399 44,440
Senegal 9 54 0 0 6 0 0 0 0 0 69 7
Serbia, Republic of 4,080 5,469 7,409 9,776 6,434 5,407 5,094 0 5,637 2,750 52,055 5,206
Seychelles 82 210 232 94 184 372 297 0 123 0 1,594 159
Sierra Leone 24 109 160 65 83 0 53 18 0 0 511 51
Solomon Islands 28 25 35 98 88 94 136 171 91 153 918 92
Somalia . . . . . . . . . . . .
South Africa 11,323 5,099 1,148 3,052 4,113 10,927 19,838 21,006 18,117 3,615 98,237 9,824
Sri Lanka 0 524 218 355 0 0 958 0 0 513 2,568 257
St. Kitts and Nevis 31 0 9 0 12 4 0 0 0 0 56 6
St. Lucia 0 0 0 0 25 0 14 190 0 0 229 23
Table 11. Non-normalized Illicit Financial Flows (CED Non-normalized+GER Non-normalized) (cont)
(in millions of U.S. dollars)
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73 Illicit Financial Flows from Developing Countries: 2001-2010
Country Names 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Cumulative Average
St. Vincent and the Grenadines 0 0 0 0 0 0 0 0 0 0 0 0
Sudan 376 490 1,140 1,018 139 70 1,620 10,973 5,663 11,095 32,583 3,258
Suriname 56 106 74 120 115 31 9 274 53 429 1,267 127
Swaziland 38 282 154 283 133 348 771 389 410 239 3,046 305
Syrian Arab Republic 13,085 93 91 13,209 1,078 2,873 1,321 1,558 1,088 158 34,553 3,455
Tajikistan 0 104 0 58 0 254 148 1,761 0 0 2,327 233
Tanzania 0 607 0 956 0 0 0 0 40 71 1,674 167
Thailand 1,898 1,490 2,957 4,715 8,352 6,095 10,517 14,880 14,894 8,534 74,332 7,433
Timor-Leste, Dem. Rep. of . . . . . 619 1,039 2,081 1,335 1,588 6,663 1,333
Togo 154 129 320 151 168 131 227 438 200 120 2,037 204
Tonga 17 15 538 3,339 9 13 43 19 6 13 4,013 401
Trinidad and Tobago 634 1,886 2,892 4,412 4,470 7,119 7,691 9,093 5,931 8,748 52,877 5,288
Tunisia 917 2,476 2,327 334 0 1,296 1,716 0 0 0 9,066 907
Turkey 6,266 12,537 6,330 2,357 1,736 17,775 25,068 11,138 8,244 3,558 95,007 9,501
Turkmenistan 694 685 600 . . . . . . . 1,978 659
Uganda 26 311 715 498 362 456 676 1,187 1,309 1,261 6,801 680
Ukraine 8,885 3,975 4,479 11,866 2,779 20,852 21,247 15,546 16,038 8,910 114,577 11,458
United Arab Emirates 5,700 7,208 16,966 27,131 44,290 50,825 45,068 97,739 24,792 8,691 328,409 32,841
Uruguay 519 4,103 353 979 286 139 763 132 1,491 3,071 11,835 1,184
Uzbekistan . . 606 792 728 1,356 2,988 3,134 4,787 3,825 18,216 2,277
Vanuatu 18 20 1 5 0 0 6 0 26 0 75 8
Venezuela, Rep. Bolivariana de 6,632 9,825 8,527 16,901 30,970 18,386 28,866 29,122 14,944 18,885 183,057 18,306
Vietnam 1,471 1,085 25 1,876 305 0 0 264 12,968 11,049 29,044 2,904
Yemen, Republic of 98 233 0 0 0 131 468 1,910 0 0 2,839 284
Zambia 95 0 439 1,121 1,279 465 1,999 357 450 2,631 8,835 884
Zimbabwe 342 992 197 297 360 1,787 96 14 103 0 4,189 419

Source: Staff estimates, Global Financial Integrity, based on offcial balance of payments and trade data reported to the IMF by member countries and
external debt data reported to the World Bank by those countries.
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74 Global Financial Integrity
Table 12. Countries for which estimation of illicit fows
could not be completed due to missing data
Country HMN
GER
DOTS IFS HK-MO Exports F.O.B.
Afghanistan x x
Algeria x
Angola x
Antigua and Barbuda x
Azerbaijan, Republic of x
Belarus x
Bhutan x x
Brunei Darussalam x x
Burkina Faso x
Cape Verde x
Central African Republic x
Chad x x
Comoros x
Congo x
Equatorial Guinea x
Eritrea x x x x
Gabon x
Gambia x
Grenada x
Guinea-Bissau x
Iran, Islamic Republic of x
Iraq x x
Kiribati x x x x x
Kosovo, Republic of x x x x x
Kyrgyz Republic x
Lebanon x
Liberia x x
Madagascar x
Mauritania x x
Montenegro x x
Niger x
Samoa x
Sao Tome and Principe x
Serbia, Republic of x
Somalia x x x
St. Kitts and Nevis x
St. Lucia x
St. Vincent and the Grenadines x
Tajikistan x x
Timor-Leste, Dem. Rep. of x x x x x
Tonga x
Turkmenistan x x x
Uzbekistan x x x x
Vanuatu x
Zimbabwe x
Country HMN GER
Afghanistan x
Algeria x
Angola
Antigua and Barbuda x
Azerbaijan, Republic of
Belarus
Bhutan x x
Brunei Darussalam x
Burkina Faso x
Cape Verde x
Central African Republic x
Chad x
Comoros x
Congo x
Equatorial Guinea x
Eritrea x x
Gabon x
Gambia x
Grenada
Guinea-Bissau x
Iran, Islamic Republic of x
Iraq x
Kiribati x x
Kosovo, Republic of x x
Kyrgyz Republic
Lebanon x
Liberia x
Madagascar x
Mauritania x
Montenegro x
Niger x
Samoa x
Sao Tome and Principe
Serbia, Republic of x
Somalia x
St. Kitts and Nevis
St. Lucia
St. Vincent and the Grenadines
Tajikistan x
Timor-Leste, Dem. Rep. of x x
Tonga x
Turkmenistan x
Uzbekistan x
Vanuatu
Zimbabwe x
Note: An X indicates missing data in each specifed category for some or all years over the period 2001-2010.
Note: HMN refers to balance of payments data required for estimation of the Hot Money Narrow method, DOTS refers to Direction of
Trade Statistics world trade data, HK-MO refers to Hong Kong and Macao trade data within the Direction of Trade Statistics, and
Exports f.o.b. refers to exports f.o.b. data from the International Financial Statistics (IFS) database.
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75 Illicit Financial Flows from Developing Countries: 2001-2010
Table 13. Geographical Regions
Africa (48) Asia (27) Developing Europe (27) MENA (15) Western Hemisphere (33)
Angola Afghanistan, Islamic Republic of Albania Algeria Antigua and Barbuda
Benin Bangladesh Armenia, Republic of Bahrain, Kingdom of Argentina
Botswana Bhutan Azerbaijan, Republic of Egypt Aruba
Burkina Faso Brunei Darussalam Belarus Iran, Islamic Republic of Bahamas, The
Burundi Cambodia Bosnia and Herzegovina Iraq Barbados
Cameroon China, P.R.: Mainland Bulgaria Jordan Belize
Cape Verde Fiji Croatia Kuwait Bolivia
Central African Republic India Cyprus Lebanon Brazil
Chad Indonesia Georgia Libya Chile
Comoros Kiribati Hungary Morocco Colombia
Congo, Democratic Republic of Lao People's Democratic Republic Kazakhstan Oman Costa Rica
Congo, Republic of Malaysia Kosovo, Republic of Saudi Arabia Dominica
Cote d'Ivoire Maldives Kyrgyz Republic Syrian Arab Republic Dominican Republic
Djibouti Mongolia Latvia Tunisia Ecuador
Equatorial Guinea Myanmar Lithuania Yemen, Republic of El Salvador
Eritrea Nepal Macedonia, FYR Grenada
Ethiopia Pakistan Malta Guatemala
Gabon Papua New Guinea Moldova Guyana
Gambia, The Philippines Montenegro Haiti
Ghana Samoa Poland Honduras
Guinea Solomon Islands Romania Jamaica
Guinea-Bissau Sri Lanka Russian Federation Mexico
Kenya Thailand Serbia, Republic of Nicaragua
Lesotho Timor-Leste, Dem. Rep. of Tajikistan Panama
Liberia Tonga Turkey Paraguay
Madagascar Vanuatu Turkmenistan Peru
Malawi Vietnam Ukraine St. Kitts and Nevis
Mali St. Lucia
Mauritania St. Vincent and the Grenadines
Mauritius Suriname
Mozambique Trinidad and Tobago
Namibia Uruguay
Niger Venezuela, Republica Bolivariana de
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Note: The total number of countries included in our analysis is 150.
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76 Global Financial Integrity
Table 14. Largest Sovereign Wealth Funds by Assets Under Management,
Developing Countries
(As of End-October 2012, in billions of U.S. dollars)
Country Sovereign Wealth Fund Name Assets Year Type Average IFFs
Algeria Revenue Regulation Fund 56.7 2000 Oil 1.6
Angola Fundo Soberano de Angola 5.0 2012 Oil 2.6
Australia Australian Future Fund 78.2 2006 Non-Commodity n/a
Azerbaijan, Republic of State Oil Fund 32.7 1999 Oil n/a
Bahrain, Kingdom of Mumtalakat Holding Company 9.1 2006 Non-Commodity 3.1
Botswana Pula Fund 6.9 1994 Diamonds & Minerals 1.4
Brazil Sovereign Fund of Brazil 11.3 2008 Non-Commodity 7.4
Brunei Darussalam Brunei Investment Agency 30.0 1983 Oil 4.2
Canada Albertas Heritage Fund 15.9 1976 Oil n/a
Chile Total 20.4 n/a Copper 12.7
Chile Social and Economic Stabilization Fund 14.7 2007 Copper n/a
Chile Pension Reserve Fund 5.7 2006 Copper n/a
China, Mainland Total 1,189.4 n/a Non-Commodity 301.4
China 2/ SAFE Investment Company 567.9 1997 Non-Commodity n/a
China China Investment Corporation 482.0 2007 Non-Commodity n/a
China National Social Security Fund 134.5 2000 Non-Commodity n/a
China China-Africa Development Fund 5.0 2007 Non-Commodity n/a
China Hong Kong Hong Kong Monetary Authority Investment Portfolio 293.3 1993 Non-Commodity n/a
Timor-Leste Timor-Leste Petroleum Fund 10.2 2005 Oil & Gas n/a
Equatorial Guinea Fund for Future Generations 0.1 2002 Oil 1.1
France Strategic Investment Fund 28.0 2008 Non-Commodity n/a
Gabon Gabon Sovereign Wealth Fund 0.4 1998 Oil 1.5
Indonesia Government Investment Unit 0.3 2006 Non-Commodity 15.7
Iran, Islamic Republic of National Development Fund of Iran 40.0 2011 Oil & Gas 1.3
Ireland National Pensions Reserve Fund 17.5 2001 Non-Commodity n/a
Italy Italian Strategic Fund 1.4 2011 Non-Commodity n/a
Kazakhstan Kazakhstan National Fund 61.8 2000 Oil 13.3
Kiribati Revenue Equalization Reserve Fund 0.4 1956 Phosphates .
Kuwait Kuwait Investment Authority 296.0 1953 Oil 25.3
Libya Libyan Investment Authority 65.0 2006 Oil 5.6
Malaysia Khazanah Nasional 34.0 1993 Non-Commodity 40.9
Mauritania National Fund for Hydrocarbon Reserves 0.3 2006 Oil & Gas .0
Mexico Oil Revenues Stabilization Fund of Mexico 6.0 2000 Oil 50.6
Mongolia Fiscal Stability Fund n/a 2011 Minerals .2
New Zealand New Zealand Superannuation Fund 15.5 2003 Non-Commodity n/a
Nigeria Nigerian Sovereign Investment Authority 1.0 2011 Oil 19.3
Norway Government Pension Fund Global 656.2 1990 Oil n/a
Oman Total 8.2 n/a Oil & Gas 2.7
Oman State General Reserve Fund 8.2 1980 Oil & Gas n/a
Oman Oman Investment Fund n/a 2006 Oil n/a
Palestine Palestine Investment Fund 0.8 2003 Non-Commodity n/a
Papua New Guinea Papua New Guinea Sovereign Wealth Fund n/a 2011 Gas .7
Peru Fiscal Stabilization Fund 7.1 1999 Non-Commodity 2.0
Qatar Qatar Investment Authority 115.0 2005 Oil 22.3
Russian Federation 3/ National Welfare Fund 149.7 2008 Oil 55.0
Saudi Arabia Total 538.1 n/a Oil 44.4
Saudi Arabia SAMA Foreign Holdings 532.8 n/a Oil n/a
Saudi Arabia Public Investment Fund 5.3 2008 Oil n/a
Singapore Total 405.0 n/a Non-Commodity n/a
Singapore Government of Singapore Investment Corporation 247.5 1981 Non-Commodity n/a
Singapore Temasek Holdings 157.5 1974 Non-Commodity n/a
South Korea Korea Investment Corporation 43.0 2005 Non-Commodity n/a
Trinidad and Tobago Heritage and Stabilization Fund 2.9 2000 Oil 5.3
United Arab Emirates Total 816.6 n/a Oil 32.8
UAE Abu Dhabi Abu Dhabi Investment Authority 627.0 1976 Oil n/a
UAE Abu Dhabi International Petroleum Investment Company 65.3 1984 Oil n/a
UAE Abu Dhabi Mubadala Development Company 53.1 2002 Oil n/a
UAE Abu Dhabi Abu Dhabi Investment Council n/a 2007 Oil n/a
UAE Dubai Investment Corporation of Dubai 70.0 2006 Oil n/a
UAE Federal Emirates Investment Authority n/a 2007 Oil n/a
UAE Ras Al Khaimah RAK Investment Authority 1.2 2005 Oil n/a
United States Total 90.7 n/a n/a n/a
US Alabama Alabama Trust Fund 2.5 1985 Oil & Gas n/a
US Alaska Alaska Permanent Fund 42.3 1976 Oil n/a
US New Mexico New Mexico State Investment Council 14.3 1958 Non-Commodity n/a
US North Dakota North Dakota Legacy Fund 0.5 2011 Oil & Gas n/a
US Texas Texas Permanent School Fund 25.5 1854 Oil & Other n/a
US Wyoming Permanent Wyoming Mineral Trust Fund 5.6 1974 Minerals n/a
Venezuela, Rep. Bolivariana de FEM 0.8 1998 Oil 18.3
Vietnam State Capital Investment Corporation 0.5 2006 Non-Commodity 2.9
1/ Source: Sovereign Wealth Fund Institute; All fgures quoted are from offcial sources, or, where the institutions concerned do not report their
assets, from other publicly available sources. Some of these fgures are best estimates as market values change daily.
2/ This number is an estimate by the Sovereign Wealth Fund Institute analysts.
3/ Includes the oil stabilization fund of Russia.
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77 Illicit Financial Flows from Developing Countries: 2001-2010
Table 15. Net Errors and Omissions in Relation to Financial Account for 10 Countries
with the Largest Sovereign Wealth Funds 1/ 2/
(in billions of U.S.dollars or in percent)

Country 3/
SWF Assets and
Concept 4/ 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Average
China, Mainland 5/ (1,189)
Financial Account 12.6 42.9 50.8 81.6 159.4 239.5 369.6 442.5 205.8 189.5 179.4
Net Errors and Omissions -4.7 7.5 7.8 12.8 22.9 3.6 13.3 18.8 -41.4 -52.9 18.6
NEO/FA 37.5 17.5 15.3 15.6 14.4 1.5 3.6 4.3 20.1 27.9 15.8
United Arab Emirates 6/ (817)
Financial Account -1.5 -10.7 -7.8 -8.4 -14.7 -17.8 28.7 -19.9 8.4 5.8 12.4
Net Errors and Omissions -4.6 8.3 -0.8 -1.0 -5.5 -11.8 6.5 -51.7 -23.5 -7.6 12.1
NEO/FA 306.7 77.6 10.3 11.9 37.4 66.3 22.6 259.8 279.8 131.0 120.3
Norway (656)
Financial Account 26.2 17.3 22.1 27.5 51.4 44.8 19.4 77.6 57.0 40.5 38.4
Net Errors and Omissions -1.2 -6.8 -6.3 -5.3 1.8 -11.0 -30.2 4.9 15.9 -11.3 9.5
NEO/FA 4.6 39.5 28.6 19.3 3.4 24.5 156.0 6.3 27.9 27.8 33.8
Saudi Arabia 7/ (533)
Financial Account 9.4 11.9 28.0 51.9 55.3 78.5 77.8 102.3 -39.8 32.4 48.7
Net Errors and Omissions 0.0 0.0 0.0 0.0 -34.8 -20.6 -15.6 -30.0 -60.8 -34.4 19.6
NEO/FA 0.0 0.0 0.0 0.0 62.8 26.2 20.1 29.4 152.7 106.2 39.7
Singapore (405)
Financial Account 7.8 12.1 24.6 19.5 27.8 36.4 45.6 28.5 34.0 55.1 29.2
Net Errors and Omissions -3.3 0.5 3.0 0.6 1.0 0.7 -0.2 2.2 3.9 -0.4 1.6
NEO/FA 42.8 4.5 12.2 2.9 3.5 2.0 0.5 7.7 11.3 0.8 8.8
Kuwait (296)
Financial Account 8.4 4.1 10.3 17.4 33.3 53.1 38.1 51.9 30.4 47.0 29.4
Net Errors and Omissions -2.9 -1.9 -0.6 1.5 2.5 7.1 -4.7 -10.0 1.0 6.6 3.9
NEO/FA 34.2 45.9 5.6 8.8 7.6 13.3 12.4 19.4 3.4 14.0 16.5
China, Hong Kong (293)
Financial Account 11.3 17.4 21.9 23.4 22.8 26.6 34.6 31.8 24.7 24.9 23.9
Net Errors and Omissions 2.7 7.0 6.5 8.0 3.3 4.1 7.7 0.2 2.1 7.2 4.9
NEO/FA 23.9 40.1 29.8 34.1 14.4 15.3 22.2 0.7 8.4 29.1 21.8
Russian Federation 8/ (150)
Financial Account 15.0 10.7 25.2 52.0 63.9 104.4 54.2 92.7 35.0 62.9 51.6
Net Errors and Omissions -9.6 -6.1 -9.2 -5.9 -7.9 9.5 -13.3 -11.3 -1.7 -8.3 8.3
NEO/FA 63.6 57.1 36.4 11.3 12.4 9.1 24.6 12.2 4.9 13.2 24.5
Qatar 6/ (115)
Financial Account -2.5 -2.6 -3.0 -8.6 -6.6 -9.6 -16.1 -29.1 2.1 -7.8 8.8
Net Errors and Omissions -2.2 -1.0 -1.3 2.5 -5.6 -4.7 1.2 -2.3 -2.2 -11.4 3.4
NEO/FA 88.8 39.1 42.1 29.3 84.6 49.2 7.6 7.9 103.3 145.9 59.8
United States (91)
Financial Account -0.4 -0.5 -0.5 -0.5 -0.7 -0.8 -0.6 -0.7 -0.2 -0.4 0.5
Net Errors and Omissions 0.0 0.0 0.0 0.1 0.0 0.0 0.1 -0.1 0.1 0.1 0.1
NEO/FA 4.2 8.6 2.2 17.5 4.6 0.8 15.0 8.1 59.4 15.5 13.6
1/ Source: Sovereign Wealth Fund Institue, IMF Balance of Payments database, IMF country reports.

2/ SWF is sovereign wealth fund, NEO is net errors and omissions, FA is fnancial account. Averages of NEO and FA are averages of the absolute
values.
3/ Ranked by size of sovereign wealth fund. China, UAE, Saudi Arabia, Singapore, the United States SWF data repressent the total of all SWFs.
4/ Sovereign Wealth Fund data has been updated through 2012.
5/ SAFE Investment Company SWF data are estimates.
6/ Net errors and omissions and fnancial account data United Arab Emirates and Qatar are taken from IMF country reports.
7/ NEO, FA, and NEO/FA averages are calculated from 2005-2010, the years for which data are available.
8/ This includes the oil stabilization fund of Russia.

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78 Global Financial Integrity
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79 Illicit Financial Flows from Developing Countries: 2001-2010
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http:/ / reali tyvi ews.blogspot.com/
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