This document summarizes a presentation given at the 2013 African Economic Conference on quantifying illicit financial flows from Africa through trade mispricing. The presentation defines illicit financial flows and outlines how they are estimated using trade data by comparing country export and import values. It finds that between 2001-2010, $409 billion left Africa through trade mispricing, concentrated in key countries, sectors, and destination countries. Modeling shows returning some illicit funds could boost African incomes and trade if spent on trade facilitation, but does not fully offset initial losses. Adopting stringent rules and transparency is critical to curb illicit financial flows in the first place and support Africa's development goals.

1. African Economic Conference 2013
28-30 October
Johannesburg, South Africa
Quantifying illicit financial flows from Africa
through trade mis-pricing
and assessing their incidence on African economies
Simon Mevel, Siope Ofa & Stephen Karingi / RITD / UN-ECA

2. Outline of the Presentation
I.
Illicit financial flows (IFF): definition and channels
II. Quantifying IFF through trade mis-pricing in Africa Methodology & Key results
III. Returning IFF money: Incidence on African
economies - Methodology & Key results
IV. Policy conclusions and implications for regional
integration in Africa

3. I.
Illicit Financial Flows (IFF) – Definition
and Channels
Source: Author’s consolidation of different concepts, 2013
 IFF can be considered as flows of money that have broken
laws:
 That is to say, money illegally earned, transferred or used, at
its origin, or during the movement of use

4. I.
IFF – Definition and Channels (Cont’d)
Source: Author’s consolidation of different concepts, 2013

Proceeds from commercial tax evasion supposed to represent the bulk of
IFF; about 65% of total IFF according to R. Baker (2005)

Transfer pricing vs. trade mis-pricing
 Transfer pricing: MNCs seeking to distribute more their worldwide profit in
lower tax countries (through subsidiaries) with objective to pay less taxes
 Trade mis-pricing or mis-invoicing: exporters and importers agree to falsify
customs’ invoices by under or over stating import and export values to usually
evade trade restrictions or move capital abroad

5. II.
Quantifying IFF through trade mispricing – Methodology

Focus on trade mis-pricing essentially due to availability of trade data
(transfer pricing requires firm level data)

Illicit financial outflows through trade mis-pricing or mis-invoicing
occurs when :
 Exports are under-invoiced:
 Exporter declaring lower value than the one being actually paid and declared
by importer (e.g.: exporter wishes to reduce apparent profit and thus income
taxes to be paid)
 AND/OR Imports are over-invoiced:
 Importer declaring higher value than the one being actually paid to exporter
and declared by him (e.g.: to shift money on a bank account in foreign
country)

If IFFMISINV i,j,k,t > 0, there are illicit financial outflows from any
African country ‘i’ to any country ‘j’ in product ‘k’ in year ‘t’

6. II.

Quantifying IFF through trade mispricing – Methodology (Cont’d)
UNDERINV_EXPi,j,k,t obtained by comparing exports of country “i” and their
reversal imports from country “j” in product “k” and year “t” after correcting
for:
 Price differences (Imports usually expressed CIF and exports are FOB)
 Time lags in reporting of transactions

If UNDERINV_EXP i,j,k,t > 0, then the African country “i” under-invoices its
exports to country “j” in product “k” in year “t”

Export and import costs are derived from World Bank Doing Business on Trading Across Borders using data
on import/export weighted average time costs from Minor and Hummels (2011)

Use of BACI which gives UN COMTRADE imports CIF converted into FOB, thanks to econometric analysis
estimating bilateral transport costs (usually a fixed 10% ratio used to convert imp. CIF to FOB)

Similar approach for computing OVERINV_IMPi,j,k,t

Important limitations: often poor reliability of trade statistics from African
countries; trade in services not captured in the computations

7. II.
Quantifying IFF through trade mispricing – Key Results
 Evolution of IFF from Africa through Trade Mis-pricing –
USD Billion – 2001-2010
80
70
60
50
40
30
20
10
0
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Source: Author’s calculations based on BACI dataset

Between 2001 and 2010, it is estimated that USD 409 billion
left Africa as IFF; strongly increased over the last few years

8. II.
Quantifying IFF through trade mispricing – Key Results (Cont’d)
 Cumulative IFF from Africa through Trade Mis-pricing – By
Country of origin – 2001-2010 – (> USD 5 Billion)
Kenya
Tanzania
Mauritania
Ghana
Cote d'Ivoire
Libya
Cameroon
Zambia
Congo
Mozambique
Tunisia
Sudan
Algeria
Morocco
Egypt
Nigeria
SACU
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
Source: Author’s calculations based on BACI dataset

IFF from Africa highly concentrated in a few countries (especially
SACU, Nigeria, Egypt, Morocco, …)

9. II.
Quantifying IFF through trade mispricing – Key Results (Cont’d)
 Cumulative IFF from Africa through Trade Mis-pricing – By
Country of destination – 2001-2010 – (> USD 5 Billion)
50
40
30
20
Russian Federation
Portugal
Austria
Angola & DRC
Korea
France
Turkey
United Kingdom
Belgium & Luxembourg
Japan
Italy
India
Germany
China
Spain
USA
0
Western Asia (Bahrain, Israel,
UAE, Qatar, Oman, …)
10
Source: Author’s calculations based on BACI dataset

IFF from Africa also highly concentrated towards a few
destinations (especially EU, USA, China, India, Western Asia, …)

10. II.
Quantifying IFF through trade mispricing – Key Results (Cont’d)
 Cumulative IFF from Africa through Trade Mis-pricing –
Top 10 main sectors – USD Billion – 2001-2010
1
2
3
4
5
6
7
8
9
10
Copper, gold and other non-ferrous metal
Crude oil
Natural gas
Construction material (cement, gravel, plaster, ...)
Refined oil and coal products
Crops nec (live plants, cut flowers, plants used in perfumery...)
Food products nec
Machinery and equipment nec
Wearing apparel
Iron & steel
Source: Author’s calculations based on BACI dataset

IFF from Africa highly concentrated in a few sectors (especially
mining and extractive industries)
84.0
69.6
34.0
33.1
20.0
17.1
16.9
16.8
14.0
13.2

11. III.
Returning IFF money: Incidence on
African economies – Methodology
 Using MIRAGE Computable General Equilibrium
(CGE) Model and GTAP database to assess impacts
on African economies from:
1) Progressive return of initially lost IFF from Africa over the
period 2006-2010 through international income transfers
2) Same as 1) but international income transfers are now
constrained such as recipient countries must spend the
additional income received towards improving trade
facilitation measures (i.e. reducing time to process goods
in customs, at African ports and during inland transportation)

12. III.
Returning IFF money: Incidence on
African economies – Key Results
 Trade and real income changes compared to the baseline –
Africa total – % and USD billion – 2017
Real income
Imports
Exports
%
USD Billion
%
USD Billion
%
USD Billion
Scenario 1 - Non-contraint
Income Transfer
21.2
25.6
33.1
167.4
-19.3
-101.8
Scenario 2 - Constraint
Income Transfer
2.7
3.3
17.9
90.4
17.7
93.1
Source: Author’s calculations based on MIRAGE model
 Scenario 1 produces the effects of a subsidy given to African
consumers, allowing them to buy more goods from RoW that have
become relatively cheaper BUT African producers are suffering
(Income transfer paradox; Samuelson, 1947)
 Scenario 2 makes both African consumers and producers better off
when part of IFF money is recovered

13. III.
Returning IFF money: Incidence on
African economies – Key Results (Cont’d)
 Special importance to devoting IFF returns to trade
facilitation measures when it comes to regional integration
 Changes in African exports compared to the baseline –
By main destinations – Broken down by main sectors –
USD billion – 2017 – Scenario 2
80
70
60
50

Boosting intraAfrican trade

Favoring
industrialization of
African exports
40
30
20
10
0
African countries
Agriculture and food
Rest of the World
Primary
Industry
Source: Author’s calculations based on MIRAGE model

14. IV.
Policy conclusions and implications
for regional integration in Africa
 IFF losses from the African continent through only trade
mis-pricing are considerable:
 Estimated at about USD 409 billion over 2001-2010

Greater than all ODA disbursements to Africa (USD 357 billion;
OECD DAC)

Greater than all FDI to Africa over the same period (USD 344
billion; UNCTADStat)

Nearly equivalent to current Africa’s external debt (USD 413
billion; AfDB-OECD-UNDP-ECA African Economic Outlook,
2013)
 IFF losses highly concentrated in a few countries and
sectors and essentially going to the EU, the US and
emerging Asian economies
 African mining and extractive industries are the most affected by
IFF (with about 2/3 of total IFF from Africa through trade mispricing)

15. IV.
Policy conclusions and implications for
regional integration in Africa (Cont’d)
 Findings show that partial returns of IFF to Africa can be
beneficial
 But only if these funds are reinvested towards targeted
reforms

Such as those aiming at improving trade facilitation
measures which could strongly support the regional
integration process:
 Enhancing intra-African trade
 Favoring industrialization of African economies
 Yet, it also appears that potential benefits of IFF returns do
not fully offset initial losses from IFF

16. IV.
Policy conclusions and implications for
regional integration in Africa (Cont’d)
 Therefore, it is critical and urgent to curb IFF in the first
place by adopting more transparent and stringent rules,
regulations and policies, such as:
 Outside partners to force their MNCs to disclose more
systematically financial data relating to their overseas operations
 African governments to:

Enforce the Extractive Industries Transparency Initiative (EITI)
which obliges locally operating firms to disclose information on
tax, dividend and royalty payments

Make public the information received
 This is paramount as costly reforms are required to make
the regional integration process more effective
 For example: Africa50Fund initiative from AfDB to support Agenda
2063 for Africa’s structural transformation needs to gather USD 100
billion/year to address Africa’s infrastructure financing gap

17. Thank you!