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Quantifying iff from africa Quantifying iff from africa Presentation Transcript

  • 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
  • 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
  • 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
  • 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
  • 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’
  • 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
  • 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
  • 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, …)
  • 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, …)
  • 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
  • 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)
  • 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
  • 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
  • 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)
  • 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
  • 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
  • Thank you!