## cr17365

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---

### 1. Distribution of Fuel Subsidy by Income Quintile

- Overview
  - Sudan maintains consumer subsidies primarily on energy (fuel products and electricity) and wheat products.
  - Subsidies are inefficient and regressive: most benefits accrue to higher income groups; they distort resource allocation, encourage over-consumption, under-investment, and can generate cross-border smuggling when domestic prices are lower than neighboring countries.
  - At end-August 2017, the premium between the official and parallel exchange rates was 216 percent, implying that moving to a unified, more flexible exchange rate would either substantially raise the budget cost of subsidies or raise retail prices.

- Channels of subsidy delivery and fiscal cost
  - Consumer subsidies are delivered through:
    - Direct budgetary transfers to producers;
    - Tax exemptions to producers and consumers of subsidized commodities;
    - Preferential access to an overvalued exchange rate by importers of subsidized commodities.
  - Fiscal Cost of Consumer Subsidies (2016–17) — Percent of GDP:
    - Energy products: Direct transfers -0.9; Tax exemptions 3.8; Exchange rate subsidy 4.2.
    - Wheat: Direct transfers 0.3; Tax exemptions n/a; Exchange rate subsidy 1.0.
  - Observations from source:
    - Currently net fuel subsidies are negative 1.0 percent of GDP in official records due to accounting practices and a recent average fuel price increase of (40 percent).
    - The FAD TA mission estimate of lost fiscal revenues from fuel tax exemptions could be as high as 3.8 percent of GDP (based on 2014 estimates).
    - Staff estimates the fiscal cost of providing access to an overvalued exchange rate for fuel and wheat imports is expected to be close to 5 percent of GDP in 2017.

- Distributional effects of subsidies
  - IMF staff assessments (2012 and 2014) based on the 2009 National Baseline Household Survey:
    - Gasoline price subsidization: top income quintile receives 68 percent of benefits; bottom income quintile receives 1 percent.
    - Electricity subsidies: top quintile receives 58 percent of benefits; lowest quintile receives 2 percent.
    - On average, households in the top 2 income quintiles receive around 74 percent of the direct benefits of fuel-product subsidies and 66 percent of the estimated indirect benefits.
    - Average monthly benefit per person: bottom quintile SDG 1; top quintile SDG 11.
  - Cross-country evidence:
    - On average the top income quintile received 6 times more benefits from generalized energy subsidies than the bottom quintile.
    - For gasoline and LPG the top quintile receives 27 and 12 times that of the bottom quintile, respectively.
  - Wheat subsidies:
    - No formal distributional assessment to date; anecdotal evidence suggests regressivity because wheat consumption is concentrated in urban and relatively higher-income areas while poorer rural areas consume more sorghum.
    - Low domestic wheat prices hurt domestic producers (wheat and sorghum).

- Impact of removing subsidies
  - Phasing out subsidies provided through access to overvalued foreign exchange would result in an increase in retail prices of at least 216 percent in fuel product prices, subsidized bread prices, and electricity tariffs.
  - Real income loss from removal of fuel and electricity subsidies (IMF staff estimate, 2014):
    - Average real income loss close to 16 percent when both direct and indirect effects are considered.
    - For the poorest 20 percent of households, eliminating fuel subsidies without offsets could imply an income loss of near 9 percent.
  - The increase in wheat prices from subsidy removal will further reduce incomes of poor households.
  - Ultimate impact depends on mitigating measures, phasing/timing, and direct support.

- Preconditions and components of successful subsidy reform
  - Six key ingredients (cross-country evidence):
    - An energy sector reform plan;
    - Communications strategy;
    - Phasing and sequencing of price increases;
    - Improving efficiency of state-owned enterprises;
    - Targeted mitigating measures;
    - Depoliticizing price setting of strategic commodities (e.g., adopting rules-based pricing mechanisms).
  - Public information campaign recommendations:
    - Launch as early as possible after a decision to phase out subsidies.
    - Wide-ranging consultations with stakeholders.
    - Inform public about high costs and unequal distribution of subsidy benefits.
    - Clearly communicate measures to protect lower income groups and the intention to reallocate budgetary savings to education and health.
    - Publish key parameters of pricing formulas for previously subsidized commodities and consider smoothing mechanisms to avoid sharp domestic price adjustments in response to global shocks (note: smoothing implies volatility in government revenue).

- Targeted mitigation: cash transfers and social protection
  - Cash transfers can mitigate impacts on lowest income groups:
    - Cost of compensating the lowest income groups for removal of fuel subsidies could be achieved at less than 1 percent of GDP a year.
    - Median loss of consumption for the poorest 50 percent would be about SDG 14 per capita per month; offsetting this could cost 0.8 percent of GDP per annum.
    - Compensating removal of all subsidies: around 2 percent of GDP could be allocated to protect the lowest income groups by raising the current cash grant and increasing coverage.
  - Existing programs and scaling needs:
    - Currently 750,000 households receive a monthly cash transfer from the budget of SDG200 per household.
    - Around 1 million households receive support from the Zakat Chamber.
    - Number of people considered to be poor was estimated at 15.4 million in 2009; substantial scaling-up of support would be required.
  - Program design suggestions:
    - Complement direct cash transfers with a productive safety net (e.g., public works) to address dependency concerns and support higher economic growth.
    - Use proxy means testing to determine eligibility when household income data are unavailable (indicators: location, dwelling quality, demographic structure, ownership of durable goods).

### 2. The enforcement of U.S. sanctions in the private sector affected significantly global transactions in U.S. dollars (USD) with Sudan

- Enforcement impact on USD transactions and correspondent banking relationships (CBRs)
  - Starting 2009, forfeitures and fines imposed on financial institutions operating in the United States for breaching the economic sanctions regime against Sudan multiplied and amounted in hundreds of millions of USD, and continued to increase significantly as with the 2012 HSBC case.
  - The 2014 settlement with BNP Paribas marked the point after which Sudan began to lose most of its CBRs.
  - Since the BNP Paribas settlement, correspondents generally refused transactions involving Sudan or Sudanese entities or individuals, including those covered by licenses issued by OFAC, to avoid potential sanctions.
  - The impact of such decisions on the Sudanese economy has been significant.

- Sanctions timeline and scope
  - European Union: 2004 regulation prohibiting military support; 2007 regulation imposed travel ban and freeze of funds and economic resources against designated persons impeding the peace process in Darfur; 2011 regulation prohibited any financing connected to military activities.
  - United Nations Security Council: March 2005 resolution 1591 imposed travel restrictions, with exemptions, and an asset freeze on designated individuals impeding the peace process in Darfur; a Committee was established to designate targets.
  - United States:
    - 1993: Sudan designated as a state sponsor of terrorism.
    - November 1997: Executive Order (E.O.) 13067 established a trade embargo and blocked the Government of Sudan’s assets.
    - 2006: E.O. 13400 imposed further sanctions related to persons in connection with the conflict in Darfur.
    - E.O. 13412 blocked property of and prohibited transactions with the Government of Sudan; prohibitions included import/export of goods, technology, services, extension of credit or loans, and all transactions related to the petroleum and oil industries in Sudan.
    - Penalties: criminal fines of up to $1 million and imprisonment of up to 20 years for willful violations.
    - OFAC issued the Sudanese Sanctions Regulations (SSR) in 1998 to implement E.O. 13067; OFAC publishes the "Specially Designated Nationals" (SDN) list.
  - Other countries (e.g., Australia, Canada, the United Kingdom) adopted similar measures including export of arms prohibitions, travel bans and asset freezes against designated persons.
  - October 2017: the U.S. revoked its sanctions on trade and financial flows against Sudan following their suspension in early 2017; the suspension in January 2017 generally authorized U.S. persons to transact with individuals and entities in Sudan, and unblocked the property of the Government of Sudan subject to U.S. jurisdiction. U.S. banks were authorized to process transactions in relation to Sudan and finance trade in USD. In October 2017, following a review period, the U.S. revoked E.O.s 13607 and 13412.
  - Consequence of October 2017 revocation: SDN list related to these E.O.s is now revoked, allowing U.S. persons to deal with those designated persons and entities.
  - Caveats after revocation:
    - Other sanctions imposed by the U.S., the UN, and other countries remain applicable.
    - Sudan remains on the U.S. SSTL, and subject to the Darfur related sanctions and SDN list under E.O. 13400; property and interests in property of persons designated pursuant to E.O. 13400 remain blocked.
    - The October revocation does not affect measures administered by the Commerce Department's Bureau of Industry and Security.
    - Main prohibitions continue to apply on the export or reexport to Sudan of items destined to military end users or for military end use and certain specified items destined to any end-user.

- FATF enhanced monitoring and AML/CFT reforms
  - February 2010: FATF identified Sudan as a jurisdiction with strategic AML/CFT deficiencies.
  - 2012: Sudan was placed under monitoring and agreed with FATF on an action plan with a timetable to address deficiencies.
  - IMF technical assistance on AML/CFT started in December 2013 and helped reform legal, regulatory, and institutional frameworks.
  - Sudan met its commitments to the FATF by end 2015 and is no longer subject to enhanced monitoring.
  - Despite removal from enhanced monitoring, Sudan has been called to improve the effectiveness of its AML/CFT regime, including implementation of its mechanism for targeted financial sanctions.

- CBRs status and data (as reported, October 2017)
  - 17 out of the 37 Sudanese commercial banks reported having CBRs.
  - A total of 155 CBRs are reported in 15 different currencies.
  - More than half of the CBRs are in currencies of GCC countries.
  - Four banks report CBRs in USD; it is not clear to what extent these are active.
  - None of these USD CBRs are with U.S., EU, or global banks, but involve instead MENA banks and one Ethiopian bank.
  - 11 banks report CBRs in EUR, and some of these with banks headquartered in the Euro area (e.g., France, Italy, Germany, Austria).
  - Sources for CBR reporting: Bankers' Almanac; figures and charts cited as "CBRs as reported by banks as of October 2017."

- Prospects for re-connecting Sudanese banks with the international financial system
  - Banking relationships are likely to be gradually reestablished.
  - Negotiations with correspondent banks reportedly started since early 2017, but material change to CBRs had not been reported as of mission reporting.
  - Foreign banks have expressed interest; European and Middle-East banks reportedly waiting for U.S. banks to begin operating in Sudan before taking action.
  - Expected delays in resuming transactions: between 6 to 12 months, despite the lifting of U.S. sanctions, due to the need for compliance reports from the requesting bank.
  - Larger domestic banks likely to attract new CBRs, potentially accentuating banking concentration.
    - Example: in May 2011, OFAC removed two designated entities from the Sudan SDN list, one of which was the largest bank in Sudan; since then this bank developed access to Euro-denominated transactions through relationships with banks in Sweden, Switzerland, Belgium, Turkey, and France, but not yet to USD-denominated ones.
  - Continued challenges even after lifting of sanctions:
    - Remaining U.S., UN, and other country sanctions require due diligence to ensure transactions are not connected to the SDN list under Darfur-related sanctions and to comply with other measures outside SSR scope.
    - U.S. financial institutions conducted enhanced and lengthy due diligence even for transactions where sanctions have been revoked.
    - Continued expectations for enhanced due diligence increase compliance budgets for correspondent banks and require no compliance failures from respondent banks.
    - Continued U.S. enforcement actions against banks breaching remaining sanctions regimes or AML/CFT requirements may discourage some correspondent banks.

- Policy recommendations to facilitate recovery of CBRs
  - Pursue efforts that lead to removal of Sudan from the SSTL, remaining U.S. sanctions, and sanctions by the UN and other countries to reduce compliance costs, time, and risks for banks processing transactions in relation to Sudan.
  - Further strengthen the effectiveness of Sudan’s AML/CFT framework:
    - Continue efforts to develop and share understanding of ML/TF risks by authorities and the private sector, especially given remaining sanctions.
    - Supervisors should establish strong regulation and oversight supported by an effective sanctioning regime.
    - Share risk and compliance-related information with domestic and foreign stakeholders.
    - Increase availability of information on beneficial ownership (e.g., national registry or other mechanisms covered by FATF standards).
  - Sudanese banks should further develop AML/CFT compliance programs:
    - Use improved understanding of risks and beneficial ownership in managing business relationships.
    - Invest in monitoring systems, especially solutions to filter transactions for SDN.
    - Update customer due diligence information and train staff.
    - Apply enhanced due diligence measures when respondent banks host nested relationships benefiting other domestic banks.
  - Domestic stakeholders should maintain ongoing communication with correspondent banks:
    - Make information on risk available to correspondent banks.
    - Seek feedback from correspondent banks on the sufficiency of compliance measures and pursue technical assistance if available.
  - Central Bank of Sudan (CBOS) actions:
    - Continue to monitor development of CBRs and help facilitate a balanced return of CBRs to Sudanese banks to maintain competition in international transaction processing.
    - Pay special attention to nested correspondent relationships and expect heightened due diligence measures by respondent banks.
    - Promote implementation of effective AML/CFT programs at smaller banks to assist them in obtaining, individually or collectively, direct access to foreign correspondents.
    - CBOS can benefit from the IMF's monitoring framework for a practical approach to monitor development of CBRs in Sudan.

### 3. Data quality issues, reconstruction of GDP, and projections (2009–22)

- Data quality issues and adjustments (2009–14)
  - Official export and import series from the national accounts "suddenly shrunk in 2009 and remained at very low levels until 2014."
  - Closer inspection suggested "a misplaced decimal point" as the likely cause; adjusting the decimal point produced more plausible exports and imports (dotted lines in chart) but raised questions about domestic demand accuracy.
  - A large revision to the official GDP series occurred in early 2017: the CBS raised the 2013-2014 real GDP growth rates from 5.3 and 1.6 percent to 6.8 and 7 percent, respectively; "the reasons for these revisions were not adequately explained by the staff of the CBS."
  - As a result, "the official data for 2009-14 was significantly adjusted, building on data from other sources (notably the BOP) and empirical estimations."

- Estimation methodology for nominal domestic demand components
  - Due to limited indicators, BOP data and proxies correlated with domestic demand components were used.
  - Private consumption growth specification:
    - ∆LOG(Private_Cons_t) = β0 + β1 ∆LOG(Agric_t) + β2 ∆LOG(M_t) + ε_t
    - Rationale: Agriculture as proxy for household income; imports correlated with domestic demand.
    - Empirical finding: agriculture production and imports growth are positively correlated with private consumption growth.
  - Private investment growth specification:
    - ∆LOG(Private_Inv_t) = β0 + β1 ∆LOG(Credit_{t-1}) + β2 ∆LOG(Oil_price_t) + ε_t
    - Main explanatory variables: lagged credit to the private sector and the oil price.
    - Finding: Both variables have the correct sign, though only credit to the private sector is statistically significant.
    - Caveat: owing to the small sample, implications should be considered cautiously.
  - Public consumption: calculated as the sum of wages and goods and services from the fiscal accounts.
  - Deflation to obtain real GDP:
    - CPI used to deflate final consumption (private and public).
    - A simple average of CPI and import prices used to deflate investment.

- Regression outputs and exact statistics (selected)
  - Private consumption regression (Sample (adjusted): 1997 2014; Included observations: 18):
    - C: Coefficient 0.06; Std. Error 0.03; t-Statistic 1.70; Prob. 0.11
    - DLOGAGRIC1: Coefficient 0.64; Std. Error 0.15; t-Statistic 4.19; Prob. 0.00
    - DLOGM: Coefficient 0.18; Std. Error 0.11; t-Statistic 1.72; Prob. 0.11
    - R-squared 0.64; Adjusted R-squared 0.59; F-statistic 13.1; Prob(F-statistic) 0.00; Durbin-Watson stat 1.87
  - Private investment regression (Sample (adjusted): 1998 2014; Included observations: 17):
    - C: Coefficient -0.08; Std. Error 0.10; t-Statistic -0.85; Prob. 0.41
    - DLOGCREDIT(-1): Coefficient 0.97; Std. Error 0.31; t-Statistic 3.16; Prob. 0.01
    - DLOGOILPRICE: Coefficient 0.07; Std. Error 0.19; t-Statistic 0.36; Prob. 0.73
    - R-squared 0.42; Adjusted R-squared 0.34; F-statistic 5.15; Prob(F-statistic) 0.02; Durbin-Watson stat 1.68
  - Domestic demand restricted regression (Sample (adjusted): 2000 2014; Included observations: 15):
    - Restriction: DLOGDD_RESTR = Domestic demand - 0.2*Non-oil revenues (coefficient -0.2)
    - DLOGAGRIC1: Coefficient 0.82; Std. Error 0.29; t-Statistic 2.85; Prob. 0.02
    - R-squared 0.48; Adjusted R-squared 0.33; F-statistic 3.35; Prob(F-statistic) 0.06; Durbin-Watson stat 1.78
  - Imports regression (Sample: 1998 2014; Included observations: 17):
    - DLOGDD: Coefficient 1.18; Std. Error 0.42; t-Statistic 2.80; Prob. 0.02
    - R-squared 0.55; Adjusted R-squared 0.40; F-statistic 3.62; Prob(F-statistic) 0.04; Durbin-Watson stat 2.25

- Reconstructed real GDP outcomes and historical comparison
  - Comparison for 2009-2014:
    - Official real GDP growth rate average: 4.9 percent
    - Official real GDP growth rate median: 5.5 percent
    - IMF/staff estimate real GDP growth rate average: -1.1 percent
    - IMF/staff estimate real GDP growth rate median: -0.2 percent
  - Conclusion: reconstructed series shows "a real growth path that is consistently lower than official data suggests."

- Extension to 2015–16 and projection approach for 2017–22
  - 2015-16: actual BOP data used for exports and imports; estimating equations used for nominal domestic demand components; appropriate deflators applied to yield real GDP estimates.
  - 2017–22 projections:
    - Exports: based on discussions with government ministries for major export sectors (livestock, oil, and gold) and staff assessment of the external environment.
    - Domestic demand and imports: based on estimated forecasting equations using agriculture production, total public expenditures, non-oil fiscal revenues, and credit to the private sector.
    - Restricted regression assumes coefficient for non-oil fiscal revenue of -0.2, "broadly in line with the likely size of the fiscal revenue multiplier"; regression results indicate the restriction is not rejected by the data.
    - Imports projected using domestic demand, credit to the private sector, nominal exchange rate, and exports as explanatory variables.
  - Caveat: data quality and availability require that staff judgement be a critical part of the process of macroeconomic projections.

*Source: cr17365 (extracted IMF staff analysis).*

### 1. Distribution of Fuel Subsidy by Income Quintile __________________________________ 6

### 1. Distribution of Fuel Subsidy by Income Quintile

### Overview
- Sudan maintains consumer subsidies primarily on energy (fuel products and electricity) and wheat products.
- Subsidies are inefficient and regressive: international and Sudan-specific evidence show most benefits accrue to higher income groups, they distort resource allocation, encourage over-consumption, under-investment, and can generate cross-border smuggling when domestic prices are lower than neighboring countries.
- At end-August 2017, the premium between the official and parallel exchange rates was 216 percent, implying that moving to a unified, more flexible exchange rate would either substantially raise the budget cost of subsidies or raise retail prices.

### Channels of subsidy delivery and fiscal cost
- Consumer subsidies in Sudan are delivered through three main channels:
  - Direct budgetary transfers to producers;
  - Tax exemptions to producers and consumers of subsidized commodities;
  - Preferential access to an overvalued exchange rate by importers of subsidized commodities.
- Table: Fiscal Cost of Consumer Subsidies (2016–17) — Percent of GDP
  - Energy products: Direct transfers -0.9; Tax exemptions 3.8; Exchange rate subsidy 4.2.
  - Wheat: Direct transfers 0.3; Tax exemptions n/a; Exchange rate subsidy 1.0.
  - (Note: 3.8 percent of GDP for fuel tax exemptions is based on 2014 estimates; exchange-rate subsidy estimate close to 5 percent of GDP in 2017 is noted in text.)
- Footnotes/observations preserved from source:
  - Currently net fuel subsidies are negative 1.0 percent of GDP in official records due to accounting practices and a recent average fuel price increase of (40 percent).
  - The FAD TA mission estimate of lost fiscal revenues from fuel tax exemptions could be as high as 3.8 percent of GDP (based on 2014 estimates).
  - Staff estimates the fiscal cost of providing access to an overvalued exchange rate for fuel and wheat imports is expected to be close to 5 percent of GDP in 2017.

### Distributional effects of subsidies
- IMF staff assessments (2012 and 2014) based on the 2009 National Baseline Household Survey:
  - Households in the top income quintile receive 68 percent of benefits from gasoline price subsidization; the bottom income quintile receives 1 percent.
  - For electricity subsidies: top quintile receives 58 percent of benefits; lowest quintile receives 2 percent.
  - On average, households in the top 2 income quintiles receive around 74 percent of the direct benefits of fuel-product subsidies and 66 percent of the estimated indirect benefits.
  - Average monthly benefit per person: bottom quintile SDG 1; top quintile SDG 11.
- Cross-country evidence: on average the top income quintile received 6 times more benefits from generalized energy subsidies than the bottom quintile; for gasoline and LPG the top quintile receives 27 and 12 times that of the bottom quintile, respectively (cited study).
- Wheat subsidies: no formal distributional assessment to date; anecdotal evidence suggests regressivity because wheat consumption is concentrated in urban and relatively higher-income areas while poorer rural areas consume more sorghum. Low domestic wheat prices hurt domestic producers (wheat and sorghum).

### Impact of removing subsidies
- Phasing out subsidies provided through access to overvalued foreign exchange would result in an increase in retail prices of at least 216 percent in fuel product prices, subsidized bread prices, and electricity tariffs.
- The real income loss from removal of fuel and electricity subsidies (IMF staff estimate, 2014):
  - Average real income loss close to 16 percent when both direct and indirect effects are considered.
  - For the poorest 20 percent of households, eliminating fuel subsidies without offsets could imply an income loss of near 9 percent.
- The increase in wheat prices from subsidy removal will further reduce incomes of poor households.
- The ultimate impact depends on mitigating measures, phasing/timing, and direct support.

### Preconditions and components of successful subsidy reform
- Six key ingredients for successful subsidy reform (cross-country evidence):
  - An energy sector reform plan;
  - Communications strategy;
  - Phasing and sequencing of price increases;
  - Improving efficiency of state-owned enterprises;
  - Targeted mitigating measures;
  - Depoliticizing price setting of strategic commodities (e.g., adopting rules-based pricing mechanisms).
- Public information campaign recommendations:
  - Launch as early as possible after a decision to phase out subsidies.
  - Wide-ranging consultations with stakeholders.
  - Inform public about high costs and unequal distribution of subsidy benefits.
  - Clearly communicate measures to protect lower income groups and the intention to reallocate budgetary savings to education and health.
  - Publish key parameters of pricing formulas for previously subsidized commodities and consider smoothing mechanisms to avoid sharp domestic price adjustments in response to global shocks (note: smoothing implies volatility in government revenue).

### Targeted mitigation: cash transfers and social protection
- Cash transfers can mitigate impacts on lowest income groups:
  - Cost of compensating the lowest income groups for removal of fuel subsidies could be achieved at less than 1 percent of GDP a year.
  - Median loss of consumption for the poorest 50 percent would be about SDG 14 per capita per month; offsetting this could cost 0.8 percent of GDP per annum.
  - Compensating removal of all subsidies: around 2 percent of GDP could be allocated to protect the lowest income groups by raising the current cash grant and increasing coverage.
- Existing programs and scaling needs:
  - Currently 750,000 households receive a monthly cash transfer from the budget of SDG200 per household.
  - Around 1 million households receive support from the Zakat Chamber.
  - Number of people considered to be poor was estimated at 15.4 million in 2009; substantial scaling-up of support would be required.
- Program design suggestions:
  - Complement direct cash transfers with a productive safety net (e.g., public works) to address dependency concerns and support higher economic growth.
  - Use proxy means testing to determine eligibility when household income data are unavailable (indicators: location, dwelling quality, demographic structure, ownership of durable goods).

*Source: cr17365 - 1. Distribution of Fuel Subsidy by Income Quintile*

### 2. The enforcement of U.S. sanctions in the private sector affected significantly global

### 2. The enforcement of U.S. sanctions in the private sector affected significantly global transactions in U.S. dollars (USD) with Sudan

### Enforcement impact on USD transactions and correspondent banking relationships (CBRs)
- Starting 2009, forfeitures and fines imposed on financial institutions operating in the United States for breaching the economic sanctions regime against Sudan multiplied and amounted in hundreds of millions of USD, and continued to increase significantly as with the 2012 HSBC case.
- The 2014 settlement with BNP Paribas (noted in the source) marked the point after which Sudan began to lose most of its CBRs.
- Since the BNP Paribas settlement, correspondents generally refused transactions involving Sudan or Sudanese entities or individuals, including those covered by licenses issued by the Office of Foreign Assets Control (OFAC), to avoid potential sanctions.
- The impact of such decisions on the Sudanese economy has been significant (see Figure 2 in source).

### Sanctions timeline and scope
- The European Union: adopted in 2004 a regulation prohibiting military support; 2007 regulation imposed measures (travel ban and freeze of funds and economic resources) against designated persons impeding the peace process in Darfur; 2011 regulation prohibited any financing connected to military activities.
- United Nations Security Council (UNSC): March 2005 resolution 1591 imposed travel restrictions, with exemptions, and an asset freeze on designated individuals impeding the peace process in Darfur; a Committee was established to designate targets.
- The United States:
  - 1993: Sudan designated as a state sponsor of terrorism.
  - November 1997: Executive Order (E.O.) 13067 established a trade embargo and blocked the Government of Sudan’s assets.
  - 2006: E.O. 13400 imposed further sanctions related to persons in connection with the conflict in Darfur.
  - E.O. 13412 blocked property of and prohibited transactions with the Government of Sudan; prohibited transactions generally included the import and export of goods, technology, and services, the extension of credit or loans, and all transactions related to the petroleum and oil industries in Sudan.
  - Penalties: Anyone who willfully violates, attempts to violate, conspires to violate, or causes a violation of the E.O.s faces criminal fines of up to $1 million and imprisonment of up to 20 years.
  - OFAC issued the Sudanese Sanctions Regulations (SSR) in 1998 to implement E.O. 13067; OFAC publishes the "Specially Designated Nationals" (SDN) list; assets of SDN are blocked and U.S. persons are generally prohibited from dealing with them.
- Other countries (e.g., Australia, Canada, the United Kingdom) adopted similar measures, generally including a prohibition on the export of arms, a travel ban and an asset freeze against designated persons.
- October 2017: the U.S. revoked its sanctions on trade and financial flows against Sudan following their suspension in early 2017; the suspension in January 2017 generally authorized U.S. persons to transact with individuals and entities in Sudan, and unblocked the property of the Government of Sudan subject to U.S. jurisdiction. U.S. banks were authorized to process transactions in relation to Sudan and finance trade in USD. In October 2017, following a review period, the U.S. revoked E.O.s 13607 and 13412.
- Consequence of October 2017 revocation: SDN list related to these E.O.s is now revoked, which allows U.S. persons to deal with those designated persons and entities.
- Caveats after revocation:
  - Other sanctions imposed by the U.S., the UN, and other countries remain applicable.
  - Sudan remains on the U.S. SSTL, and subject to the Darfur related sanctions and SDN list under E.O. 13400; the property and interests in property of persons designated pursuant to E.O. 13400 remain blocked.
  - The October revocation does not affect measures administered by the Commerce Department's Bureau of Industry and Security.
  - Main prohibitions continue to apply on the export or reexport to Sudan of items destined to military end users or for military end use and certain specified items destined to any end-user.

### FATF enhanced monitoring and AML/CFT reforms
- February 2010: FATF identified Sudan as a jurisdiction with strategic AML/CFT deficiencies.
- Two years later (2012), Sudan was placed under monitoring due to remaining deficiencies and agreed with FATF on an action plan with a timetable to address deficiencies.
- IMF technical assistance on AML/CFT started in December 2013 and helped reform legal, regulatory, and institutional frameworks.
- Sudan met its commitments to the FATF by end 2015 and is no longer subject to enhanced monitoring.
- This development prompted foreign governments to encourage Sudan to pursue reforms to help reconnect its economy with the rest of the world.
- Despite removal from enhanced monitoring, Sudan has been called to improve the effectiveness of its AML/CFT regime, including implementation of its mechanism for targeted financial sanctions.

### CBRs status and data (as reported)
- Data are scarce but suggest the existence of a number of CBRs, albeit limited and fragile.
- As of October 2017:
  - 17 out of the 37 Sudanese commercial banks reported having CBRs.
  - A total of 155 CBRs are reported in 15 different currencies.
  - More than half of the CBRs are in currencies of GCC countries.
  - Four banks report CBRs in USD; it is not clear to what extent these are active.
  - None of these USD CBRs are with U.S., EU, or global banks, but involve instead MENA banks and one Ethiopian bank.
  - 11 banks report CBRs in EUR, and some of these with banks headquartered in the Euro area (e.g., France, Italy, Germany, Austria).
- Sources for CBR reporting: Bankers' Almanac; figures and charts cited as "CBRs as reported by banks as of October 2017."

### Prospects for re-connecting Sudanese banks with the international financial system
- Banking relationships between Sudanese banks and foreign correspondents are likely to be gradually reestablished.
- Negotiations with correspondent banks reportedly started since early 2017, but material change to CBRs had not been reported as of mission reporting.
- Foreign banks have expressed interest in dealing with Sudan again; European and Middle-East banks reportedly waiting for U.S. banks to begin operating in Sudan before taking action.
- Expected delays in resuming transactions: between 6 to 12 months, despite the lifting of U.S. sanctions, due to the need for compliance reports from the requesting bank.
- Larger domestic banks are likely to attract new CBRs, potentially accentuating banking concentration in Sudan.
  - Example: in May 2011, OFAC removed two designated entities from the Sudan SDN list, one of which was the largest bank in Sudan; since then this bank developed access to Euro-denominated transactions through relationships with banks in Sweden, Switzerland, Belgium, Turkey, and France, but not yet to USD-denominated ones.
  - Near-exclusive access to CBRs by a single bank reportedly contributed to its growth and market dominance.
- From correspondent banks' perspective, dealing with a large bank is more substantial, lucrative, less costly, and supported by better risk management and compliance.
- Even with lifting of sanctions, establishing new CBRs may continue to pose challenges:
  - Remaining U.S., UN, and other country sanctions require due diligence to ensure transactions are not connected to the SDN list under Darfur-related sanctions and to comply with other measures outside SSR scope.
  - U.S. financial institutions conducted enhanced and lengthy due diligence even for transactions where sanctions have been revoked.
  - Continued expectations for enhanced due diligence increase compliance budgets for correspondent banks and require no compliance failures from respondent banks.
- Continued U.S. enforcement actions against banks breaching remaining sanctions regimes or AML/CFT requirements may discourage some correspondent banks due to the risk of civil and criminal sanctions and perceived low profitability of CBRs in Sudan.

### Possible remedial measures for a balanced recovery of CBRs in Sudan (policy recommendations)
- Pursue efforts that lead to removal of Sudan from the SSTL, remaining U.S. sanctions, and sanctions by the UN and other countries to reduce compliance costs, time, and risks for banks processing transactions in relation to Sudan.
- Further strengthen the effectiveness of Sudan’s AML/CFT framework to improve compliance with international standards and to provide comfort to correspondent banks:
  - Continue efforts to develop and share understanding of ML/TF risks by authorities and the private sector, especially given remaining sanctions.
  - Supervisors should establish strong regulation and oversight supported by an effective sanctioning regime.
  - Share risk and compliance-related information with domestic and foreign stakeholders.
  - Increase availability of information on beneficial ownership (e.g., national registry or other mechanisms covered by FATF standards) to assist domestic and correspondent banks in identifying transactions connected to designated persons and in managing risks more generally.
- Sudanese banks should further develop AML/CFT compliance programs:
  - Use improved understanding of risks and beneficial ownership in managing business relationships.
  - Invest in monitoring systems, especially solutions to filter transactions for SDN.
  - Update customer due diligence information and train staff.
  - Apply enhanced due diligence measures when respondent banks host nested relationships benefiting other domestic banks.
- Domestic stakeholders should maintain ongoing communication with correspondent banks:
  - Make information on risk available to correspondent banks to help them make informed decisions about initiation, management, or continuation of CBRs.
  - Seek feedback from correspondent banks on the sufficiency of compliance measures and pursue technical assistance if available.
- Central Bank of Sudan (CBOS) actions:
  - Continue to monitor development of CBRs and help facilitate a balanced return of CBRs to Sudanese banks to maintain competition in international transaction processing.
  - Pay special attention to nested correspondent relationships and expect heightened due diligence measures by respondent banks.
  - Promote implementation of effective AML/CFT programs at smaller banks to assist them in obtaining, individually or collectively, direct access to foreign correspondents.
  - CBOS can benefit from the IMF's monitoring framework for a practical approach to monitor development of CBRs in Sudan (reference to IMF Working Paper WP/17/216 in source).

*International Monetary Fund — extracted from "2. The enforcement of U.S. sanctions in the private sector affected significantly global transactions in U.S. dollars (USD) with Sudan" (cr17365).*

### 22.4 percent for the IMF estimate, with no

### cr17365 - 22.4 percent for the IMF estimate, with no

### Data quality issues and adjustments (2009–14)
- Official export and import series from the national accounts "suddenly shrunk in 2009 and remained at very low levels until 2014."
- Closer inspection suggested "a misplaced decimal point" as the likely cause; adjusting the decimal point produced more plausible exports and imports (dotted lines in chart) but raised questions about domestic demand accuracy.
- A large revision to the official GDP series occurred in early 2017: the CBS raised the 2013-2014 real GDP growth rates from 5.3 and 1.6 percent to 6.8 and 7 percent, respectively; "the reasons for these revisions were not adequately explained by the staff of the CBS."
- As a result, "the official data for 2009-14 was significantly adjusted, building on data from other sources (notably the BOP) and empirical estimations."

### Estimation methodology for nominal domestic demand components
- For nominal GDP components, BOP data and proxies correlated with domestic demand components (private and public consumption and investment) were used due to limited alternative indicators in Sudan.
- Private consumption growth specification:
  - ∆LOG(Private_Cons_t) = β0 + β1 ∆LOG(Agric_t) + β2 ∆LOG(M_t) + ε_t
  - Rationale: Agriculture production used as a proxy for household income; imports included as correlated with domestic demand.
  - Empirical finding: "estimates show that agriculture production and imports growth are positively correlated with private consumption growth."
- Private investment growth specification:
  - ∆LOG(Private_Inv_t) = β0 + β1 ∆LOG(Credit_{t-1}) + β2 ∆LOG(Oil_price_t) + ε_t
  - Main explanatory variables: lagged credit to the private sector and the oil price.
  - Finding: "Both variables have the correct sign, though only credit to the private sector is statistically significant."
  - Caveat: "owing to the small sample, we should be mindful of the implications for such analysis."
- Public consumption: calculated as the sum of wages and goods and services from the fiscal accounts.
- Deflation to obtain real GDP:
  - CPI used to deflate final consumption (private and public).
  - A simple average of CPI and import prices used to deflate investment (which uses imported goods).

### Regression outputs and exact statistics
- Private consumption regression (Dependent Variable: DLOGPRIV_CONS; Method: Least Squares; Sample (adjusted): 1997 2014; Included observations: 18 after adjustments):
  - C: Coefficient 0.06, Std. Error 0.03, t-Statistic 1.70, Prob. 0.11
  - DLOGAGRIC1: Coefficient 0.64, Std. Error 0.15, t-Statistic 4.19, Prob. 0.00
  - DLOGM: Coefficient 0.18, Std. Error 0.11, t-Statistic 1.72, Prob. 0.11
  - R-squared 0.64; Adjusted R-squared 0.59; Mean dependent var 0.21; S.D. dependent var 0.12
  - S.E. of regression 0.08; Sum squared resid 0.09; Log likelihood 21.98
  - Akaike info criterion -2.11; Schwarz criterion -1.96; Hannan-Quinn criter. -2.09
  - F-statistic 13.1; Prob(F-statistic) 0.00; Durbin-Watson stat 1.87
- Private investment regression (Dependent Variable: DLOGGCF2; Method: Least Squares; Sample (adjusted): 1998 2014; Included observations: 17 after adjustments):
  - C: Coefficient -0.08, Std. Error 0.10, t-Statistic -0.85, Prob. 0.41
  - DLOGCREDIT(-1): Coefficient 0.97, Std. Error 0.31, t-Statistic 3.16, Prob. 0.01
  - DLOGOILPRICE: Coefficient 0.07, Std. Error 0.19, t-Statistic 0.36, Prob. 0.73
  - R-squared 0.42; Adjusted R-squared 0.34; Mean dependent var 0.19; S.D. dependent var 0.26
  - S.E. of regression 0.21; Sum squared resid 0.60; Log likelihood 4.24
  - Akaike info criterion -0.15; Schwarz criterion 0.00; Hannan-Quinn criter. -0.13
  - F-statistic 5.15; Prob(F-statistic) 0.02; Durbin-Watson stat 1.68
- Domestic demand restricted regression (Dependent Variable: DLOGDD_RESTR; Method: Least Squares; Sample (adjusted): 2000 2014; Included observations: 15 after adjustments):
  - DLOGDD_RESTR = Domestic demand - 0.2*Non-oil revenues (restriction imposed with coefficient -0.2)
  - C: Coefficient -0.03, Std. Error 0.08, t-Statistic -0.39, Prob. 0.70
  - DLOGAGRIC1: Coefficient 0.82, Std. Error 0.29, t-Statistic 2.85, Prob. 0.02
  - DLOGG: Coefficient 0.26, Std. Error 0.15, t-Statistic 1.69, Prob. 0.12
  - DLOGCREDIT: Coefficient 0.24, Std. Error 0.16, t-Statistic 1.45, Prob. 0.17
  - R-squared 0.48; Adjusted R-squared 0.33; Mean dependent var 0.21; S.D. dependent var 0.11
  - S.E. of regression 0.09; Sum squared resid 0.09; Log likelihood 17.23
  - Akaike info criterion -1.76; Schwarz criterion -1.57; Hannan-Quinn criter. -1.77
  - F-statistic 3.35; Prob(F-statistic) 0.06; Durbin-Watson stat 1.78
- Imports regression (Dependent Variable: DLOGM; Method: Least Squares; Sample: 1998 2014; Included observations: 17):
  - C: Coefficient -0.13, Std. Error 0.10, t-Statistic -1.29, Prob. 0.22
  - DLOGDD: Coefficient 1.18, Std. Error 0.42, t-Statistic 2.80, Prob. 0.02
  - DLOGCREDIT: Coefficient 0.23, Std. Error 0.24, t-Statistic 0.93, Prob. 0.37
  - DLOGNER: Coefficient 0.30, Std. Error 0.28, t-Statistic 1.05, Prob. 0.31
  - DLOGX: Coefficient 0.09, Std. Error 0.11, t-Statistic 0.80, Prob. 0.44
  - R-squared 0.55; Adjusted R-squared 0.40; Mean dependent var 0.19; S.D. dependent var 0.19
  - S.E. of regression 0.15; Sum squared resid 0.26; Log likelihood 11.54
  - Akaike info criterion -0.77; Schwarz criterion -0.52; Hannan-Quinn criter. -0.75
  - F-statistic 3.62; Prob(F-statistic) 0.04; Durbin-Watson stat 2.25

### Reconstructed real GDP outcomes and historical comparison
- The nominal GDP components were deflated using the procedures above to calculate a reconstructed real GDP series.
- Comparison of central measures for 2009-2014:
  - Official real GDP growth rate average: 4.9 percent
  - Official real GDP growth rate median: 5.5 percent
  - IMF/staff estimate real GDP growth rate average: -1.1 percent
  - IMF/staff estimate real GDP growth rate median: -0.2 percent
- The results show "a real growth path that is consistently lower than official data suggests."

### Extension to 2015–16 and projection approach for 2017–22
- For 2015-16 (absence of published official data), methodology:
  - Actual BOP data used for exports and imports.
  - Estimating equations above used to estimate components of nominal domestic demand.
  - Appropriate deflators applied to yield estimates of real GDP.
- Projections for 2017–22:
  - Forecasting exports: based on discussions with government ministries for major export sectors (livestock, oil, and gold) and staff assessment of the external environment.
  - Forecasting domestic demand and imports: based on estimated forecasting equations.
  - Domestic demand explanatory variables: agriculture production, total public expenditures, non-oil fiscal revenues, and credit to the private sector.
    - Because of simultaneity bias between domestic demand and non-oil fiscal revenues, a restricted regression was estimated where the coefficient for non-oil fiscal revenue was assumed to be -0.2 (stated as "broadly in line with the likely size of the fiscal revenue multiplier"); "Regression results indicate that the restriction is not rejected by the data."
    - Estimated coefficients: agriculture production is the most significant variable with the largest coefficient, followed by public spending and credit.
  - Imports projected using: domestic demand, credit to the private sector, nominal exchange rate, and exports as explanatory variables. Some coefficients are not statistically significant but have "plausible size and sign" supporting forecast quality.

### Policy and analytical implications
- The estimated set of equations are presented as "relevant inputs for medium-term growth projection in Sudan."
- Twofold aim of the note:
  - Reconstruct a real GDP growth series that better represents actual economic performance.
  - Estimate a medium-term projection of real GDP growth from the aggregate demand side to assess implications of monetary and fiscal policies on growth.
- Important caveat: "data quality and availability requires that staff judgement be a critical part of the process of macroeconomic projections."

*International Monetary Fund, Staff analysis as presented in the provided content.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2017/cr17365.pdf_