## The D4D Fund Context (Presentation by Johannes Mueller, IMF Statistics Department, January 29, 2019)

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**Canonical URL:** [The D4D Fund Context (Presentation by Johannes Mueller, IMF Statistics Department, January 29, 2019)](https://www.imf.org/-/media/files/capacity-developement/d4d/presentation-context-mueller.pdf)

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### Overview and Objective
- Objective: Putting more and better data in the hands of decision makers to enhance evidence-based macroeconomic policies and support achievement of the sustainable development goals.
- Focus: Strengthen national statistical systems to cope with a more challenging national and international policy environment, promote transparency and accountability, and offer efficient solutions to capacity development (CD).
- Conceptual foundation: Needs assessment considering (i) demand from countries, (ii) policy relevance, (iii) identified data gaps and data quality concerns, (iv) complementarity with IMF RTACs and bilateral CD projects, and (v) flexibility to react to country capacity and readiness.
- Target countries: Mainly Low- and lower middle-income countries world-wide, including fragile states.
- Size: US$33 million (including a 7 percent IMF trust fund management fee).
- Duration: Five years (FY2019-24).

### Drivers of Statistics Capacity Development
- Enumerated drivers:
  - Development of national statistical systems.
  - Data gaps and quality concerns revealed by crises (e.g., 2007/08).
  - Compilation focus and compliance with international standards (e.g., SNA 2008, BPM6).
  - Recognition of policy relevance of data and global push for evidence-based policy-making.
  - 2030 UN Agenda for Sustainable Development: 230+ indicators.
  - Methodological advances and greater awareness of measurement challenges (digital economy, “Data Revolution”).
  - Transparency and accountability: focus on dissemination and engagement beyond compilers.
  - Newly emerging sources: Big Data.
- Implication highlighted: New user demands, including outside governments, and the SDG framework present unprecedented demand on national statistical systems.

### Assessment of Needs and Country Ownership
- Key findings:
  - Statistics CD needs remain pervasive despite considerable TA and training.
  - Various assessments point toward entry points for statistics CD.
  - Addressing statistics CD needs must be country-driven.
  - Country ownership and results focus are key for fundamentally addressing statistical reforms.
- World Bank Statistical Capacity Score (2004 and 2017) by country grouping (100=max.):
  - Low-income: 53.4 (2004) and 67.0 (2017)
  - Lower Middle-Income: 70.4 (2004) and 45.2 (2017) [note: preserve sequence as in source]
  - Upper Middle-Income: 59.7 (2004) and 69.6 (2017)
  - Fragile states: 74.0 (2004) and 53.0 (2017)
  - (Presentation shows these values under the heading "World Bank Statistical Capacity Score, 2004 and 2017 (by country grouping (100=max.))".)

### Global Trends and Funding for Statistics CD
- Observations:
  - While statistics CD needs are high, global support has been stagnating at best.
  - The IMF has steadily increased its TA and training in statistics, making it one of the top global providers.
- Trends and figures (as presented):
  - ODA spending on statistics (percent) trend shown 2011–2019 alongside IMF statistics missions (number), with separate trendlines for each (numeric chart not recapitulated beyond trend statement).
- Top global providers of Statistics CD, 2018 (in percent):
  - World Bank 37%
  - EU/Eurostat 20%
  - IMF 5%
  - UNFPA 5%
  - UNICEF 5%
  - Canada 6%
  - Other 22%

### SDGs: Opportunity, Challenge, and Funding Scenarios
- SDG monitoring challenge:
  - 17 SDG targets with 230+ indicators create unprecedented demand on national statistical systems.
  - Two-thirds of indicators pose data challenges (as described).
- Paris21 investment scenarios for national statistical systems (NSS) related to SDGs:
  - Scenario types: Low (basic production of SDG monitoring framework); Medium (basic production plus statistical CD to ensure sustainability); High (full implementation of CTGAP, including efficient data dissemination).
  - Countries’ own resources are paramount for long-term sustainability.
  - Funding gap for high-ambition scenario would amount to a doubling of current ODA.
- Annual funding gap figures shown by scenario (in billions of USD) — bar values presented in source charts:
  - Low-ambition: Total cost 2.9; Cost paid from domestic sources 2.2; Current volumes of ODA for statistics 0.6; Funding gap 0.1
  - Medium-ambition: Total cost 4.2; Cost paid from domestic sources 3.3; Current volumes of ODA for statistics 0.6; Funding gap 0.3
  - High-ambition: Total cost 5.6; Cost paid from domestic sources 4.3; Current volumes of ODA for statistics 0.6; Funding gap 0.7

### Global Initiatives and Calls to Action
- 2017 Cape Town Global Action Plan (CTGAP) for Sustainable Development Data:
  - Blueprint to guide implementation related to the 2030 Agenda to ensure “no one is left behind.”
  - Emphasized quality, timely, relevant, open and disaggregated data for SDG monitoring.
  - Called for a global pact to modernize countries’ NSS and derived six strategic objectives, including partnerships, resource mobilization, and coordination for statistics CD.
- 2018 Dubai Declaration:
  - Reaffirmed CTGAP objectives.
  - Called for enhanced support for strengthening data collection and capacity building for NSS.
  - Made the case for new and innovative forms of CD and political commitment to allocate adequate domestic resources.
  - Advocated a global funding mechanism for statistics CD.

### IMF’s Relevance to SDG Monitoring and Positive Externalities
- IMF role and linkages:
  - If macroeconomic statistics improve, monitoring of more than one-third of SDG indicators will improve.
  - Examples of SDG indicators related to IMF domains:
    - Real sector/GDP-related: 7.3.1; 9.2.1; 10.1.1; 12.c.1
    - Monetary/Financial sector and FSIs: 8.10.2; 9.3.2
    - ESS-related: 8.a.1; 9.a.1; 17.4.1
    - GFS-related: 1.a.2; 2.a.1; 16.6.1
- IMF as custodian agency for selected SDG indicators and related CD vehicles:
  - Indicator 8.10.1 (Financial Access Survey database http://fas.imf.org) → CD vehicle: D4D Fund.
  - Indicator 10.5.1 (FSI database http://fsi.imf.org) → CD vehicle: FSSF.
  - Indicator 17.1.1 on government revenue as share of GDP (GFS database http://data.imf.org/gfs) → CD vehicles: RCDCs, D4D Fund, bilateral projects.
  - Indicator 17.1.2 on share of domestic budget funded by domestic taxes (GFS database http://data.imf.org/gfs) → CD vehicles: RCDCs, D4D Fund, bilateral projects.

### IMF Statistics CD Architecture and the Role of the D4D Fund
- Medium-term steady state architecture components:
  - Field-based Regional Technical Assistance Centers (RTACs): Provide CD on “bread-and-butter” statistics to establish strong methodological foundation: RSS (close to full coverage), GFS (increasing coverage), ESS (limited coverage).
  - Financial Sector Stability Fund (FSSF) (LLMICs): Financial Soundness Indicators; Balance Sheet Approach for Monetary and Financial Statistics, International Investment Position, GFS balance sheets.
  - Bilateral & IMF’s own resources.
  - D4D Fund (LLMICs): RSS, GFS, ESS, Financial Access Survey (FAS), Online learning, Statistical Information Management.
- Aim: Fill gaps in coverage, address topical gaps and expand regional coverage on ESS basics, provide policy-relevant high-frequency data and source data.

### Demand, Design Features, and Implementation Approach of the D4D Fund
- Demand and impact design principles:
  - Scope aligned with newly emerging issues.
  - Agility through modular structure and wide LLMIC coverage.
  - Country-led approach leveraging IMF as trusted advisor.
- Implementation and results approach:
  - Focus on data for policy-making.
  - New products, techniques and data sources, including Big Data.
  - Combination of TA and training/workshops to sustain CD.
  - Close results-monitoring, including by partners; enhanced data and TA report dissemination.
- Technology and synergies:
  - Online learning.
  - Coordination internally and externally.
  - Efficient CD administration.
- Key success factors summarized:
  - Demand-driven, modular, results-focused, scalable online learning, coordination with partners, and leveraging IMF credibility.

### D4D Fund Modules and Activities
- Online Learning (Module objective):
  - Develop a new structured online learning curriculum to exponentially enhance reach of statistics training.
  - Offer online learning to allow truly global delivery.
  - Develop eight fundamental statistics courses (English and up to five other languages).
- Core modules / submodules identified:
  - M1: Real Sector Statistics (RSS)
  - M2: External Sector Statistics (ESS)
  - M3: Government Finance Statistics (GFS)
  - M4: Financial Access Survey (FAS)
    - Provide policymakers and analysts with high-quality statistics in this financial inclusion database covering 189 economies (http://data.imf.org/fas).
    - Improve and expand FAS to new areas, such as new access points, gender-disaggregated statistics, and costs of financial access.
    - Provide monitoring basis for SDG financial inclusion indicator.
  - Statistical Information Management:
    - Develop advice to help statistical offices, central banks, and ministries streamline, standardize, and automate data management practices and infrastructure.
    - Enhance countries’ ability to compile and disseminate macroeconomic and financial statistics.

### Targeting, Beneficiaries, and Allocation
- D4D Fund focus:
  - Focus on Low- and lower middle-income countries (LLMICCs) and fragile states.
  - The D4D Fund is presented as rising share in the IMF’s externally-funded statistics CD budget and anchoring future IMF statistics CD.
- Current beneficiary countries: Low- and lower-middle income countries (LLMICCs), with LLMICCs and Fragile States highlighted.
- Note: Additional countries can become eligible if income status changes, a country becomes an IMF member, and/or the IMF Executive Board authorizes CD to a non-member country.

### Results Framework and Evidence of Impact
- IMF RBM (Results-Based Management) framework for statistics CD:
  - Since inception in FY2016, a wealth of data has been accumulated to assess progress and results of country CD interventions.
  - RBM framework allows slicing information by criteria (country, region, funding source, delivering unit) to inform CD decisions.
  - IMF Statistics CD RBM: 2 work streams, 26 outcomes.
- Outcomes and milestone achievement ratings (FY2016-19):
  - Fully Achieved 67%
  - Largely Achieved 16%
  - Partially Achieved 13%
  - Not Achieved 4%
- Evidence of country-level results (examples presented):
  - Montenegro: BOP components (percent of GDP, average of 2010-16) — charts indicate measurable changes before and after TA missions.
  - São Tomé and Príncipe: Fiscal Outturn, 2017 (percent of GDP) — charts indicate improvement in fiscal reporting after TA missions.
  - CARTAC countries: Quarterly GDP estimates — shown expansion of coverage between “Before 2015” and “2018” for listed Caribbean countries.

### Strategic Rationale and Final Points
- The IMF’s statistics CD is projected to have the highest growth rate of all IMF CD areas over the next three years and responds specifically to country requests identifying statistics CD as the area of highest demand in the context of the 2018 CD Strategy Review.
- The D4D Fund is positioned as a core vehicle to:
  - Support data dissemination, targeted CD (regional harmonization, source data), online learning, and statistical information management.
  - Provide CD across RSS, GFS, ESS, FAS and develop country capacity to monitor SDG-related indicators.

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_Source: https://www.imf.org/-/media/files/capacity-developement/d4d/presentation-context-mueller.pdf_