## The D4D Fund in the Global Statistical Capacity Development Architecture

## Source details

**Canonical URL:** [The D4D Fund in the Global Statistical Capacity Development Architecture](https://www.imf.org/-/media/files/capacity-developement/d4d/presentation-the-d4d-fund-in-the-global-statistical-capacity-development-architecture.pdf)

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### Global statistics capacity development agenda
- Drivers of statistics capacity development identified by the IMF Statistics Department (STA):
  - Development of national statistical systems; compilation focus.
  - Data gaps and quality concerns revealed by crises (e.g., 2007/08).
  - Compliance with international standards (e.g., SNA 2008, BPM6).
  - Recognition of policy relevance of data linked to the 2030 UN Agenda for Sustainable Development: “Global push for evidence-based policy-making,” “230+ SDG indicators,” and “Data challenges for two-thirds of indicators.”
  - Methodological advances and awareness of measurement challenges (Digital economy, “Data Revolution”).
  - Transparency and accountability: focus on dissemination and engagement beyond compilers.
  - Newly emerging items: Big Data.
- OECD (2017) Action Items to Bridge the Data Divide highlighted:
  - Boost statistical capacity and data literacy through new CD approaches.
  - Improve data on development financing to understand state of SDG support.
  - Improve quantity and quality of financing for data by countries and partners.
  - Make statistical laws, regulations, and standards fit for evolving data needs.
  - Invest in and use country-led strategies and data ecosystems to monitor SDGs.
  - Increase efficiency and impact of investment in data and CD through coordinated country-led approaches.

### IMF capacity development in statistics (STA)
- STA CD scope and delivery:
  - FY2018 regional capacity development allocation shares (in field time delivery): Sub-Saharan Africa 43%, Western Hemisphere 18%, Middle East & Central Asia 13%, Europe 7%, Asia & The Pacific 19%.
  - Statistical areas emphasized: Real Sector, Government Finance, External Sector, Monetary and Financial, Data Dissemination & Multisector.
  - 84% increase in CD since FY2011.
- Funding and provider landscape (Paris21 PRESS 2016/2017 data):
  - Global ODA commitments: Statistics $541m (2015).
  - Global Commitments to Statistics (as percentage of ODA) time series presented for 2006–2015 with values: 0.14%, 0.23%, 0.20%, 0.24%, 0.26%, 0.35%, 0.30%, 0.39%, 0.29%, 0.30%.
  - Top Five Statistics CD Providers (2014 shares): The World Bank 37%, Others 28%, European Commission / EUROSTAT 18%, Food and Agriculture Organizations of the United Nations (FAO) 7%, International Monetary Fund 5%, Canada 5%.
  - STA CD focuses on macroeconomic statistics where IMF is an international standard setter.
- Estimated funding needs and gaps (Global Partnership for Sustainable Development Data, 2016):
  - ODA resources: USD 0.7 billion per year.
  - Total minimum costs to produce data for SDG indicators for 144 developing countries: USD 2.8-3 billion per year.
  - ODA funding gap for producing SDG indicators in developing countries: about USD 0.7 billion per year up to 2030, implying a need for annual additional aid for statistics of USD 0.2 billion over 2015.
- Perception of IMF statistics technical assistance (IMF IEO survey results):
  - Positive responses (Strongly agree/Agree) shown as numeric values: 95, 94, 93, 88, 93, 86, 94.

### Recalibration needs and strategic priorities for IMF CD in statistics
- Identified needs for recalibration:
  - Demand and impact: recognize newly emerging issues; be agile in responding to country and international community demands (e.g., fiscal and debt reporting); safeguard country-led approach; leverage IMF’s position as a trusted advisor; use technology in CD; cooperate internally and with other CD providers; emphasize multi-partner CD vehicles (trust funds); participate in global outreach.
  - Technology and synergies: balance advocating standards and promoting “methodology fit for purpose”; be open to new products, techniques, and data sources including Big Data; sustain impact through TA plus training and peer learning; measure results chain and outcomes via a Results-Based Monitoring (RBM) Framework; enhance dissemination of data and TA reports.
- Strategic priorities (IMF Board Paper SM/18/29, February 12, 2018):
  - Priority 1: Agility in identifying data needs.
  - Priority 2: Build a Global Data Commons.
  - Priority 3: Support the use of Big Data.
  - Priority 4: Ensure secure and seamless access and sharing of high-quality data.
  - Priority 5: Aim at a greater cross-country data comparability.
  - Priority 6: Address data weaknesses.
  - STA will work with other IMF departments to introduce a more transparent assessment of data adequacy for surveillance purposes.

### STA Results-Based Monitoring (RBM) system and objectives
- RBM purpose:
  - Provide greater clarity of purpose of CD activities; better tracking of progress toward results; understanding of past successes/failures; transparency and accountability; promotion of global best practices.
- RBM scope:
  - RBM includes all IMF-and partner-financed STA projects.
  - STA RBM comprises 2 work streams and 26 outcomes.
- Objective 1 (Work Stream 1): Strengthen compilation and dissemination of macroeconomic and financial statistics for decision making according to internationally accepted standards. Outcomes listed include institutional/legal environment, resources adequacy, staff capacity increases, compilation using latest manuals/guides across concepts, coverage, classification, sectorization, basis of recording, statistical techniques, source data adequacy and assessment, higher frequency data compilation, improved timeliness, internal consistency, intersectoral consistency, longer time series, revision studies/policies, new datasets, improved public access and metadata, documentation, and regional harmonization.
- Objective 2 (Work Stream 2): Enhance transparency via participation in the e-GDDS, subscription to the SDDS, adherence to the SDDS Plus, with outcomes 23–26 detailing steps for participation, NSDP/ARC strengthening, and SDDS/SDDS Plus subscription/adherence milestones.

### Data for Decisions (D4D) Fund — overview and design
- Objective (as stated by IMF STA): 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 CD.
- Conceptual foundation: Needs assessment considering (i) demand from countries, (ii) policy relevance, (iii) identified data gaps and data quality concerns, (iv) complementarity of CD provided out of the IMF’s Regional Technical Assistance Centers (RTACs) and bilateral CD projects, and (v) the need for flexibility to react to a country’s capacity and readiness to accept CD.
- 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).

### D4D Fund scope, modules, and links to SDGs
- Core statistical domains under D4D:
  - National Accounts and Prices.
  - Government Finance Statistics.
  - Balance of Payments (External Sector).
  - Financial Access Survey (FAS).
- SDG linkages highlighted by STA:
  - Impacts SDG Goals: 1, 2, 7, 8, 9, 10, 11, 12, and 14 (linked to National Accounts and Prices).
  - Impacts SDG Goals: 1, 7, 10, 11, 14, 16, and 17 (linked to Government Finance Statistics).
  - Balance of Payments linked directly to indicators under SDG Goals 7 and 17.
  - Financial Access Survey linked directly to an indicator under SDG Goal 8.
  - Noted: “More than one-third of SDG Indicators include economic variables” and “SDG Indicators 232”.
- STA CD under D4D focus:
  - Targeting Low-and lower-middle income countries (LLMICs) and fragile states.
  - Coverage examples enumerated across country groupings (slide shows counts by region: WH, DAF, REF, EUR, MCD, APD, AFR but source content lists numbers: 5, 17, 2, 10, 13, 1, 21, 1, 7, 5 — presented in the slides as regional tallies).

### Implementation modalities and complementary initiatives
- IMF medium-term steady state CD architecture and complementarities:
  - Field-based Regional Technical Assistance Centers (RTACs): provide CD on “bread-and-butter” statistics to establish methodological foundations (RSS close to full coverage; GFS increasing coverage; ESS limited coverage); aim to fill gaps.
  - Financial Sector Stability Fund (LLMICs): focus on Financial Soundness Indicators, Balance Sheet Approach across Monetary and Financial Statistics, International Investment Position, and GFS balance sheets.
  - Bilateral & IMF’s own resources complement D4D Fund activities.
  - D4D Fund (LLMICs) supports RSS, GFS, ESS, FAS, online learning, and Statistical Information Management; focuses on policy-relevant high-frequency data and source data, topical gaps, and expanded regional coverage on ESS basics.
- Implementation and results emphasis under D4D:
  - Demand and impact: modular structure, wide LLMIC coverage, country-led approach, leveraging IMF as trusted advisor.
  - Implementation and results: focus on data for policy-making; take up new products, techniques and data sources including Big Data; combine TA and training/workshops; close results-monitoring including by partners; enhanced data and TA report dissemination.
  - Technology and synergies: online learning, internal and external coordination, and efficient CD administration.

### Online learning, statistical information management, and FAS
- Online learning goals:
  - Develop a new structured online learning curriculum to exponentially enhance reach of statistics training and offer truly global delivery.
  - Develop eight fundamental statistics courses (English and up to five other languages).
- Statistical Information Management (M4):
  - Provide advice to statistical offices, central banks, and ministries on streamlining, standardizing, and automating data management practices and infrastructure to enhance compilation and dissemination of macroeconomic and financial statistics.
- Financial Access Survey (FAS) priorities (M3):
  - Provide policymakers and analysts with high-quality statistics in FAS covering 189 economies (http://data.imf.org/fas).
  - Sustain 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.

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_Source: https://www.imf.org/-/media/files/capacity-developement/d4d/presentation-the-d4d-fund-in-the-global-statistical-capacity-development-architecture.pdf_