## Facilitating Data Dissemination: IMF Data Standards Initiatives and SDMX

## Source details

**Canonical URL:** [Facilitating Data Dissemination: IMF Data Standards Initiatives and SDMX](https://www.imf.org/-/media/files/capacity-developement/d4d/june-2024/03-d4d-dsi.pdf)

## Other formats

- [Markdown version](/-/media/files/capacity-developement/d4d/june-2024/03-d4d-dsi.pdf.md)
- [Structured JSON version](/-/media/files/capacity-developement/d4d/june-2024/03-d4d-dsi.pdf.json)

---

### Introduction to IMF Data Standards
- IMF Data Standards Initiatives are described as a multilateral effort promoting data transparency as a global public good.
- Purposes attributed to the Initiatives:
  - Promotes data transparency, beneficial for statistical development and market access.
  - Provides a framework for interagency coordination.
  - Involves collaboration with AfDB and World Bank.
  - Is closely integrated with SDMX technological advances.
- Presentation date and event attribution: JUNE19, 2024, D4D FUND STEERING COMMITTEE MEETING (IMF Statistics Department).

### Three-Tiered Framework for Data Dissemination
- The IMF three-tiered framework:
  - Tier 1 — e-GDDS (Enhanced General Data Dissemination System): countries in earlier stages of statistical development.
  - Tier 2 — SDDS (Special Data Dissemination Standard): countries seeking access to international capital markets.
  - Tier 3 — SDDS Plus (Special Data Dissemination Standard Plus): countries with systemically important financial sectors.
- Additional detail:
  - Tiers 2 and 3 carry additional data categories and more stringent requirements.
  - Additional data categories (encouraged) from 2022 include climate, debt and other indicators.

### Participation and Reach
- Growth of participation in IMF Data Standards Initiatives:
  - From 42 participants in 1996 to 190 participants* in 2024 (185 IMF Member Countries).
  - *Includes territories.

### Benefits and Evidence of Impact
- Stated benefits of implementing IMF Data Standards Initiatives:
  - Strengthens data transparency.
  - Facilitates economic monitoring.
  - Improves sovereign financing conditions.
  - Enhances interagency coordination.
  - Supports convergence of regional and global practices.
- Empirical evidence referenced:
  - Effect on sovereign spreads of implementing the IMF Data Standards Initiatives (Source: IMF Working Paper, Improving Sovereign Financing Conditions through Data Transparency, 2022).
  - Note in referenced work: *** (**, *) = significant at 1 (5, 10) percent level.

### Fragile and Conflict-Affected States (FCS) and Small States
- Participation trajectory:
  - FCS and Small States journey to e-GDDS: participation increased from 2 countries in 2016 to 36 in 2023.
- Benefits for FCS and Small States:
  - Efficiency gains from standardized dissemination.
  - Powerful tool for interagency cooperation.
- Challenges for FCS and Small States:
  - Weak cooperation among data producing agencies.
- Source for challenges and benefits: IMF Working Paper, Building Statistical Capacity in Fragile and Conflict-Affected States, 2022.

### Roadmap Ahead: e-GDDS and SDDS (Operational Observations)
- e-GDDS countries:
  - Deterioration of timeliness/coverage during pandemic and requires further engagement.
  - Extensive engagement is helping launch NSDP — Example of Niger.
- SDDS countries:
  - Ongoing work motivated by better capital market terms and political commitment to transparency.
  - Provision of data gaps analysis and support in implementing workplan to join SDDS and SDDS Plus.
- Source attribution for roadmap observations: IMF Staff Calculations.

### SDMX: Modernization and Operational Benefits
- Objectives and intended improvements from enhanced SDMX usage:
  - Improve timeliness and accessibility.
  - Enable access to global ecosystems.
  - Support modernization and ensure trust.
  - Reduce costs and reporting burden.
  - Ensure data can be discovered, understood, and used, facilitated by global standards.
  - Promote automated processes — dissemination as an event.
  - Encourage open source, community sharing, and automation.
  - Foster ecosystems of support and linked open data.
- SDMX defined as an international initiative aiming at standardizing and modernizing ("industrializing") mechanisms and processes for dissemination of statistical data and metadata.

### Open Data (5-Star) and AI Use Case
- Five-star dissemination ambition and practices:
  - Mentioned formats: PDF and HTML; SDMX-enabled NSDP at the high end.
  - Data dissemination TA delivers and enables the jump to higher-star dissemination; dissemination practices with 1 to 3 stars persist.
  - Reference: https://5stardata.info/en/
- 5 Star Use Case — AI Supported Data Querying:
  - Vision: AI-supported data querying that:
    - Removes silos between datasets.
    - Creates opportunities to inform users.
    - Uses SDMX interface to retrieve data and metadata with certainty.
    - Operates faster than faceted searching.

### Implementation: From Tables to Data Cubes and Tooling
- Transition described:
  - From statistical table to data cube using SDMX tools and third-party software.
  - SDMX-enabled workflows intended to improve accessibility and enable downstream automated and AI-enabled usage.
  - Source example: African Development Bank Training Material.

### Country Engagements and Coordination Examples
- Ongoing dialogues between IMF Statistics Department (STA) and World Bank task teams in West and Central Africa include country-level engagements with timing and objectives (examples summarized from the region):
  - Ghana: HISWA (2020-2025) — Advance to SDDS.
  - Guinea: CA-SOP 1 (2022-2027) — Improve performance within e-GDDS and advance work towards subscription to SDDS.
  - Burkina Faso, Côte d'Ivoire, Sierra Leone: HISWA (2020-2025) — Improve performance within e-GDDS.
  - Central African Republic: CA-SOP 2 (2023-2029) — Outside the standards: establish an NSDP.
  - Guinea-Bissau, Liberia, Niger: CA-SOP 1 (2022-2027) or HISWA (2020-2025) — Outside the standards: establish an NSDP.
- Country engagement entries are presented as joint STA–World Bank coordination for priority projects and timing.

### Key Challenges and Collaborative Solutions
- Identified challenges:
  - Outdated metadata.
  - Weak interagency coordination.
  - Poor statistics and physical infrastructure.
  - Low absorptive capacity requiring training.
  - Human resource constraints.
  - Lack of census and surveys to compile data.
  - Need for data quality improvements (e.g., GDP rebasing, geographical coverage).
- Collaborative solutions with the World Bank and IMF staff:
  - Hire experts to help countries update metadata.
  - Establish mechanisms and processes to improve interagency coordination (jointly with IMF area department teams/STA economists).
  - Use World Bank project funds for modest furnishings and refurbishment.
  - Support provision of ICT infrastructure (e.g., tablets, computers, scanners, photocopiers) and reliable internet connectivity using World Bank project money.
  - Provide on-the-job training and workshops (STA and WB); improve staff qualifications in the education system (example: new undergraduate training program at the University of Liberia).
  - Address human resource policy weaknesses (for example, salary structure, promotion policy).
  - Fund censuses and surveys (household income and expenditure surveys, business surveys) fully aligned with statistical methodology to improve external sector statistics, national accounts and prices data.
  - Implement GDP rebasing and other international-standard practices to improve data quality and availability.

---


_Source: https://www.imf.org/-/media/files/capacity-developement/d4d/june-2024/03-d4d-dsi.pdf_