“UN SQAF ensures quality and consistency in global statistical systems and is a benchmark for international statistics producers.

UN NQAF provides practical guidance for countries to enhance their statistical systems and align with global standards.”

International Quality Assurance Frameworks

In the early 2000s, international organisations began developing quality assurance frameworks (QAFs) to ensure reliable, consistent, and transparent statistics for evidence-based policymaking and global initiatives. The International Monetary Fund’s Data Quality Assessment Framework (DQAF) (2003) provides a structured approach to evaluating data quality, emphasising integrity, accuracy, and accessibility.

European Statistical System Quality Assurance Framework (ESS QAF) (2009, updated 2017): This framework guides quality assurance across EU statistical systems, incorporating the principles of accuracy, independence, and continuous improvement.

OECD Quality Framework and Guidelines (QFG) (2011): Promotes transparency, professional independence, and user engagement in statistical activities.

UN Statistical Quality Assurance Framework (UN-SQAF) (2017): This framework enhances quality within the UN Statistical System, focusing on data coordination for global goals like the 2030 Agenda.

UN National Quality Framework (UN-NQAF) (2019): An NQAF provides a coherent and holistic system for statistical quality management that assures trust in and the quality of official statistics.

These frameworks share a common goal: to build public trust by implementing rigorous standards to produce accurate, timely, and relevant statistics for policymakers, researchers, and the public.

“During a workshop - - - in Vienna in July 2007, the participants agreed that the business process model used by Statistics New Zealand would provide a good basis for developing a Generic Statistical Business Process Model. ”
— GSBPM, Version 5.0, 2013

GSBPM and Quality Assurance Frameworks

The Generic Statistical Business Process Model (GSBPM), developed by UNECE in 2009 (latest version: v5.1, 2019), standardises statistical production phases and sub-processes, ensuring consistency and quality in official statistics. It is a model for documenting, monitoring, improving, and harmonising statistical processes. The GSBPM has eight main phases and 44 sub-processes, covering the statistical production process.

GSBPM is adopted by national and international statistical organisations for quality and efficiency.

GSBPM in QAF Integration

The UN-NQAF emphasises aligning statistical production processes with key quality dimensions such as sound methodology, coherence, and transparency. The GSBPM is a backbone for identifying quality checkpoints and ensuring adherence to international quality standards throughout the statistical production lifecycle with quality dimensions like soundness and transparency.

IMF DQAF Maps GSBPM phases to evaluate methodological soundness and diagnose data quality. The "Analyze" phase supports the DQAF’s emphasis on ensuring statistical accuracy and relevance through robust analysis, while the "Archive" phase addresses data integrity and sustainability.

OECD QFG: GSBPM supports transparency and workflow improvements across data lifecycles. It also helps identify bottlenecks and inefficiencies, improve workflow, and align production processes with user needs.

ESS QAF: Facilitates Eurostat and NSI peer reviews for compliance with the Code of Practice. Used as a benchmarking tool to evaluate end-to-end statistical production processes.

FAO-SQAF: Adapts GSBPM for agricultural and food security data processes, ensuring methodological rigour and alignment with international practices.

Learn more about GSBPM/GAMSO/GSIM.

UN Statistical Quality Assurance Framework

The UN-SQAF is designed to enhance the quality and consistency of statistics produced by United Nations agencies. The framework supports developing and implementing quality assurance practices across the United Nations Statistical System (UNSS), aiming to build trust and ensure that statistical outputs meet users' needs, particularly for monitoring international development agendas such as the 2030 Agenda for Sustainable Development.

The framework outlines:

  1. Quality Dimensions:

    • Covers output quality (relevance, accuracy, timeliness), process quality (sound methodology, cost-effectiveness), and institutional quality (professional independence, transparency).

  2. Guidelines and Governance:

    • Provides guidelines for statistical processes, infrastructure, and quality governance.

    • Emphasises coordination and data sharing within the UNSS.

  3. Scope:

    • Applicable to all statistical activities undertaken by UN agencies, including data collection, processing, analysis, dissemination, and infrastructure development.

  4. Actions for Quality Assurance:

    • Promotes the adoption of Generic SQAFs by individual UN agencies.

    • Encourages quality self-assessment and external assessments.

About UN-SQAF

  • Prepared By: UNCTAD (United Nations Conference on Trade and Development) and a Task Team

  • Division: Committee of the Chief Statisticians of the United Nations System (CCS-UNS)

  • Date: 28 February 2017

  • Document Reference: UNSYSTEM/2017/3

  • Location: Presented at the United Nations Headquarters, New York

  • Scope:

    • Applies to all UN agencies involved in statistical work.

    • Covers the development, implementation, and monitoring of statistical quality assurance frameworks within the UN Statistical System.

  • Purpose:

    • To establish a common understanding of quality assurance.

    • To support the development of SQAFs tailored to the unique needs of each UN agency.

    • To ensure the quality and reliability of statistical outputs for global decision-making.

  • Reference: UN-SQAF

This framework underscores the importance of quality in official statistics and provides tools and guidelines to help UN agencies maintain high standards in their statistical processes.

UN National Quality Assurance Framework

Focus on quality assurance throughout the entire statistical production process. Promote institutional and organisational frameworks that support high-quality outputs.

The framework defines key dimensions of quality, including:

  • Relevance: Meeting the needs of users.

  • Accuracy and Reliability: Producing error-free, trustworthy data.

  • Timeliness and Punctuality: Delivering statistics within a reasonable time.

  • Coherence and Comparability: Ensuring statistics are internally consistent and comparable across time and domains.

  • Accessibility and Clarity: Making data and metadata easily available and understandable.

Implementation Guidelines:

  • Encourages countries to adopt a system-wide approach to quality assurance.

  • Provides tools for self-assessment and external reviews.

  • Suggests the development of quality assurance plans tailored to national contexts.

Integration with International Standards:

  • Aligns with frameworks like the GSBPM and the European Statistics Code of Practice.

Adaptability:

  • Flexible enough to be customised to a country's specific institutional, legal, and cultural context.

Purpose of UN NQAF:

  • Ensure trust in official statistics by making quality assurance a central part of statistical activities.

  • Help countries meet global demands, such as those arising from the Sustainable Development Goals (SDGs).

Reference: UN-NQAF

The UN NQAF serves as both a guideline and a tool for countries seeking to enhance the credibility and reliability of their statistical systems.

IMF Data Quality Assessment Framework

The DQAF, created by the IMF, is designed to assess the quality of statistics produced by national and international agencies. The framework covers the entire statistical process, from data collection to dissemination, and is used to diagnose strengths and weaknesses in statistical systems.

Key Elements:

  • Covers six dimensions of quality: prerequisites of quality, integrity, methodological soundness, accuracy and reliability, serviceability, and accessibility.

  • Used for assessments and as a diagnostic tool for improving statistical practices.

  • Scope: National and international statistical systems

  • Reference: IMF DQAF​

Quality Framework and Guidelines for OECD Statistical Activities

The OECD QFG outlines procedures for ensuring the quality of statistics produced and disseminated by the OECD. To maintain high-quality statistics, it emphasises transparency, professional independence, and user engagement. The framework applies to all OECD statistical activities and provides a benchmark for continuous quality improvement.

Key Elements:

  • Focus on quality dimensions: relevance, accuracy, credibility, timeliness, accessibility, interpretability, and coherence.

  • Guidelines for managing quality throughout the data lifecycle.

  • Scope: OECD statistical activities

  • Reference: OECD Quality Framework​

ECB Statistics Quality Framework

The ECB SQF provides a structured approach to ensuring the quality of statistics produced by the ECB. It covers professional independence, confidentiality, and accountability principles, ensuring that the statistics support the ECB’s monetary policy and other functions.

Key Elements:

  • Based on the principles of the European Statistics Code of Practice.

  • Framework for continuous quality improvement, monitoring, and reporting.

  • Scope: ECB statistical activities

  • Reference: ECB SQF​

FAO Statistics Quality Assurance Framework

The FAO-SQAF was developed by the Food and Agriculture Organization (FAO) to ensure the quality of agricultural and food-related statistics. It focuses on delivering accurate, reliable, and timely data for global food security and agricultural development.

Key Elements:

  • Addresses quality dimensions: relevance, accuracy, coherence, and accessibility.

  • Emphasises quality management throughout the statistical process.

  • Scope: FAO statistical activities

  • Reference: FAO SQAF​

ESS Quality Assurance Framework

The ESS QAF is a comprehensive framework developed by Eurostat to support the implementation of the European Statistics Code of Practice (CoP). It is a practical guide to ensure the quality of European statistics produced by National Statistical Institutes (NSIs) and Eurostat. The framework provides a structured approach to quality assurance, detailing methods, tools, and activities to maintain and improve statistical quality across the European Statistical System (ESS).

The framework ensures that statistics are produced with high professional independence, impartiality, accuracy, and reliability standards.

Key Elements

  1. Quality Assurance Activities:

    • Specifies activities, methods, and tools to support adherence to the principles of the Code of Practice.

  2. Sixteen Principles:

    • Covers the institutional environment, statistical processes, and statistical outputs.

  3. Peer Reviews:

    • Mechanism for assessing compliance with the Code of Practice through regular peer reviews and follow-up actions.

  4. Continuous Improvement:

    • Encourages regular quality assessments, feedback mechanisms, and improvements in statistical practices.

About ESS QAF

  • Date: 2017 (aligned with the revised 2017 edition of the Code of Practice)

  • Scope:

    • Applies to the European Statistical System (ESS), including Eurostat and the National Statistical Institutes of EU Member States and EFTA countries.

  • Legal Basis:

    • Regulation (EC) No 223/2009 on European statistics, amended by Regulation (EU) 2015/759.

  • Reference:

  • Related Framework:

  • Purpose

  • To ensure high-quality, reliable, and comparable statistics across Europe.

  • To provide a common framework for quality assurance within the ESS.

  • To support evidence-based policymaking and transparency in statistical production.

This QAF, together with the Code of Practice, represents a commitment by European statistical authorities to maintain high standards and continuously improve statistical quality.

This page was updated in January 2025.