亚开行-可持续发展目标数据分类实用指南（英文）-2021.5-117页_1mb

Summary of Practical Guidebook on Data Disaggregation for the Sustainable Development Goals

Core Content

This guidebook provides a comprehensive overview of the concept and practice of data disaggregation in the context of the Sustainable Development Goals (SDGs), emphasizing its importance in ensuring inclusive and equitable development. It outlines the role of disaggregated data in identifying vulnerable groups, measuring progress, and supporting evidence-based policy-making under the "Leave No One Behind" (LNOB) principle of the 2030 Agenda for Sustainable Development.

Main Topics Covered

1. Data Disaggregation and the SDG Indicators

• Definition: Data disaggregation refers to the process of breaking down data into more detailed categories, such as income, sex, age, ethnicity, disability, and geographic location, to reveal disparities and support targeted interventions.
• Importance: Disaggregated data enables more precise understanding of development progress and helps identify the most vulnerable populations, ensuring that policies address inequalities effectively.
• Challenges: Disaggregation requires significant data resources and operational capacity, which can be a burden for national statistical systems (NSSs).
• Key Dimensions: The guidebook outlines the minimum set of dimensions for data disaggregation, including income, sex, age, ethnicity, migratory status, disability, and geographic location. It also discusses other relevant dimensions and potential future additions.

2. Integrating Policy Demands with Data

• Policy-Data Nexus: Disaggregated data is essential for inclusive development and LNOB. It supports the formulation and monitoring of policies aimed at vulnerable groups.
• Human Rights-Based Approach: The guidebook advocates for a human rights-based lens in data collection and analysis, ensuring that marginalized groups are not overlooked.
• Tools for Integration: It presents tools such as the Advanced Data Planning Tool (ADAPT) and StaTact to align data with policy priorities and enhance the use of data in decision-making.

3. Sources of Disaggregated Data

• Censuses: Provide comprehensive data but may lack detail on certain dimensions.
• Sample Surveys: Offer detailed insights but require careful design to ensure representativeness.
• Administrative Systems: Useful for real-time data but may lack granularity and coverage.
• Small Area Estimation (SAE): Allows for more detailed subnational data, though it requires technical expertise.
• Big Data and Geospatial Data: Offer new opportunities for data collection but raise concerns about data quality, standardization, and privacy.
• Data Integration: Combines multiple data sources to improve accuracy and coverage, though it involves complex processes and requires coordination between agencies.

4. Analysis of Disaggregated Data for LNOB

• Equity Assessments: The guidebook introduces tools like the Health Equity Assessment Toolkit (HEAT) to analyze disparities and support equity-focused policy-making.
• Multilevel Disaggregation: Highlights the need to analyze data at different levels (national, subnational, local) to capture nuanced patterns of inequality.
• Case Studies: Includes examples from Bangladesh and the Philippines to illustrate how disaggregated data can inform policy and monitoring.

5. Reporting, Communicating, and Using Disaggregated Data

• Reporting Practices: Emphasizes the importance of transparent and accessible reporting of SDG indicators.
• Data Visualization: Suggests the use of tools like Spider Chart Visualization to communicate multidimensional inequalities effectively.
• Multilevel Analysis: Encourages the use of Automated Development Economics Poverty Tables (ADePT) to improve the efficiency and accuracy of data analysis.

6. Barriers and Capacity Development

• Barriers: Includes limited data availability, lack of technical capacity, and institutional challenges in producing and using disaggregated data.
• Capacity Development: Highlights the need for training and resources to build the skills of national statistics offices (NSOs) and other stakeholders.
• Investment in Data: Encourages investment in data infrastructure and the development of tools and standards for better data collection and analysis.
• Systematic Approach: Recommends a structured framework for capacity development, including policy alignment, training, and collaboration with international partners.

Key Information

• Disaggregation Dimensions: The guidebook identifies key dimensions such as income, sex, age, ethnicity, disability, and geographic location as essential for SDG monitoring.
• Tools and Methods: Provides an overview of various tools and methodologies for data collection, analysis, and reporting, including ADAPT, ADePT, and HEAT.
• International Collaboration: The guidebook was developed in collaboration with the United Nations Statistics Division (UNSD), UN Women, WHO, and other international agencies, reflecting a global effort to support SDG monitoring.
• Future Updates: The guidebook is part of an ongoing series and will be updated as new tools and standards become available, particularly through the IAEG-SDGs and ADB platforms.

Main Viewpoints

• Disaggregated data is crucial for achieving the SDGs and ensuring that no one is left behind.
• The LNOB principle requires data to be broken down by various dimensions to understand and address inequalities.
• There is a need for better data collection, analysis, and dissemination to support inclusive policy-making.
• The integration of data and policy is essential for effective implementation of the SDGs.
• Capacity development is a key priority to enhance the ability of NSSs and other organizations to produce and use disaggregated data.

Conclusion

This guidebook serves as a practical resource for statisticians, policy-makers, and data analysts involved in SDG monitoring. It provides a framework for understanding and applying data disaggregation, along with tools and examples to improve the quality and relevance of data for inclusive development. The guidebook is intended to support the production, analysis, and communication of disaggregated data, contributing to more equitable and effective policy outcomes.