世界银行-欧盟_利用次国家数据实施凝聚力政策（英）-2025_180页_13mb

Summary of "Using Subnational Data for the Operationalization of the Cohesion Policy"

Core Content

This report explores the use of subnational data to better operationalize the European Union's (EU) cohesion policy, with a focus on improving the understanding and measurement of poverty and social exclusion at the local level. It highlights the importance of detailed, geographically disaggregated data in informing targeted policy interventions and fostering equitable development across EU regions.

The document outlines a comprehensive approach to analyzing poverty and social exclusion through various dimensions such as income, education, health, employment, and housing. It emphasizes the shift from one-dimensional economic indicators to multidimensional measures that capture a broader spectrum of well-being and deprivation. These measures include the Multidimensional Poverty Index (MPI), the Multidimensional Deprivation Index (MDI), and the Human Development Index (HDI), which integrate non-monetary aspects of life.

Subnational data production and usage are evaluated through case studies of five EU member states: Czechia, Italy, Poland, Romania, and Spain. The report also draws lessons from other countries such as Germany, the Netherlands, Canada, and the United States to provide a broader perspective on best practices in subnational data collection and application.

Main Points

1. Concepts of Poverty and Social Exclusion

• Poverty is traditionally defined as insufficient economic resources, measured in absolute or relative terms.
  • Absolute poverty is based on a fixed income threshold for basic needs.
  • Relative poverty compares income to the median or mean income in a society.
• Social exclusion refers to the inability of individuals to access essential institutions and services, and is often approximated using multidimensional poverty indicators.

2. Subnational Data Landscape in the EU

• Subnational data is essential for effective policy design and implementation.
• Data sources include surveys, administrative registers, and statistical databases.
• Geographical levels of analysis vary from NUTS 3 regions to local administrative units (LAUs) and municipalities.
• Challenges include data quality, availability, and the need for integration across different data systems.

3. Case Studies of Five EU Member States

• Czechia: Social Exclusion Index (SEI) is used at the municipal level.
• Italy: The Best SYSTEM and Statistical Atlas of Municipalities are examples of innovative data platforms.
• Poland: The STRATEG System provides a policy framework for using subnational data.
• Romania: Uses a mix of administrative and survey data for poverty and social exclusion indicators.
• Spain: The SIDAMUN and ADRH (Household Income Distribution Atlas) offer detailed territorial insights.

4. Good Practices in Subnational Data Production and Usage

• Croatia: Income-based poverty indicators at the county and municipality level.
• Spain: At-risk-of-poverty indicators at the census level.
• Slovenia: At-risk-of-poverty indicators at the NUTS 3 level.
• Germany: Uses a well-being index that integrates multiple dimensions.
• Netherlands: Monitor of Wellbeing (MoW) and Regional Monitor of Wellbeing (RMoW) provide comprehensive well-being data.
• Canada: The Canadian Index of Multiple Deprivation (CIMD) is used to assess deprivation at the municipal level.
• United States: SAIPE and SAHIE provide detailed poverty and health indicators at the county level.

5. Data Dissemination and Policy Use

• Innovative territorial data platforms enable public consultation and engagement.
• Examples include:
  • SMUP-Poland
  • SIDAMUN - Spain
  • Best SYSTEM - Italy
  • Statistical Atlas of Municipalities - Italy
  • Data PAQ-Czechia
• Subnational data supports informed policy decisions, particularly in the context of cohesion policy and the Europe 2030 Strategy.

6. Recommendations

• Policy Recommendations for Enhancing Subnational Indicators:
  • High-level readiness: Strengthen data infrastructure and ensure continuous data updates.
  • Mid-level readiness: Improve data integration and accessibility.
  • Low-level readiness: Build capacity for data collection and analysis at the subnational level.
• Policy Recommendations for Optimizing Data Usage:
  • Promote the use of multidimensional indicators.
  • Enhance data sharing and interoperability between national and local authorities.
  • Support the development of user-friendly data platforms for public and policy engagement.

Key Information

• Territorial disparities are significant across EU member states, with over 94 million people in the EU at risk of poverty or social exclusion in 2023.
• Subnational data is critical for the effective implementation of cohesion policy.
• Multidimensional poverty and social exclusion are increasingly recognized as more comprehensive indicators of deprivation.
• The report highlights the importance of administrative data, survey data, and small area estimation (SAE) techniques in generating accurate and detailed subnational indicators.
• Data platforms such as STAGE Portal, Well-being in Germany, and MoW are showcased as successful models for data dissemination and usage.

Conclusion

The report underscores the necessity of leveraging subnational data to enhance the precision and impact of EU cohesion policy. It calls for a unified approach to data collection, analysis, and dissemination, emphasizing the role of multidimensional indicators in capturing the full scope of poverty and social exclusion. By adopting best practices from across the EU and beyond, member states can better address regional inequalities and support inclusive development.