2025-05-13-世界银行-欧盟_利用次国家数据实施凝聚力政策（英）_180页_11mb

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

Core Content

This document explores the use of subnational data in the operationalization of the European Union's (EU) cohesion policy. It provides an in-depth analysis of the data landscape across 27 EU member states, focusing on how poverty and social exclusion are measured and utilized at the subnational level. The report highlights best practices and lessons learned from various countries, offering policy recommendations to improve the production and usage of such data.

Main Objectives and Structure

• Objective: To enhance the use of subnational data in EU cohesion policy by understanding the current data landscape, identifying good practices, and proposing actionable recommendations.
• Structure: The report is divided into two main parts:
  • Part 1: Diagnostic – Analyzes the data landscape, including definitions, data sources, and country-specific features.
  • Part 2: Recommendations – Offers policy suggestions for improving data readiness and usage at the subnational level.

Key Data Concepts

Poverty

• Poverty is traditionally defined as the inability to meet basic economic needs, measured either in absolute (fixed income threshold) or relative (fraction of median income) terms.
• Multidimensional poverty includes deprivations in health, education, and living conditions, providing a more comprehensive view of poverty.

Social Exclusion

• Social exclusion refers to the inability of individuals to access basic institutions and services.
• It is a multidimensional, dynamic, and relational concept, impacting long-term opportunities and social integration.
• Often approximated through multidimensional poverty indicators due to the complexity of measurement.

Key Countries Analyzed

The report focuses on Czechia, Italy, Poland, Romania, and Spain, with a deep-dive into their subnational data production and usage. It also draws lessons from Germany, Croatia, Slovenia, the Netherlands, and the United States.

Data Sources and Indicators

• Main data sources include surveys (e.g., EU-SILC, HBS, LFS), administrative registers (e.g., tax, social security, health insurance), and statistical databases.
• Indicators cover:
  • Poverty: At-risk-of-poverty (AROP), at-risk-of-poverty or social exclusion (AROPE), income-to-poverty ratio (IPR), and Equivalent Economic Situation Indicator (ISEE).
  • Social Exclusion: Includes health, education, employment, housing, and energy-related indicators.
  • Well-being: Various dimensions such as income, employment, health, education, and housing are used to assess well-being.
• Geographical Disaggregation: Data is often broken down to NUTS 3 (regional level), municipal level, district level, and block group level.

Good Practices in Subnational Data Production and Usage

Production

• Croatia: Uses income-based poverty indicators at county and municipality levels.
• Spain: Calculates at-risk-of-poverty at the census level.
• Slovenia: Produces at-risk-of-poverty indicators at the regional level (NUTS 3).
• Germany: Uses a well-being framework with multiple dimensions.
• Italy: Develops the Bes (Equitable and Sustainable Well-being) indicators at the local level.
• Netherlands: Implements the Monitor of Wellbeing (MoW) and uses administrative data for well-being indicators.
• Canada: Utilizes the Canadian Index of Multiple Deprivation (CIMD).
• United States: Employs the Multidimensional Deprivation Index (MDI) and Small Area Estimation (SAE) techniques.

Usage

• Informed Policy Making: Countries like Poland and Italy use subnational data for targeted policy interventions.
• Data Platforms: Innovative platforms such as SMUP-Poland, SIDAMUN-Spain, and Data PAQ-Czechia facilitate public consultation and data dissemination.
• Data Integration: Techniques like microintegration and BLUP (Best Linear Unbiased Prediction) are used to enhance data robustness and accuracy.

Policy Recommendations

1. Enhancing Subnational Poverty and Social Exclusion Indicators:

   • High-level readiness: Strengthen data collection and dissemination.
   • Mid-level readiness: Improve data integration and use of administrative data.
   • Low-level readiness: Develop capacity for data production and analysis.

2. Optimizing Data Usage for Territorial Policies:

   • Promote the use of multidimensional indicators to capture a broader picture of poverty and social exclusion.
   • Encourage the development of territorial data platforms to support evidence-based policy making.
   • Foster data sharing and collaboration between national statistical offices and policymakers.

Conclusion

The report underscores the importance of subnational data in understanding and addressing poverty and social exclusion within the EU. It highlights the need for more accurate, timely, and multidimensional data to support effective cohesion policy. The lessons from various countries and the proposed recommendations aim to improve the operationalization of these policies at the local and regional levels.

Key Information

• EU Poverty Rate: In 2023, over 94 million people in the EU were at risk of poverty or social exclusion, representing 21% of the population.
• Rural and Small Towns: 58 million of the at-risk population live in small towns and rural areas.
• Data Dissemination: Platforms like STAGE, Bes of Territories, and MoW are used to present and analyze subnational data.
• Methodologies: Techniques such as small area estimation, data matching, and principal component analysis (PCA) are employed to enhance data quality and usability.

References and Annexes

The report includes references and annexes with detailed methodologies, data sources, and indicator lists from the analyzed countries. These provide a comprehensive overview of the data landscape and support the findings and recommendations presented.