2016年-世界发展银行全球_Pinpointing_Poverty_in_the_Slovak_Republic_2页_1mb

Summary of Poverty in Europe: Pinpointing Poverty in the Slovak Republic

Core Content

The document provides an analysis of poverty and social exclusion in the Slovak Republic within the context of the broader European Union (EU). It highlights the importance of using high-resolution poverty maps to better understand and address regional disparities in poverty rates.

Main Points

1. Variability in Poverty Rates Across the EU and Within Member States

• Poverty and social exclusion rates vary significantly across EU member states.
• The EU has allocated €1 trillion in its 2014-20 multiannual financial framework to reduce the number of people at risk of poverty or social exclusion by 20 million by 2020.
• The Slovak Republic has set a national target to reduce the number of poor and socially excluded individuals by 170,000.

2. High-Resolution Poverty Maps

• The EU and the World Bank, in collaboration with member states, developed high-resolution poverty maps.
• These maps offer more detailed geographical insights into poverty distribution compared to previous sub-national data.
• They enable more effective targeting of resources for poverty reduction programs.

3. Poverty Distribution in the Slovak Republic

• Previous surveys indicated that the eastern oblasts (regions) had the highest poverty rates.
• The district-level poverty map reveals greater spatial heterogeneity in poverty incidence.
• High poverty rates are concentrated in specific districts along the borders with the center and Ukraine (e.g., Rožnava, Poprád, Sobrance, Snina).
• In contrast, Košice in the east has relatively low poverty incidence.
• Central districts like Revúca, Rimavská Sobota, and Poltár also show high poverty rates, despite moderate overall rates in the center.

4. Statistical Differences Between Districts and Oblasts

• In 23 out of 27 districts, the poverty headcount is statistically different from that of the oblast it belongs to.
• This indicates that poverty is not uniformly distributed within larger administrative regions.

5. Limitations of Poverty Maps

• Poverty maps alone cannot provide all the necessary information for policy decisions.
• They should be combined with local expertise and additional data to understand the root causes of poverty.
• Reasons for poverty can vary significantly between regions and may include inadequate infrastructure, lack of economic activity, and an insufficiently skilled workforce.

6. Poverty Headcount vs. Poverty Rates

• Poverty headcount (number of people living in poverty) is generally correlated with the absolute size of the poor population.
• However, this is not always the case. For example, districts like Žilina, Nitra, Trnava, and Trenčín have low poverty rates but high absolute numbers of poor people.
• Conversely, districts like Poltár, Sobrance, Stropkov, and Krupina have higher poverty headcounts but represent a smaller share of the total population in poverty.

7. Importance of Targeting Poor Areas and People

• Policymakers need to consider both areas with high poverty rates and those with large numbers of poor people.
• This distinction is crucial for efficient resource allocation and poverty reduction strategies.

Key Information

• Poverty Threshold: Defined as 60% of the median national equalized income after social transfers.
• Geographical Classification: Uses NUTS (Nomenclature des Unités Territoriales Statistiques) and LAU (Local Administrative Units) classifications.
• Data Sources: The maps are based on data from the 2011 EU-SILC survey and the 2011 Population and Housing Census.
• License: The document is subject to a CC BY 3.0 IGO license, allowing reuse with proper attribution.

Conclusion

The poverty maps offer a more detailed and nuanced understanding of poverty distribution in the Slovak Republic. They highlight the need for targeted interventions that consider both the geographic concentration of poverty and the number of people affected. While these maps are a valuable tool, they should be used in conjunction with other data and local insights to develop comprehensive and effective poverty reduction strategies.