Measuring Small-Scale At-Risk-of-Poverty in Germany – a Methodical Overview

Kaup, Stefan (2015) Measuring Small-Scale At-Risk-of-Poverty in Germany – a Methodical Overview. REAL CORP 2015. PLAN TOGETHER – RIGHT NOW – OVERALL. From Vision to Reality for Vibrant Cities and Regions. Proceedings of 20th International Conference on Urban Planning, Regional Development and Information Society. pp. 819-825.

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Regarding the EU 2020 initiative of the European Commision, one of the main targets for the next years is to reduce the number of people in or at risk of poverty and social exclusion in Europe by 20 million. (European Commission 2014a). This target aims different aspects of the financial setting and social participation of individuals and groups and has to be operationalizes in mesurable indicators, that could cover the major domains and dimensions of the complex theme (Copus 2014). The latest publication of the German Federal Statistical Office speaks of 20.3 % of the German population affected by poverty or social exclusion. This term is a multi-variate definition based on indicators related to people at risk of poverty (16.1 %), people affected by massive material depriviation (5.4 %) and people living in households with very low income (9.9 %) (Destatis 2014a). It focuses mainly on the financial aspects. The first of the three indicators is measured by the so called At-Risk-of-Poverty rate, which is defined by Eurostat as the “share of people with an equivalised disposable income (after social transfer) below the At-Risk-of-Poverty threshold, which is set at 60 % of the national median equivalised disposable income after social transfers” (Eurostat 2014a). The underlying datasets for the German indicators come from the EU-SILC, an EU wide annual survey of income and living conditions (Eurostat 2014c). This survey provides the possibility to calculate statistics down to the NUTS 2 regions. A regional level that in Germany is called Government regions. For decision makers on the regional or local level, this computation is not good enough in terms of spatial resolution. So how to come to more useful numbers? The author here discusses possibilities to create At-Risk-of-Poverty rates in Germany on a higher spatial resolution. An own elaboration based on a linear regression model is cross compared with an approach based on the German Microcensus.

Item Type: Article
Uncontrolled Keywords: linear regression, Germany, at-risk-of-poverty, EU 2020, AROP
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HM Sociology
H Social Sciences > HN Social history and conditions. Social problems. Social reform
Depositing User: REAL CORP Administrator
Date Deposited: 23 Mar 2016 13:50
Last Modified: 23 Mar 2016 13:50

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