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Erschienen in:
Personal Pensions and Homeownership in the EU: An Overview Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Old-Age Poverty and Residential Property in the EU: An Analysis with the EU-SILC 2014 Data

verfasst von : Eszter Megyeri

Erschienen in: Old-Age Provision and Homeownership – Fiscal Incentives and Other Public Policy Options

Verlag: Springer International Publishing

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Abstract

On the basis of available EU-SILC data, this paper examines the relationship between old-age poverty and residential property in the EU member states. The descriptive section of the paper provides a comparative analysis of poverty indicators across EU states and different groups of the elderly population as well as a comparison of the elderly populations’ homeownership rates. The following sections look at the poverty risk and homeownership rate of the age group 65+ in conjunction, clustering the EU states according to these characteristics. For this purpose, an aggregated poverty risk index is developed, which includes both monetary and nonmonetary poverty indicators. The cluster analysis accounts for five groups of EU states, two of which (Latvia, Romania, Bulgaria, Croatia, and Lithuania followed by Estonia, Portugal, Greece, Poland, Slovenia, Italy, Hungary, and Ireland) can be considered target markets for pension products such as equity release schemes.

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Vorheriges Kapitel Personal Pensions and Homeownership in the EU: An Overview
Nächstes Kapitel Holding on and Letting Go in Ireland: Examining the Policy and Fiscal Environment for Supplementing Retirement Income from Residential Property
Fußnoten
1
Persons who are at risk of poverty receive incomes below the threshold of 60% of the median equivalized income after transfers (more details below).
 
2
2014 is the last year for which complete data is available.
 
3
Micro datasets were not available.
 
4
“‘(T)he poor’ shall be taken to mean persons, families and groups of persons whose resources (material, cultural and social) are so limited as to exclude them from the minimum acceptable way of life in the member states in which they live” (European Council 1985, p. 24).
 
5
According to the Eurostat definition, the relative median at-risk-of-poverty gap “is calculated as the difference between the median equivalized disposable income of people below the at-risk-of-poverty threshold and the at-risk-of-poverty threshold, expressed as a percentage of the at-risk-of-poverty threshold (cut-off point: 60% of national median equivalized disposable income)” (Eurostat Glossary 2014c).
 
6
The share of the population that is at risk of poverty, suffering from severe material deprivation and living in a household with extremely weak labour market participation.
 
7
Six Eastern European countries (Hungary, Poland, Lithuania, Latvia, Bulgaria, and Romania) as well as the rest of the EU.
 
9
A hypothesis is that more women than men over 65 live in single households.
 
10
A cluster analysis with the classification characteristics homeownership rate and at-risk-of-poverty rate yields an optimal cluster number of three. The clusters are marked by colour, and the country groups are circled.
 
11
While the at-risk-of-poverty threshold of single persons in Luxemburg was, for example 20,592 Euro, in Hungary the threshold was 2707 Euro (see Eurostat – Code: ilc_li01).
 
12
When calculating the mean, it has to be taken into account that the indicators “average equivalized total net income” and “relative median income ratio” have a negative sign. For the other indicators, a higher value corresponds to a higher risk of poverty. For these two indicators, it is the other way round; thus the negative sign is needed.
 
13
The SPSS software programme (version 23) was used.
 
14
This measure is widely used; see Brosius (2013, p. 720).
 
15
This method is recommended due to higher transparency; see Brosius (2013, p. 720).
 
16
For more information on hierarchical cluster analysis and the methods used, see Bühl (2014, pp. 633–656), and Brosius (2013, pp. 711–744).
 
17
For more information on the optimal cluster number, see Bühl (2014, p. 640). The method of agglomeration was “linkage between the groups” (see above). The also widely used Ward method yields the same optimal cluster number.
 
18
The other classification characteristic (homeownership rate) has not been changed but only standardized.
 
Literatur
Antczak, R., & Zaidi, A. (2016). Risk of poverty among older people in EU countries. DICE Report, 14(1), 37.
Brosius, F. (2013). SPSS 21. Frechen: MITP-Verlags GmbH & Co. KG.
Bühl, A. (2014). SPSS 22. Einführung in die moderne Datenanalyse (14th ed.). München.
European Commission. (2015). The 2015 Pension Adequacy Report. Current and future income adequacy in old age in the EU, Vol. I, Luxembourg.
European Council. (1985). Council Decision of 19 December 1984 on Specific Community Action to Combat Poverty, 85/8/EEC, Official Journal of the European Union, No. L 2/24, Vol. 28, 1.1985, pp. 24–25.
Hauser, R. (2008). Altersarmut in der Europäischen Union. In WSI Mitteilungen, Vol. 3, pp. 125–132.
Hennecke, P., Murro, P., Neuberger, D., & Palmisano, F. (2016). Pensions and housing wealth. Quantitative data on market conditions for equity release schemes in the EU, Thünen-Series of Applied Economic Theory, No. 146.
Metadaten
Titel
Old-Age Poverty and Residential Property in the EU: An Analysis with the EU-SILC 2014 Data
verfasst von
Eszter Megyeri
Copyright-Jahr
2018
Buch
Old-Age Provision and Homeownership – Fiscal Incentives and Other Public Policy Options

Print ISBN: 978-3-319-75210-5

Electronic ISBN: 978-3-319-75211-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75211-2_2