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Social Indicators Research

, Volume 130, Issue 3, pp 1025–1049 | Cite as

The Interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators

  • Sara Ayllón
  • András Gábos
Article

Abstract

The aim of this paper is to analyse dynamically the three indicators of poverty and social exclusion covered by the EU2020 poverty target, while focusing on state dependence and feedback effects. We are interested in learning the extent to which the fact of being at risk of poverty, severe material deprivation or low work intensity in a given year is related to having the same status one year on, and whether being at risk in one domain in one year is a predictor of being at risk in one of the other domains in subsequent years. Our results are based on data from the EU-SILC for eight countries and indicate that the three social indicators of the EU2020 strategy capture different aspects of economic hardship in the majority of European countries analysed. We show that the three phenomena are affected by a considerable degree of genuine state dependence, but there is weak evidence for one-year lagged feedback effects—apart from in Hungary and Poland, where feedback loops between the three segments are to be found. Mostly, interrelationships occur at the same point in time via current effects, initial conditions and correlated unobserved heterogeneity. In terms of policy implications, our results suggest that the three phenomena should be addressed by different interventions while it is expected that spill-over effects across time will be marginal.

Keywords

Europe 2020 indicators Poverty Material deprivation Low work intensity State dependence Feedback effects EU-SILC 

JEL Classification

I32 I31 

Notes

Acknowledgments

The research on which this paper is based is financially supported by the European Union’s Seventh Framework Programme (FP7/2012–2016) under Grant agreement No. 290613 (project title: ImPRovE). Sara Ayllón also acknowledges financial support from NEGOTIATE (Horizon 2020, Grant agreement No. 649395) and the Spanish projects ECO2013-46516-C4-1-R and 2014-SGR-1279. We would like to thank Tim Goedemé and Fabienne Montaigne for providing the do-file that helped us construct the low work intensity indicator. We are grateful to Karel Van den Bosch (Herman Deleeck Centre for Social Policy) and participants at the ImPRovE Meeting (Budapest, November 2014), at the SASE 2015 (London, July 2015) and at the SIS 2015 Statistical Conference (Treviso, September 2015) for their useful comments. We also want to thank the editor, Filomena Maggino, and one anonymous reviewer for helping us improving the paper. Any errors or misinterpretations are our own.

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Economics and EQUALITASUniversitat de GironaGironaSpain
  2. 2.TÁRKI Social Research InstituteBudapestHungary

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