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The Interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators

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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.

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Notes

  1. For the detailed methodology of the composite and the three sub-indicators see http://ec.europa.eu/social/BlobServlet?docId=10421&langId=en.

  2. http://ec.europa.eu/europe2020/pdf/targets_en.pdf.

  3. See, for example, Guio (2009) for the material deprivation indicator; and Ward and Özdemir (2013) and Corluy and Vandenbroucke (2013) for the low work intensity indicator.

  4. See Pemberton et al. (2013) for a comprehensive review of the qualitative aspects of poverty persistence.

  5. Oxley et al. (2000) contains further evidence in a comparative analysis of six countries on how the probability of exiting poverty falls with previous experiences.

  6. See a brief discussion of the persistent material deprivation indicator from the ‘Social Situation Monitor’ at http://ec.europa.eu/social/main.jsp?catId=1050&intPageId=1997&langId=en.

  7. Eurostat has only recently decided to include private pension plans as part of household income.

  8. Using the same approach as other authors, missing values in the different deprivation items or activities are treated as no deprivation (see D’Ambrosio 2013).

  9. Note the impossibility of adding controls relative to the labour market attachment of household members, as the low work intensity indicator (its initial condition and lagged value) captures part of this information.

  10. The Nordic countries and the Netherlands have a sub-sampling procedure according to which they collect the variables of main activity status throughout the preceding year only for selected respondents aged 16+ in the sampled households, so the household work intensity variable cannot be calculated, as we do not have the information on other adults in those sampled households.

  11. The only exception is the UK, which starts the panel with one rotational group less than the other countries.

  12. The number of waves would have been too small for countries such as Bulgaria, Malta or Romania since they did not start their participation in the EU-SILC from the beginning.

  13. Each point on the graphs presented in Sect. 4 shows the average across years (and not the average of annual rates).

  14. We use this definition to proxy state dependence. Instead, Eurostat defines persistent poverty as the share of persons with an equivalised disposable income below the at-risk-of-poverty threshold in the current year and in at least two of the preceding three years. See http://epp.eurostat.ec.europa.eu/portal/page/portal/structural_indicators/documents/sc031_-_At_persistant_risk_of_poverty.

  15. See Jenkins and Van Kerm (2014) for an in-depth analysis of the near-linear relationship between the current poverty rate and the persistent poverty rate (using the EU standard definition) with data from the EU-SILC.

  16. The notation draws heavily on Ayllón (2015). See the same reference for a review of the previous literature that has used a similar model.

  17. Note that such a panel structure makes it nearly impossible to include higher-order dynamics.

  18. See Skrondal and Rabe-Hesketh (2014) for a recent review of the different strategies that have dealt with the initial conditions problem.

  19. The full models with all the coefficients are available from the authors upon request and from the Web Appendix in www.saraayllon.eu.

  20. For the sake of robustness, we ran another model, where the equation for severe material deprivation was the first one and that for poverty the second. This meant that, instead of capturing the effect of current material deprivation on poverty, we obtained the effect of current poverty on severe material deprivation. This model ensured that if a time bias relative to the reference period for poverty and material deprivation exists, no future values of material deprivation would enter as explanatory variable in the poverty equation. The results are very similar to those presented, with two main exceptions: we found a positive effect of current poverty on material deprivation in Italy, while the effect of lagged poverty on material deprivation in Poland was not found (we would like to thank Alessio Fusco (LISER) and Iva Tasseva (ISER) for suggesting this model structure to us).

  21. Recall that Spain and Poland were the countries where the difference between the probability of being poor according to past low work intensity status was the smallest in Fig. 3c.

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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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  1. Department of Economics and EQUALITAS, Universitat de Girona, C/ Universitat de Girona, 10, 17071, Girona, Spain

    Sara Ayllón

  2. TÁRKI Social Research Institute, Budaörsi u. 45, P.O. 1518 Pf. 71, Budapest, 1112, Hungary

    András Gábos

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  1. Sara Ayllón
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Correspondence to Sara Ayllón.

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Ayllón, S., Gábos, A. The Interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators. Soc Indic Res 130, 1025–1049 (2017). https://doi.org/10.1007/s11205-015-1212-2

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