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Use of Panel Data Analysis for V4 Households Poverty Risk Prediction

  • Lukáš SobíšekEmail author
  • Mária Stachová
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

One of the main approaches to tracking causality between income, social inclusion and living conditions is based on regression models estimated using various statistical methods. This approach takes into account quantitative and qualitative information about individuals or households that is collected in different periods of time (years in particular), thus allowing it to be transformed into multidimensional data sets, called panel data. Regression models based on panel data are able to describe the dynamics over time periods, so that the patterns can be related to changes in other characteristics. This paper utilises one of these approaches to panel data analysis RE-EM trees which are used to predict the risk-of-poverty rate of households located in the four “Visegrad” countries. The risk-of-poverty rate of individual households is computed on the basis of cluster analysis results, and it takes into account household living conditions as well as income. Subsequently, the risk-of-poverty rate is used as the outcome for the prediction model above. Certain household characteristics were chosen as predictors including: information about the “head” of the household (age, education level, marital status, etc.) and information about the number of members in the household. The results show slight differences in poverty determinants among Visegrad countries. The determinants with the highest impact on the risk-of-poverty rate are: number of household members (Czech Republic, Hungary and Slovakia) and education level (Poland).

Keywords

Material Deprivation Household Poverty Poverty Risk Cluster Cluster Household Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The present paper was supported by VEGA1/0127/11 grant project entitled “The poverty distribution within the EU countries”. EU-SILC microdata were provided for research purposes under EU-SILC/2011/33 agreement concluded between the European Commission, Eurostat and the Technical University in Košice, Slovakia. Eurostat bears no responsibility for the results and conclusions reached by the authors. Data were analysed by Lukáš Sobíšek who cooperates in the project. This work was also supported by projects Mobility—enhancing research, science and education at Matej Bel University, ITMS code: 26110230082, under the Operational Program Education co-financed by the European Social Fund.

References

  1. Andriopoulou, E., & Tsakloglou, P. (2011). The determinants of poverty transitions in Europe and the role of duration dependence. IZA Discussion Paper No. 5692. Bonn: The Institute for the Labor.Google Scholar
  2. Baltagi, B.H. (2012). Econometric analysis of panel data. Chichester: Wiley.zbMATHGoogle Scholar
  3. Eurostat (2014). Income distribution statistics. Luxembourg: Eurostat.Google Scholar
  4. Fusco, A., Guio, A. C., & Marlier, E. (2010). Income poverty and material deprivation in European countries. Luxembourg: Office for Official Publication of the European Communities.Google Scholar
  5. Nolan, B., & Whelan, C. T. (2011). Poverty and deprivation in Europe. Oxford: Oxford University Press.CrossRefGoogle Scholar
  6. Perry, B. (2002). The mismatch between income measures and direct outcome measures of poverty. Social Policy Journal of New Zealand, 19, 101–127.Google Scholar
  7. Pinheiro, J., & Bates, D. (2009). Mixed-effects models in S and S-PLUS. New York: Springer.zbMATHGoogle Scholar
  8. Reinstadler, A., & Ray, J.-C. (2010). Macro Determinants of Individual Income Poverty in 93 Regions of Europe. CEPS-INSTEAD Working Paper no. 2010–13.Google Scholar
  9. Santini, I., & De Pascale, A. (2012). Social Capital and Household Poverty: The Case of European Union. Working Paper no. 109 from Sapienza University of Rome.Google Scholar
  10. Sela, R. J., & Simonoff, J. S. (2011a). RE-EM trees: A data mining approach for longitudinal and clustered data. Machine Learning, 86, 169–207.Google Scholar
  11. Sela, R. J., & Simonoff, J. S. (2011b). REEMtree: Regression trees with random effects. R package version 0.90.3.Google Scholar
  12. Vermunt, J. K., & Magidson, J. (2005). Technical guide for latent GOLD 4.0: Basic and advanced [online]. Belmont, MA: Statistical Innovations Inc.Google Scholar
  13. Ward, T., Lelkes, O., Sutherland, H., & Tóth, I. G. (Eds.) (2009). European inequalities: Social inclusion and income distribution in the European Union. Budapest: Tárki.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Informatics and StatisticsUniversity of EconomicsPragueCzech Republic
  2. 2.Faculty of EconomicsMatej Bel UniversityBanská BystricaSlovakia

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