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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.
Baltagi, B.H. (2012). Econometric analysis of panel data. Chichester: Wiley.
Eurostat (2014). Income distribution statistics. Luxembourg: Eurostat.
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.
Nolan, B., & Whelan, C. T. (2011). Poverty and deprivation in Europe. Oxford: Oxford University Press.
Perry, B. (2002). The mismatch between income measures and direct outcome measures of poverty. Social Policy Journal of New Zealand, 19, 101–127.
Pinheiro, J., & Bates, D. (2009). Mixed-effects models in S and S-PLUS. New York: Springer.
Reinstadler, A., & Ray, J.-C. (2010). Macro Determinants of Individual Income Poverty in 93 Regions of Europe. CEPS-INSTEAD Working Paper no. 2010–13.
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.
Sela, R. J., & Simonoff, J. S. (2011a). RE-EM trees: A data mining approach for longitudinal and clustered data. Machine Learning, 86, 169–207.
Sela, R. J., & Simonoff, J. S. (2011b). REEMtree: Regression trees with random effects. R package version 0.90.3.
Vermunt, J. K., & Magidson, J. (2005). Technical guide for latent GOLD 4.0: Basic and advanced [online]. Belmont, MA: Statistical Innovations Inc.
Ward, T., Lelkes, O., Sutherland, H., & Tóth, I. G. (Eds.) (2009). European inequalities: Social inclusion and income distribution in the European Union. Budapest: Tárki.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sobíšek, L., Stachová, M. (2016). Use of Panel Data Analysis for V4 Households Poverty Risk Prediction. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-25226-1_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25224-7
Online ISBN: 978-3-319-25226-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)