ecms_neu_mini.png

Digital Library

of the European Council for Modelling and Simulation

 

Title:

The Use Of Cluster Analysis For Demographic Policy Development: Evidence From Russia

Authors:

Oksana Shubat, Anna Bagirova, Abilova Makhabat, Anton Ivlev

Published in:

 

 

(2016).ECMS 2016 Proceedings edited by: Thorsen Claus, Frank Herrmann, Michael Manitz, Oliver Rose, European Council for Modeling and Simulation. doi:10.7148/2016

 

 

ISBN: 978-0-9932440-2-5

 

30th European Conference on Modelling and Simulation,

Regensburg Germany, May 31st – June 3rd, 2016

 

Citation format:

Oksana Shubat, Anna Bagirova, Abilova Makhabat, Anton Ivlev (2016). The Use Of Cluster Analysis For Demographic Policy Development: Evidence From Russia, ECMS 2016 Proceedings edited by: Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose  European Council for Modeling and Simulation. doi:10.7148/2016-0159

DOI:

http://dx.doi.org/10.7148/2016-0159

Abstract:

Russia has been experiencing a demographic crisis since the 1990s. The most obvious manifestations include an excess of mortality over fertility rates, population decline and an ageing population. The last 20 years have seen considerable activity to come up with new demographic policy measures to mitigate these adverse trends, with single solutions developed for all regions in Russia. This paper presents the results of a study where cluster analysis was applied to enable the identification of groups of regions with significant differences in the dynamics of socio-demographic indicators. We used hierarchical cluster analysis to classify and group Russian regions on the basis of social and economic development indices for 2002 and 2008. The validity of the profiling was confirmed using parametric and nonparametric tests. The analysis identified three clusters of Russian regions. These clusters have significant differences in socio-demographic indicators and the associated dynamics. The results of our analysis identified ‘growth points’ for each cluster: the fertility correlates that should be factored into the development of effective demographic policy measures.

 

Full text: