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

Shubat, Oksana; Bagirova, Anna; Makhabat, Abilova; Ivlev, Anton Vasilyevich

doi:10.7148/2016-0159

Cited by 8 publications

(8 citation statements)

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“…We have previously demonstrated the profiling of these clusters and the assessment of the statistical significance of the obtained model of Russia's demographic space (Shubat et al 2016).…”

Section: Resultsmentioning

confidence: 99%

“…The clustering of Russian regions on the basis of indicators describing the conditions for the formation of demographic potential at the macro-level -that is, the regional level (Study 1) -was based on the following variables:  Birth rate;  Perinatal mortality rate;  Infant mortality rate;  Under-five mortality rate;  Pregnancy rate. We presented the results of this clustering previously (Shubat et al 2016).…”

Section: Methodsmentioning

confidence: 99%

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The Use Of Cluster Analysis To Assess The Demographic Potential Of Russian Regions

Shubat¹,

Bagirova²,

Shmarova³

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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In recent years, Russia has been grappling with a serious economic crisis. The slowing pace of economic development is accompanied by adverse demographic trends. The purpose of our study is to assess the demographic potential of Russian regions and identifying groups that require the implementation of specific measures aimed at its development. We used hierarchical cluster analysis to model Russia's demographic space and segment regions with comparable problems related to forming demographic potential. Clustering was based on the indicators describing the demographic potential at the macro-level (regional) and meso-level (family level). The analysis identified the groups of regions that have the best and the worst conditions for the development of demographic potential. We proposed a set of measures that would be most relevant to the needs of specific groups of regions and could directly drive the development and actualisation of demographic potential. The analysis showed the need to use multifactor classification in the demographics of countries that have a high level of regional differentiation. Modelling the demographic space on the basis of cluster analysis can be seen as an element of the system of supporting administrative decision-making and the development of effective demographic policy.

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“…We have previously demonstrated the profiling of these clusters and the assessment of the statistical significance of the obtained model of Russia's demographic space (Shubat et al 2016).…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Use Of Cluster Analysis To Assess The Demographic Potential Of Russian Regions

Shubat¹,

Bagirova²,

Shmarova³

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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“…Moreover, the differentiation of Russian regions was not taken into consideration, which fully negated the possible impact of introducing the maternity capital programme. For example, our earlier research showed that countries with a large number of constituent parts with high variance in their development require a demographic policy that is differentiated by type of region (Shubat et al 2016). Such development of targeted measures for different types of regions would improve the effectiveness of Russia's overall demographic policy.…”

Section: Discussionmentioning

confidence: 99%

The Use Of Econometric Models In The Study Of Demographic Policy Measures (Based On The Example Of Fertility Stimulation In Russia)

Shubat¹,

Bagirova²

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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Russia is experiencing steady population decline. One of the reasons for this is low fertility. The other major problem is insufficient housing availability. In today's political discussion, these two problems are often presented as interconnected. The aim of our research is to analyse the relationship between fertility dynamics and provision of housing in Russia in order to subsequently assess the effectiveness of the most expensive measure for stimulating fertility in the state's history-the so-called "maternity capital". We estimated regression models for the time series of fertility rates and the availability of housing. To assess the strength of relationship between the time series, we analysed correlation between regressions' residuals in two models.A retrospective analysis of the time series showed no correlation between the two in a historical context. Throughout the time that the maternity capital was in place the correlation analysis also revealed no relationship between them. Our analysis showed that these variables were not significantly correlated either in urban or rural Russian areas. We can conclude that the introduction of maternity capital in Russia was not underpinned by profound statistical and demographic analysis. Our results also give reason to question the effectiveness of maternity capital.

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“…It is broadly acknowledged that multivariate cluster analysis is an effective analytical tool to carry out regional segmentation (Johnson and Wichern, 2007;Shubat et al, 2007). It is also evident that regional segmentation, i.e., identifying homogeneous (or heterogeneous) groups is an effective and need-based approach for designing and implementing the community-based research.…”

Section: Discussionmentioning

confidence: 99%

Demographic and Socioeconomic Homogeneity among Districts and District Towns in Bangladesh

Hossain

2019

J. biomed. anal.

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Exploring the homogeneity (or heterogeneity) at sub-national level is crucial as it associated with design, budget allocation and implementation of a research project. Since demographic and socioeconomic factors depict the first valuable insight of a community, it is imperative to explore the homogeneity within a country by considering these variables. Yet, the information on this aspect is scarce in Bangladesh. Therefore, the present study aimed to identify the district and district town specific homogeneity in Bangladesh. The data for this study were extracted from the most recent Housing and Population Census of the country, and the multivariate cluster analysis was employed to identify the natural groups or segments. We found that Bangladesh could be classified into three distinct clusters both at district and district town levels based on demographic and socioeconomic factors. The findings of this study would provide insights to the policymakers and researchers for designing and implementing community-based research initiatives, particularly in the area of public health and social science as well as market analysis research. The findings could also be helpful in the situation when the national representation of data is required with budget and time constraints.

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

Cited by 8 publications

References 1 publication

The Use Of Cluster Analysis To Assess The Demographic Potential Of Russian Regions

The Use Of Cluster Analysis To Assess The Demographic Potential Of Russian Regions

The Use Of Econometric Models In The Study Of Demographic Policy Measures (Based On The Example Of Fertility Stimulation In Russia)

Demographic and Socioeconomic Homogeneity among Districts and District Towns in Bangladesh

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