Abstract
The increasingly pronounced ageing process of the population is followed by a) a relatively low mortality in all age groups and a stabilization of the mortality rate and b) an increase in mortality rate due to the growing proportion of the elderly in the total population. According to the literature, when the number of deaths is higher than the number of live births, then a process of depopulation occurs. This chapter aims to extend our knowledge on mortality determinants in the ageing and depopulation context within Western Balkans countries. Furthermore, our study provides a novel approach into the explanatory determinants that may impact the change of mortality rates. The research work applies an empirical analysis on the annual country level data between a sample of two Western Balkans countries (Albania and Serbia) during 1991–2021 in order to determine the context of influential determinants to mortality rate. An elastic net regularization (ENR) approach is estimated to test the relationship between different variables on crude mortality rate. ENR analyses reveal that the average value of mortality rate in the two selected Western Balkans countries tend to be affected mostly by proxies for ageing variables, i.e. MEDIANAGE and life expectancy at birth (LEAB). Results further confirm that demographic changes pose a major challenging factor in determining the average value of mortality rate within these countries. Thus, it was found that net migration rate (NMR) emerged as commonly the most non-significant determinant when it comes to its effect as predictors on values of mortality rate. The insights about the relationship of mortality rate with determinants under this study evoke for setting up policy directions for mid- and long-term within these two countries as well as within Western Balkans.
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Notes
- 1.
Kriging is a type of regression that provides least-squares estimation of data. It uses z-scores to generate an estimated surface model from the spatial description of scattered data points. It originated in mining geology but now is an important part in geostatistics. It also has uses in computer engineering, remote sensing and environmental studies. One advantage of this type of interpolation is that it not only generates an interpolated spatial model, but also generates an estimate of the uncertainty of each point in that model. Source: Remy et al. (2011).
- 2.
Recently, an important area of research is that of sparse learning, which refers to the use of learners who select and use only a reduced number of attributes in a dataset, as opposed to all. Apart from guaranteeing reduced computational complexity for prediction when applying several attributes and thus reducing the costs of data collection and storage, sparse learners are effective in solving the common problem of overloading by excluding attributes that may not be informative and can increase the variability of the prediction. Additionally, by selecting and using a small set of attributes, sparse learners can be more easily interpreted and subsequently used as a basis for further investigation of particular phenomena. Source: Molinari et al. (2020).
- 3.
Western Balkans include: Serbia, Bosnia and Herzegovina, Albania, Montenegro and Macedonia (TFYR).
- 4.
XK—Kosovo (under United Nations Security Council Resolution 1244/99).
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The datasets generated during and/or analyzed during the current study are available in the UN repository: https://population.un.org/wpp/.
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The author is solely responsible for conception and design of the study. The author fully and independently carried out the empirical analysis and interpreted both the theoretical and empirical results of the manuscript.
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Miladinov, G. (2024). Population Ageing Process and Depopulation Context in Western Balkans. In: Zafeiris, K.N., Kotzamanis, B., Skiadas, C. (eds) Population Studies in the Western Balkans. European Studies of Population, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-031-53088-3_3
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