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http://dx.doi.org/10.5351/KJAS.2021.34.5.823

Factors affecting regional population of Korea using Bayesian quantile regression  

Kim, Minyoung (Department of Statisctics, Ewha Womans University)
Oh, Man-Suk (Department of Statisctics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.5, 2021 , pp. 823-835 More about this Journal
Abstract
Identification of factors influencing regional population is critical for establishing government's population policies as well as for improving residents' social, economic and cultural well-being in the region. In this study we analysed the data from 2019 Population Housing Survey in Korea to identify the factors affecting the population size in each of the three regions: Seoul, metropolitan cities, and provincial regions. We applied a Bayesian quantile regression to account for asymmetry and heteroscedasticity of data. The analysis results showed that the effects of factors vary greatly between the three regions of Seoul, metropolitan cities, and provincial regions as well as between sub regions within the same region. These results suggest that population-related variables have very heterogeneous characteristics from region to region and therefore it is important to establish customized population policies that suit regional characteristics rather than uniform population policies that apply to every region.
Keywords
Bayesian inference; quantile regression; Markov chain Monte Carlo; population of Korea;
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1 Kim BS and Seo WS (2014). Investigating socio-economic characteristics affecting regional population changes : comparing capital region to non-capital region, The Korean Regional Development Association, 26, 1-14.
2 Kim LY and Seo WS (2016). Investigating spatial patterns and urban influential factors of the school-age population using spatial econometric analysis, The Korean Regional Development Association, 28, 113-129.
3 Koenker R and Bassett G (1978). Regression quantiles, Econometrica, 46, 33-50.   DOI
4 Lee JH and Jun MJ (2018). An analysis on the redistributive effects of population in the capital region due to the Sejong City construction, The Korean Regional Development Association, 30, 47-65.
5 Min BG (2017). Study on intra-urban migration patterns and the classification of the school-age population in Seoul, The Korean Regional Development Association, 29, 47-72
6 Oh MS (2019). Bayesian Data Analysis Using JAGS, Freedom Academy Press, Seoul, Korea.
7 Plummer M (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Technische Universit, Wien, Austria, 125.
8 Son DG and Hur JW (2018). A study on the effectiveness of the relocating public organization on the reduction of population concentration in the Seoul Metropolitan area, Journal of Korea Planning Association, 53, 5-18.
9 Yi YJ and Choi MS (2018). Determinants of the elderly's spatio-temporal concentration - using big data of de facto population of Seoul, The Seoul Institute, 19, 149-168.
10 Gelman A and Rubin DB (1992). Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-472.   DOI
11 Lee YC and Kim CH (2020). A study on the performance evaluation and validation of innovation city development, The Korean Regional Development Association, 32, 47-68.
12 Kim LY and Yang KS (2013). Empirical analysis of regional characteristic factors determining net inflow and outflow of the population, The Korean Regional Development Association, 25, 1-19.