IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v11y2022i9p389-d901919.html
   My bibliography  Save this article

A Spatial Analysis of the Voting Patterns in the South Korean General Elections of 2016

Author

Listed:
  • Hyun-Chool Lee

    (Department of Political Science, Konkuk University, Seoul 05029, Korea)

  • Alexandre Repkine

    (Department of Economics, Konkuk University, Seoul 05029, Korea)

Abstract

In this study we analyze the spatial patterns in the South Korean voting behavior in the context of the 2016 general election along with the socio-economic determinants of the South Korean voters’ choice. To this end we applied spatial econometric analysis to a unique dataset on the outcomes of the 2016 general elections in South Korea at a highly disaggregate level of 229 provinces. Our empirical model accounts for three types of spatial dependence in the data that has to do with the fact that geographic proximity may imply similar voting behavior. Our empirical findings align well with the existing evidence on South Korean voting behavior, in particular regarding the influence produced by the voters’ region of origin, and their age. Surprisingly, we do not find economic characteristics such as the regional income per capita or the rate of unemployment to produce a statistically significant effect on South Korean voters’ choice. However, our results imply that a sound fiscal policy by the local government may act as a signaling device distinguishing between a conservative and a liberal political agenda. Our finding of the older voters leaning towards the conservative edge of the political spectrum suggests that the “silver democracy” now actively discussed in the South Korean media is increasingly assuming more conservative traits.

Suggested Citation

  • Hyun-Chool Lee & Alexandre Repkine, 2022. "A Spatial Analysis of the Voting Patterns in the South Korean General Elections of 2016," Social Sciences, MDPI, vol. 11(9), pages 1-24, August.
  • Handle: RePEc:gam:jscscx:v:11:y:2022:i:9:p:389-:d:901919
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/11/9/389/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2076-0760/11/9/389/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    2. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    3. António Caleiro & Gertrudes Guerreiro, 2005. "Understanding the election results in Portugal," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 4(3), pages 207-228, December.
    4. Adolfo Maza & José Villaverde & María Hierro, 2019. "The 2017 Regional Election in Catalonia: an attempt to understand the pro-independence vote," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 36(1), pages 1-18, April.
    5. Happy, J. R., 1992. "The Effect of Economic and Fiscal Performance on Incumbency Voting: The Canadian Case," British Journal of Political Science, Cambridge University Press, vol. 22(1), pages 117-130, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    2. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    3. Geloso, Vincent & Kufenko, Vadim, 2019. "Can markets foster rebellion? The case of the 1837–38 rebellions in Lower Canada," Journal of Economic Behavior & Organization, Elsevier, vol. 166(C), pages 263-287.
    4. Minmeng Tang & Deb Niemeier, 2021. "How Does Air Pollution Influence Housing Prices in the Bay Area?," IJERPH, MDPI, vol. 18(22), pages 1-13, November.
    5. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    6. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    7. Marcos Herrera Gomez, 2015. "Econometría espacial usando Stata. Breve guía aplicada para datos de corte transversal," Working Papers 13, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    8. Patricia Carracedo & Ana Debón, 2021. "Spatiotemporal Econometrics Models for Old Age Mortality in Europe," Mathematics, MDPI, vol. 9(9), pages 1-18, May.
    9. Youngme Seo, 2020. "Varying Effects of Urban Tree Canopies on Residential Property Values across Neighborhoods," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    10. Rüttenauer, Tobias, 2023. "Spatial Data Analysis," SocArXiv mq7te, Center for Open Science.
    11. Tobias Ruttenauer, 2024. "Spatial Data Analysis," Papers 2402.09895, arXiv.org.
    12. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2020. "Treatment Effects With Heterogeneous Externalities," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 826-838, October.
    13. Sandy Fréret & Denis Maguain, 2017. "The effects of agglomeration on tax competition: evidence from a two-regime spatial panel model on French data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(6), pages 1100-1140, December.
    14. Gupta, Abhimanyu & Robinson, Peter M., 2015. "Inference on higher-order spatial autoregressive models with increasingly many parameters," Journal of Econometrics, Elsevier, vol. 186(1), pages 19-31.
    15. Kristien Werck & Bruno Heyndels & Benny Geys, 2008. "The impact of ‘central places’ on spatial spending patterns: evidence from Flemish local government cultural expenditures," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 32(1), pages 35-58, March.
    16. Padovano, Fabio & Petrarca, Ilaria, 2014. "Are the responsibility and yardstick competition hypotheses mutually consistent?," European Journal of Political Economy, Elsevier, vol. 34(C), pages 459-477.
    17. Guohuan Su & Adam Mertel & Sébastien Brosse & Justin M. Calabrese, 2023. "Species invasiveness and community invasibility of North American freshwater fish fauna revealed via trait-based analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    18. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    19. Matthieu Leprince & Sonia Paty & Emmanuelle Reulier, 2005. "Choix d'imposition et interactions spatiales entre collectivités locales. Un test sur les départements français," Recherches économiques de Louvain, De Boeck Université, vol. 71(1), pages 67-93.
    20. Jan Jacobs & Jenny Ligthart & Hendrik Vrijburg, 2010. "Consumption tax competition among governments: Evidence from the United States," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 17(3), pages 271-294, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jscscx:v:11:y:2022:i:9:p:389-:d:901919. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.