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Factors affecting youth citizenship in accordance with socioeconomic background

Author

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  • Kim, Hyun Ju
  • Chung, Jae Young

Abstract

This research aims to analyze how youth citizenship manifests differently according to their socioeconomic background and identify the factors involved in the interaction between socioeconomic background and youth citizenship. The sample was divided into three socioeconomic background groups of the upper, middle, and lower according to household monthly income and parent academic attainment. Then, the differences in consequences pertaining to youth citizenship were identified. Both cognitive and affective-behavioral citizenship were found to be lowest with youths belonging to the lower group, and increased with the socio-economic background indicators of the family. On the other hand, an analysis of the factors that affected youth citizenship in relation to the socioeconomic background of the family showed that higher self-esteem, mature occupational view, and good peer relations were correlated with higher levels of youth citizenship. In particular, youths in lower groups showed greater levels of citizenship with the increase in the number of books read and the democratic pedagogical methods utilized within the classroom.

Suggested Citation

  • Kim, Hyun Ju & Chung, Jae Young, 2020. "Factors affecting youth citizenship in accordance with socioeconomic background," Children and Youth Services Review, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:cysrev:v:111:y:2020:i:c:s0190740919312551
    DOI: 10.1016/j.childyouth.2020.104847
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    References listed on IDEAS

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    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Government of India Ministry of Women and Child Development, 2013. "The National Policy for Children, 2013," Working Papers id:5358, eSocialSciences.
    3. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    4. Beck, Paul Allen & Jennings, M. Kent, 1982. "Pathways to Participation," American Political Science Review, Cambridge University Press, vol. 76(1), pages 94-108, March.
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