Analyzing the Relationships among the Factors Affecting Educational Competitiveness: An Application of the Structural Equation Modeling Approach

Young-Chool Choi, Ji-Hye Lee



This study was conducted in order to investigate the relationships between different factors affecting educational competitiveness, which is crucial to enhancing national competitiveness in every country, and to put forward policy implications whereby each country may raise the level of its educational competitiveness. PISA score was selected as an indicator representing the educational competitiveness of OECD countries, and this included a number of independent variables, such as per capita GDP, total public expenditure on education as a percentage of GDP, and total per capita public expenditure on education (US dollars), affecting educational competitiveness. We employed the structural equation modeling approach to analyze the complex causal relationships among the factors affecting educational competitiveness. The research results show that the significant factors affecting PISA are: edusys (educational system), puptec (pupil–teacher ratio), and privat exp (total expenditure on education by private source as a percentage of GDP), and that the most influential factor affecting PISA directly is edusys (the extent to which the education system meets the needs of a competitive economy). Finally, the study suggests that each country should endeavor to enhance its own educational competitiveness, considering how the factors associated with this relate to each other.


educational competitiveness, PISA, educational factors, structural equation modeling

Full Text:




Adam, A., Delis, M., Kammas, P. (2008). Fiscal decentralization and public sector efficiency: evidence from OECD countries. CESIFA working paper no. 2364.

Biever, T. and Martens, K. (2011). The OECD PISA study as a soft power in education? Lessons from Switzerland and US, European Journal of Education, 46(1), part 1.

Borgonovi, F., Montt, G. (2012). Parental involvement in selected PISA countries and economies. OECD Education working paper no. 73.

Cakar, F. and Karatas, Z. (2012). The self-esteem, perceived social support and hopelessness in adolescents: the structural equation modeling, Educational Sciences: Theory and Practice 12(4), 2406–12.

Chien, H., Kao, C., Yeh, I. and Lin, K. (2012). Examining the relationship between teachers’ attitudes and motivation toward web-based professional development: a structural equation modeling approach, TOJET, 11(2), 120–7.

Choi, Y. C. (2008). Relationships between national competitiveness and decentralization, Korean Association of Local Government Studies Summer Conference Proceedings.

Enikolopov, R., Zhuravskaya, E. (2007). Decentralization and political institutions, Journal of Public Economics, 91, 2261–90.

Fisman, R., Gatti, R., (2002). Decentralization and corruption: evidence across countries, Journal of Public Economics 83, 325–46.

Gao, S., Mokhtarian, P. and Johnston, R. (2008). Exploring the connections among job accessibility, employment, income, and auto ownership using structural equation modeling, Ann Reg Sci, 42, 341–56.

Hayduck, L. A. (1987) Structural Equation Modeling with LISREL: Essentials and advances. Baltimore, MD: Johns Hopkins.

Holzinger, K. and Knill, C. (2008). Theoretical framework: causal factors and convergence expectations, in K. Holzinger, C. Knill and B. Arts (eds), Environmental Policy Convergence in Europe: The impact of international institutions and trade. Cambridge: Cambridge University Press.

Huh, J. (2013). Advanced Structural Equation Modeling by AMOS. Seoul: Hanare Academy.

IMD (2013). World Competitiveness Yearbook. Geneva: IMD.

KEDI (2010). Analysis of Effects of Education on National Competitiveness. Seoul: KEDI.

KICE (Korea Institute for Curriculum and Evaluation). Homepage.

Laglera, J. M., Collado, J. and Oca, J. A. M. (2013). Effects of leadership on engineers: a structural equation model, Engineering Management Journal, 25(4), 7–16.

Lee, C. and Lee, K. H. (2006). Analysis of the Conditions of Korea Education Competiveness Index of IMD World Competitiveness Yearbook. The Journal of Korean Education, 33(1), 173-197.

Lingard, B. and Grek, S. (2007). The OECD, indicators and PISA: an exploration of events and theoretical perspectives. Edinburgh, ESRC/ESP Research Project.

OECD (2010). Education at a Glance. Paris: OECD.

OECD (2013). PISA 2012 Results in Focus. Paris: OECD.

Rowan, Correnti, and Miller (2002). What large scale,survey research tells us about teacher effects on student achievement: insight from the “prospect” study of elementary schools. CPRE research report series.

Shin, H. S. and Joo, Y. H. (2013). Global governance and educational policy in Korea, Korean Journal of Educational Research, 51(3), 133–59.

Tanzi, V. and Schuknecht, L. (1998). Can small governments secure economic and social well being?, in Grubel, H. (ed.), How To Spend the Fiscal Dividend: What is the optimal size of government? Vancouver: Fraser Institute.

Yavuz, M. (2009). Factors that affect mathematics-science (MS) scores in the secondary education institutional exam: an application of structural equation modeling, Educational Sciences: Theory and Practice, 9



  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License


If you find difficulties in submitting manuscript please forward your doc file to Our support team will assist you in submission process and other technical matters.

In order to get notifications on inbox please add in your email safe list.

Journal of Arts and Humanities (Print) ISSN:2167-9045

Journal of Arts and Humanities (Online) ISSN: 2167-9053

[Journal of Arts and Humanities previously published by MIR Center for Socio-Economic Research, MD, USA. From February 2018 this journal is published by the LAR Center Press, OR, USA]