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Inequalities in Educational Attainment

  • Dilaka LathapipatEmail author
Chapter
Part of the Education in the Asia-Pacific Region: Issues, Concerns and Prospects book series (EDAP, volume 42)

Abstract

In recent decades, Thailand has been highly successful in expanding coverage of its basic education system. However, a growing body of empirical evidence indicates that there remain serious issues related to low learning outcomes and rising inequalities in student performance in standardized assessments. For example, in the PISA 2012 reading assessment, one-third of Thai 15-year-old students were classified as “functionally illiterate,” lacking critical skills for many jobs in a modern economy. Students in rural areas, who predominantly attend small schools which are severely lacking in adequate teachers and infrastructure, are not receiving the same quality education that their counterparts in bigger, urban schools are receiving. These rural students, often from Thailand’s poorest families, are also falling further behind. The gaps in learning outcomes at the lower education levels inevitably lead to a concentration of enrolment disparities between socioeconomic groups at the upper secondary and, particularly, the tertiary level. Based on recent research evidence, this chapter identifies the most important equity and quality challenges facing the Thai education system. It argues that Thailand has the resources to build a high-performing education system – one built on schools that utilize the full potential of high-quality teachers and prepare students with the critical skills for success in a modern economy. However, a strong political will is needed if the types of reforms suggested here are to be implemented successfully.

Keywords

PISA Programme For International Student Assessment (PISA) Economic, Social And Cultural Status (ESCS) Office Of The Basic Education Commission (OBEC) AFQT Scores School Received 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aigner, Dennis J., C.A. Knox Lovell, and Peter Schmidt. 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6: 21–37.CrossRefGoogle Scholar
  2. Belley, Philippe, and Lance Lochner. 2007. The changing role of family income and ability in determining educational achievement. Journal of Human Capital 1 (1): 37–89.CrossRefGoogle Scholar
  3. Cameron, Stephen V., and James J. Heckman. 1998. Life cycle schooling and dynamic selection bias: Models and evidence for five cohorts of American males. The Journal of Political Economy 106: 262–333.CrossRefGoogle Scholar
  4. ———. 1999. Can tuition policy combat rising wage inequality? In Financing college tuition: Government policies and educational priorities, ed. Marvin H. Kosters, 76–124. Washington, DC: American Enterprise Institute Press.Google Scholar
  5. ———. 2001. The dynamics of educational attainment for Black, Hispanic and White males. The Journal of Political Economy 109: 455–499.CrossRefGoogle Scholar
  6. Carneiro, Pedro, and James J. Heckman. 2002. The evidence on credit constraints in post-secondary schooling. Economic Journal 112: 989–1018.CrossRefGoogle Scholar
  7. Chularat Saengpassa. 2018. Merge small schools, urges World Bank. The Nation, September 3, p. 3A.Google Scholar
  8. Dilaka Lathapipat. 2013. The influence of family wealth on the educational attainments of youth in Thailand. Economics of Education Review 37: 240–257.CrossRefGoogle Scholar
  9. ——— . 2015.School-level governance: Decentralized decision-making for improved learning outcomes, Unpublished mimeo.Google Scholar
  10. ———. 2016. Inequality in education and wages. In Unequal Thailand: Aspects of income, wealth, and power, ed. Pasuk Phongpaichit and Chris Baker, 43–54. Singapore: NUS Press.Google Scholar
  11. Dilaka Lathapipat, and Lars Sondergaard. 2015. Thailand – wanted: A quality education for all. Washington, DC: World Bank Group.Google Scholar
  12. Firpo, Sergio, Nicole Fortin, and Thomas Lemieux. 2009. Unconditional quantile regressions. Econometrica 77 (3): 953–973.CrossRefGoogle Scholar
  13. Fry, Gerald W., and Pham Lan Huong. 2011. Vietnam as an outlier: Past, tradition and change in education. In Education in Southeast Asia, ed. Colin Brock and Loraine Symaco, 221–243. Oxford: Oxford Studies in Comparative Education Series.Google Scholar
  14. Hanushek, Eric A., and Ludger Woessmann. 2012. Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. Journal of Economic Growth 17: 267–321.CrossRefGoogle Scholar
  15. Little, Angela. 2006. Education for all and multigrade teaching: Challenges and opportunities. Dordrecht: Springer. http://public.eblib.com/choice/publicfullrecord.aspx?p=303577.CrossRefGoogle Scholar
  16. Meeusen, Wim, and Julien van den Broeck. 1977. Efficiency estimation from Cobb-Douglas production function with composed error. International Economic Review 8: 435–444.CrossRefGoogle Scholar
  17. ———. 2015. PISA 2015 results in focus. Paris: OECD. http://www.oecd.org/pisa/pisa-2015-results-in-focus.pdf

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.World Bank-ThailandPathumwan, BangkokThailand

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