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.
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Notes
- 1.
Children are divided into four wealth quartiles (the poorest in Quartile 1 and the richest in Quartile 4) according to their family per capita monthly expenditure, expressed in “adult-equivalent” units. In order to compare expenditures across households, it is important to correct for household composition and household size by dividing total consumption expenditure by the number of “adult equivalents” in the household to obtain the per capita monthly expenditure in adult-equivalent scale. Each child under age 15 is treated as equivalent to 0.5 adults. All individuals in each round of the SES data set are then classified into four wealth quartiles based on their household’s per capita expenditure.
- 2.
See Chap. 2 in Dilaka and Sondergaard (2015) for more in-depth discussion on the expansion of educational access in Thailand.
- 3.
These papers use the National Longitudinal Survey of Youth (NLSY) cohort data sets, which contain rich measures of family background characteristics, as well as measures of scholastic ability embodied in Armed Forces Qualifying Test (AFQT) scores.
- 4.
Using NLSY79, Carneiro and Heckman (2002) find strong family income effects on college enrolment for white males when they do not control for AFQT scores. However, when they do control for AFQT scores, they find that the enrolment gaps by quartile compared to the richest quartile (see Table 3 in their paper) are not jointly significantly different from zero at conventional levels.
- 5.
The PISA is an international survey that aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. The tests are designed to assess the extent to which students can apply their knowledge to real-life situations and be prepared for full participation in society. To date, students from more than 70 countries have participated in the assessment, which is conducted every 3 years (see www.oecd.org/pisa/ for more details).
- 6.
The PISA index of economic, social, and cultural status (ESCS) was derived from the following three indices: highest occupational status of parents, highest education level of parents, and home possessions. The index of home possessions comprises all items on the indices of family wealth, cultural possessions, home educational resources, as well as books in the home.
- 7.
The student performance index (ranges from 0 to 100), constructed by Dilaka and Sondergaard (2015), is a weighted index of the 2010 Ordinary National Education Test (O-NET) exams in mathematics and science for students in Grades 6, 9, and 12. For details of the computation of the index, see Appendix A5.3 in Dilaka and Sondergaard (2015).
- 8.
The estimation strategy employed is a two-stage modeling framework which can be represented using the following equation:
$$ {T}_{is}=f\left({F}_{is},;{R}_s,;\beta \right)\times Ef{f}_s $$where f(∙) is an educational production function or production frontier whose arguments are the inputs or factors of production denoted by vectors F is and R s. The elements of vector F is consist of student i’s individual and family background characteristics, while those of R s consist of educational resources of school s where student i is attending. By definition, the production function f(∙) gives the maximum output or the highest PISA test score obtainable for student i for a given feasible combination of inputs. The term Eff s denotes the technical efficiency where Eff s = 1 shows that student i in school s obtains the maximum feasible score, while Eff s < 1 provides a measure of the shortfall of the observed test score from the maximum.
In practice, the parameters of the above production process are estimated in the first stage using stochastic frontier analysis (SFA) for panel data (Meeusen and Van den Broeck 1977; Aigner et al. 1977) where each school represents a panel. The estimated regression equation has the following specification:
$$ {T}_{is}={\beta}_0+{F}_{is}^{\prime }{\beta}_F+{R}_s^{\prime }{\beta}_s+{v}_{is}-{u}_s $$where T is is the PISA test score for student i in school s, the vector β is the production technology parameter to be estimated, and \( {v}_{is}\sim iid\;N\left(0,{\sigma}_v^2\right) \) is a stochastic component describing the random shocks affecting the production process. Notice that each student i in school s faces a different shock, but the shocks are randomly distributed with zero mean and variance \( {\sigma}_v^2 \). The random variable \( {u}_s\sim iid\;{N}^{+}\left(\begin{array}{c}\\ {}\mu, {\sigma}_u^2\end{array}\right) \) is the nonnegative distance from the production frontier for school s and is assumed to have a truncated normal distribution (truncated at 0) with mean μ and variance \( {\sigma}_u^2 \). Therefore, u s ≥ 0 by construction. The random variables v is and u s are also assumed to be distributed independently from each other.
The first stage SFA panel regression parameter estimates are then used to compute the school-level efficiency score using the equation:
$$ {\hat{Eff}}_s=\frac{\hat{f}\left(\overline{F_s},{R}_s\right)-{\hat{u}}_s}{\hat{f}\left(\overline{F_s},{R}_s\right)} $$where \( \hat{f}\left(\overline{F_s},{R}_s\right)={\hat{\beta}}_0+{\overline{F}}_s^{\prime }{\hat{\beta}}_F+{R}_s^{\prime }{\hat{\beta}}_s \) is the estimated educational production frontier, \( \hat{\beta} \) is the estimated vector of production technology parameters, \( {\hat{u}}_s \) is the estimated distance from the frontier for school s, and \( \overline{F_s} \) is the vector of average student body characteristics of school s.
In the second stage, the impacts of school governance practices on the entire distribution of school-level efficiency score are evaluated using the unconditional quantile regression (UQR) method proposed by Firpo. See Fortin and Lemieux – FFL (2009) for further analysis. In particular, the focus of the second stage is to analyze the effects of decentralization of decision-making to schools with regard to curriculum, budget, and personnel autonomy. The impacts of increasing school autonomy in these different areas are investigated under different accountability regimes.
- 9.
The PISA index on the school’s material resources was computed on the basis of six items measuring the school principals’ perceptions of potential factors hindering instruction at school. These are shortage or inadequacy of (1) science laboratory equipment, (2) instructional materials, (3) computers for instruction, (4) Internet connectivity, (5) computer software, and (6) library materials. All items were reversed for scaling so that more positive values on this index indicate higher quality of material resources at a school.
- 10.
However, it should be mentioned that on average, around 94% of teachers in Thai secondary schools are fully certified (according to PISA 2012 data).
- 11.
The PISA index on teacher shortage was derived from four items measuring the school principal’s perceptions of potential factors hindering instruction at school. The four items indicate shortages of qualified teachers in (1) science, (2) mathematics, (3) test language of the country (e.g., Thai), and (4) other subjects. A larger value on this index indicates a higher degree of teacher shortage at a school.
- 12.
- 13.
The United Nations Department of Economic and Social Affairs projects that the number of school-age children (3–17 years old) in Thailand will decline from 7.3 million in 2016 to 6.1 million in 2026.
- 14.
See Box 3.1 in Dilaka and Sondergaard (2015).
- 15.
The empirical study uses a 2010 cross-sectional school data collected by the Office of the Basic Education Commission (OBEC).
- 16.
Student achievement is measured by the student performance index, which is a weighted index of mathematics and science scores in the 2010 Ordinary National Education Test (O-NET) exams for Grades 6, 9, and 12. The index is constructed as explained in detail in Section A5.3 in Technical Appendix to Annex A5 in Dilaka and Sondergaard (2015).
- 17.
The “unobserved teacher quality index” approximately captures variations arising from the discretionary wage component (such as performance pay), the average academic ranking of the teacher workforce, and other school average teacher characteristics unobserved by the researcher.
- 18.
For schools ranked at or below the 2nd percentile of the performance distribution, the average number of teachers per classroom is 0.79. For schools ranked between the 2nd and 4th percentiles, the figure improves slightly to 0.93. The figure improves further to 1.0 for schools ranked between the 4th and 6th percentiles and to 1.06 for schools ranked between the 6th and 20th percentiles. For those schools that are ranked above the 20th percentile, the average number of teachers per classroom rises to 1.18. These figures once again confirm that teacher shortages are a very serious problem constraining Thai schools.
- 19.
Dilaka and Sondergaard (2015) define a small school as a school with 20 students or less per grade on average. This is different from OBEC’s definition which classifies a school with less than 120 enrolled students as small.
- 20.
M-schools are defined as having enrolment size between 120 and 299 and are not classified as small schools (less than 20 students per grade).
- 21.
A school is defined as isolated if there is no school of a similar type (meaning some/all grade levels taught at the schools overlap) located within 20 min from it or if the subdistrict where the school is situated is more than 500 m above sea level.
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Lathapipat, D. (2018). Inequalities in Educational Attainment. In: Fry, G. (eds) Education in Thailand. Education in the Asia-Pacific Region: Issues, Concerns and Prospects, vol 42. Springer, Singapore. https://doi.org/10.1007/978-981-10-7857-6_13
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