The impact of education expenditure on economic growth in the Southeast Asia region
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DOIhttp://dx.doi.org/10.21511/kpm.09(1).2025.11
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Article InfoVolume 9 2025, Issue #1, pp. 148-159
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As education gains prominence in sustainable development strategies, assessing the effectiveness of public education spending has become essential, particularly in Southeast Asia. Despite being seen as a driver of economic growth, its actual impact remains debated due to differing outcomes across countries and stages of development. This study aims to analyze the impact of public education expenditure on economic growth in nine Southeast Asian countries: Brunei, Cambodia, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam, during the period 1998–2022. The study employs secondary data sourced from the World Development Indicators. Econometric techniques used include Ordinary Least Squares, Fixed Effects Model, Random Effects Model, and the Generalized Method of Moments to ensure consistency and robustness in the estimations.
The findings show that public spending on education in Southeast Asia during the studied period did not produce a clearly positive effect on economic growth. In several models, the coefficient of education spending on GDP growth was statistically insignificant or even negative. For instance, in the GMM model, the coefficient was –0.031 (p > 0.1) – a negative, though statistically insignificant, impact. Meanwhile, the institutional quality variable exhibited a positive and statistically significant effect, suggesting that the effectiveness of public spending depends strongly on the quality of governance and fiscal management. The study concludes that public education spending is unlikely to be effective without strong governance. Therefore, in addition to increasing budgets, countries should focus on institutional reform and improving education quality to ensure long-term economic benefits.
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JEL Classification (Paper profile tab)I22, O40, H52
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References40
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Tables4
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Figures0
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- Table 1. Description of variables and data source
- Table 2. Descriptive statistics of variables’ data
- Table 3. Matrix of correlation coefficients of variables
- Table 4. Estimation results of OLS, FEM, REM, and GMM models
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