The Impact of Aging Society on Public Health Expenditure in Thailand
Keywords:
Elderly Population, Public Health ExpenditureAbstract
This article aimed (1) examine the overview of the elderly population and government expenditure on public health in Thailand, and (2) investigate the relationship between the elderly population and government expenditure on public health in Thailand. The researcher conducted data analysis using both descriptive and quantitative statistics. Time-series data was collected from relevant agency websites. The analysis involved descriptive statistics, such as means and percentages, and quantitative statistical analysis using annual secondary data from 1985 to 2022. Long-term relationships were analyzed using the method proposed by Pesaran, Shin, and Smith, while short-term adjustments to long-term equilibrium were assessed using the Error Correction Model. The result of the study found that: Prior to Thailand becoming an aging society, the average proportion of government expenditure on public health was approximately 7%. After Thailand transitioned into an aging society, this proportion increased to an average of about 10%, reflecting a noticeable increase in average government expenditure on public health. The elderly population and the COVID-19 situation did not impact long-term public health expenditure. However, GDP per capita had a positive effect on long-term public health expenditure at a 99% confidence level, with a short-term adjustment to long-term equilibrium of 41.55%.
References
พัชราพรรณ กิจพันธ์. (2561). ประเทศไทยสู่สังคมผู้สูงอายุ. วารสารอาหารและยา, 25(3), 4-8.
สถาบันวิจัยประชากรและสังคม มหาวิทยาลัยมหิดล. (2565). ประชากรของประเทศไทย พ.ศ. 2556. สืบค้น 25 กรกฎาคม 2566. จาก https://plan.dmh.go.th/forums/index.php?action=dlattach;topic=822.0;attach=1374.
สำนักงบประมาณ. (2566). งบประมาณโดยสังเขป (ฉบับปรับปรุง). สืบค้น 17 กรกฎาคม 2566. จาก https://www.bb.go.th/topic.php?gid=548&mid=311.
Baharin, R., & Saad, S. (2018). Ageing population and health care expenditure: evidence using time series analysis. Geografia, 14(4), 65-73.
Engle, R.F. & Granger, C.W.J. (1987). Cointegration and error correction representation: Estimation and testing. Econometrica, 55, 251-276.
Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive model. Oxford: Oxford University Press.
Nordin, N., Nordin, N., & Ahmad, N. A. (2015, May). The effects of the ageing population on healthcare expenditure: A comparative study of China and India. In International Conference on Economics and Banking (ICEB-15) (pp. 297-310). Dordrecht: Atlantis Press.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.
Tamakoshi, T., & Hamori, S. (2015). Health-care expenditure, GDP and share of the elderly in Japan: a panel cointegration analysis. Applied Economics Letters, 22(9), 725-729.
Tchoe, B., & Nam, S. (2022). The Real Effect of Aging Population on Health Expenditures in OECD Countries. Korea and the World Economy, 23(1), 25-34.
World Bank. (2023). Birth rate, crude (per 1,000 people). Retrieved 1 June 2023 form https://data.worldbank.org/indicator/SP.DYN.CBRT.IN.
World Bank. (2023). Population ages 65 and above (% of total population). Retrieved 1 June 2023 form https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS.
World Bank. (2023). Population ages 65 and above, total. Retrieved 1 June 2023 form https://data.worldbank.org/indicator/SP.POP.65UP.TO.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Social Science Panyapat

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.