Forecasting labour force trends among older persons in Malaysia using time series analysis

Authors

  • Masrol Hafizal Ismail Department of Statistics Malaysia
  • Muhammad Mat Yusof Universiti Utara Malaysia
  • Norhayati Yusof Universiti Utara Malaysia

DOI:

https://doi.org/10.32890/jcia2026.5.1.5

Keywords:

labour force participation, older persons, productive ageing, time series forecasting

Abstract

Malaysia has transitioned into an ageing society faster than the previous demographic model suggested. The latest report from the Department of Statistics Malaysia (DOSM) confirms that as of 2025, individuals aged 65 and older already comprise 8% of the total population. To address the objectives of Sustainable Development Goal (SDG) 8.5 regarding productive employment, this study forecasts labour force participation trends among Malaysians aged 60 to 64 through the year 2030. This specific age group represents a critical segment for extending working lives and maintaining national productivity. The analysis utilises annual time series data from 1982 to 2021 for model estimation and evaluation, while actual observations from 2022 to 2024 serve as an ex-post benchmark to verify the forecast accuracy. This study applies four forecasting techniques, including double exponential smoothing (DES), Holt’s exponential smoothing (HES), autoregressive integrated moving average (ARIMA), and time series regression (TSR). Following evaluation via mean absolute percentage error (MAPE), root mean square error (RMSE), and geometric root mean square error (GRMSE), the HES emerged as the most reliable, achieving a precision rate of 99.27% (0.73% error) against 2024 actuals. The final forecast trends indicate steady expansion, with the labour force participation expected to reach 533,020 older workers by 2030, which is a 10.47% cumulative increase from 2024. These findings confirm that prolonged workforce participation is no longer a temporary shift but a structural reality. Consequently, Malaysia requires immediate policy interventions focused on flexible retirement frameworks, targeted reskilling, and the creation of an age-inclusive workplace environment to sustain economic stability.

References

Barusch, A. S., Luptak, M., & Hurtado, M. (2009). Supporting the labor force participation of older adults: An international survey of policy options. Journal of Gerontological Social Work, 52(6), 584–599. https://doi.org/10.1080/01634370802609221

Bärnighausen, T., Liu, Y., Zhang, X., & Sauerborn, R. (2007). Willingness to pay for social health insurance among informal sector workers in Wuhan, China: A contingent valuation study. BMC Health Services Research, 7, Article 114. https://doi.org/10.1186/1472-6963-7-114

Department of Statistics Malaysia. (2025). Labour force survey report, Malaysia, 2024. https://www.dosm.gov.my/portal-main/release-content/labour-force-survey-2024

Frees, E. W. (2003). Stochastic forecasting of labor force participation rates. Insurance: Mathematics and Economics, 33(2), 317–336. https://doi.org/10.1016/S0167-6687(03)00156-2

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/

Ismail, N. A., Ramzi, N. A., & Mah, P. J. W. (2022). Forecasting the unemployment rate in Malaysia during COVID-19 pandemic using ARIMA and ARFIMA models. Malaysian Journal of Computing, 7(1), 982–994. https://doi.org/10.24191/mjoc.v7i1.14641

Kashkooli, M. (2018). Forecasting labor force participation at the regional level in the United States: The case of Maine [Honors thesis, University of Maine]. DigitalCommons@UMaine. https://digitalcommons.library.umaine.edu/honors/344

Leynes, A. G. (2021). Modeling the labor force participation rate of the Philippines through multiple linear regression. Turkish Journal of Computer and Mathematics Education, 12(3), 4987–4998. https://doi.org/10.17762/turcomat.v12i3.2011

Lim, B. M., Abdul Rahman, N., & Arsad, Z. (2021). Determinants of labour force participation rate in Malaysia from gender perspective. Journal of Statistical Modeling and Analytics, 3(2), 109–121. https://doi.org/10.22452/josma.vol3no2.7

Nor, M. E., Saharan, S., Lin, L. S., Salleh, R. M., & Asrah, N. M. (2018). Forecasting of unemployment rate in Malaysia using exponential smoothing methods. International Journal of Engineering & Technology, 7(4.30), 451–453. https://doi.org/10.14419/ijet.v7i4.30.22365

OECD. (2023). Retaining talent at all ages (Ageing and Employment Policies). OECD Publishing. https://doi.org/10.1787/00dbdd06-en

Ramely, A., Ahmad, Y., & Mohamed Harith, N. H. (2022). The effects of Malaysian older people’s participation and engagement in the local labour market. Malaysian Journal of Social Sciences and Humanities, 7(7), Article e001685. https://doi.org/10.47405/mjssh.v7i7.1685

Ramli, S. F., Ismail, N., Isa, Z., & Kamaruddin, H. S. (2024). Forecasting the retirement period of employees in Malaysia 2001–2047. Sains Malaysiana, 53(9), 3011–3019. https://doi.org/10.17576/jsm-2024-5309-08

Romzi, M. S., & Hamdan, M. F. (2022). Analysis of labor force participation rate using multiple linear regression. Proceedings of Science and Mathematics, 9, 270–277. https://science.utm.my/procscimath/volume/2022-2/vol-9/

Siegrist, J., von dem Knesebeck, O., & Pollack, C. E. (2004). Social productivity and well-being of older people: A sociological exploration. Social Theory & Health, 2(1), 1–17. https://doi.org/10.1057/palgrave.sth.8700014

Sulaiman, N. N. (2024). Malaysia on ageing population labour participation. International Journal of Academic Research in Business and Social Sciences, 14(8), 201–207. https://doi.org/10.6007/IJARBSS/v14-i8/22383

Tan, V. N., Md Yusof, Z., Misiran, M., & Supadi, S. S. (2021). Assessing youth unemployment rate in Malaysia using multiple linear regression. Journal of Mathematics and Computing Science, 7(1), 23–34. https://ir.uitm.edu.my/id/eprint/49087/

United Nations Department of Economic and Social Affairs. (2025). The Sustainable Development Goals report 2025. United Nations. https://unstats.un.org/sdgs/report/2025/

Whiting, E. (2005). The labour market participation of older people. Labour Market Trends, 113(7), 285–296.

Schmillen, A. D., Wang, D., Yap, W. A., Bandaogo, M. A. S. S., Simler, K., Ali Ahmad, Z. B., & Abdur Rahman, A. B. (2020). A silver lining: Productive and inclusive aging for Malaysia. World Bank.

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Published

31-01-2026

How to Cite

Ismail, M. H., Mat Yusof, M., & Yusof, N. (2026). Forecasting labour force trends among older persons in Malaysia using time series analysis. Journal of Computational Innovation and Analytics (JCIA), 5(1), 72-82. https://doi.org/10.32890/jcia2026.5.1.5

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Harvested 2026-08-30
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Identifiers DOI 10.32890/jcia2026.5.1.5 OpenAlex W7130950039

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