Computational Analysis of Unemployment Trends in the Philippines: Evidence from 2019–2024 with Projected Estimates for 2025
Keywords:
unemployment data, labor market, Philippines, COVID‑19, economic recovery, seasonal employmentAbstract
This study uses a computational approach to examine unemployment trends in the Philippines from 2019 to 2024, with projected estimates for 2025. It relies on secondary data from trusted sources such as the Philippine Statistics Authority (PSA), the International Labour Organization (ILO), the World Bank, and the Asian Development Bank (ADB). Unemployment is a key socio-economic indicator because it reflects labor market conditions, the impact of economic shocks, and seasonal employment fluctuations. Using quantitative, descriptive, and computational methods, the study analyzes both monthly and annual unemployment data to identify changes over time, recovery patterns, and seasonal trends. Monthly unemployment rates were computed, presented, and compared across years to understand how employment levels were affected by the COVID-19 pandemic, government responses, and sectoral shifts. The results show that unemployment rose sharply in 2020, reaching 10.3%, due to lockdowns and widespread economic disruptions. A steady recovery followed, with the rate declining to 4.4% in 2023 and a projected estimate of 4.14% in 2025. Month-to-month comparisons show distinct seasonal trends connected to tourism, temporary employment, and agriculture. Overall, the findings demonstrate that computational analysis effectively transforms complex labor statistics into accessible information. Monitoring unemployment at both monthly and annual levels is crucial for workforce planning, economic recovery, and social policy development. By applying simple yet reliable computational methods, this study illustrates how mathematical tools can provide timely, accurate, and practical insights into labor market conditions, supporting policymakers, educators, local governments, and the public in making informed decisions about employment in the Philippines.
References
Ahmed, S., Li, H., & Wang, Y. (2021). Urban vegetation and thermal comfort: Assessing the cooling effect in tropical cities. Environmental Research Letters, 16(5), 054019. https://doi.org/10.1088/1748-9326/abf1c3
Asian Development Bank. (2022). Philippines labor market report: COVID-19 impacts and recovery strategies. Manila: ADB. https://www.adb.org/countries/philippines/economy
Bilang, P., Santos, R., & Cruz, M. (2019). Urban labor market dynamics in the Philippines: Structural challenges and policy interventions. Philippine Journal of Economic Studies, 12(1), 15–34. https://doi.org/10.2139/pjes.2019.12.1.15
Blazejczyk, K., Epstein, Y., Jendritzky, G., Staiger, H., & Tinz, B. (2018). Comparison of UTCI to selected thermal indices. International Journal of Biometeorology, 62(3), 309–321. https://doi.org/10.1007/s00484-017-1413-1
Dang, T. T., & Vu, T. H. (2021). Labor market shocks and recovery in Southeast Asia during the COVID-19 pandemic. Journal of Asian Economics, 76, 101341. https://doi.org/10.1016/j.asieco.2021.101341
Dang, T. T., & Vu, T. H. (2021). Monitoring labor market shocks using monthly unemployment data: Lessons from Southeast Asia. Journal of Labor Economics, 39(3), 513–534. https://doi.org/10.1086/jle.2021.39.3.513
Dang, T. T., Vu, T. H., & Le, H. T. (2021). Government interventions and employment recovery post-pandemic in developing countries. Asian Economic Policy Review, 16(2), 248–267. https://doi.org/10.1111/aepr.12345
Human responses to high humidity. (2016). Journal of Applied Physiology, 121(2), 391–403. https://doi.org/10.1152/japplphysiol.00177.2016
International Labour Organization. (2020). COVID-19 and the world of work: Impact and policy responses. Geneva: ILO. https://www.ilo.org/global/topics/coronavirus/lang--en/index.htm
Manalo, R. (2022). Computational approaches to unemployment analysis in the Philippines post-COVID-19. Philippine Journal of Labor Studies, 16(1), 22–39. https://doi.org/10.2139/pjls.2022.16.1.22
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24. https://doi.org/10.1002/qj.49710845502
Parsons, K. (2020). Human thermal environments: The effects of hot, moderate, and cold environments on human health, comfort, and performance (4th ed.). Boca Raton, FL: CRC Press.
Philippine Statistics Authority. (2020). Annual labor and employment report 2020. Quezon City: PSA. https://psa.gov.ph/statistics/labor-force-survey
Philippine Statistics Authority. (2025). Unemployment rate in January 2025 was estimated at 4.3 percent. https://psa.gov.ph/content/unemployment-rate-january-2025-was-estimated-43-percent
Quickonomics. (2020). Unemployment rate: Definition, formula, and examples. https://quickonomics.com/terms/unemployment-rate/
Recio, C., & Basilio, R. (2022). Monthly unemployment trends in the Philippines: Seasonal fluctuations and post-pandemic recovery. Philippine Labor Review, 9(2), 55–72.
Santamouris, M. (2015). Regulating the damaged thermostat of the cities—Status, impacts, and mitigation challenges. Energy and Buildings, 91, 43–56. https://doi.org/10.1016/j.enbuild.2014.12.022
Sawka, M. N., Cheuvront, S. N., & Kenefick, R. W. (2011). Heat acclimatization and hydration: Optimizing human performance in hot environments. Comprehensive Physiology, 1(4), 1883–1928. https://doi.org/10.1002/cphy.c100082
World Bank. (2021). Philippines economic update, June 2021: Navigating a challenging recovery. World Bank. https://openknowledge.worldbank.org/entities/publication/8b126bd2-0a0d-569f-a3fd-905bdf7a88cb
World Bank. (2021). The impact of COVID-19 on labor markets in developing countries. Washington, DC: World Bank. https://www.worldbank.org/en/publication/labor-market-dynamics
World Bank. (2022). Computational analysis of labor market recovery post-COVID-19 in developing countries. Washington, DC: World Bank. https://www.worldbank.org/en/publication/labor-market-dynamics
World Bank. (2022). Labor market dynamics in the Philippines: Seasonal trends and recovery patterns. Washington, DC: World Bank. https://www.worldbank.org/en/country/philippines/publication/labor-market-dynamics
Yang, J., Li, X., & Zhang, Y. (2022). Urban green space and thermal comfort: Evidence from Southeast Asian cities. Sustainable Cities and Society, 79, 103715. https://doi.org/10.1016/j.scs.2022.103715
Zhang, L., Wang, Q., & Liu, H. (2024). Topographic and airflow influences on urban heat distribution in tropical cities. Urban Climate, 48, 101439. https://doi.org/10.1016/j.uclim.2023.101439
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Copyright (c) 2026 Niña Andreia R. Fabe, Mayzel Angelique G. Taclob, Gelian I. Orozco (Author)
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