Analysis of Opportunities and Threats of the Demographic Bonus Towards Unemployment in Indonesia
DOI:
https://doi.org/10.55927/z0bjz127Keywords:
Demographic Bonus, Unemployment, ARIMA Model, Time Series Analysis, Labor Market PolicyAbstract
This study examines the opportunities and threats of Indonesia’s demographic bonus on the unemployment rate and its implications for labor absorption and employment policy. The research employs an associative approach to analyze the relationship between the demographic bonus and unemployment. The data consist of time series data on Indonesia’s annual unemployment rate from 1980 to 2024, collected through documentation techniques and measured in percentage terms. The Box-ARIMA model is used as the analytical method. The findings indicate that Indonesia’s unemployment rate is projected to fluctuate with an overall upward trend during the 2025–2045 period, reaching an estimated 5.76 percent by 2045. These results suggest that the demographic bonus does not automatically generate economic benefits. Without significant economic improvement and sufficient job creation, the demographic bonus may instead become a serious threat by increasing unemployment levels. The study emphasizes the importance of targeted and sustainable labor market policies to ensure that the expanding working-age population can be absorbed productively. In the absence of such policies, the demographic bonus may place additional pressure on the labor market. However, the study has limitations, as it relies on a univariate ARIMA model and long-term projections based solely on historical trends, which may not fully capture structural changes or external shocks
References
Adelowokan, O. A., Maku, O. E., Babasanya, A. O., & Adesoye, A. B. (2019). Unemployment, poverty and economic growth in Nigeria. Journal of Economics and Management, 35(1). https://doi.org/10.22367/jem.2019.35.01
Agyei, A., Baah, G., Anowuo, I., & Owusu, E. (2025). Policy strategies for balancing digital financial inclusion with environmental sustainability and economic growth : lessons for sustainable development in Sub-Saharan African economies. Sustainable Futures, 10(12). https://doi.org/10.1016/j.sftr.2025.101503
Al-khraif, R. M., Salam, A. A., Fadzil, M., & Rashid, A. (2022). Demographic dividend in Saudi Arabia : From age structural changes to economic gains. Journal of Economics and Management, 44, 19–37. https://doi.org/10.22367/jem.2022.44.02
Aprianti, D. I., Suyanto, & Choirudin, S. (2022). Tantangan Bonus Demografi Bagi Pemerintah. Nusantara Innovation Journal, 1(1), 10–18. https://doi.org/10.70260/nij.v1i1.12
Armand, A. (2026). Can subsidized employment tackle long-term unemployment ? Experimental evidence from North Macedonia $. Journal of Development Economics, 178(July 2025), 103598. https://doi.org/10.1016/j.jdeveco.2025.103598
Baah-Boateng, W. (2015). Unemployment in Ghana: a cross sectional analysis from demand and supply perspectives. African Journal of Economic and Management Studies, 6(4), 402–415. https://doi.org/10.1108/ajems-11-2014-0089
Baah‐Boateng, W. (2013). Determinants of Unemployment in Swaziland. Journal of Applied Sciences, 15(9), 1190–1195. https://doi.org/10.3923/jas.2015.1190.1195
Bayraktar, Y., Ozyilmaz, A., Toprak, M., Olgun, M. F., & Isik, E. (2023). The role of institutional quality in the relationship between financial development and economic growth: Emerging markets and middle-income economies. Borsa Istanbul Review, 23(6), 1303–1321. https://doi.org/10.1016/j.bir.2023.10.002
Bhattarai, K. (2016). Unemployment-inflation trade-offs in OECD countries. Economic Modelling, 58, 93–103. https://doi.org/10.1016/j.econmod.2016.05.007
Bian, N., & Chen, Y. (2024). Testing for stationary of housing prices in China : An examination using efficient unit root tests. Heliyon, 10(1), e23891. https://doi.org/10.1016/j.heliyon.2023.e23891
Buhamra, S., Smaoui, N., & Gabr, M. (2003). The Box – Jenkins analysis and neural networks : prediction and time series modelling. Applied Mathematical Modelling, 27, 805–815. https://doi.org/10.1016/S0307-904X(03)00079-9
Celik, S. (2015). Estimation of Number Of Small Cattle Through ARIMA Models in Turkey. Journal of Mathematics and System Science, 10(5). https://doi.org/10.17265/2159-5291/2015.11.003
Dawn, L., Verma, A., & Manglani, H. (2025). Research in Globalization ICT , trade , economic growth , and unemployment nexus : panel evidence from the Indian Ocean Rim Association. Research in Globalization, 11(October), 100322. https://doi.org/10.1016/j.resglo.2025.100322
Gil-hernández, C. J., Marqués-perales, I., & Fachelli, S. (2017). Research in Social Strati fi cation and Mobility Intergenerational social mobility in Spain between 1956 and 2011 : The role of educational expansion and economic modernisation in a late industrialised country. Research in Social Stratification and Mobility, 51(April 2016), 14–27. https://doi.org/10.1016/j.rssm.2017.06.002
Gomado, K. M., & Amedanou, I. (2026). Unemployment impact of network sectors and employment protection legislation reforms : Evidence from selected african countries. World Development, 200(December 2025), 107304. https://doi.org/10.1016/j.worlddev.2025.107304
Hegerty, S. W. (2022). Time - series dynamics of Baltic trade flows : Structural breaks , regime shifts , and exchange - rate volatility. Journal of Economics and Management, 44, 96–118. https://doi.org/10.22367/jem.2022.44.05
Hummel, A. J. (2021). Unemployment and Tax Design. SSRN Electronic Journal, 246(March), 105359. https://doi.org/10.2139/ssrn.3885420
Jeyaraj, J. J., Chong, S. C., Chin, M. Y., & Foo, L. P. (2025). Gig labour regulation thresholds and youth unemployment : A dynamic panel threshold model analysis. Journal of Digital Economy, 4(February), 168–181. https://doi.org/10.1016/j.jdec.2025.07.003
Jolianis, Adrimas, Bachtiar, N., & Muharja, F. (2020). Unemployment Duration of Educated Workers in the Provinces of Indonesia: A Cross Sectional Analysis From Labor Supply Perspectives. Journal of Applied Economic Sciences, 1(67), 97–105. https://doi.org/10.14505/jaes.v15.1
Jolianis, Areva, D., Sulkaisi, N., Amelia, M., Ronald, J., & Stevani. (2022). Determination of Sectoral Economic Growth on Educated Unemployment in All Provinces of Indonesia. Economica, 11(1), 60–69. https://doi.org/10.22202/economica.2022.v11.i1.6270
Jolianis, Farlis, F., & Sari, P. (2024). Simultaneous Analysis of Economic Growth and Unemployment in Indonesia. Jurnal Trikonomika, 23(2), 63–73. https://doi.org/10.23969/trikonomika.v23i2.12716
Karimah, L. N., Shafwan, A.-F. V., & Tambunan, N. (2023). Analisis Inflasi Terhadap Pengangguran Di Indonesia. Community Development Journal, 4(2), 4572–4577. https://doi.org/10.30997/karimahtauhid.v2i4.8884
Laelia, S. M., & Priyarsono, D. S. (2023). A Study on the Use of Google Trends Data: The Case of Youth Unemployment Forecasting. Bina Ekonomi, 27(2), 100–123. https://doi.org/10.26593/be.v27i2.5276.1-24
Lan, B., Li, N., & Liu, T. (2025). How do economic growth and unemployment affect green development in Latin America nations? International Review of Economics and Finance, 98(February), 103955. https://doi.org/10.1016/j.iref.2025.103955
Li, L., Zhang, H., Ren, X., & Zhang, J. (2021). A novel recursive learning identification scheme for Box – Jenkins model based on error data R. Applied Mathematical Modelling, 90, 200–216. https://doi.org/10.1016/j.apm.2020.08.076
Lin, Y., & Xu, X. (2025). Intelligence-driven Growth : Exploring the dynamic impact of digital transformation on China ’ s high-quality economic development. International Review of Economics and Finance, 101(March), 104240. https://doi.org/10.1016/j.iref.2025.104240
Liu, Z., Wang, H., & Zhou, Y. (2024). Assessment of emission reduction effects in China’s economic transformation and sustainable development strategy. Ecological Indicators, 167(January), 112522. https://doi.org/10.1016/j.ecolind.2024.112522
Maryati, S. (2015). Dinamika Pengangguran Terdidik: Tantangan Menuju Bonus Demografi di Indonesia. Economica, 3(2), 124–136. https://doi.org/10.22202/economica.2015.v3.i2.249
Nakakuni, K. (2024). Journal of The Japanese and International Economies Macroeconomic analysis of the child benefit : Fertility , demographic. Journal OfThe Japanese and International Economies, 73(December 2023). https://doi.org/10.1016/j.jjie.2024.101325
Niken, K., Haile, M. A., & Berecha, A. (2023). On the nexus of inflation, unemployment, and economic growth in Ethiopia. Heliyon, 9(4), e15271. https://doi.org/10.1016/j.heliyon.2023.e15271
Nuriman, E. J., Hidayat, R., Setiabudi, A., & Dewi, M. P. (2025). Bonus Demografi : Peluang atau Tantangan Bagi Kemajuan Indonesia di Tahun 2045. PANDITA: Interdisciplinary Journal of Public Affairs, 8(1), 149–161. https://doi.org/10.61332/ijpa.v8i1.266
Nurman, S., & Nusrang, M. (2022). Analysis of Rice Production Forecast in Maros District Using the Box-Jenkins Method with the ARIMA Model. Journal of Mathematics and Applied Science, 2(1), 36–48. https://doi.org/10.35877/mathscience731
Oyedepo, E. O. (2024). Labor force dynamics and economic performance : A case of Nigeria, India, and China. Journal of Economics and Management, 46, 143–170.
Permata, J. M. A. C., & Habibi, M. (2023). Autoregressive Integrated Moving Average ( ARIMA ) Models For Forecasting Sales Of Jeans Products. Telematika: Jurnal Informatika Dan Teknologi Informasi, 20(1), 31–40. https://doi.org/10.31515/telematika.v20i1.7868
Pompei, F., & Selezneva, E. (2019). Unemployment and Education Mismatch in the EU before and after the financial crisis Fabrizio. Journal of Policy Modeling, 2(1). https://doi.org/10.1016/j.jpolmod.2019.09.009
Poschke, M. (2024). Wage employment, unemployment and self-employment across countries. Journal of Monetary Economics, 149(March 2022), 103684. https://doi.org/10.1016/j.jmoneco.2024.103684
Purbaningrat, B. W., Apriyanto, I. N. P., & Deksino, G. R. (2024). Demographic Bonus Management Strategies In Facing The Industrial Revolution 4.0 With The Perspective Of Defense Science And State Defense. International Hournal of Humanities Education and Social Science (IJHESS), 3(5), 2354–2360. https://doi.org/10.55227/ijhess.v3i5.851
Putri, F. R. G., & Suhartini, A. M. (2024). Pengaruh Bonus Demografi Terhadap Pengangguran Terdidik Dan Pengangguran Usia Muda Di Indonesia. Jurnal Pembangunan Dan Pemerataan, 4(2), 269–278. https://doi.org/10.34123/semnasoffstat.v2024i1.2152
Qomariyah, N., Dewi, J., Ningtyas, A., Tamara, K., & Ismanto, K. (2023). Analisis Peluang Dan Tantangan Adanya Bonus Demografi Ditahun 2045 Terhadap Perekonomian Indonesia. Jurnal Sahmiyya, 2(1), 180–186. https://doi.org/10.18592/alhadharah.v16i32.1992
Razali, F. A., & Haron, N. F. (2023). Forecasting Data Using Box - Jenkins Procedure : A Case Study For Unemployed People in Malaysia. Journal of Science and Technology, 6(1), 31–39. https://doi.org/2637-0018 Unemployment
Riani, A. R., & Haryatiningsih, R. (2023). Analisis Faktor Yang Mempengaruhi Pengangguran Terdidik di Kota Padang. Jurnal Riset Ilmu Ekonomi Dan Bisnis, 1(2), 114. https://doi.org/10.24014/ekl.v1i2.7101
Rujiwattanapong, W. S. (2025). Unemployment dynamics and endogenous unemployment insurance extensions $. European Economic Review, 178, 105106. https://doi.org/10.1016/j.euroecorev.2025.105106
Sacchi, S., & Samuel, R. (2024). Variation in unemployment scarring across labor markets. A comparative factorial survey experiment using real vacancies. Research in Social Stratification and Mobility, 93(October 2023), 100959. https://doi.org/10.1016/j.rssm.2024.100959
Safitri, I., Rusnita, A. N., Hasibuan, R. S., Tarigan, F. F., & Siregar, T. M. (2023). Antisipasi dan Tantangan Bonus Demografi : Permasalahan Pengangguran di Indonesia Menuju Tahun 2045. Jurnal Pendidikan Tambusai, 7(3), 28450–28457.
Salma, M., Abdelilah, K., Sara, S., & Mohamed, E. A. (2025). Stability analysis of a fractional order unemployment model with a non-linear job creation. Scientific African, 29, e02828. https://doi.org/10.1016/j.sciaf.2025.e02828
Santos, A., & Conte, A. (2025). Measuring regional economic transformation : Which European regions are leading the competitiveness and sustainability challenge ? Global Challenges & Regional Science, 4(April), 100020. https://doi.org/10.1016/j.gcrs.2025.100020
Siburian, E. S., Ginting, E. M., Syahfitri, M. D., & Purba, B. (2025). Bonus Demografi Sebagai Peluang dan Tantangan Bagi Indonesia. Jurnal Ilmiah Wahana Pendidikan, 11, 123–128. https://doi.org/DOI: peneliti.net/index.php/JIWP/article/view/9738
Smith, A. (2025). Labour migration, mass unemployment and the state: Class, gender and work in the Land Settlement Association in inter-war rural England. Journal of Rural Studies, 114(March 2024). https://doi.org/10.1016/j.jrurstud.2024.103498
Smith, J. D., & Hasan, M. (2020). Quantitative approaches for the evaluation of implementation research studies. Psychiatry Research, 283(August 2019), 112521. https://doi.org/10.1016/j.psychres.2019.112521
Tiao, G. C. (2015). Time Series : ARIMA Methods. In International Encyclopedia of Social & Behavioral Sciences (Second Edi, Vol. 23). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.42182-3
Triatmanto, B., & Bawono, S. (2023). The interplay of corruption, human capital, and unemployment in Indonesia_ Implications for economic development. Journal of Economic Criminology, 2(September), 100031. https://doi.org/10.1016/j.jeconc.2023.100031
Virrankoski, J. (2025). A note on indeterminacy of unemployment equilibrium. Economics Letters, 246(November 2024), 112091. https://doi.org/10.1016/j.econlet.2024.112091
Wallwey, C., & Kajfez, R. L. (2023). Methods in Psychology Quantitative research artifacts as qualitative data collection techniques in a mixed methods research study. Methods in Psychology, 8(February), 100115. https://doi.org/10.1016/j.metip.2023.100115
Wineman, A., Alia, D. Y., & Anderson, C. L. (2020). Definitions of “rural” and “urban” and understandings of economic transformation: Evidence from Tanzania. Journal of Rural Studies, 79(July), 254–268. https://doi.org/10.1016/j.jrurstud.2020.08.014
Yasmin, S., & Moniruzzaman, M. (2024). Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 16(April), 101203. https://doi.org/10.1016/j.jafr.2024.101203
Yenilmez, I., & Mugenzi, F. (2023). Estimation of conventional and innovative models for Rwanda ’ s GDP per capita : A comparative analysis of artificial neural networks and Box – Jenkins methodologies. Scientific African, 22(August). https://doi.org/10.1016/j.sciaf.2023.e01902
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jolianis, Putri Melizasari, Dina Amaluis (Author)

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






















