Trapped in the Grip of Income Vulnerability: Who Earns Below or Above the Minimum Among Indonesian Gig Workers?

Abstract

The development of the gig economy in the digital era has opened up new opportunities in the job market. Jobs in this era offer flexibility in time, location, and work patterns. However, this flexibility also presents challenges in the form of income vulnerability. In general, gig workers are classified as informal workers, a category that is expected to remain dominant in the Indonesian workforce structure until 2024. Their proportion exceeds 50 percent and has even increased recently. Vulnerability will become even more apparent in 2025, when nearly 87 percent of regions in Indonesia will have self-employed workers in the service sector with an average monthly income below the minimum wage. Although several studies have highlighted the gig worker phenomenon in Indonesia, unfortunately, none have examined gig worker income vulnerability simultaneously at the individual, household, and regional levels. This study aims to analyze the determinants of gig worker income vulnerability in Indonesia. The research was conducted in stages, at three levels: individual, household, and regional, using a hierarchical logistic regression approach and drawing on data from the February 2024 Sakernas. The analysis results indicate that male gig workers, those with spouses, and those with work experience have a lower risk of earning below the minimum wage. Conversely, gig workers living in households with four or more members are at greater risk. Furthermore, high open unemployment and regional inflation further increase the risk of gig workers earning below the minimum wage. These findings confirm that gig worker vulnerability is not solely an individual issue, but is also influenced by household conditions and regional macroeconomic dynamics. Therefore, the government needs to design social protection policies, adaptive wage regulations, and skills development programs to protect gig workers better and increase their competitiveness. Furthermore, the government is expected to strengthen labor market stabilization and inflation control at the regional level. 

References

Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268–281. https://doi.org/10.1016/j.gloenvcha.2006.02.006 Akbar, F. (2022). Gender Wage Gap: Evidence from Employment in Informal Sector. The Journal of Indonesia Sustainable Development Planning, 3(2), 104–117. https://doi.org/10.46456/jisdep.v3i2.301 Azen, R., & Walker, C. M. (2021). Categorical Data Analysis for the Behavioral and Social Sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780429330308 Bitana, E. B., Lachore, S. T., & Utallo, A. U. (2024). The influence of household size on socioeconomic conditions of rural farm households in Damot Woyde District, Wolaita Zone, Southern Ethiopia. Cogent Social Sciences, 10(1). https://doi.org/10.1080/23311886.2024.2358153 Blanchflower, D., & Oswald, A. (2005). The Wage Curve Reloaded. https://doi.org/10.3386/w11338 BPS. (2025a, May 6). Proporsi Lapangan Kerja Informal Menurut Provinsi. Badan Pusat Statistik. BPS. (2025b, August 11). Rata-Rata Pendapatan Bersih Sebulan Pekerja Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (Ribu Rupiah). Badan Pusat Statistik. Chiswick, B. R. (2023). Estimating Returns to Schooling and Experience: A History of Thought (Issue 1365). Global Labor Organization (GLO). https://hdl.handle.net/10419/280895

Cook, C., Diamond, R., Hall, J. V, List, J. A., & Oyer, P. (2021). The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers. The Review of Economic Studies, 88(5), 2210–2238. https://doi.org/10.1093/restud/rdaa081 Danquah, M., Iddrisu, A. M., Boakye, E. O., & Owusu, S. (2021). Do gender wage differences within households influence women’s empowerment and welfare? Evidence from Ghana. Journal of Economic Behavior & Organization, 188, 916–932. https://doi.org/10.1016/j.jebo.2021.06.014 Dong, Z., Peng, J., & Liu, S. (2024). Gender Pay Gap in the Gig Economy. China Finance and Economic Review, 13(1), 3–22. https://doi.org/10.1515/cfer-2024-0001 e-Conomy SEA. (2024, November 13). e-Conomy SEA 2024: Perekonomian digital Indonesia akan mencapai GMV $90 miliar pada tahun 2024. Google, Tamasek, Bain & Company. Field, A. P. (2018). Discovering Statistics Using IBM SPSS Statistics (5th edition.). SAGE. Hasanah, H., Nachrowi, N. D., Wisana, I. D. G. K., & Siregar, H. (2024). Could the minimum wage policy reduce food insecurity among households of formal workers in Indonesia? Agriculture & Food Security, 13(1), 7. https://doi.org/10.1186/s40066-023-00451-3 Huang, N., Burtch, G., Hong, Y., & Pavlou, P. A. (2020). Unemployment and Worker Participation in the Gig Economy: Evidence from an Online Labor Market. Information Systems Research, 31(2), 431–448. https://doi.org/10.1287/isre.2019.0896 Jokinen, J. (2020). The wage curve and local monopsony power. The Annals of Regional Science, 64(1), 159–183. https://doi.org/10.1007/s00168-019-00966-3 Jordà, Ò., & Nechio, F. (2023). Inflation and wage growth since the pandemic. European Economic Review, 156, 104474. https://doi.org/10.1016/j.euroecorev.2023.104474 Kadolkar, I., Kepes, S., & Subramony, M. (2025). Algorithmic management in the gig economy: A systematic review and research integration. Journal of Organizational Behavior, 46(7), 1057 1080. https://doi.org/10.1002/job.2831 Kurian, J. S., & Bindu Madhavi, N. (2024). Navigating the gig economy: exploring challenges and motivations for the wellbeing of Gen Y and Gen Z gig workers. Cogent Psychology, 11(1). https://doi.org/10.1080/23311908.2024.2357458 Mediating Li, Y., Xie, Z., Tui, Z., & Woo, D. (2025). The Impact of Digital Economy on Flexible Employment: The Role of Laborer Perceptions. SAGE Open, 15(1). https://doi.org/10.1177/21582440251327017 Liu, R., & Yin, H. (2024). How Algorithmic Management Influences Gig Workers’ Job Crafting. Behavioral Sciences, 14(10), 952. https://doi.org/10.3390/bs14100952 Marsela, T., & Mulyani, E. (2024). The Influence of Income, Education, and Number of Family Members on Household Consumption Expenditures in Bandar Sari Village, Way Tuba District, Way Kanan Regency. INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS, 07(03). https://doi.org/10.47191/ijmra/v7-i03-69

Mu, X., & Chen, S. (2022). Family-Size Effect on Intergenerational Income Mobility under China’s Family Planning Policy: Testing the Quantity–Quality Trade-Off. Sustainability, 14(19), 12559. https://doi.org/10.3390/su141912559 Natalia, C., & Putranto, F. G. F. (2023). Kerentanan Kesejahteraan Gig Worker di Indonesia Pascapandemi. Jurnal Ekonomi Indonesia, 12(2), 173–186. https://doi.org/10.52813/jei.v12i2.479 Olivo-Villabrille, M. (2022). The marital earnings premium: an IV approach. Empirical Economics, 62(2), 709–747. https://doi.org/10.1007/s00181-021-02021-7 Pampel, F. (2000). Logistic Regression. SAGE Publications, Inc. https://doi.org/10.4135/9781412984805 Pérez Pérez, J. (2020). The minimum wage in formal and informal sectors: Evidence from an inflation shock. World Development, 133, 104999. https://doi.org/10.1016/j.worlddev.2020.104999 Permana, M. Y., Izzati, N. R., & Askar, M. W. (2022). Measuring The Gig Economy in Indonesia: Typology, Characteristics, and Distribution. Jurnal Manajemen Teknologi, 21(3), 339–358. https://doi.org/10.12695/jmt.2022.21.3.7 Pilossoph, L., & Wee, S. L. (2021). Household Search and the Marital Wage Premium. American Economic Journal: Macroeconomics, 13(4), 55–109. https://doi.org/10.1257/mac.20180092 Pituch, K. A., & Stevens, J. P. (2015). Applied Multivariate Statistics for the Social Sciences. Routledge. https://doi.org/10.4324/9781315814919 Prestianawati, S. A., Fawwaz, M., & Teguh, A. L. J. (2023). Analisis Determinan Tenaga Kerja yang Bekerja pada GIG Economy. International Journal For Multidisciplinary Research, 5(4), 1–24. https://doi.org/10.36948/ijfmr.2023.v05i04.4612 Putranto, F. G. F., Natalia, C., & Pitriyani, N. K. D. (2024). Closing the Gap Between Education and Labor Market Requirement: Do Vocational Education Matter? The Journal of Indonesia Sustainable Development Planning, 5(3), 181–191. https://doi.org/10.46456/jisdep.v5i3.614 Rodríguez-Modroño, P., Pesole, A., & López-Igual, P. (2022). Assessing gender inequality in digital labour platforms in Europe. Internet Policy Review, 11(1). https://doi.org/10.14763/2022.1.1622 Sihombing, D. A. (2022). Does Marriage Affect Men’s Labor Market Outcomes? Evidence from Indonesia. Proceedings of the Eighth Padang International Conference On Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PICEEBA-8 2021), 222, 457–566. https://doi.org/10.2991/aebmr.k.220702.069 Sitorus, A. A., & Kornitasari, Y. (2024). Analisis Tingkat Kesejahteraan Pekerja Gig di Indonesia. Journal of Development Economic and Social Studies, 3(2), 537–551. https://doi.org/10.21776/jdess.2024.03.2.16 Stanford, J. (2017). The resurgence of gig work: Historical and theoretical perspectives. The Economic and Labour Relations Review, 28(3), 382–401. https://doi.org/10.1177/1035304617724303

Sultana, N., Rahman, M. M., & Khanam, R. (2022). Informal Sector Employment and Economic Growth: Evidence from Developing Countries in SDG Perspective. Sustainability, 14(19), 11989. https://doi.org/10.3390/su141911989 Toczek, L., Bosma, H., & Peter, R. (2021). The Gender Pay Gap: Income Inequality Over Life Course – A Multilevel Analysis. Frontiers in Sociology, 6. https://doi.org/10.3389/fsoc.2021.815376 Vallas, S., & Schor, J. B. (2020). What Do Platforms Do? Understanding the Gig Economy. Annual Review of Sociology, 46(1), 273–294. https://doi.org/10.1146/annurev-soc-121919-054857 Wiener, M., Cram, W. A., & Benlian, A. (2023). Algorithmic control and gig workers: a legitimacy perspective of Uber drivers. European Journal of Information Systems, 32(3), 485–507. https://doi.org/10.1080/0960085X.2021.1977729 Woodcock, J., & Graham, M. (2020). The Gig Economy: A Critical Introduction (1st ed., Vol. 1). Polity Press. Workers: Yang, H., Li, C., & Sun, Z. (2023). The Impact Mechanism of Work Experience on the Income of Flexible Evidence https://doi.org/10.3390/su152316422 from China. Sustainability, 15(23), 16422. Zhang, L., Yang, J., Zhang, Y., & Xu, G. (2023). Gig worker’s perceived algorithmic management, stress appraisal, and destructive deviant behavior. PLOS ONE, 18(11), e0294074. https://doi.org/10.1371/journal.pone.0294074

How to Cite

“Trapped in the Grip of Income Vulnerability: Who Earns Below or Above the Minimum Among Indonesian Gig Workers?” (2025) Indonesia Forum on Labour and Productivity, 1. Available at: https://iflp.kemnaker.go.id/index.php/naker/article/view/31 (Accessed: 29 October 2026).