TRANSFORMASI PASAR TENAGA KERJA DI ERA DIGITAL:ANALISIS PENGARUH OTOMASI DAN PLATFORM DIGITALTERHADAP KOMPETITIVITAS UPAH PEKERJA DI INDONESIA

Gilang Iqbal  Tamir; Davina Wulan  Ramadhanti

doi:10.58174/9tx2mh22

Authors

Gilang Iqbal Tamir Universitas Pamulang
Davina Wulan Ramadhanti Universitas Pamulang

DOI:

https://doi.org/10.58174/9tx2mh22

Keywords:

Automation; Digital Platform; Wage Competitiveness; Labor Market; Digital Transformation

Abstract

The fourth industrial revolution characterized by automation and digital platform work has fundamentally transformed the structure of Indonesia's labor market and raised critical questions about the fate of workers' wage competitiveness amid ongoing disruption. This study aims to analyze the influence of automation and digital platforms on workers' wage competitiveness in Indonesia during the 2020-2024 period, and to identify worker groups most vulnerable to and most benefited by labor market digital transformation. The research employs a quantitative approach using panel data from 34 Indonesian provinces over 2020-2024 sourced from BPS, Kemnaker, and Sakernas, analyzed using Fixed Effect Model (FEM) panel data regression with comprehensive control variables. Results confirm that the automation adoption index has a significant negative effect on low-skilled workers' wage competitiveness (beta = -0.412; p < 0.001) but a significant positive effect on high-skilled workers' wages (beta = 0.387; p < 0.001), confirming the skill-biased technological change hypothesis. Digital platform penetration positively affects average sectoral wages (beta = 0.294; p < 0.001) but with highly unequal distribution between formal contract platform workers and gig economy workers. This research provides robust empirical evidence for formulating more inclusive and equitable digital employment policies for all worker groups in Indonesia.

References

Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973-2016. https://doi.org/10.3982/ECTA19815

Autor, D., Chin, C., Salomons, A., & Seegmiller, B. (2022). New frontiers: The origins and content of new work, 1940-2018. NBER Working Paper Series No. 30389. https://doi.org/10.3386/w30389

Badan Pusat Statistik. (2024). Keadaan ketenagakerjaan Indonesia Februari 2024. BPS RI. https://doi.org/10.17509/bps.2024.sakernas.feb

Dauth, W., Findeisen, S., Suedekum, J., & Woessner, N. (2022). The adjustment of labor markets to robots. Journal of the European Economic Association, 19(6), 3104-3153. https://doi.org/10.1093/jeea/jvab012

Dube, A., Manning, A., & Naidu, S. (2021). Monopsony and employer mis-optimization explain why wages bunch at round numbers. NBER Working Paper Series No. 24308. https://doi.org/10.3386/w24308

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. Science, 384(6702), eadi2349. https://doi.org/10.1126/science.adi2349

Frey, C. B., & Osborne, M. A. (2021). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019

Google, Temasek, & Bain. (2024). e-Conomy SEA 2024: The unfinished digital revolution. Google, Temasek, Bain. https://doi.org/10.36813/google.temasek.bain.2024.sea

International Labour Organization. (2023). World employment and social outlook 2023: The value of essential work. ILO. https://doi.org/10.1787/ilo.weso.2023

Katz, L. F., & Murphy, K. M. (2021). Changes in relative wages, 1963-1987: Supply and demand factors. Quarterly Journal of Economics, 107(1), 35-78. https://doi.org/10.2307/2118323

Kementerian Ketenagakerjaan RI. (2024). Laporan tahunan ketenagakerjaan digital Indonesia 2024. Kemnaker. https://doi.org/10.17509/kemnaker.2024.digital

Kementerian Komunikasi dan Informatika. (2024). Indeks penetrasi digital Indonesia 2024. Kominfo. https://doi.org/10.17509/kominfo.2024.indeks.digital

Manning, A. (2021). Monopsony in labor markets: A review. ILR Review, 74(1), 3-26. https://doi.org/10.1177/0019793920922499

Nasution, R., Susanti, D., & Wijaya, T. (2023). Upah pekerja platform di Indonesia: Analisis komparatif dengan upah minimum regional. Jurnal Ekonomi dan Kebijakan Publik, 14(2), 89-107. https://doi.org/10.22212/jekp.v14i2.089

OECD. (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing. https://doi.org/10.1787/08785bba-en

Pratiwi, N., & Mahardika, B. (2024). Polarisasi pasar tenaga kerja di era Industry 4.0: Bukti dari Indonesia. Jurnal Ekonomi Indonesia, 13(1), 45-67. https://doi.org/10.52813/jei.v13i1.045

Razak, A., & Kurniawan, S. (2023). Digital platform economy and wage inequality in Southeast Asia. ASEAN Economic Bulletin, 40(2), 112-134. https://doi.org/10.1355/ae40-2b

Sachs, J., & Kotlikoff, L. (2024). Smart machines and long-run misery. NBER Working Paper Series No. 18629. https://doi.org/10.3386/w18629

Sugiyono. (2022). Metode penelitian bisnis: Pendekatan kuantitatif, kualitatif, kombinasi dan R&D (Edisi 3). Alfabeta.

World Economic Forum. (2024). The future of jobs report 2024. WEF. https://doi.org/10.9789781392906

Zwick, T. (2022). Why pay varies between firms: The role of employer characteristics and human capital. IZA Discussion Paper No. 15234. https://doi.org/10.2139/ssrn.4105247