Beyond Flexibility: Evaluating the Impact of Gig Work on Household Well-being in Rural and Urban Indonesia
Keywords:
GIG workers Quality of Life rural–urban Lewbel Instrumental VariableAbstract
Indonesia's digital transformation has driven the rise of platform-based jobs (gig work), particularly in the online transportation, delivery, and freelance services sectors. This work is often viewed as providing flexibility, but its impact on household quality of life remains debated, particularly given the differences between rural and urban conditions. This study aims to analyze how gig work affects household quality of life and examine the disparities in its impact between rural and urban areas. The study used data from the 2023 National Socioeconomic Survey (SUSENAS), which contains information on demographics, employment, household consumption, and access to social security. Gig households were identified based on the presence of household members working in the transportation and digital platform-based services sectors. The analysis was conducted using Lewbel's IV (heteroskedasticity-based instruments) to address potential endogeneity due to unobservable factors such as work preferences or individual abilities. Quality of life indicators were measured through per capita consumption, diversification of nutritious food consumption, and the proportion of education and health expenditures. The results indicate that gig work contributes positively to household consumption in urban areas, but its impact is more limited in rural areas due to limited digital infrastructure and market access. This finding indicates a digital divide in the benefits of the gig economy. By applying Lewbel IV, this study provides a more robust estimate of the impact of gig work, while also providing policy insights regarding the need for social protection interventions, financial literacy, and more equitable expansion of digital infrastructure in Indonesia.
References
Aloisi, A., Rainone, S., & Countouris, N. (2023). An unfinished task? Matching the Platform Work
Directive with the EU and international social acquis. International Labour Organization Working
Paper,
https://www.ilo.org/sites/default/files/wcmsp5/groups/public/%40ed_dialogue/%40actrav/docu
ments/publication/wcms_907189.pdf
Antara News. (2024, May 8). Menghadapi ledakan pekerja sektor informal di era ekonomi gig.
https://www.antaranews.com/berita/4416613/menghadapi-ledakan-pekerja-sektor-informal-di
era-ekonomi-gig
Anwar, A., & Graham, M. (2023). Financial resilience and digital gig work: Evidence from developing
economies. World Development, 169, 106240. https://doi.org/10.1016/j.worlddev.2023.106240
Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of
confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.
https://doi.org/10.1080/00273171.2011.568786
Berg, J., Furrer, M., Harmon, E., Rani, U., & Silberman, M. S. (2018). Digital labour platforms and
the future of work: Towards decent work in the online world. Geneva: International Labour
Organization,
https://www.ilo.org/sites/default/files/wcmsp5/groups/public/%40dgreports/%40dcomm/%40pu
bl/documents/publication/wcms_645337.pdf
Chen, M. K., Rossi, P., & Chevalier, J. (2021). The value of flexibility: Evidence from Uber drivers.
American Economic Review, 111(8), 2431–2476. https://doi.org/10.1257/aer.20191389
Courtemanche, C. J. (2020). An application of Lewbel’s instrumental variables method. National
Bureau of Economic Research Working Paper 26732,
https://www.nber.org/system/files/working_papers/w26670/w26670.pdf
De Stefano, V., Durri, I, & Wouters, M. (2021). Regulating platform work: Institutional contexts and
worker well-being in comparative perspective, ILO Working Paper No 27, March 2021.
Fiers,F.(2024). Resilience in the gig economy: digital skills in online freelancing, Journal of
Computer-Mediated Communication, Volume 29, Issue 5, September 2024, zmae014, https://doi.org/10.1093/jcmc/zmae014
Fisher, J. (2024). Worker welfare in the gig economy: A structural approach. Industrial Relations
Journal, 55(2), 185–206. https://doi.org/10.1111/irj.12490
Hall, J. V., & Krueger, A. B. (2018). An analysis of the labor market for Uber’s driver-partners in the United States. Industrial and Labor Relations Review, 71(3), 705–732.
https://doi.org/10.1177/0019793917717222
Haryanto, A, T. (2024). APJII: Jumlah Pengguna Internet Indonesia Tembus 221 Juta Orang,
Heinrich, C., Maffioli, A., & Vázquez, G. (2010). A primer for applying propensity-score matching.
Inter-American Development Bank, http://dx.doi.org/10.18235/0008567
Indra, R., & Nawangsari, S. A. (2023). Legal protection for gig economy workers from the perspective of labor law in Indonesia. Hakim: Jurnal Ilmu Hukum dan Sosial, 3(1).
https://doi.org/10.51903/hakim.v3i1.2289
Klaus, D. (2023). Dependency and social recognition of online platform workers. Social Inclusion,
11(3), 67–80. https://doi.org/10.17645/si.v11i3.6990
Kassi, O., & Lehdonvirta, V. (2018). Online labour index: Measuring the online gig economy for
policy and research. Development Policy Review, 36(4), 419–434.
https://doi.org/10.1111/dpr.12328
Lewbel, A. (2012). Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous
Regressor Models. Journal of Business & Economic Statistics, 30(1), 67–80.
https://doi.org/10.1080/07350015.2012.643126
Lewbel, A. (2018). Identification and estimation using heteroskedasticity without instruments, The
binary endogenous regressor case, Economics Letters, 25(19), 1361–1365.
https://doi.org/10.1016/j.econlet.2018.01.003
Magister, I., & Putranto, F. G. (2024). Kerentanan kesejahteraan gig worker di Indonesia
pascapandemi. Jurnal Ekonomi Indonesia, 12(2), 133–152.
https://doi.org/10.52813/jei.v12i2.479
Mehta, R., & Awasthi, A. (2023). Work, earnings, and precarity in the Indian gig economy. Industrial Relations Journal, 54(5), 421–442. https://doi.org/10.1111/irj.12464
Nilsen, M., & Kongsvik, T. (2023). Health, safety, and well-being in platform-mediated work: A job
demands–resources perspective. Safety Science, 163, 106130.
https://doi.org/10.1016/j.ssci.2023.106130
Pusat Kajian Anggaran. (2022). Realisasi Pembangunan Infrastruktur TIK Serta Potensi dan
Tantangannya, Budget Issue Brief: Politik dan Keamanan, Vol 02, Ed 8,
https://berkas.dpr.go.id/pa3kn/analisis-tematik-apbn/public-file/bib-public-118.pdf
Rachmawati, R. (2021). Urban gig workers in Indonesia during COVID-19. Work Organisation, Labour & Globalisation, 15(2), 45–58. https://doi.org/10.13169/workorgalaboglob.15.2.0045
Shen, C. (2025). How does the digital economy affect the urban–rural income gap? Journal of
Development Economics, 165, 103119. https://doi.org/10.1016/j.habitatint.2025.103327
Sitorus, A., & Kornitasari, Y. (2024). Analisis tingkat kesejahteraan pekerja gig di Indonesia. Journal of Development Economic and Social Studies, 3(2), 125–138.
https://doi.org/10.21776/jdess.2024.03.2.16
Syabani, A. H., Silvianita, A., & Widodo, A. (2023). Flexibility and social security in gig workers: A
study of gig worker welfare in Bandung City. Formosa Journal of Multidisciplinary Research,
3(6), 45–59. https://doi.org/10.55927/fjmr.v3i6.9453
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press.
Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and
algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56–75.
