Occupational Mismatch and Income Effects in Indonesia’s Gig Economy: A Gender Perspective
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Abstract
This study examines the relationship between occupational mismatch and income in Indonesia’s gig economy, with particular attention to gender differences. Using nationally representative data from the 2020 Indonesian National Labor Force Survey, gig workers are identified based on self-employment status and digital engagement in work activities. To address potential endogeneity, this study employs a treatment-effects model using district-level mismatch rates as an instrumental variable. The results show a negative association between occupational mismatch and income, indicating that mismatched workers have lower income than their well-matched counterparts after accounting for selection bias. This finding is consistent across ordinary least squares (OLS), propensity score matching (PSM), and treatment-effects models, supporting the robustness of the results. Gender-disaggregated analysis further shows that the negative effect is statistically significant for male workers but not for female workers, indicating important gender heterogeneity. Quantile regression results show that income penalties are present across the income distribution for male workers and become more pronounced at higher income levels. In contrast, the estimated effects for female workers remain statistically insignificant across quantiles. These findings suggest that occupational mismatch in digitally mediated labor markets reflects structural constraints rather than efficient skill allocation. This study contributes to the literature by providing evidence on the distributional and gender-specific effects of occupational mismatch. It also offers policy implications for improving skill alignment and reducing regional labor-market disparities.
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References
• Addison, J. T., Chen, L., & Ozturk, O. D. (2020). Occupational skill mismatch: Differences by gender and cohort. ILR Review, 73(3), 730–767. https://doi.org/10.1177/0019793919873864
• Ajaiyeoba, I. O. (2025). Diversity and emotional labor in the gig economy. Equality, Diversity and Inclusion: An International Journal, 44(6), 777–791. https://doi.org/10.1108/EDI-11-2023-0394
• Alacovska, A., Bucher, E., & Fieseler, C. (2024). A relational work perspective on the gig economy: Doing creative work on digital labour platforms. Work, Employment and Society, 38(1), 161–179. https://doi.org/10.1177/09500170221103146
• Arranz, J. M., & García-Serrano, C. (2025). Has it gone down the drain? The influence of overeducation on the wages of young workers. International Journal of Manpower, 46(5), 973–996. https://doi.org/10.1108/IJM-03-2024-0200
• Bahl, S., & Sharma, A. (2024). Informality, education-occupation mismatch, and wages: Evidence from India. Applied Economics, 56(19), 2260–2294. https://doi.org/10.1080/00036846.2023.2186364
• Becker, G. S. (1993). A treatise on the family (Enlarged ed.). Harvard University Press.
• Bedemariam, R., & Ramos, J. (2021). Over-education and job satisfaction: The role of job insecurity and career-enhancing strategies. European Review of Applied Psychology, 71(3), Article 100632. https://doi.org/10.1016/j.erap.2021.100632
• Benson, A., Sojourner, A., & Umyarov, A. (2020). Can reputation discipline the gig economy? Experimental evidence from an online labor market. Management Science, 66(5), 1802–1825. https://doi.org/10.1287/mnsc.2019.3303
• Blecker, R. A., & Braunstein, E. (2022). Feminist perspectives on care and macroeconomic modeling: Introduction to the special issue. Feminist Economics, 28(3), 1–22. https://doi.org/10.1080/13545701.2022.2085880
• Boto-García, D., & Escalonilla, M. (2022). University education, mismatched jobs: Are there gender differences in the drivers of overeducation? Economia Politica, 39(3), 861–902. https://doi.org/10.1007/s40888-022-00270-y
• BPS-Statistics Indonesia. (2015, November 30). Keadaan angkatan kerja di Indonesia Agustus 2015 [Labor force situation in Indonesia August 2015]. Badan Pusat Statistik. https://www.bps.go.id/id/publication/2015/11/30/311dc33e7624d47529ec4800/keadaan-angkatan-kerja-di-indonesia-agustus-2015.html
• BPS-Statistics Indonesia. (2021, January 21). BPS: 270,20 juta penduduk Indonesia hasil SP2020 [BPS: Indonesia’s population reached 270.20 million based on the 2020 Population Census]. Badan Pusat Statistik. https://www.bps.go.id/id/news/2021/01/21/405/bps--270-20-juta-penduduk-indonesia-hasil-sp2020.html
• BPS-Statistics Indonesia. (2023, December 22). Booklet Survei Angkatan Kerja Nasional Agustus 2023 [National Labor Force Survey booklet August 2023]. Badan Pusat Statistik. https://www.bps.go.id/id/publication/2023/12/22/ffb3e2d42b94d727d97e78d8/booklet-survei-angkatan-kerja-nasional-agustus-2023.html
• Calvo, A. G. (2024). Work in the platform economy: A broad perspective and its policy implications. In M. Valizadeh & C. J. F. Pickvance (Eds.), Work, employment and flexibility (pp. 95–111). Edward Elgar Publishing. https://doi.org/10.4337/9781035309368.00013
• Cavanagh, T. M., Kraiger, K., & Henry, K. L. (2020). Age-related changes on the effects of job characteristics on job satisfaction: A longitudinal analysis. The International Journal of Aging and Human Development, 91(1), 60–84. https://doi.org/10.1177/0091415019837996
• Cerulli, G. (2014). Ivtreatreg: A command for fitting binary treatment models with heterogeneous response to treatment and unobservable selection. The Stata Journal, 14(3), 453–480. https://doi.org/10.1177/1536867X1401400301
• Chaudhary, K., Manchanda, N. K., Timilsina, R., Rahut, D. B., & Sonobe, T. (2025). From platforms to paychecks: Comparative insights into wages of low-skilled platform and nonplatform workers in India (ADBI Working Paper No. 1496). Asian Development Bank Institute. https://doi.org/10.56506/LOHF9401
• Chua, K., & Chun, N. (2016, February). In search of a better match: Qualification mismatches in developing Asia (ADB Economics Working Paper Series No. 476). Asian Development Bank. https://www.adb.org/publications/search-better-match-qualification-mismatches-developing-asia
• Díaz, M. Y. (2022). Making it work: How women negotiate labour market participation after the transition to motherhood. Advances in Life Course Research, 53, Article 100500. https://doi.org/10.1016/j.alcr.2022.100500
• Doeringer, P. B., & Piore, M. J. (1985). Internal labour markets and manpower analysis. Routledge. https://doi.org/10.4324/9781003069720
• Duggan, J., Carbery, R., McDonnell, A., & Sherman, U. (2023). Algorithmic HRM control in the gig economy: The app-worker perspective. Human Resource Management, 62(6), 883–899. https://doi.org/10.1002/hrm.22168
• Eguia, B., Rodriguez Gonzalez, C., & Serrano, F. (2023). Overeducation and scarring effects on the wages of young graduates. International Journal of Manpower, 44(4), 755–771. https://doi.org/10.1108/IJM-02-2022-0075
• Faik, I., Gwee, M., Tan, F. T. C., Leong, C., & Hastiadi, F. F. (2026). When digital platforms enter informal sectors: Work formalization and institutional change. The Journal of Strategic Information Systems, 35(1), Article 101941. https://doi.org/10.1016/j.jsis.2025.101941
• Farré, L., Jofre-Monseny, J., & Torrecillas, J. (2023). Commuting time and the gender gap in labor market participation. Journal of Economic Geography, 23(4), 847–870. https://doi.org/10.1093/jeg/lbac037
• Flores-Saviaga, C., Li, Y., Hanrahan, B. V., Bigham, J. P., & Savage, S. (2020). The challenges of crowd workers in rural and urban America. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 159–162. https://doi.org/10.1609/hcomp.v8i1.7475
• Foweraker, B., & Cutcher, L. (2020). An ageless gift: Reciprocity and value creation by and for older workers. Work, Employment and Society, 34(4), 533–549. https://doi.org/10.1177/0950017019841521
• Gerber, C. (2022). Gender and precarity in platform work: Old inequalities in the new world of work. New Technology, Work and Employment, 37(2), 206–230. https://doi.org/10.1111/ntwe.12233
• Graham, M., Hjorth, I., & Lehdonvirta, V. (2017). Digital labour and development: Impacts of global digital labour platforms and the gig economy on worker livelihoods. Transfer: European Review of Labour and Research, 23(2), 135–162. https://doi.org/10.1177/1024258916687250
• Gussek, L., & Wiesche, M. (2023). IT professionals in the gig economy: The success of IT freelancers on digital labour platforms. Business & Information Systems Engineering, 65(5), 555–575. https://doi.org/10.1007/s12599-023-00812-z
• Hasibuan, E., & Handayani, D. (2021). Pengaruh qualification mismatch terhadap upah tenaga kerja di Indonesia [The effect of qualification mismatch on wages in Indonesia]. Jurnal Ekonomi dan Pembangunan, 29(1), 1–16. https://doi.org/10.14203/jep.29.1.2021.1-16
• Herrmann, A. M., Zaal, P. M., Chappin, M. M. H., Schemmann, B., & Lühmann, A. (2023). “We don’t need no (higher) education”: How the gig economy challenges the education-income paradigm. Technological Forecasting and Social Change, 186, Article 122136. https://doi.org/10.1016/j.techfore.2022.122136
• Huertas, I. P. M., & Raymond, J. L. (2024). Education, educational mismatch and occupational status: An analysis using PIAAC data. Economia Politica, 41(3), 717–738. https://doi.org/10.1007/s40888-024-00328-z
• International Finance Corporation. (2021, May). Women and e-commerce in Southeast Asia. World Bank Group. https://doi.org/10.1596/37837
• Jacobs, V., Rycx, F., & Volral, M. (2022). Wage effects of educational mismatch according to workers’ origin: The role of demographics and firm characteristics. De Economist, 170(4), 459–501. https://doi.org/10.1007/s10645-022-09413-9
• Jacobs, V., Pineda-Hernández, K., Rycx, F., & Volral, M. (2023). Does over-education raise productivity and wages equally? The moderating role of workers’ origin and immigrants’ background. Education Economics, 31(6), 698–724. https://doi.org/10.1080/09645292.2022.2156981
• 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
• Kain, J. F. (1968). Housing segregation, negro employment, and metropolitan decentralization. The Quarterly Journal of Economics, 82(2), 175–197. https://doi.org/10.2307/1885893
• Khoiruddin, M. A., Setyanti, A. M., Suman, A., Prasetyia, F., & Susilo, S. (2024). Exploring determinants of education-job mismatch among educated workers in Indonesia. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan, 25(2), 263–281. https://doi.org/10.23917/jep.v25i2.23994
• Klasen, S., Le, T. T. N., Pieters, J., & Santos Silva, M. (2021). What drives female labour force participation? Comparable micro-level evidence from eight developing and emerging economies. The Journal of Development Studies, 57(3), 417–442. https://doi.org/10.1080/00220388.2020.1790533
• Lott, Y., & Abendroth, A. K. (2020). The non-use of telework in an ideal worker culture: Why women perceive more cultural barriers. Community, Work & Family, 23(5), 593–611. https://doi.org/10.1080/13668803.2020.1817726
• Lukac, M., & Grow, A. (2021). Reputation systems and recruitment in online labour markets: Insights from an agent-based model. Journal of Computational Social Science, 4(1), 207–229. https://doi.org/10.1007/s42001-020-00072-x
• McKenzie, M. D. J. (2022). Micro-assets and portfolio management in the new platform economy. Distinktion: Journal of Social Theory, 23(1), 94–113. https://doi.org/10.1080/1600910X.2020.1734847
• McKinnish, T. (2020, January 30). Marriage and labor market outcomes. Marriage and labor market outcomes. In A. Banerjee (Ed.), Oxford Research Encyclopedia of Economics and Finance. Oxford University Press. https://doi.org/10.1093/acrefore/9780190625979.013.503
• Mincer, J. (1974). Schooling, experience, and earnings. Columbia University Press.
• Mori, K., Odagami, K., Inagaki, M., Moriya, K., Fujiwara, H., & Eguchi, H. (2024). Work engagement among older workers: A systematic review. Journal of Occupational Health, 66(1), Article uiad008. https://doi.org/10.1093/joccuh/uiad008
• Mukherjee, A., & Sarkhel, S. (2025). Patriarchal norms and women’s labour market outcomes. Review of Development Economics, 29(2), 747–774. https://doi.org/10.1111/rode.13145
• Munoz, I., Kim, P., O’Neil, C., Dunn, M., & Sawyer, S. (2024). Platformization of inequality: Gender and race in digital labor platforms. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), Article 108. https://doi.org/10.1145/3637385
• Pater, R., Cherniaiev, H., & Kozak, M. (2022). A dream job? Skill demand and skill mismatch in ICT. Journal of Education and Work, 35(6–7), 641–665. https://doi.org/10.1080/13639080.2022.2128187
• Prasetyo, E. H. (2024). Digital platforms’ strategies in Indonesia: Navigating between technology and informal economy. Technology in Society, 76, Article 102414. https://doi.org/10.1016/j.techsoc.2023.102414
• Rani, U., Castel-Branco, R., Satija, S., & Nayar, M. (2022). Women, work, and the digital economy. Gender & Development, 30(3), 421–435. https://doi.org/10.1080/13552074.2022.2151729
• Ross, A. G., McGregor, P. G., & Swales, J. K. (2024). Labour market dynamics in the era of technological advancements: The system-wide impacts of labour-augmenting technological change. Technology in Society, 77, Article 102539. https://doi.org/10.1016/j.techsoc.2024.102539
• Salas-Velasco, M. (2021). Mapping the (mis)match of university degrees in the graduate labour market. Journal for Labour Market Research, 55(1), Article 14. https://doi.org/10.1186/s12651-021-00297-x
• Schor, J. B., Attwood-Charles, W., Cansoy, M., Ladegaard, I., & Wengronowitz, R. (2020). Dependence and precarity in the platform economy. Theory and Society, 49(5), 833–861. https://doi.org/10.1007/s11186-020-09408-y
• Seo, H., Altschwager, D., Choi, B.-Y., Song, S., Britton, H., Ramaswamy, M., Schuster, B., Ault, M., Ayinala, K., Zaman, R., Tihen, B., & Yenugu, L. (2021). Informal technology education for women transitioning from incarceration. ACM Transactions on Computing Education, 21(2), Article 16. https://doi.org/10.1145/3425711
• Sevilla, M. P., Farías, M., & Luengo-Aravena, D. (2021). Patterns and persistence of educational mismatch: A trajectory approach using Chilean panel data. Social Sciences, 10(9), Article 333. https://doi.org/10.3390/socsci10090333
• Siatan, M. S., Gustiyana, S., & Nurfitriani, S. (2024). Infrastructure development and regional disparities. KnE Social Sciences, 9(16), 799–806. https://doi.org/10.18502/kss.v9i16.16289
• Sitorus, F. M., & Wicaksono, P. (2020). The determinant of educational mismatch and its correlation to wages. Jurnal Ekonomi Pembangunan, 18(2), 163–176. https://doi.org/10.29259/jep.v18i2.12788
• Smith, J. (2022). Treatment effect heterogeneity. Evaluation Review, 46(5), 652–677. https://doi.org/10.1177/0193841X221090731
• Stoll, M. A. (2005). Geographical skills mismatch, job search and race. Urban Studies, 42(4), 695–717. https://doi.org/10.1080/00420980500060228
• Sun, H., & Kim, G. (2022). The wage effects of overeducation across overall wage distribution on university graduates: Incidence, heterogeneity and comparison. International Journal of Manpower, 43(5), 1144–1165. https://doi.org/10.1108/IJM-03-2021-0181
• Suparman, S., & Muzakir, M. (2023). Regional inequality, human capital, unemployment, and economic growth in Indonesia: Panel regression approach. Cogent Economics & Finance, 11(2), Article 2251803. https://doi.org/10.1080/23322039.2023.2251803
• Topel, R. (1991). Specific capital, mobility, and wages: Wages rise with job seniority. Journal of Political Economy, 99(1), 145–176. https://doi.org/10.1086/261744
• Tran, T. Q., Vu, N. B. T., Van Le, D., & Vu, L. H. (2025). Vertical and horizontal job-education mismatches and wages: A quantitative analysis of university graduates in Vietnam. International Journal of Educational Development, 117, Article 103323. https://doi.org/10.1016/j.ijedudev.2025.103323
• Voces, C., & Caínzos, M. (2021). Overeducation as status inconsistency: Effects on job satisfaction, subjective well-being and the image of social stratification. Social Indicators Research, 153(3), 979–1010. https://doi.org/10.1007/s11205-020-02516-3
• Wicaksono, P., Theresia, I., & Al Aufa, B. (2023). Education–occupation mismatch and its wage penalties: Evidence from Indonesia. Cogent Business & Management, 10(3), Article 2251206. https://doi.org/10.1080/23311975.2023.2251206
• Wood, A. J., & Lehdonvirta, V. (2023). Platforms disrupting reputation: Precarity and recognition struggles in the remote gig economy. Sociology, 57(5), 999–1016. https://doi.org/10.1177/00380385221126804
• 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. https://doi.org/10.1177/0950017018785616
• Wulandari, H., & Damayanti, A. (2021). Qualification mismatch dan upah di Indonesia [Qualification mismatch and wages in Indonesia]. Jurnal Ekonomi dan Kebijakan Publik Indonesia, 8(1), 45–57. https://doi.org/10.24815/ekapi.v8i1.21168
• Yoselina, A. I., Marta, J., & Dina, R. (2024). Pengaruh horizontal mismatch terhadap upah tenaga kerja di Indonesia [The effect of horizontal mismatch on wages in Indonesia]. Jurnal Kajian Ekonomi dan Pembangunan, 6(2), 39–48. https://doi.org/10.24036/jkep.v6i2.16613
