Axellina Muara Setyanti, Khusnul Ashar, Dwi Budi Santoso, Nurul Badriyah
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. © 2027 Mahidol University, Institute for Population and Social Research. All rights reserved.
Faculty of Economics and Business, Universitas Brawijaya, Indonesia
