In the era of Industry 4.0, characterised by automation, artificial intelligence, and digital platforms, gig workers face increasing challenges in maintaining competitiveness. To thrive in this dynamic environment, upskilling and reskilling—enhancing existing skills and acquiring new competencies—are essential for navigating evolving job demands and achieving key success metrics, such as higher income and project diversity (OECD, 2023).

While formal education provides structured and comprehensive pathways for the long-term development of worker capabilities, upskilling and reskilling in the gig economy demand more agile, flexible, and targeted approaches. The gig economy challenges the traditional education-income paradigm as workers increasingly acquire skills through self-directed learning and hands-on practical experience rather than formal education (Herrmann et al., 2023). Emerging evidence highlights the potential of non-formal learning pathways, such as crowdwork and platform-based gig work, to serve as channels for skill development and expansion (Barnes et al., 2015; Cedefop, 2020; Pouliakas & Ranieri, 2022). Despite this, the effectiveness of these non-formal approaches in improving gig worker outcomes remains underexplored, particularly within the dynamic landscape of the online gig economy. This study addresses this gap by examining the extent to which upskilling and reskilling measures contribute to the success of online gig workers.

To address this question, a longitudinal research design is employed to examine profile data from 36,831 gig workers on a leading digital platform across four waves of observation over a two-year period. The analysis focuses on key success indicators, specifically changes in gig workers' requested hourly rates and their monthly earned income. Skill development dynamics, captured through three parts, education, qualifications, and certifications, serve as the primary independent variables. To account for temporal dependencies and potential endogeneity, the study employs a Dynamic Panel Data (DPD) model, applying appropriate estimation techniques to ensure robust and unbiased results.

We anticipate that upskilling and reskilling will positively contribute to income growth, highlighting their potential role in enabling gig workers to adapt and access higher-value opportunities. By examining these dynamics, the study aims to offer insights into the evolving nature of gig work, inform the design of effective platform-based training initiatives, and underscore the policy relevance of promoting continuous learning to foster a resilient and adaptable workforce in the digital age.