Mathematical and Statistical Analysis of Gig Work in the Platform Economy

This paper investigates the structure, challenges, and optimization strategies of gig employment in Chinas rapidly expanding platform economy. As digital platforms like Meituan, Didi, and Ele.me continue to reshape labor relations, millions of workers engage in task-based, algorithm-mediated work arrangements without formal employment protections. Using a mixed-methods approachcombining statistical modeling, algorithmic system analysis, and empirical case studieswe identify three primary issues: high income volatility, opaque and biased algorithmic dispatch systems, and a widespread absence of social protections such as insurance, paid leave, or representation. To address these concerns, a set of integrated solutions is proposed, including transparent and auditable dispatch algorithms, fairness-aware machine learning frameworks, portable benefits schemes, and government-enforced minimum income standards. Analytical tools such as multivariate regression, Markov chain simulations, and fairness metrics (e.g., demographic parity) are employed to model and evaluate interventions. Ultimately, this study advocates for a multi-level, data-driven approach that combines mathematical optimization with regulatory reform to protect gig workers rights, enhance economic resilience, and promote the long-term sustainability of platform ecosystems.