As a transformative force in the labor market, the gig economy has rapidly expanded in China in recent years, employing over 200 million workers. Understanding the determinants is crucial for grasping how the gig economy reshapes employment structures and worker livelihoods in the digital era. Surprisingly, the underlying factors driving Chinese workers' decisions to engage in gig work remain insufficiently explored. Our study addresses this gap by analyzing the determinants of gig economy entry using more than 15,000 individuals from the China Labor-force Dynamics Survey (CLDS) (2014, 2016, 2018). This research adopts a broader definition of the gig economy, encompassing both platform-based and non-platform-based flexible employment, enabling a more inclusive analysis of workers' choices. 

Theoretically, using Self-Determination Theory (SDT), Labor Market Segmentation Theory (LMST), and the Precarity Framework (PF), this study explores both intrinsic motivations (e.g., wage incentives, work flexibility) and external constraints (e.g., gender discrimination, lack of social security) that shape workers' choices. Methodologically, the study employs three complementary machine learning models—Artificial Neural Networks (ANN), Random Forest (RF), and XGBoost—to predict gig economy participation and assess the relative importance of the features. SHAP value analysis and logistic regression are used to further identify the direction and magnitude of these influences.

The findings reveal that wage is the most critical determinant of gig economy participation, with higher earnings significantly increasing the likelihood of entry. Work flexibility is also a major factor, as workers who have greater autonomy over their work tasks, progress, and intensity are more likely to engage in gig work. However, gender inequality persists, as women are less likely to enter the gig economy, reflecting systemic barriers such as family responsibilities and less access to gig work opportunities. Additionally, lack of social security coverage is a significant factor, indicating that many workers turn to gig work out of necessity rather than choice.

This research contributes to the literature by providing a comprehensive, data-driven analysis of gig economy participation in China. It highlights the complex interplay between economic incentives and structural constraints. The findings offer targeted policy recommendations, including minimum wage protections, enhanced social security mechanisms for gig workers, and incentive measures for female workers. Finally, this study provides insights into future trends in digital labor and the evolving role of flexible employment in China's labor market.