Thesis Title: Mapping Gendered Employment Trajectories in China: A Data-Driven Approach to Labor Market Inequality

This study examines the gendered and spatially stratified patterns of employment trajectories among Chinese workers during 2018–2022. Using three waves of China Family Panel Studies data ( N = 12,622), we employ sequence analysis to identify six distinct trajectory types for each gender and apply explainable machine learning (XGBoost-SHAP) to quantify the effects of five composite pressure indices. Key findings include: (1) Pronounced gender disparities exist, with 81.2 % of men maintaining continuous employment compared to 63.8 % of women, as caregiving responsibilities induce greater discontinuity in women’s trajectories; (2) Career development pressure emerges as the strongest predictor of women’s employment-to-care transitions (SHAP value +0.80), while for men it primarily shapes exit pathways rather than employment probability; (3) Urban-rural stratification operates through differential institutional resource allocation, providing “protection effects” for urban residents while creating “deprivation mechanisms” for rural populations, particularly rural women, systematically pushing them toward precarious trajectories; (4) Rural women face “triple disadvantage” of economic constraints, caregiving burdens, and institutional exclusion, rendering their caregiving choices essentially involuntary “forced exits.” These findings reveal how macro-level institutional arrangements shape gendered employment inequality and inform evidence-based policy interventions for China’s sustainable development agenda, including expanding socialized care infrastructure, reforming hukou-based barriers to promote urban-rural labor market integration, and developing gender-responsive employment support strategies that address structural roots of inequality.