Is China’s Urbanization Inclusive?—Comparative Research Based on Machine Learning Algorithms

Improving the inclusiveness of urban development is crucial to improving wages low- and middle-income workers. In this study, we used machine learning cluster labor into low, middle, high socioeconomic groups in order analyze effects economic agglomeration compare them with results, which were classified according income. The results showed that has improved low middle groups; estimated wage spillover effect was 3.9%. By contrast, result based on by a single index income 20.3%, represents an overestimation agglomeration. This method often explain China’s urbanization, leading overestimation. Further mechanism analysis found characteristics industrial structure affect change elasticity caused agglomeration, moderating differing associated responsible for disparities China.