Uncovering income-based unequal access to schooling using multi-source big data approach

Income-based unequal access to schooling threatens inclusive urban growth and fair education in the rapidly urbanizing Global South, but measuring it remains difficult due to limited data. This study addresses the issue by using a new multi-source data method, analyzing the residence-school relationship of over one million students through mobile phone population mobility data in Chinese megacities. We reveal a complex pattern of inequality: students at top schools travel long distances, while lower-quality schools cluster low-income students in isolated groups. Top schools, although showing greater income diversity within their student bodies, are still predominantly attended by high-income students. Segregation decreases from primary to high-school levels, driven by the shift from proximity-based school districting to merit-based admission. However, urban expansion, despite the increase in school provisioning, reproduces these inequalities and segregation instead of solving them. This paper validates the Effectively Maintained Inequality theory in the Global South context and provides empirical evidence on unequal access to schooling in urban China.