Peer discrimination toward rural migrant students and academic performance in urban China: A machine learning approach. (December 2022) - Lee, Hanol; Song, Eunbi
Abstract: Rural students migrating to cities in China encounter discrimination from their local peers, largely due to the institution known as Hukou . Using micro-level data from urban middle schools in China, we confirm that peer discrimination toward migrant students is negatively associated with the academic performance. We perform a simulation study using a novel machine learning technique to generate synthetic data where the bias in the original data is removed. Our finding shows the negative association disappears when students are exposed to a setting that mitigates discrimination. For policy implication, this study implies that practices to increase migrants' awareness of discrimination or reduce locals' unfavorable behavior would be helpful. Such practices should be multidimensional, with students, teachers, and schools. Highlights: Rural students who migrate to cities face discrimination from their local peers. Discrimination is negatively associated with students' academic performance. Our simulation study generates the environment where discrimination is mitigated. Negative association between discrimination and test score no longer appears. Practices that raise awareness of discrimination or reduce bias would be helpful.