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China’s student population has drastically changed in recent decades to include rural migrants residing in urban areas and rural youth residing without one or both parents at home, often referred to as “left-behind” youth. While emerging bodies of work have compared their educational attainment to their urban and rural peers, less work has examined how teachers – key figures in the educational lives of youth – matter. Moreover, education policies can dictate teacher credentials as well as mandate better teacher training for working with diverse student populations.
Using a nationally-representative study of adolescents and a large-scale study of migrant and urban first-graders in Shanghai, we provide a comparative portrait of teacher qualifications and teacher perspectives of students and parents belonging to different groups. The first dataset, the China Education Panel Study 2013-2014, randomly sampled 28 county-level units, then 112 schools and 438 classrooms, including approximately 20,000 7th and 9th graders. The second dataset is the 2014 Child Well-Being Study, which was conducted in Shanghai, China, and surveyed 2,282 1st grade students from diverse socioeconomic backgrounds. The sample included children who were born in Shanghai and held urban hukou status (e.g., household registration system to distinguish between people holding urban versus rural status) as well as children who had rural hukou status and were considered migrants. We employ both descriptive statistics, as well as multilevel models, to gain a clear understanding of patterns of group difference.
Small inequalities exist that favor urban youth in terms of teacher level and quality of education, certification, and years of and awards for teaching. We also find that teachers have less favorable perceptions of migrant, left-behind, and rural youth – and their parents – compared to teachers of urban youth. We end by discussing how education policy and teacher education programs can better train future teachers to work with China’s diverse student population to address biases that likely perpetuate, rather than eliminate, existing inequalities.