Rural migrant concentration and performance inequality in Chinese middle schools: A machine learning approach

Hanol Lee

The Journal of Mathematical Sociology, 2025, vol. 49, issue 3, 175-191

Abstract: This study proposes a new methodological approach by utilizing a machine learning-based clustering algorithm to measure academic performance inequality in Chinese middle schools. Unlike traditional methods that use single summary statistics, our approach clusters schools based on the entire empirical cumulative distribution function of student test scores, capturing more complex patterns of inequality. We classify schools into three distinct clusters reflecting varying degrees of inequality. Our findings reveal that schools with higher concentrations of rural migrant students are more likely to fall into more unequal clusters, where students face greater academic challenges. By comparing our method with traditional measures, we demonstrate its ability to detect subtle inequality patterns that traditional measures may overlook. This methodology provides valuable insights for targeted policy interventions to address disparities.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0022250X.2025.2481371 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gmasxx:v:49:y:2025:i:3:p:175-191

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gmas20

DOI: 10.1080/0022250X.2025.2481371

Access Statistics for this article

The Journal of Mathematical Sociology is currently edited by Noah Friedkin

More articles in The Journal of Mathematical Sociology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().