The Role of Teachers and School Infrastructure in Overcoming Educational Inequality: Evidence from the Kazakhstani Olympiad

Friday, 11 July 2025
Location: SJES026 (Faculty of Legal, Economic, and Social Sciences (JES))
Distributed Paper
Aliya SARSEKEYEVA, "El Umiti" corporate fund, Kazakhstan
Aslanbek AMRIN, 'El Umiti' Corporate Fund, Kazakhstan
Sulushash KAZTAYEVA, El Umiti corporate fund, Kazakhstan
This study presents the results of the All-Kazakhstan Rural Schoolchildren Olympiad, conducted among 80,000 sixth-grade students in 2024, including an analysis of schools’ and teachers’ effect on academic achievements of participants. The Olympiad has been held annually by the non-governmental organization El Umiti since 2020. The Olympiad provides an opportunity for gifted students to gain places in prestigious schools – thus achieving social mobility and receiving better educational opportunities. An innovative approach to the implementation of the Olympiad is also not only measurements in academic subjects (mathematics, science, languages, etc.), but also measurements in functional and spatial thinking, closely related to non-cognitive skills. The study uses open data on school infrastructure, participants and results of the Olympiad, as well as support for teachers in preparing for the Olympiad. We study the structure of the data, first using classical statistical methods (regression analysis) with elements of spatial analysis to find mechanisms for the reproduction of inequality, and then more modern methods of network analysis, assessing the contribution of teachers. The study contributes to the existing academic and practical discussion on educational inequality and the specifics of teaching in rural schools. At the same time, the data used allow us to draw conclusions that are relevant specifically in the context of educational innovations based on NGO, which is important due to the recognized lack of such works on local material by the expert community. From the methodological side, we emphasize how the network approach enriches the understanding of the data compared to classical statistics.