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Ambiguous high scores: The All-Russian Olympiad in economics during the COVID-19 pandemic


  • Magzhanov, Timur

    (Moscow State University, CMASF, Moscow, Russian Federation;)

  • Sagradyan, Anna

    (Moscow State University, Moscow, Russian Federation)


This paper evaluates the change in the contribution of factors to the probability of students’ success at the All-Russian Olympiad in Economics during the COVID-19 pandemic using classical econometric models and binary quantile regression (BQR). No works were found in the Russian literature where BQR would be applied. However, in our opinion, it has great potential both for studying the effects’ heterogeneity and for solving probability prediction problems. Empirical results show that the contribution of school rating to success at the municipal stage decreased in the 2020/21 season compared to the 2019/20 season. High score at the municipal stage (winner status) became a weaker predictor of success at the regional stage in the 2020/21 season compared to the 2019/20 season. The reason for this change may lie in a decrease in the tasks’ complexity (due to a change in their structure), a higher opportunity for cheating (due to weak and non-mandatory proctoring) or both.

Suggested Citation

  • Magzhanov, Timur & Sagradyan, Anna, 2023. "Ambiguous high scores: The All-Russian Olympiad in economics during the COVID-19 pandemic," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 89-108.
  • Handle: RePEc:ris:apltrx:0472

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    References listed on IDEAS

    1. Peresetsky, Anatoly & Davtian, Misak, 2011. "Russian USE and olympiads as instruments for university admission selection," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 23(3), pages 41-56.
    2. Aucejo, Esteban M. & French, Jacob & Ugalde Araya, Maria Paola & Zafar, Basit, 2020. "The impact of COVID-19 on student experiences and expectations: Evidence from a survey," Journal of Public Economics, Elsevier, vol. 191(C).
    3. Clark, Andrew E. & Nong, Huifu & Zhu, Hongjia & Zhu, Rong, 2021. "Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic," China Economic Review, Elsevier, vol. 68(C).
    4. Zamkov, Oleg & Peresetsky, Anatoly, 2013. "Russian Unified National Exams (UNE) and academic performance of ICEF HSE students," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 93-114.
    5. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2020. "Brq: an R package for Bayesian quantile regression," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 313-328, December.
    6. Dries F. Benoit & Dirk Van den Poel, 2012. "Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1174-1188, November.
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    More about this item


    online education; All-Russian Olympiad in economics; COVID-19; binary quantile regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education


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