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News
September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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A Comparison of the Missing-Indicator Method and Complete Case Analysis in Case of Categorical Data

2019.
Zhuchkova S., Rotmistrov A.
The research aims to provide a complex analysis of the missing-indicator method’s performance in case of a categorical independent variable in regression in comparison with complete case analysis. While the latter seems to be the most popular way to handle missing data, the former appears to be a simple and effective alternative that allows making a full sample available for analysis. By means of a statistical experiment and simulated data, we examined how these methods perform in conditions that differ in a mechanism of missingness, proportion of missing data, and model specification. The final results show that, overall, both methods produce unbiased estimates of regression coefficients, but crucially biased estimates of their standard errors and additional statistics such as R2, adjusted R2, and F-statistic, especially in case of a missing-indicator method. We explain these results by contribution of a missing-indicator variable, coefficient of which always turns out to be significant and far away from zero.
Priority areas: sociology
Language: English
Full text
Keywords: пропущенные данныерегрессионный анализregression analysismissing datacategorical datamissing indicator methodкатегориальные данныеметод индикаторной переменной
Publication based on the results of:
Обоснование преимуществ поиска эффектов взаимодействия и их учета в социологических регрессионных моделях (2018)
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