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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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Интегральный индикатор медиакоммуникационной индустрии: инструмент анализа новой социально-экономической категории

Вестник Московского университета. Серия 10: Журналистика. 2025. Т. 50. № 1. С. 3–32.
Vartanov S., Nefedova Y., Nefedov S.

The paper presents a methodological framework for developing a quantitative analytical tool for a new socio-economic category – the media communication industry (MCI). The proposed tool, the integral indicator of the MCI, enables the assessment of the industry’s development level across various aspects and manifestations based on statistical data from national media communication industry databases. The methodology for calculating integral indicators follows an econometric approach and builds upon theoretical foundations and contributions from Russian and international scholars (including the authors) employing the principal component method to reduce the dimensionality of socio-economic indicator sets. The construction of the indicator incorporates empirical data spanning a ten-year period across the constituent entities of the Russian Federation. As a result, the developed research tool provides an accurate representation of the media communication industry’s qualitative state at both national and regional levels. Importantly, it is mathematically robust and preserves essential variability. The study establishes key principles for classifying the country’s regions based on the level of development and operational characteristics of the media communication industry, as well as its contribution to the regional economy. Furthermore, it formulates the main parameters of a mathematical model linking regional media communication industry development levels with broader macroeconomic indicators.

Research target: Media and Communications Mathematics
Language: Russian
DOI
Text on another site
Keywords: медиаметод главных компонентmediaрейтинг регионовintegral indicatorинтегральный индикаторregional rankingsprincipal component methodмедиакоммуникационная индустрияmedia communication industryMediamonitorМедиамонитор
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