Introduction. Generative artificial intelligence (GenAI) is transforming the labour market, yet existing methodological approaches to assessing its impact remain fragmented and insufficiently adapted to the Russian context of labour shortages.
Aim. To systematize and comparatively analyze methodological approaches for assessing the impact of GenAI on labour market dynamics and to identify directions for their application in Russian research.
Methods. A systematic review and comparative analysis of scientific literature (2023– 2026), including studies from international research centers (IZA, NBER) and Russian publications.
Results. An original classification of methods is proposed: partial and general equilibrium models, econometric approaches (including survival analysis), agent-based modeling, big data analysis, task automation assessment methodology (verb-noun semantic pairs), and the analogy method. It is shown that no single method is universal, necessitating a hybridization of approaches. The Russian context is characterized by labour shortages, shifting the focus from substitution risks to workforce compensation opportunities. Econometric methods based on administrative microdata and an adapted task automation assessment methodology hold the greatest potential for Russian research.
Scientific novelty. This is the first systematic classification of methods for assessing the impact of GenAI on the labour market, emphasizing their applicability in the Russian context, considering spatial heterogeneity and institutional features.
Practical significance. The findings can inform the development of regional employment programs, skills demand monitoring, and active labour market policy measures (retraining, career guidance) amidst technological transformation.