Evolution of Approaches to Anticipating Labour Demand and Supply

Abstract

Anticipating labour supply and demand remains relevant due to significant transformations in the labor market caused by socio-economic, demographic, and technological factors. In addition, infrastructure destruction, job losses, and large-scale flows of displaced persons have further exacerbated these problems. Adapting educational programs, forecasting the necessary professional skills, and training specialists for the restoration and reconstruction of the country are currently important for ensuring stability and economic growth. The issue of forecasting supply and demand is on the government's agenda. The priority task for demographic development and economic growth in general is to regulate supply and demand in the labor market. In accordance with the Program of Activities of the Cabinet of Ministers of Ukraine in 2025, a draft National Employment Strategy 2030 has been developed, which focuses, in particular, on better balancing labor supply and demand in the labor market. The article systematizes methodological approaches to forecasting labor supply and demand, ranging from classical macroeconomic and stock-flow models to modern agent-oriented approaches. It is determined that the evolution of labor demand and supply forecasting models reflects changes in economic theory paradigms — from deterministic to stochastic, from aggregate to micro-oriented. While macroeconomic balance and trend methods dominated in the mid-20th century, since the 1990s the focus has gradually shifted to modeling the behavior of individual economic agents — employers, employees, and households. This has allowed for a deeper understanding of the mechanisms of market equilibrium formation and responses to technological, demographic, and institutional changes. The experience with approaches to forecasting labor supply and demand has been generalized, and the advantages and limitations of different methods have been identified, taking into account their suitability for analyzing labor market dynamics. The stock–flow method laid the foundation for quantitative analysis of labor flows; dynamic stochastic general equilibrium (DSGE) models with a job search and matching mechanism introduced a behavioral component; BeTa-type models demonstrated how policy and skills can be integrated into a single macro structure; and agent-based modeling opened up the possibility of studying individual dynamics, adaptation, and interaction. Thus, the modern paradigm of labor market forecasting is increasingly focused on microdynamics, flexibility, and complex behavior of agents, which makes it the most promising for the development of effective employment policies. Scientifically sound recommendations have been formulated on the application of the approaches considered for assessing and forecasting labor supply and demand in the Ukrainian labor market, particularly during the recovery period.