Inter-status mobility in Ukraine’s labor market

Abstract

The article studies the functioning of Ukraine's labor market in 2019–2021 through the prism of the status flows of labor force, for which various methodological techniques of analytical research are consistently applied, which, complementing each other, allow analyzing the flows from different angles of view. So, using micro data on labor force indicators and their characteristics, probabilistic matrices of transitions of Ukraine's population between employment, unemployment and economic inactivity are constructed, assuming that such transitions occur according to the Markov process. As a result, the scope, nature and dominant vectors of the movements of Ukrainians between the three main statuses on the labor market are revealed. Based on the algorithms for calculating Shorrock's indices – proxy indices of mobility, the author carries out an integral assessment of the intensity of inter-status movement in Ukraine's labor market. A similar assessment for a number of European countries makes it possible to propose a basis for cross-country comparison of the level of mobility in Ukraine. Using economic-mathematical modeling of multiple choice, the author reveals socio-demographic factors determining the individual's status on Ukraine's labor market, and in so doing also answers the question of stability of the observed status. It is shown that the analysis of inter-status mobility of labor force provides a powerful basis for better understanding of the functioning of the labor market, characterizes the mechanisms of adaptation of the latter and allows observing the direction and intensity of flows behind any specific change in gross employment, unemployment or economic inactivity, which makes relevant policy measures on the labor market more targeted. In particular, since the analyzed period was marked by increased unemployment in Ukraine, the author establishes the role of flows in the above mentioned dynamics and in the distribution of the risk of job loss, taking into account such socio-demographic characteristics of individuals as gender, age and education level. Understanding such connections is important for developing high quality solutions aimed at reducing unemployment in the country.