Publications

We document how worker flows between employment, unemployment, and out of the labor force, vary by age and gender for a large panel of European countries. We develop and calibrate an extended Diamond-Mortensen-Pissarides model that captures all the salient features of these data. The model assigns a major role to the production technology in driving differences in aggregate employment, while, in contrast to Standard analyses, labor-market policies play only a secondary role. Search intensity and a laborforce participation decision are key for propagating the effects of technology across age and gender groups, and for explaining the variation in aggregate employment.

Keywords: Employment, Unemployment, Labor Force Participation, Life cycle, Worker Flows, Labor Market Institutions

JEL classification: E02, E24, J21, J64, J82

We use survey micro-data for 31 European countries, and estimate the life-cycle profiles of worker transition probabilities across employment, unemployment and nonparticipation. The estimated transition probabilities are then used to explain aggregate difference in employment rates between Lithuania and Europe. We show that the separations from employment is a key in understanding differences in labor market outcomes of both genders, and that demographics play a large negative role for Lithuanian employment rates. The results have important implications for economic policies that are discussed at the end of the analysis.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

Available only in Lithuanian

In this paper, we first provide a comprehensive account of the relationship between cross-country differences in aggregate employment and disaggregated differences in worker flows along the life cycle. To this end, we use survey micro-data for 31 European countries, and estimate the life-cycle profiles of transition probabilities across employment, unemployment and non-participation for each country. We develop a decomposition measuring the contribution of these transition probabilities to aggregate employment differences. We find substantial cross-country and cross-gender heterogeneity with respect to the role of worker flows between each labor market state.

JEL Codes: E02, E24, J21, J64, J82.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.