Publications

This document explores the incidence of the minimum wage in Lithuania. The descriptive analysis exploits high-frequency data on monthly labor income coming from Social Security records between July 2013 and July 2020 to characterize (i) the evolution of the monthly minimum wage, (ii) the percentage of workers who earn the minimum wage, (iii) the bite of the minimum wage in the wage distribution, and (iv) the heterogeneity of the findings with respect to gender and age. The evidence shows that the minimum wage was raised 7 times with an average (real) increase of 7.3% and, on average, less than 10% of the workers earn at most the minimum wage but low-pay incidence is around 20%. In terms of the impact of the wage distribution, the minimum wage relative to the average wage in the economy fluctuates between 45 and 50 percent. Females and young workers exhibit a larger low-pay incidence and minimum wage bite.

JEL Codes: J38, J48

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

Despite Lithuania’s household income inequality being among the highest in the European Union (EU), little empirical work has been carried out to explain such disparities. In this article, we use the EU Statistics on Income and Living Conditions sample micro data. We confirm that income inequality in Lithuania is high compared to the EU average and find that it is robust to inequality measure or equivalence scale used. We have also decomposed household disposable income inequality by subgroups and factors. We find that the number of employed household members in Lithuania’s households affects income inequality more as compared to the EU. It is related to a larger labour income, and self-employment income in particular, contribution to inequality in Lithuania as opposed to the EU. Moreover, transfers and taxes have a smaller impact on reducing inequality in Lithuania than in the EU.

JEL Codes: D31.

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

This paper investigates the importance of unemployment and credit in determining the potential level of real activity for a small-open economy with a low degree of financialization. We estimate a multivariate unobserved component model (MUC) to derive the potential output and its associated output gap for the Lithuanian economy. The model is estimated via Bayesian methods and the time-paths of unobserved variables are extracted via the Kalman filter. We find that the inclusion of unemployment into the MUC model substantially improves the estimates of output gap in real-time. Once information about unemployment is accounted for, adding information about credit does not substantially alter either the estimates of output gap or its performance in real time. We uncover a strong negative correlation between the model-implied unemployment gap (without credit) and real credit growth. This explains the relatively muted impact of the financial variable on the level and dynamics of the output gap. Data revisions appear not to be the primary source of revisions on output gaps estimates.

JEL Codes: C11, C32, E24, E32.

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