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No 86
2021-03-19

Productivity-Enhancing Reallocation during the Great Recession: Evidence from Lithuania

  • Abstract

    This paper studies the impact of the Great Recession on the relationship between reallocation and productivity dynamics in Lithuania. Using detailed microlevel data, we first document the aggregate contribution of firm exit and employment reallocation to productivity growth. Next, we estimate firm-level regressions to confirm the findings and to perform a heterogeneity analysis. This analysis shows that productivity shielded firms from exit, and that this relationship became stronger during the Great Recession. Moreover, we demonstrate that more productive firms experienced on average lower employment losses, and that this effect was even stronger during the economic slump. Taken together, our results suggest that reallocation was productivity-enhancing during the Great Recession. However, the analysis also indicates that reallocation intensity varied with sector's dependence on external financing or international trade as well as market concentration.

    Keywords: firm dynamics, job reallocation, productivity, Great Recession

    JEL Codes E24, E32, L11, J23

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

No 82
2020-12-18

Statistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US

  • Abstract

    In the US, black workers spend more time in unemployment, lose their jobs more rapidly, and earn lower wages than white workers. This paper quantifies the contributions of statistical discrimination, as portrayed by negative stereotyping and screening discrimination, to such employment and wage disparities. We develop an equilibrium search model of statistical discrimination with learning based on Moscarini (2005) and estimate it by indirect inference. We show that statistical discrimination alone cannot simultaneously explain the observed differences in residual wages and monthly job loss probabilities between black and white workers. However, a model with negative stereotyping, larger unemployment valuation and faster learning about the quality of matches for black workers can account for these facts. One implication of our findings is that black workers have larger returns to tenure.

     Keywords: Learning; Screening discrimination; Job search; Indirect inference.

    JEL Codes: J31; J64; J71.

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

No 23
2020-11-09

A First Glance at the Minimum Wage Incidence in Lithuania using Social Security Data

  • Abstract

    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.

No 32
2020-02-07

The life-cycle profile of worker flows in Lithuania

  • Abstract

    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.

No 71
2020-01-24

Changes in income inequality in Lithuania: the role of policy, labour market structure, returns and demographics

  • Abstract

    We model the household disposable income distribution in Lithuania and explore the drivers of the increase in income inequality between 2007 and 2015. We quantify the contributions of four factors to changes in the disposable income distribution: (i) demographics; (ii) labour market structure; (ii) returns and prices; and (iv) tax-benefit system. Results show that the effects of the factors were substantial and reflected heterogeneous developments over two sub-periods: changes in the tax and benefit system successfully accommodated a rapid rise in market income inequality due to the global financial crisis during 2007-2011, but failed to do so during the subsequent years of economic expansion, when rising returns in the labour and capital markets significantly increased disposable income inequality. We also find that declining marriage rates contributed to the increase of income inequality in Lithuania.

    JEL Codes: D31, H23, J21, J31, I38.

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

No 31
2020-01-21

The Challenges of Lithuania’s Economic Convergence and Labour Market

  • Abstract

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


    Available only in Lithuanian

     

No 16
2019-12-04

The Life-cycle Profile of Worker Flows in Europe: an Empirical Investigation

  • Abstract

    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.

No 58
2019-01-15

The changing nature of gender selection into employment over the Great Recession

  • Abstract

    Online appendix (204.3 KB )


    The Great Recession has strongly influenced employment patterns across skill and gender groups. This paper analyzes how the resulting changes in non-employment have affected selection into jobs and hence gender wage gaps. Using data for the European Union, we show that male selection into the labour market, traditionally disregarded, has become positive. This is particularly so in Southern Europe, where dramatic drops in male unskilled employment have taken place during the crisis. As regards female selection, traditionally positive, we document two distinct effects. An added-worker effect has increased female labour force participation and hence reduced selection in some countries. In others, selection has become even more positive as a result of adverse labour demand shifts in industries which are intensive in temporary work, a type of contract in which women are over-represented. Overall, our results indicate that selection has become more important among men and less so among women, thus changing traditional gender patterns and calling for a systematic consideration of male non-employment when studying gender wage gaps.

    JEL Codes: J31.

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