Bank of Lithuania
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No 40
2021-10-18

Beyond the Traditional Unemployment Rate during Covid-19 in Lithuania

  • Abstract

    This paper provides empirical evidence on the impact of Covid-19 on unemployment and underemployment in Lithuania. Based on the Labor Force Survey, we document the evolution of the unemployment rate using broader definitions that incorporate the underemployed and marginally attached workers. Our results show that, compared to previous recessions, Covid-19 had a milder impact on the Lithuanian labor market. Moreover, Lithuania fared reasonably well relative to other Eurozone countries. However, the data reveal a substantial increase in marginal workers and underemployment during 2020, with women, young workers and individuals in rural areas being most affected by the pandemic-induced recession.

    Keywords: labor market statistics, labor force, unemployment.

    JEL codes: E24, J21, J64

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

     

     

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 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 76
2020-06-18

Workers' job mobility in response to severance pay generosity

  • Abstract

    This paper studies the impact of severance pay generosity on workers' voluntary mobility decisions. The identification strategy exploits a major labor market reform in Spain in February 2012 together with the exposure of some workers to a layoff shock. I rely on rich administrative data to estimate a discrete time duration model with dynamic treatment effects. The results show that a decrease in mobility costs induced by a reduction in severance pay made workers who expected to be displaced in the near future more likely to voluntarily leave their employers. The results indicate that policies targeting employers may also affect workers' behavior. They further reveal the relevance of taking into account interactions between employment protection and unemployment insurance.

    JEL Codes: J62, J63, J65.

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

No 68
2019-11-29

Employment and Wages over the Business Cycle in Worker-Owned Firms: Evidence from Spain

  • Abstract

    This paper compares worker-owned firms and mainstream capital-owned enterprises over the business cycle. Specifically, I study whether conventional employees in worker-owned firms enjoy greater employment stability than similar workers in traditional enterprises over the business cycle, and investigate whether this stability is associated with greater volatility of working-time or wages. Unlike the literature that has compared partners of cooperatives to wage-earners of mainstream firms, I compare wage-earners across both type of organizations along the three margins of adjustment. To perform the econometric analysis, I rely on rich Spanish administrative data and panel data methods to account for composition differences between the two types of organizations. The results show that worker-owned firms offer higher job security because they do not adjust employment levels over the business cycle as much as mainstream enterprises. Wages and working-time, instead, are equally responsive across the two types of firms. The findings can be rationalized by the presence of similar labor regulations and differences in the objectives of the two type of organizations. Namely, both types of firms are constrained by regulations, such as the national Labor Code and collective bargaining, on the adjustments they can impose on wages and working-time. However, the social nature of worker-owned firms mitigates employment volatility in these organizations.

    JEL Codes: J21, J31, J54.

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