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The German Statutory Minimum Wage and Its Effects on Regional Employment and Unemployment

Holger Bonin, Ingo E. Isphording, Annabelle Krause-Pilatus, Andreas Lichter, Nico Pestel and Ulf Rinne

Abstract

This paper studies the effects of the introduction of Germany’s statutory minimum wage in 2015 on employment and unemployment on the level of regional labor markets. Using variation in the regional exposure to the new wage floor, we employ a difference-in-differences approach that compares the evolution of employment and unemployment between regions with varying minimum wage bites. Overall, we find no statistically significant effect of the introduction of the German minimum wage on regular employment subject to social insurance, but a statistically significant negative effect on marginal employment. The reduction is not accompanied by a proportional increase in unemployment.

JEL Classification: J21; J31; J38

Acknowledgements

The results presented in this paper are based on a study conducted on behalf of the German Minimum Wage Commission (Bonin et al. 2018), which served as a basis for the Commission’s Second Report on the Effects of the Statutory Minimum Wage (Mindestlohnkommission 2018). The authors would like to thank two anonymous referees for their helpful comments and suggestions.

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Article note

This article is part of the special issue “Effects of the Introduction of the Statutory Minimum Wage in Germany” published in the Journal of Economics and Statistics. Access to further articles of this special issue can be obtained at www.degruyter.com/journals/jbnst.

Appendix

A

Table 3:

Measures of the minimum wage bite.

RegionGermanyWestEast
Average wage gap (in EUR)0.160.120.33
Share of workers with hourly wage < 8.50 EUR (in  %)11.49.420.8
Kaitz index (relative to average wage, in  %)49.948.358.8
Kaitz index (relative to median wage, in  %)57.755.969.0

  1. Source: SES (2014), own calculations.

Figure 6: Geographic spread of minimum wage bite by measure. Sources: SES (2014), own calculations.

Figure 6:

Geographic spread of minimum wage bite by measure. Sources: SES (2014), own calculations.

Table 4:

Characteristics of labor market regions prior to the introduction of the minimum wage.

Minimum wage bite (wage gap)ControlTreatmentTotal
Average wage gap (2014, in EUR)0.110.300.20
Total employment
2013–2014155,551115,028135,369
2015–2016161,464118,404140,018
Agglomeration type (share in  %)
Sparsely populated, rural25.635.230.4
Rural with population centers20.928.924.9
Urban53.535.944.8
East Germany (share in  %)0.042.221.0
2013 Employment structure (share in  %)
Agriculture2.32.42.4
Services13.613.613.6
Manufacturing30.427.729.1
Public Sector29.531.830.6
Wholesale24.324.524.4
Population share 18-64 years (2013, in  %)62.662.262.4
Number of labor market regions129128257

  1. Source: SES (2014), Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) and Destatis, own calculations.

    Notes: The classification of labor market regions by agglomeration type follows the definition of the BBSR. Information on employment statistics by sector for 2013 and data on population levels are taken from the Regional Statistics of the Federal Statistical Office (Destatis).

Table 5:

Robustness Checks: Share of affected employees as alternative bite (simple DiD approach).

Outcome(1)(2)(3)(4)(5)
RegularMarginalMarginalTotalUnemployment
Empl.Empl. (total)Empl. (excl.)Empl.
Treatment0.00030.0162**0.0150*0.00390.0164**
effect(0.0024)(0.0074)(0.0086)(0.0025)(0.0067)
Observations41121028102810284112
R20.9340.4260.6250.9140.739
LMR type/Time/XXXXX
Region FE
Sector-time-trendsXXXXX

  1. Source: BA Statistics and SES (2014).

    Notes: Standard errors (in parentheses) are clustered at the labor market level. All specifications include labor market region and time fixed effects. LMR type/Time/Region FE: time-specific dummy variables that allow for different effects by labor market region type (sparsely populated rural region, rural region with population concentrations, urban region) in East and West Germany. Sector-specific time trends: linear time trends for East and West Germany interacted with the share of employees in (i) agriculture, (ii) public services and (iii) manufacturing as of 1/2013. The usual confidence levels apply: * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 6:

Robustness Checks: Non-weighted regressions (simple DiD approach).

Outcome(1)(2)(3)(4)(5)
RegularMarginalMarginalTotalUnemployment
Empl.Empl. (total)Empl. (excl.)Empl.
Treatment0.00150.0118**0.0108*0.0044**0.0047
effect(0.0022)(0.0045)(0.0059)(0.002)(0.0064)
Observations41121028102810284112
R20.8960.4120.6310.8690.691
LMR type/Time/XXXXX
Region FE
Sector-specificXXXXX
time trends

  1. Source: BA Statistics and SES (2014).

    Notes: Standard errors (in parentheses) are clustered at the labor market level. All specifications include labor market region and time fixed effects. LMR type/Time/Region FE: time-specific dummy variables that allow for different effects by labor market region type (sparsely populated rural region, rural region with population concentrations, urban region) in East and West Germany. Sector-specific time trends: linear time trends for East and West Germany interacted with the share of employees in (i) agriculture, (ii) public services and (iii) manufacturing as of 1/2013. The usual confidence levels apply: * p < 0.1, ** p < 0.05, *** p < 0.01.

Received: 2018-09-07
Revised: 2019-03-01
Accepted: 2019-03-14
Published Online: 2019-06-28
Published in Print: 2020-02-25

© 2020 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston