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Unemployment Compensation and Wages: A Difference-in-Differences Approach to Assessing the Wage Effects of the German Hartz Reforms

Johannes B. D. Weskott

Abstract

This paper examines the influence of the level of unemployment assistance (Arbeitslosengeld II) on the wage level by exploiting a quasi-natural experiment formed by the German Hartz reforms in 2005. Estimations are based on data from the Socioeconomic Panel ranging from 2000 to 2007. As dependent variables both real monthly gross salary and real hourly gross wage are used. Firstly, following the approach taken by Arent and Nagl (2013, Unemployment Compensation and Wages: Evidence from the German Hartz Reforms. Jahrbücher für Nationalökonomie und Statistik 233 (4): 450–466), a before-after estimator is applied. Secondly, in contrast to the replication study by Ludsteck and Seth (2014, Comment on „Unemployment Compensation and Wages: Evidence from the German Hartz Reforms“ by Stefan Arent and Wolfgang Nagl. Jahrbücher für Nationalökonomie und Statistik 234 (5): 635–644) a control group is constructed and a difference-in-differences estimator (DiD) is used for further assessment. The results of the before-after estimation indicate a negative influence of the unemployment assistance reform on wages. However, the corresponding placebo regressions cast doubt on whether the estimated effect is a policy effect. The DiD approach shows that substantial time effects exist. This indicates that the before-after estimator is not suitable for assessing the policy effect. Applying the DiD estimator, a negative significant policy effect is only identified for men in West Germany.

JEL Classification: J08; J31; J65

Acknowledgements

Thanks to Prof. Dr. Viktor Steiner for his encouraging comments and very helpful advice and to Dr. Claudius Gräbner for advice and support.

References

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Appendix

Table 8:

Variables employed in the replication.

Variable used bySOEP Variable employed
Arent and Nagl (2013)in the replication
GenderGender [variable: sex]
Agegenerate from: Birth Year, 4-digit [variable: gebjahr]
Monthly salaryGross Income Last Month [variable: plc0013]
Hourly wagegenerated from Gross Income Last Month [variable: plc0013]
and Actual Weekly Work Time [variable: pgtatzt]
Job tenureLength Of Time With Firm [variable: pgerwzt]
EducationNumber of Years of Education [variable: d11109]
Firm sizeCore Category Size Of The Company [variable: pgallbet]
Industry sector1 Digit Industry Code of Individual [variable: e11106]
Occupational positionOccupational Position [variable: pgstib]
Full-time employmentEmployed Full-Time No. Months Prev. Yr [variable: kal1a02]
Age structure of firm
Figure 1: Development of hours worked (SOEP; own calculations).

Figure 1:

Development of hours worked (SOEP; own calculations).

Table 9:

Data added for DiD estimation.

Data for Control Group ConstructionSOEP Variable
Real estate (owner occupied)Prop. Prim. Resid. Share of Value imp.x
[variable: p0101x]
Real estateOther Real Estate Share of Value imp.x
[variable: e0101x]
Cash; money in current accounts,Financial Assets Share of Value imp.x
    Time deposit accounts,[variable: f0101x]
    Night money accounts, savings accounts,
    Bonds and securities, etc
Capital-sum life insurance,Private Insurances Market Value imp.x
    Home loan savings contract, etc[variable: i0100x]
Jewelry, stamp collections, etcTangible Assets Market Value imp.x
[variable: t0100x]
Size of living spaceSize of Housing Unit in Square Meters
[variable: hgsize]
Ownership of housing indicatorTenant Or Owner Of Dwelling
[variable: hgowner]

Table 10:

Summary statistics: real monthly gross salary.

msalaryAgeLow educMedium educHigh educYears educ
Control Group
Mean2677.9543.938.76 %48.37 %41.61 %13.00
p502523.364412
Min511.05177
Max56246418
Treatment Group
Mean2111.5538.7711.91 %58.77 %25.79 %12.12
p5019943811.5
Min511.19177
Max56246418

  1. Source: SOEP; own calculations.

Figure 2: Comparison of dependent variable log real monthly gross salary (SOEP; own calculations).

Figure 2:

Comparison of dependent variable log real monthly gross salary (SOEP; own calculations).

Table 11:

Summary statistics: real hourly gross wage.

hwageAgeLow educMedium educHigh educYears educ
Control Group
Mean15.9043.898.33 %48.79 %41.61 %13.00
p5015.344412
Min4.34177
Max30.706518
Treatment Group
Mean12.8738.9211.14 %59.40 %25.94 %12.13
p5012.283811.5
Min4.35177
Max30.706518

  1. Source: SOEP; own calculations.

Table 12:

Summary statistics: Number of observations.

Number of ObservationsPercent
Control Group30 83648.60
Treatment Group32 61151.40
Total63 447100.00

  1. Source: SOEP; own calculations.

Table 13:

DiD estimation: real monthly gross salary (SOEP high income sample excluded).

Men westWomen westMen eastWomen east
Time effect–0.004–0.029***–0.012–0.036***
(0.005)(0.010)(0.011)(0.013)
Policy effect–0.016**0.027**–0.0020.001
(0.007)(0.012)(0.013)(0.015)
Observations27,17815,8328,5426,833
Number of pid5,9063,5891,8941,520
Adjusted R-squared0.2870.2550.1650.149

  1. Robust standard errors in parentheses

  2. *** p<0.01, ** p<0.05, * p<0.1

Table 14:

DiD estimation: real hourly gross wage (SOEP high income sample excluded).

Men westWomen westMen eastWomen east
Time effect–0.013**–0.022***–0.013–0.044***
(0.005)(0.009)(0.011)(0.012)
Policy effect–0.017***–0.002–0.0070.015
(0.007)(0.010)(0.012)(0.014)
Observations26,65315,8018,2716,611
Number of pid5,9403,6171,8731,508
Adjusted R-squared0.1130.1350.0530.053

  1. Robust standard errors in parentheses

  2. *** p<0.01, ** p<0.05, * p<0.1

Received: 2018-02-27
Revised: 2018-07-03
Accepted: 2019-02-12
Published Online: 2019-06-18
Published in Print: 2020-01-28

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