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.
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
Arent, S., W. Nagl (2013), Unemployment Compensation and Wages: Evidence from the German Hartz Reforms. Jahrbücher für Nationalökonomie und Statistik 233 (4): 450–466.10.1515/jbnst-2013-0402Search in Google Scholar
Arent, S., W. Nagl (2014), Unemployment Compensation and Wages: Evidence from the German Hartz Reforms – Reply. Jahrbücher für Nationalökonomie und Statistik 234 (5): 645–646.10.1515/jbnst-2013-0402Search in Google Scholar
Federal Law Gazette (2001), Arbeitslosenhilfe-Verordnung. Federal Law Gazette 70 (1): 3734–3736. http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl101s3734.pdf Accessed March 19, 2016).(Search in Google Scholar
Federal Law Gazette (2002a), Erstes Gesetz für moderne Dienstleistungen am Arbeitsmarkt. Federal Law Gazette 87 (1): 4607–4620. http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl102s4607.pdf Accessed March 19, 2016).(Search in Google Scholar
Federal Law Gazette (2002b), Zweites Gesetz für moderne Dienstleistungen am Arbeitsmarkt. Federal Law Gazette 87 (1): 4621–4636. http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl102s4621.pdf Accessed March 19, 2016).(Search in Google Scholar
Federal Law Gazette (2003), Viertes Gesetz für moderne Dienstleistungen am Arbeitsmarkt. Federal Law Gazette 66 (1): 2954–3000. http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl103s2954.pdf Accessed March 19, 2016).(Search in Google Scholar
Federal Law Gazette (2006), Gesetz zur Fortentwicklung der Grundsicherung für Arbeitssuchende. Federal Law Gazette 36 (1): 1706–1720. https://www.bgbl.de/xaver/bgbl/start.xav?start=%2F%2F*%5B%40attr_id%3D%27bgbl106s1706.pdf%27%5D#__ bgbl__%2F%2F*%5B%40attr_id%3D%27bgbl106s1706.pdf%27%5D__1559478788039 Accessed March 19, 2016).(Search in Google Scholar
Federal Law Gazette (2015), Bekanntmachung über die Höhe der Regelbedarfe nach § 20 Absatz 5 des Zweiten Buches Sozialgesetzbuch für die Zeit ab 1. Januar 2016. Federal Law Gazette 41 (1). http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl115s1792.pdf Accessed March 20, 2016).(Search in Google Scholar
Ludsteck, J., S. 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): 635–644.10.1515/jbnst-2014-0504Search in Google Scholar
Stuart, E.A., H.A. Huskamp, K. Duckworth, J. Simmons, Z. Song, M.E. Chernew, C.L. Barry (2014), Using Propensity Scores in Difference-in-Differences Models to Estimate the Effects of a Policy Change. Health Services and Outcomes Research Methodology 14 (4): 166–182.10.1007/s10742-014-0123-zSearch in Google Scholar
Wooldridge, J.M. (2010), Econometric Analysis of Cross Section and Panel Data. Cambridge Massachusetts, MIT Press.Search in Google Scholar
Appendix
Table 8:
Variables employed in the replication.
| Variable used by | SOEP Variable employed |
|---|---|
| Arent and Nagl (2013) | in the replication |
| Gender | Gender [variable: sex] |
| Age | generate from: Birth Year, 4-digit [variable: gebjahr] |
| Monthly salary | Gross Income Last Month [variable: plc0013] |
| Hourly wage | generated from Gross Income Last Month [variable: plc0013] |
| and Actual Weekly Work Time [variable: pgtatzt] | |
| Job tenure | Length Of Time With Firm [variable: pgerwzt] |
| Education | Number of Years of Education [variable: d11109] |
| Firm size | Core Category Size Of The Company [variable: pgallbet] |
| Industry sector | 1 Digit Industry Code of Individual [variable: e11106] |
| Occupational position | Occupational Position [variable: pgstib] |
| Full-time employment | Employed Full-Time No. Months Prev. Yr [variable: kal1a02] |
| Age structure of firm | – |
Figure 1:
Development of hours worked (SOEP; own calculations).
Table 9:
Data added for DiD estimation.
| Data for Control Group Construction | SOEP Variable |
|---|---|
| Real estate (owner occupied) | Prop. Prim. Resid. Share of Value imp.x |
| [variable: p0101x] | |
| Real estate | Other 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, etc | Tangible Assets Market Value imp.x |
| [variable: t0100x] | |
| Size of living space | Size of Housing Unit in Square Meters |
| [variable: hgsize] | |
| Ownership of housing indicator | Tenant Or Owner Of Dwelling |
| [variable: hgowner] |
Table 10:
Summary statistics: real monthly gross salary.
| msalary | Age | Low educ | Medium educ | High educ | Years educ | |
|---|---|---|---|---|---|---|
| Control Group | ||||||
| Mean | 2677.95 | 43.93 | 8.76 % | 48.37 % | 41.61 % | 13.00 |
| p50 | 2523.36 | 44 | 12 | |||
| Min | 511.05 | 17 | 7 | |||
| Max | 5624 | 64 | 18 | |||
| Treatment Group | ||||||
| Mean | 2111.55 | 38.77 | 11.91 % | 58.77 % | 25.79 % | 12.12 |
| p50 | 1994 | 38 | 11.5 | |||
| Min | 511.19 | 17 | 7 | |||
| Max | 5624 | 64 | 18 | |||
Source: 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.
| hwage | Age | Low educ | Medium educ | High educ | Years educ | |
|---|---|---|---|---|---|---|
| Control Group | ||||||
| Mean | 15.90 | 43.89 | 8.33 % | 48.79 % | 41.61 % | 13.00 |
| p50 | 15.34 | 44 | 12 | |||
| Min | 4.34 | 17 | 7 | |||
| Max | 30.70 | 65 | 18 | |||
| Treatment Group | ||||||
| Mean | 12.87 | 38.92 | 11.14 % | 59.40 % | 25.94 % | 12.13 |
| p50 | 12.28 | 38 | 11.5 | |||
| Min | 4.35 | 17 | 7 | |||
| Max | 30.70 | 65 | 18 | |||
Source: SOEP; own calculations.
Table 12:
Summary statistics: Number of observations.
| Number of Observations | Percent | |
|---|---|---|
| Control Group | 30 836 | 48.60 |
| Treatment Group | 32 611 | 51.40 |
| Total | 63 447 | 100.00 |
Source: SOEP; own calculations.
Table 13:
DiD estimation: real monthly gross salary (SOEP high income sample excluded).
| Men west | Women west | Men east | Women 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** | –0.002 | 0.001 |
| (0.007) | (0.012) | (0.013) | (0.015) | |
| Observations | 27,178 | 15,832 | 8,542 | 6,833 |
| Number of pid | 5,906 | 3,589 | 1,894 | 1,520 |
| Adjusted R-squared | 0.287 | 0.255 | 0.165 | 0.149 |
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Table 14:
DiD estimation: real hourly gross wage (SOEP high income sample excluded).
| Men west | Women west | Men east | Women 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 | –0.007 | 0.015 |
| (0.007) | (0.010) | (0.012) | (0.014) | |
| Observations | 26,653 | 15,801 | 8,271 | 6,611 |
| Number of pid | 5,940 | 3,617 | 1,873 | 1,508 |
| Adjusted R-squared | 0.113 | 0.135 | 0.053 | 0.053 |
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
© 2020 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston