The Cross-sectional Model 2 of the Linked-Employer-Employee Data 1993-2010 (LIAB QM2 9310) from the German Institute of Employment Research (IAB) combines survey data on establishments from the annual waves of the IAB Establishment Panel (IAB EP) with administrative data on individuals drawn from the Integrated Employment Biographies (IEB) (for details see Heining et al. 2013). The data was accessed via on-site use at the Research Data Center (FDZ) and subsequently via remote data access.
The IAB EP (for details see Fischer et al. 2008) is a stratified sample of German es-tablishments with at least one employee liable for social security payments as of June 30 in the year prior to the survey. For the years under consideration, the annual sample size amounts to roughly 16,000 establishments representing approximately 1% of the universe of German establishments. The data on individuals are drawn from the IEB and entail administrative data from the Employee History (BeH) which covers all employees liable for social security payments. Since the data basis of the BeH is the integrated notification procedure for health, pension and unemployment insurance, it is considered to be highly reliable. The individual-level data are supplemented with basic establishment information (e.g., 3-digit industry code) from the Establishment History Panel (BHP) (for details see Gruhl et al. 2012).
The LIAB QM2 merges the data from these various sources using a unique establishment identifier. It is constructed according to the following procedure: First, all establishments from the IAB EP with a valid interview in the respective year are selected. Subsequent, for each year information on all individuals that are employed at one of these establishments at the cut-off date June 30 are drawn from the IEB. Although not all surveyed establishments
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can be linked to individual-level data from the IEB, the yearly coverage rate of 89 to 98%
is fairly high and maintains the representativity of the sample for the universe of German establishments.
I process the LIAB QM2 9310 as follows: I keep individuals who are regular employ-ees liable for social security payments (erwstat =101), employees in partial retirement (erwstat =103), student trainees (erwstat =106), or marginally employed (erwstat = 109, 209). Individuals in vocational training (stib=0) or owner and executive staff (beru f = 751 anderwstat=101, 102 andstib6=0, 1, 7) are only considered in the denominator of the worker turnover rates. Full-time equivalent weights are 0.5 for part-time employees not eligible for unemployment benefits (stib=8), student trainees, and marginal employees, 0.75 for part-time employees eligible for unemployment benefits (stib=9), and 1 otherwise.
All wages are converted in Euros and deflated by the Consumer Price Index, with 2000 as the base year. I impute missing or unknown values for education following Fitzenberger et al.
(2006) and aggregate education levels to three groups:lowfor individuals without vocational training or missing information,mediumfor individuals with a vocational qualification, and highfor individuals with a university degree or more. Since I do not have information on the hours of work, I limit the attention to individuals who are working full-time (stib<8) in the analysis of firms’ log mean daily wages. I impute right-censored wages following Dustmann et al. (2009). For each year and gender, I estimate separate Tobit models that control for all possible interactions between the three imputed education levels and eight age categories. All individual-level data are aggregated using a unique establishment identifier (idnum). I exclude establishments in the shipping and aircraft transport industry, agricultural and mining sectors as well as non-profit firms and private households. Moreover, I abstract from establishment entries and exits and only keep firms that are always present during the sample periods 2000 to 2007. For further sample restrictions in each of the four assignment methods to the treatment and control group see section 3.4.2 and Table 3.2.
To obtain firm-level shares for workers on fixed-term contracts (FTC) or temporary agency (TA) worker, I merge the aggregated data with establishment-level data from the IAB EP. Since there are multiple sources for inconsistent establishment matches (e.g., a new establishment identifier is issued whenever a plant changes ownership only in the IEB), I follow the procedure described in Alda (2005) and drop establishments with substantial differences in the establishment size according to the two data sources. Therefore, I define firm size-contingent limits for the allowed difference in the number of employees according to the data sources. For establishments with up to five employees, the limit is 40%, for establishments with five to 19 employees 30%, and for establishments with 20 to 100 employees 20%. If the limit is exceeded in one of the sample periods under consideration, the establishment is removed from the sample. Depending on the assignment method, the procedure reduces the sample by 32 to 39%. The reduction in sample size appears reasonable given Alda (2005) finds annual rates exceeding the tolerance levels of up to 30%.
Furthermore, I drop establishments with a share of FTC or TA employment larger than one
B.1. SAMPLEPROCESSING
from the sample used for the analysis of the use of temporary employment. The share of FTC employment can exceed one only due to misreporting while the share of TA employment may also exceed one if the number of TA workers in a given firm exceeds its total number of employees (which does not include TA workers).
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B.2. Appendix Tables
Table B.1.: Overview of Treatment Assignment Methods Used in Other Studies
3-years in ’before’ periods: Martins (2009) Bellmann et al. (2014) 4-years in ’before’ periods: Bauernschuster (2013) Always the same: Kugler and Pica (2008) Period-by-period: Bauer et al. (2007)
Centeno and Novo (2012) von Below and Thoursie (2010) Notes:Table lists different assignment methods to the treatment and control group and studies that applied the respective assign-ment method. The method3-Years (4-Years) in ’Before’ Periods assigns firms to the treatment and control group that remain in a specified firm size interval for three (four) periods before the treat-ment. The methodAlways the sameassigns firms to the treatment and control group that remain in a specified firm size interval for all observation periods before and after the treatment. The method Period-by-Periodassigns firms to the treatment and control group based on the firm size in each period.
B.2. APPENDIXTABLES
Table B.2.: Differences in Means Between Treatment and Control Group by Assignment Method
2-Years 4-Years
in ’Before’ in ’Before’ Always Adjacent Periods Periods the same Periods
(1) (2) (3) (4)
Treatment identifier
FTE establishment size −7.404∗∗∗ −7.388∗∗∗ −7.195∗∗∗ −7.404∗∗∗
Outcome variables
Hiring rate −0.004 −0.014 −0.006 −0.005
Separation rate 0.003 0.001 −0.009 0.002
Job flow rate −0.007 −0.015 0.003 −0.007
Churning rate −0.009 −0.018 −0.016 −0.009
Industry distribution
Manufacturing −0.021 −0.019 −0.057 −0.022
Construction 0.006 0.005 −0.021 0.005
Wholesale and retail trade 0.031 0.048 0.030 0.030
Real estate −0.014 −0.020 0.011 −0.011
Others −0.001 −0.014 0.037 −0.002
Geographic distribution
North −0.024 −0.039 −0.030 −0.025
East −0.029 −0.055 −0.032 −0.027
Berlin region 0.015 0.008 0.003 0.014
South 0.047 0.050 0.037 0.046
West −0.008 0.036 0.022 −0.009 Average worker characteristics
Avg. share of women 0.045∗ 0.056∗ 0.068∗ 0.046∗
Avg. share of blue-collar worker −0.028∗ −0.024 −0.031 −0.029∗ Avg. share of part-time worker 0.012 0.027 0.033 0.012
Avg. share of apprentices 0.001 0.002 0.002 0.001
Mean age −0.002 −0.158 −0.616 −0.003
Mean age squared 8.490 −0.851 −42.205 8.330
Firms 587 422 247 588
Notes:Table shows the mean differences in the indicated variables between the treatment and control group. Each column refers to one of the four assignment methods described in section 3.4.2. For details on the industry and geographic categories see notes to Table 3.3. Asterisks denote significance oft-test for mean equality between treatment and control group. Significance levels: * 10%, ** 5%, and *** 1%. Data source: LIAB QM2.
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B.3. Appendix Figures
Figure B.1.: Shares of Establishments Exceeding Firm Size Intervals of Treatment and Control Group
A. 2-Years in ’Before’ Period B. 4-Years in ’Before’ Period
Notes:Figure shows the share of establishments that exceed the firm size intervals of five to 10 full-time equivalent weighted employees (treatment group) and 10 to 20 full-time equivalent weighted employees (control group) in the periods beyond the assignment periods. In panel A, the assignment periods are 2002 and 2003. In panel B, the assignment periods are 2000 to 2003. Data source: LIAB QM2.