Evidence from a 2004 German Reform
3.3. Identification Strategy
3.5.2. Consolidation with Previous Findings
To consolidate my statistically insignificant estimates on the hiring rate with the positive effect on this outcome detected by Bauernschuster (2013), I apply the four assignment methods described in section 3.4.2 to the IAB Establishment Panel (IAB EP) after I
pre-61
processed the data following Bauernschuster (2013).26 Thereby, I extend Bauernschuster (2013)’s analysis for the years 2004 and 2005 by the sample periods 2006 and 2007. Since the IAB EP refers to hires within the first six months of each year, I conduct a comparable analysis based on the LIAB QM2 that distinguishes between hires in the first (June to December in yeart−1) and the second (January to June in yeart) half of the twelve month observation period. More precisely, following the definition of flow rates in section 3.4.3, I compute half-year hiring rates for each period by replacing annual hires with six months hires in the numerator. This is possible since I know the exact entry date of each worker in a firm. Eventually, I estimate the effect on the half-year hiring rates (January to June) based on models (3.1) and (3.2) for the sample periods 2001 to 2007 for both the IAB EP and LIAB QM2. To obtain separate estimates for a short- and medium-term effect that can be compared to results in Bauernschuster (2013), I re-estimate equation (3.1) once more whereby I substituteρS(Di(t)×Shortt) +ρM(Di(t)×Mediumt)forρ1(Di(t)×Postt).Shortt
is a dummy variable that takes the value 1 in the years 2004 and 2005 and 0 otherwise.
Likewise,Mediumt is a dummy variable that takes the value 1 in the years 2006 and 2007 and 0 otherwise.
The left side of Table 3.6 shows the extension of Bauernschuster (2013)’s estimates based on the IAB EP. Column 2 corresponds to the assignment method used in his main analysis (Table 1, p. 301). The results are comforting in that I obtain a statistically significant short-term effect (DiD 2004-05) of 1.5 percentage points, while his original estimates for the years 2004 and 2005 range between 1.3 and 2.1 percentage points.27In contrast, estimates from assignment methods (1), (3), and (4) for the short-term effect are smaller in absolute terms and statistically insignificant. The picture is similar for the total effect (DiD 2004-07) where only assignment method (2) leads to a statistically significant DiD coefficient. Taken together, the results based on the IAB EP already cast some doubt on Bauernschuster (2013)’s finding of a causal short-term effect on the hiring rate.
The right side of Table 3.6 depicts the analogous estimates based on the LIAB QM2.
Interestingly, looking at column 6 which represents assignment method (2), the coefficient of the total effect from the LIAB QM2 of 1.5 percentage points (significant at the 10% level) is very close to the respective estimate from the IAB EP of 1.6 percentage points (significant at the 5% level), despite the fact that the former is only driven by a significant short-term effect while the latter is driven by both a statistically significant short- and medium term effect.28 Even more important, results for the total effect from the other assignment methods
26I thank Stefan Bauernschuster for making available the statistical programs for the data processing of the IAB EP.
27The slightly lower significance in my estimates could potentially be explained by the smaller sample as the extension of the sample periods require firms from the initial sample to also be present in the periods 2006 and 2007.
28There are a number of reasons that may explain differences in the estimates from the two data sources. To name a few, the LIAB QM2 constitutes administrative data whereas the IAB EP is obtained from surveys, the hires in the LIAB QM2 are determined as point-in-time comparisons whereas hires in the IAB EP refer to all hires, and the LIAB QM2 allows for a more precise full-time equivalent weighting scheme as opposed to global part-time weights used for the IAB EP.
3.5.RESULTS
Table 3.6.: Difference-in-Differences Results: IAB EP vs. LIAB QM2
IAB EP LIAB QM2
2-Years 4-Years 2-Years 4-Years
in ’Before’ in ’Before’ Always Adjacent in ’Before’ in ’Before’ Always Adjacent Periods Periods the same Periods Periods Periods the same Periods
(1) (2) (3) (4) (5) (6) (7) (8)
Six month DiD 2004-05 0.013 0.015∗ 0.008 −0.007 0.014∗ 0.023∗∗∗ 0.017 0.016∗∗
hiring rate (0.010) (0.008) (0.009) (0.007) (0.008) (0.008) (0.011) (0.008)
(Jan. DiD 2006-07 0.002 0.017∗∗ 0.005 −0.005 0.001 0.008 0.007 0.005
to June) (0.009) (0.008) (0.010) (0.007) (0.008) (0.009) (0.011) (0.008)
DiD 2004-07 0.007 0.016∗∗ 0.006 −0.006 0.008 0.015∗ 0.001 0.011
(0.009) (0.007) (0.009) (0.007) (0.007) (0.008) (0.010) (0.007)
DiD 2001-02 0.007 0.008 0.006 0.001 0.011 0.010 0.004 0.004
(0.010) (0.007) (0.010) (0.008) (0.008) (0.008) (0.011) (0.008)
Firms 558 383 349 1184 587 422 247 957
Observations 3906 2681 2443 4304 4109 2954 1729 4304
Notes:Table shows coefficients of difference-in-differences (DiD) estimates (ρ) as given by empirical model (3.1) for the sample periods 2001 to 2007, with the indicated variable as the outcome and establishment-year as the unit of observation. The years after DiD indicate the pooled periods under consideration. The year 2003 is the baseline period. Each column presents separate estimates for one of the four assignment methods described in section 3.4.2. Columns 1 to 4 refer to estimates for the six month hiring rate as the outcome based on the IAB EP and columns 5 to 8 to estimates for the analogous outcome based on the LIAB QM2. Standard errors are clustered at the establishment level.
Significance levels: * 10%, ** 5%, and *** 1%. See text for additional details. Data sources: LIAB QM2 9310 and IAB Establishment Panel.
63
in columns 5, 7, and 8 are again statistically insignificant, although the short-term effect remains statistically significant in method (1) and (4).29
To sum up, this supplementary analysis highlights the dependence of previous findings from Bauernschuster (2013) on the assignment methods to the treatment and control group.
As discussed in section 3.4.2, there is no obvious justification for prioritizing one assignment method over the other and therefore the findings based on both data sources do not allow one to draw a clear-cut conclusion on the causal effect of the relaxed dismissal protection on firm hiring rates. If anything, the findings suggest a short-term effect on the six months hiring rate which does not persist in the medium-term.
3.5.3. Heterogeneity
Although I do not find an effect on the firm overall worker turnover rates, the PADA reform might have differential impacts on subgroups of workers or firms. To this end, I first exploit worker-level information on gender and age and construct an array of subgroup-specific turnover rates for which I re-estimate model (3.1). In analogy to the overall rates, the men’s (women’s) hiring rate is determined by dividing the FTE hires of men (women) in periodt by the average of the FTE firm size in periodst−1 andt. Age-specific rates are constructed in the same way based on two age groups, i.e., young workers (aged<35 years) and older workers (aged ≥35 years).30 As in Centeno and Novo (2014), I use the cut-off age at 35 so that the group of young workers predominantly comprises employees that are still establishing themselves in the labor market and have a reasonable likelihood to pursue further education. In addition, I assess differential effects by union status and the East-West divide between firms. To this end, I split the establishment samples by the respective observable firm-level characteristic and re-estimate model (3.1) for each subsample.
From a theoretical point of view, differential effects of the PADA reform on worker subgroups could be explained by differences in labor supply elasticities (Bertola et al. 2007;
Centeno and Novo 2014). The labor supply of women and young workers is generally more elastic relative to prime-aged men given that women are more likely to decide between home production and market work and young workers between market work and education (Blundell and MaCurdy 1999; Bertola et al. 2007; Evers et al. 2008).31 Therefore, a less
29In unreported estimations, I also look at the dynamic pattern of the effects. Since the reform became effective in the beginning of 2004 and the outcome flow variables of the LIAB QM2 are point-in-time comparisons of the June 30 of consecutive years, the first treatment period 2004 is only subject to the reform in the last six month (January to June 2004). Hence, I would expect an effect on the hiring rate only in the second half of this observation period. Reassuringly, estimates reveal that positive and statistically significant effects on the hiring rate in 2004 for the different assignment methods are indeed driven by the second half of the observation period.
Coefficients range between 2.0 and 2.7 percentage points and are significance at least at 10% levels. In contrast, estimates for the first six months (July to December 2003) vary between -0.1 and 0.5 percentage points and are uniformly statistically insignificant.
30By construction, the sums of subgroup-specific hiring, separation, and job flow rates for men/women and young/older workers are equal to the respective overall flow rate. Consequently, the sums of the subgroup-specific total DiD coefficients are equal to the respective total DiD coefficients from section 3.5.1.
31For example, Prifti and Vuri (2013) find that a strengthening of the dismissal protection in Italy in 1990 had a positive and sizable causal effect on women’s fertility decisions.
3.5. RESULTS
stringent dismissal protection should have more pronounced employment effects on women and younger workers as employment rather than wage is the more important margin of adjustment (relative to men) (Bertola et al. 2007). Consequently, worker turnover rates of women and younger workers are more likely to be affected by the reform than rates of men and older workers.
Gender: Table 3.7 summarizes the results for the gender-specific turnover rates. The left side shows the results for the men’s rates, the right side the analogous estimates for the women’s rates. DiD estimates for the hiring, separation, and job flow rates in the pre-treatment periods are statistically insignificant which supports the common time trend assumption for these subgroup-specific outcome variables. In contrast, the women’s churning rate exhibits differential trends in the pre-reform periods for two of the four assignment methods. Consequently, I focus on the gender-specific hiring, separation, and job flow rates and disregard the results for the churning rate. Looking at the post-reform periods, I find evidence for a positive effect of the PADA reform on the women’s hiring rate (see panel A in Table 3.7). Depending on the assignment method, the rate increased by 1.3 to 2.1 percentage points with estimates being statistically significant at 5% and 1% levels. In comparison, estimates for the men’s hiring rate are in absolute terms not larger than 0.8 percentage points and uniformly statistically insignificant. The significant increase of the women’s hiring rate of 1.3 to 2.1 percentage points is sizeable. Relative to the women’s mean rate of 4.3% in the baseline year 2003, it is a 30 to 48% increase. Taking into account that the estimates for the gender-specific separation rates are very small, and for both men and women statistically insignificant (see panel B in Table 3.7), the DiD coefficients for the women’s job flow rates are, as expected, positive (see panel C in Table 3.7). Moreover, given all assignment methods yield positive estimates that range between 1.5 and 2.3 percentage points of which three out of four are statistically significant at the 5% level, the results further suggest a positive causal effect of the PADA reform on the women’s job flow rate.
Age groups:Contrary to the gender-specific findings which are consistent with the initial hypothesis on labor supply elasticities, the results do not support the prediction of a differen-tial effect for younger workers. As panel A to D in Table 3.8 show, the age-specific estimates are almost exclusively statistically insignificant.
Union status: To test for heterogeneity across the union status of firms, I divide the sample into two groups, one with firms that are covered by either a firm-level or an industry-wide union agreement and one with firms without any union coverage. As union membership is generally associated with more rigid wages, I expect worker turnover rates of firms in the group within unionized firms to be more susceptible to a reduction in dismissal protection as these firms are more likely to adjust along the employment margin. Panel A in Table 3.9 depicts the results on the hiring rate by firm unionization status. The total effect ranges from 1.7 to 3.1 percentage points, but is only statistically significant in two cases. I do not regard this as convincing evidence for a positive effect on the hiring rate of unionized firms.
65
FFECTSOFRELAXEDEMPLOYMENTPROTECTIONONLABORMARKETOUTCOMES
Men Women
2-Years 4-Years 2-Years 4-Years
in ’Before’ in ’Before’ Always Adjacent in ’Before’ in ’Before’ Always Adjacent Periods Periods the same Periods Periods Periods the same Periods
(1) (2) (3) (4) (5) (6) (7) (8)
Panel A: Hiring rate DiD 2004-07 −0.008 0.002 −0.006 −0.006 0.013∗∗ 0.018∗∗∗ 0.021∗∗∗ 0.014∗∗
(0.008) (0.008) (0.010) (0.008) (0.005) (0.006) (0.008) (0.006)
DiD 2001-02 0.007 0.003 −0.008 −0.004 0.005 0.007 0.010 0.006
(0.008) (0.008) (0.010) (0.008) (0.007) (0.007) (0.009) (0.006)
Panel B: Separation rate DiD 2004-07 0.000 0.003 0.005 0.000 −0.006 −0.005 0.005 −0.002
(0.008) (0.010) (0.010) (0.008) (0.007) (0.008) (0.008) (0.006)
DiD 2001-02 0.000 −0.007 −0.014 −0.004 −0.001 0.000 0.004 0.001
(0.008) (0.009) (0.011) (0.008) (0.007) (0.008) (0.010) (0.007)
Panel C: Job flow rate DiD 2004-07 −0.008 −0.001 −0.012 −0.007 0.019∗∗ 0.023∗∗ 0.016 0.015∗∗
(0.011) (0.012) (0.014) (0.011) (0.008) (0.009) (0.011) (0.008)
DiD 2001-02 0.007 0.010 0.006 0.000 0.007 0.007 0.005 0.005
(0.011) (0.012) (0.015) (0.011) (0.009) (0.010) (0.013) (0.009)
Panel D: Churning rate DiD 2004-07 −0.001 0.006 0.009 0.003 0.011 0.022∗∗ 0.029∗∗∗ 0.012
(0.010) (0.011) (0.012) (0.010) (0.008) (0.008) (0.010) (0.008)
DiD 2001-02 −0.006 −0.008 −0.013 −0.009 0.006 0.019∗ 0.026∗∗ 0.014
(0.010) (0.010) (0.012) (0.010) (0.010) (0.010) (0.013) (0.010)
Firms 587 422 247 957 587 422 247 957
Observations 4109 2954 1729 4304 4109 2954 1729 4304
Notes:Table shows coefficients of difference-in-differences (DiD) estimates (ρ) as given by empirical model (3.1) for the sample periods 2001 to 2007, with the indicated variable as the outcome and establishment-year as the unit of observation. The years after DiD indicate the pooled periods under consideration. The year 2003 is the baseline period. Each column presents separate estimates for one of the four assignment methods described in section 3.4.2. Columns 1 to 4 refer to estimates for the gender-specific worker turnover rates of men and columns 5 to 8 to estimates for the gender-specific worker turnover rates of women. Standard errors are clustered at the establishment level. Significance levels: * 10%, ** 5%, and *** 1%. Data source: LIAB QM2 9310.
3.5.RESULTS
Table 3.8.: Difference-in-Differences Results: Turnover Rates by Age Groups
Young (workers aged<35) Older (workers aged≥35)
2-Years 4-Years 2-Years 4-Years
in ’Before’ in ’Before’ Always Adjacent in ’Before’ in ’Before’ Always Adjacent Periods Periods the same Periods Periods Periods the same Periods
(1) (2) (3) (4) (5) (6) (7) (8)
Panel A: Hiring rate DiD 2004-07 0.004 0.009 0.012 0.003 0.000 0.011 0.002 0.004
(0.006) (0.007) (0.008) (0.006) (0.008) (0.008) (0.009) (0.007)
DiD 2001-02 0.004 0.005 0.006 0.002 0.008 0.005 −0.005 −0.001
(0.006) (0.006) (0.008) (0.006) (0.008) (0.009) (0.011) (0.008)
Panel B: Separation rate DiD 2004-07 −0.001 0.007 0.013 0.003 −0.005 −0.010 −0.002 −0.005
(0.006) (0.007) (0.008) (0.006) (0.009) (0.010) (0.011) (0.008)
DiD 2001-02 −0.003 −0.001 −0.005 −0.002 0.001 −0.005 −0.005 −0.001
(0.007) (0.008) (0.010) (0.007) (0.009) (0.009) (0.012) (0.009)
Panel C: Job flow rate DiD 2004-07 0.006 0.002 0.000 0.000 0.005 0.020∗ 0.005 0.009
(0.008) (0.009) (0.011) (0.008) (0.011) (0.012) (0.014) (0.010)
DiD 2001-02 0.007 0.006 0.012 0.004 0.007 0.010 0.000 0.001
(0.008) (0.010) (0.013) (0.009) (0.011) (0.012) (0.015) (0.011)
Panel D: Churning rate DiD 2004-07 −0.001 0.009 0.018∗∗ 0.001 −0.007 0.000 0.003 −0.002
(0.007) (0.008) (0.009) (0.008) (0.010) (0.010) (0.013) (0.010)
DiD 2001-02 −0.012 −0.006 −0.001 −0.006 −0.002 0.000 −0.007 −0.004
(0.008) (0.008) (0.009) (0.008) (0.011) (0.011) (0.013) (0.011)
Firms 587 422 247 957 587 422 247 957
Observations 4109 2954 1729 4304 4109 2954 1729 4304
Notes:Table shows coefficients of difference-in-differences (DiD) estimates (ρ) as given by empirical model (3.1) for the sample periods 2001 to 2007, with the indicated variable as the outcome and establishment-year as the unit of observation. The years after DiD indicate the pooled periods under consideration. The year 2003 is the baseline period. Each column presents separate estimates for one of the four assignment methods described in section 3.4.2. Columns 1 to 4 refer to estimates for the age-specific worker turnover rates of young workers (aged<35) and columns 5 to 8 to estimates for the age-specific worker turnover rates of older workers (aged≥35). Standard errors are clustered at the establishment level. Significance levels: * 10%, ** 5%, and *** 1%. Data source: LIAB QM2 9310.
67
FFECTSOFRELAXEDEMPLOYMENTPROTECTIONONLABORMARKETOUTCOMES
Union agreement No union agreement
2-Years 4-Years 2-Years 4-Years
in ’Before’ in ’Before’ Always Adjacent in ’Before’ in ’Before’ Always Adjacent Periods Periods the same Periods Periods Periods the same Periods
(1) (2) (3) (4) (5) (6) (7) (8)
Panel A: Hiring rate DiD 2004-07 0.025∗∗ 0.031∗∗ 0.027 0.017 −0.012 0.010 0.005 −0.002
(0.013) (0.014) (0.018) (0.013) (0.014) (0.015) (0.018) (0.014)
DiD 2001-02 0.021 0.015 0.023 0.015 0.005 0.005 −0.017 −0.010
(0.014) (0.014) (0.018) (0.013) (0.015) (0.015) (0.019) (0.015)
Panel B: Separation rate DiD 2004-07 0.002 0.008 0.014 0.005 −0.014 −0.012 0.007 −0.008
(0.016) (0.019) (0.019) (0.014) (0.015) (0.017) (0.019) (0.014)
DiD 2001-02 0.001 −0.002 −0.011 −0.007 −0.005 −0.011 −0.010 0.000
(0.016) (0.018) (0.022) (0.016) (0.015) (0.017) (0.021) (0.016)
Panel C: Job flow rate DiD 2004-07 0.023 0.022 0.013 0.013 0.001 0.022 −0.002 0.006
(0.018) (0.019) (0.024) (0.017) (0.018) (0.022) (0.025) (0.018)
DiD 2001-02 0.019 0.017 0.034 0.023 0.009 0.016 −0.007 −0.010
(0.020) (0.022) (0.029) (0.020) (0.020) (0.021) (0.027) (0.019)
Panel D: Churning rate DiD 2004-07 0.009 0.033 0.043∗ 0.012 −0.010 0.003 0.000 −0.002
(0.019) (0.021) (0.026) (0.019) (0.019) (0.020) (0.024) (0.020)
DiD 2001-02 −0.004 0.007 0.016 −0.005 −0.012 0.004 −0.008 0.006
(0.018) (0.021) (0.028) (0.019) (0.021) (0.021) (0.025) (0.021)
Firms 263 199 118 429 324 223 129 528
Observations 1841 1393 826 1944 2268 1561 903 2360
Notes:Table shows coefficients of difference-in-differences (DiD) estimates (ρ) as given by empirical model (3.1) for the sample periods 2001 to 2007, with the indicated variable as the outcome and establishment-year as the unit of observation. The years after DiD indicate the pooled periods under consideration. The year 2003 is the baseline period. Each column presents separate estimates for one of the four assignment methods described in section 3.4.2. Columns 1 to 4 refer to estimates for unionized establishments and columns 5 to 8 to estimates for non-unionized establishments. An establishment is considered as unionized if it is covered by either a firm-level or an industry-wide union agreement. Standard errors are clustered at the establishment level. Significance levels: * 10%, ** 5%, and *** 1%.
Data source: LIAB QM2 9310.
3.5.RESULTS
Table 3.10.: Difference-in-Differences Results: Turnover Rates by East-West Divide of Establishments
West Germany East Germany
2-Years 4-Years 2-Years 4-Years
in ’Before’ in ’Before’ Always Adjacent in ’Before’ in ’Before’ Always Adjacent Periods Periods the same Periods Periods Periods the same Periods
(1) (2) (3) (4) (5) (6) (7) (8)
Panel A: Hiring rate DiD 2004-07 −0.002 0.030∗∗ 0.022 0.007 0.013 0.012 0.010 0.008
(0.014) (0.014) (0.019) (0.013) (0.014) (0.015) (0.017) (0.014)
DiD 2001-02 0.012 0.006 −0.012 −0.014 0.012 0.013 0.011 0.018
(0.015) (0.014) (0.019) (0.013) (0.014) (0.015) (0.019) (0.015)
Panel B: Separation rate DiD 2004-07 −0.005 0.001 0.027∗ 0.007 −0.007 −0.002 −0.003 −0.011
(0.015) (0.017) (0.016) (0.014) (0.016) (0.018) (0.020) (0.015)
DiD 2001-02 −0.002 −0.017 −0.024 −0.015 −0.002 0.004 0.002 0.008
(0.015) (0.017) (0.020) (0.015) (0.016) (0.018) (0.022) (0.016)
Panel C: Job flow rate DiD 2004-07 0.003 0.029 −0.006 0.000 0.019 0.014 0.013 0.019
(0.018) (0.020) (0.023) (0.018) (0.019) (0.021) (0.025) (0.018)
DiD 2001-02 0.014 0.023 0.013 0.001 0.013 0.009 0.009 0.009
(0.020) (0.021) (0.028) (0.019) (0.020) (0.022) (0.028) (0.020)
Panel D: Churning rate DiD 2004-07 −0.002 0.021 0.026 0.015 0.000 0.014 0.014 −0.008
(0.018) (0.020) (0.024) (0.019) (0.020) (0.022) (0.025) (0.021)
DiD 2001-02 −0.024 −0.013 −0.032 −0.017 0.008 0.023 0.031 0.020
(0.020) (0.020) (0.023) (0.020) (0.021) (0.022) (0.028) (0.021)
Firms 315 215 115 538 272 207 132 419
Observations 2205 1505 805 2342 1904 1449 924 1961
Notes:Table shows coefficients of difference-in-differences (DiD) estimates (ρ) as given by empirical model (3.1) for the sample periods 2001 to 2007, with the indicated variable as the outcome and establishment-year as the unit of observation. The years after DiD indicate the pooled periods under consideration. The year 2003 is the baseline period. Each column presents separate estimates for one of the four assignment methods described in section 3.4.2. Columns 1 to 4 refer to estimates for West German establishments and columns 5 to 8 to estimates for East German establishments. Standard errors are clustered at the establishment level.
Significance levels: * 10%, ** 5%, and *** 1%. Data source: LIAB QM2 9310.
69
Unreported sensitivity checks (in analogy to the sensitivity tests conducted in section 3.5.5) further support this conclusion in that the positive effect on the hiring rate of unionized firms becomes in most of the tests statistically insignificant. Panel B, C, and D of Table 3.9 further show that estimates for both subgroups on the separation, job flow, and churning rate are predominantly statistically insignificant and thus do not provide evidence for a differential effect by firm unionization status.
East-West divide:Finally, I analyze differences in the effects on firms located in former West Germany versus firms located in former East Germany.32Although the PADA reform was introduced country-wide, persistent structural differences in the economies of the two former halves of Germany may still yield differential reform effects. However, as Table 3.10 shows, the DiD coefficients for both groups are again almost exclusively statistically insignificant and thus there is no evidence that firms in either part of Germany responded differently to the PADA reform.