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1 The Effects of Hiring Subsidies for Older Workers on Unemployment Durations in Germany Andreas Ammermüller Bernhard Boockmann Michael Meier Thomas Zwick.

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Presentation on theme: "1 The Effects of Hiring Subsidies for Older Workers on Unemployment Durations in Germany Andreas Ammermüller Bernhard Boockmann Michael Meier Thomas Zwick."— Presentation transcript:

1 1 The Effects of Hiring Subsidies for Older Workers on Unemployment Durations in Germany Andreas Ammermüller Bernhard Boockmann Michael Meier Thomas Zwick Centre for European Economic Research (ZEW), Mannheim

2 2 Outline (1) Introduction (2) Description of hiring subsidies (3) The data (4) Estimation approach and implementation (5) Results (6) Conclusions

3 3 Two questions should be distinguished: 1.Do hiring subsidy programmes causally lead to earlier exit from unemployment to employment in the group of eligible persons as compared to the situation in which no subsidies are available? 2.Do subsidised hirings causally lead to more unsubsidised employment? Forslund et al. (2004), Sianesi (2003), and Hujer et al. (2002) In this paper, the focus is on the first question Introduction

4 4 Static model with perfect competition on the labour market: hiring subsidy lowers net wages paid by the employer and increases demand for subsidised employees Reaction depends on wage elasticity: dlnL/ds =  /(  +  ), where L is subsidised employment If elasticity is low, deadweight effects (Buslei and Steiner, 1999; Hujer and Caliendo, 2003; Meyer, 1995a) occur Empirically, wage elasticities differ between male/female and single/married and East/West Germany Conditions for the effectiveness of hiring subsidies

5 5 Hiring subsidies may provide too low an incentive and / or may not be known among eligible firms and workers  implementation study In addition to deadweight effects, hiring subsidies may be ineffective due to substitution or a displacement effect Since our approach is based on individual-level data, we concentrate on deadweight effects. The particular interest in the deadweight effect is whether the programme is effective for the targeted group More reasons why hiring subsidies may fail to affect the number of hirings

6 6 Deadweight effects involve a counterfactual that must be estimated In this paper we use changes to the eligibility rules as „natural“ variation Before 2002, the Integration Supplement for older workers was only available for hiring long-term unemployed workers. Taking effect on January 1st, 2002, this condition was dropped Taking effect on January 1st, 2004, the EGZ subsidy for older workers was integrated into the framework of the general EGZ, so that workers aged 50+ lost preferential treatment Estimating deadweight effects

7 7 One of the major instruments of German active labour market policy Legal basis: German Social Code (SGB), Volume III EGZ are paid to the employer as a percentage of standardised labour costs (maximum 50 per cent) for up to 24 months (other limits for workers with specific disadvantages) If the employment relationship is terminated before a minimum period after the expiration of the subsidy, the employer is legally obliged to refund parts of the subsidy No legal claim to EGZ either by the worker or the employer Description of hiring subsidies (EGZ) in Germany

8 8 Employment contracts subsidised by Integration Supplements

9 9 EGZ is regarded as an important instrument by employment agencies There is substantial scope for decision-making concerning the allocation of the subsidy In the majority of cases, an initial contact between a worker and an employer already existed It will often be difficult for placement officers to decide whether the company would refrain from hiring without EGZ The implementation study strongly confirms the notion that deadweight effects are a major issue Implementation of the programme

10 10 Evaluation is based on the Integrated Employment Biographies (IEB) The IEB are composed of four separate data bases: Employment Register (BeH) Benefit Claimants Register (LeH) Programme-Participants Comprehensive Data Base (MTG) Job Applicant Files (ASU) The data

11 11 The paper uses two legal changes in eligibility as natural variation First, before 1-1-2002 eligibility was limited to individuals aged 50+ who were either long-term unemployed (in the legal definition) or had been unemployed for more than 6 consecutive months; this criterion was dropped in 2002 Hence, workers 50+ and not fulfilling the criterion are used as the treatment group Second, on 1-1-2004 EGZ subsidies for older workers were integrated into the general EGZ framework; workers 50+ lost preferential status Hence, workers 50+ are used as the treatment group Estimation approach and implementation

12 12 Definition of age groups: oworkers aged 50 to 50+6 months at the time of entering unemployment (treatment group) oworkers aged between 49 and 49+6 months (control group) Treatment and control group are observed before and after the legal changes We observe individuals belonging to a 3-months entry cohort during a period of 180 days after entry in unemployment Estimation approach and implementation

13 13 Time frame for the difference-in-differences analysis Policy change

14 14 Three difference-in-difference estimators (1)time varying unconditional effect obtained from Kaplan-Meier- Survivor functions (DD1): (2)estimation of a PH model and calculation of the treatment effect on the survivor function (DD2):

15 15 Three difference-in-difference estimators (3)time varying conditional effect calculated from the difference- in-differences of the baseline hazard rate of the Cox partial likelihood model (DD3)

16 16 The most important assumption underlying the difference-in- differences estimator is that all differences in the changes of the outcome variable between the treatment and the control group are due to the treatment A number of reasons why this could by invalid: 1.Other programmes affecting treatment or control group differently 2.Other influences on particular age groups 3.Anticipation effects Validity of the DD estimators

17 17 Note: Number of individuals in parentheses, t-statistics estimated robustly. Exits into subsidised employment 2001200220032004 Treatment Group (Age 50 to 50+6 months) 1.16 (6209) 3.72 (7320) 3.03 (14406) 0.94 (16389) Control Group (Age 49 to 49+6 months) 0.82 (6453) 1.14 (6947) 1.25 (14374) 0.52 (17181)

18 18 DD1-effect on employment, 2002

19 19 DD1-effect on employment, 2002 Men, WestMen, East Women, West Women, East

20 20 DD1-effect on employment, 2004

21 21 DD1-effect on employment, 2004 Men, WestMen, East Women, West Women, East

22 22 All workersMen WestMen EastWomen WestWomen East After treatment (2002) 1,000,981,011,070,88 (-0,01)(-0,28)(0,07)(0,9)(-0,87) Treatment group (age 50 to 50+6) 0,951,060,920,940,64 (-1,15)(0,93)(-1,01)(-0,91)(-2,94) Interaction term1,03 (0,6) 0,98 (-0,22) 1,05 (0,41) 0,99 (-0,15) 1,64 (2,32) PH test, p-level 0,570,900,920,310,99 DD2 Number of obs. 0.68 23760 -0,43 8870 1,25 3895 -0,26 8440 6,88 2555 LR chi2367,75131,8287,8184,2931,72 Conditional DD Results (DD2-Effect), 2002

23 23 AllMen WestMen EastWomen WestWomen East After treatment (2004) 0,99 (-0,51) 0,88 (-2,7) 0,97 (-0,48) 1,06 (1,07) 1,18 (2,02) Treatment group (age 50 to 50+6) 0,97 (-1,12) 0,89 (-2,39) 0,99 (-0,17) 1,02 (0,36) 1,04 (0,46) Interaction term 0,95 (-1,23) 1,10 (1,39) 0,99 (-0,06) 0,80 (-2,88) 0,85 (-1,43) PH test, p-level 0,240,130,960,130,04 DD2 Number of obs. -0,74 57449 1,48 20490 -0,10 10154 -2,84 18253 -1,92 8552 LR chi2 954,51333,36363,27139,5180,05 Conditional DD Results (DD2-Effect), 2004

24 24 Conditional DD Results (DD3-Effect), 2002

25 25 Conditional DD Results (DD3-Effect), 2004

26 26 Conditional DD Results (DD3-Effect), 2002 West East Men Women

27 27 Conditional DD Results (DD3-Effect), 2004 West East Men Women

28 28 Evidence on deadweight effects How does the number of jobs created with the help of hirings subsidies compare to the number of subsidies disbursed? Compare number of exits into subsidised and unsubsidised jobs – if the latter declines by as much as the first increases, crowding out is complete Competing risks framework using differences of differences to the cumulative incidence functions of exit into subsidised and unsubsidised jobs All results are unconditional on covariates

29 29 DD of the Cumulative Incidence Function, 2002

30 30 DD of the Cumulative Incidence Function, 2004

31 31 Conditional DD Results (DD3-Effect), 2002 West East Men Women

32 32 Conditional DD Results (DD3-Effect), 2002 West East Men Women

33 33 We have used a natural experiment design to answer the question whether hiring subsidy programmes are effective Two changes were compared: extension of eligibility for workers aged 50+ in 2002 and abolishment of preferential treatment for the same group in 2004 Application of three DiD estimators shows that employment effects for workers as a whole are insignificant and small compared to the number of subsidised hirings Competing risks framework suggests that the EGZ is not very effective in the group of treated individuals and deadweight effects are important However, for East German Women (in 2002) and Women in both parts of Germany (in 2004), some effects are significant and large relative to subsidised hirings  deadweight effects much smaller here Conclusions


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