Andreas Ammermüller Bernhard Boockmann Michael Meier Thomas Zwick

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

Outline

(1) Introduction

(2) Description of hiring subsidies

(3) The data

(4) Estimation approach and implementation

(5) Results

(6) Conclusions

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

• 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

• 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

• 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

• 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

80

30,000

60,000

90,000

120,000

150,000

180,000

Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05

All

East

West

Employment contracts subsidised

by Integration Supplements

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• 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

• 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

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

Definition of age groups:

o workers aged 50 to 50+6 months at the time of entering unemployment (treatment group)

o workers 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

Time frame for the difference-in-differences analysis

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2001/2003 2002/2004

Entry into unemployment

Entry into unemployment

Policy change

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):

1 , 1 , 0 , 1 , 0( ) ( ) ( ) ( ) ( ) .h t h t k t k tDD S S S S

2 0 2 3 0 2ˆ ˆ( ) ( ) exp exp( ´ ) ( ) exp exp( ´ ) .h h h

t i iDD S d d z S d z

0 1 2 3( ) ( ) exp( ´ ).h ht t id d d z

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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)

3 , 1 , 0 , 1 , 0ˆ ˆ ˆ ˆ( ) ( ) ( ) ( ) ( ) .h t h t k t k tDD S S S S

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• 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

Note: Number of individuals in parentheses, t-statistics estimated robustly.

Exits into subsidised employment

2001 2002 2003 2004

Treatment Group(Age 50 to50+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)

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DD1-effect on employment, 2002

0,7

0,75

0,8

0,85

0,9

0,95

1

1 30 59 88 117 146 175

Days

Sh

are

Un

emp

loye

d

Control group, after treatment

Treatment group, after treatment

Control group, before treatment

Treatment group, before treatment

19

0.7

0.75

0.8

0.85

0.9

0.95

1

1 30 59 88 117 146 175

Days

Sh

are

un

em

plo

ye

d

Kontrollgruppe, nach Änderung Treatmentgruppe, nach Änderung

Kontrollgruppe, vor Änderung Treatmentgruppe, vor Änderung

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1 30 59 88 117 146 175

Tage

Arb

eit

slo

sig

ke

its

an

teil

Kontrollgruppe, nach Änderung Treatmentgruppe, nach Änderung

Kontrollgruppe, vor Änderung Treatmentgruppe, vor Änderung

0.7

0.75

0.8

0.85

0.9

0.95

1

1 30 59 88 117 146 175

Tage

Arb

eits

losi

gke

itsa

nte

il

Kontrollgruppe, nach Änderung Treatmentgruppe, nach Änderung

Kontrollgruppe, vor Änderung Treatmentgruppe, vor Änderung

0.7

0.75

0.8

0.85

0.9

0.95

1

1 30 59 88 117 146 175

Tage

Arb

eits

losi

gke

itsa

nte

il

Kontrollgruppe, nach Änderung Treatmentgruppe, nach Änderung

Kontrollgruppe, vor Änderung Treatmentgruppe, vor Änderung

DD1-effect on employment, 2002

Men, West Men, East

Women, West Women, East

20

DD1-effect on employment, 2004

0,7

0,75

0,8

0,85

0,9

0,95

1

1 30 59 88 117 146 175

Days

Sh

are

un

em

plo

ye

d

Control group after treatment

Treatment group, after treatment

Control group, before treatment

Treatment group, before treatment

21

DD1-effect on employment, 2004

0,7

0,75

0,8

0,85

0,9

0,95

1

1 30 59 88 117 146 175

Days

Sh

are

un

em

plo

ye

d

Control group, after change Treatment group, after change

Control group, before change Treatment group, before change

0,7

0,75

0,8

0,85

0,9

0,95

1

1 30 59 88 117 146 175

Days

Sh

are

un

emp

loye

d

Control group, after change Treatment group, after change

Control group, before change Treatment group, after change

0,7

0,75

0,8

0,85

0,9

0,95

1

1 30 59 88 117 146 175

Days

Sh

are

un

em

plo

ye

d

Control group, after change Treatment group, after change

Control group, before change Treatment group, before change

0,7

0,75

0,8

0,85

0,9

0,95

1

1 30 59 88 117 146 175

Days

Sh

are

un

emp

loye

d

Control group, after change Treatment group, after change

Control group, before change Treatment group, after change

Men, West Men, East

Women, West Women, East

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All workers Men West Men East Women West

Women East

After treatment (2002)

1,00 0,98 1,01 1,07 0,88

(-0,01) (-0,28) (0,07) (0,9) (-0,87)

Treatment group (age 50 to 50+6)

0,95 1,06 0,92 0,94 0,64

(-1,15) (0,93) (-1,01) (-0,91) (-2,94)

Interaction term 1,03 (0,6)

0,98 (-0,22)

1,05 (0,41)

0,99 (-0,15)

1,64 (2,32)

PH test, p-level 0,57 0,90 0,92 0,31 0,99

DD2Number of obs.

0.6823760

-0,438870

1,253895

-0,268440

6,882555

LR chi2 367,75 131,82 87,81 84,29 31,72

Conditional DD Results (DD2-Effect), 2002

23

All Men West Men East Women West Women East

After treatment(2004)

0,99(-0,51)

0,88 (-2,7)

0,97 (-0,48)

1,06 (1,07)

1,18(2,02)

Treatmentgroup (age 50to 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 0,24 0,13 0,96 0,13 0,04

DD2

Number of obs.

-0,74

57449

1,48

20490

-0,10

10154

-2,84

18253

-1,92

8552

LR chi2 954,51 333,36 363,27 139,51 80,05

Conditional DD Results (DD2-Effect), 2004

24

Conditional DD Results (DD3-Effect), 2002

25

Conditional DD Results (DD3-Effect), 2004

26

Conditional DD Results (DD3-Effect), 2002

West East

Men

Women

27

Conditional DD Results (DD3-Effect), 2004

West East

Men

Women

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

DD of the Cumulative Incidence Function, 2002

30

DD of the Cumulative Incidence Function, 2004

31

Conditional DD Results (DD3-Effect), 2002

West East

Men

Women

32

Conditional DD Results (DD3-Effect), 2002

West East

Men

Women

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