are training programs more effective when unemployment is high?

Are Training Programs More EffectiveWhen Unemployment is High?

Michael Lechner and Conny Wunsch

www.siaw.unisg.ch/lechner

October 2006

© Michael Lechner, Conny Wunsch, p. 2

Idea of the paper

Understand whether the effectiveness of training programs for the unemployed depends on the state of the labour market

Why is that important?

• optimize timing and volume of ALMP over the business cycle• better understanding of empirical evidence for different time periods and

countries

Problem: hard to find suitable data

• regional cross-sectional studies face a lot of regional heterogeneity• macro studies cannot address relevant selection problem • meta studies have a lot of individual study-specific heterogeneity• experiments typically do not trace program entries long enough


What do we know so far?

• Meta study by Kluve (2006) seems to indicate a positive dependence of program effectiveness on unemployment

• Johansson (2001) uses variation of Swedish active labour market programs over municipalities programs prevent unemployed from leaving the labour force during a downturn positively related to UE

• Raaum, Torp, Zhang (2002):- 12 cohorts of labour market training participants in Norway 1991-1996 - 4 subgroups: men/women with/without UB- outcome variable: annual earnings 1/2/3 years after program- meta analysis: (i) pool all estimates, (ii) exploit county-level variation- results: positive correlation of the ATET with unemployment rate as well as exit rate to employment at outcome measurement- do not control for changing composition of participants and program mix


Our contribution

Systematic investigation of the relationship between program effectiveness and labour market conditions using administrative data for West Germany 1980-2003

• 10 years of monthly program entries (1986-1995)

• 8 years after program start to observe outcomes

• at least 6 years before program start to control for selectivity

• institutional environment relatively stable

• control for changes in composition of participants and program mix


The West German economy 1984-2003

Business cycle movements

-2

0

2

4

6

8

10

12

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

GDP growth Unemployment rate Employment rate / 10


Training as a part of German ALMP

Program DescriptionJob search assistance

Learn how to write CV / application, how to locate job vacancies; practice job interviews etc. (only until end of 1992)

Short training Further training: general update or adjustment of skills; planned duration 6 months.

Practice firms Simulate working in a specific professionLong training Same types as short training with a planned duration > 6 months.

Retraining Training to obtain a new professional degree in a field other than the profession currently held.

Ranked in ascending order of planned program duration:

min. 1 weekmax. 48 months


Training as a part of German ALMP

Participants in training West Germany 1986-1995

0

10000

20000

30000

40000

50000

60000

186 686 1186 487 987 288 788 1288 589 1089 390 890 191 691 1191 492 992 293 793 1293 594 1094 395

Job search assistance Retraining Further training All programmes


Institutional changes

1994

• (1-3%) cut in replacement rates of unemployment benefits (UB), unemployment assistance (UA) and benefits during participation in training (maintenance allowance, MA)

• eligibility: reduction in required work experience before program by 3 years

changes small and can be controlled for in the data


The data

Employment subsample Benefit payment register Training participant data

Source Employer supplied mandatory social insurance entries.

Benefit payment register of the FEA.

Questionnaires filled in by caseworker for statistical purposes (ST35).

Population

1% random sample of persons covered by social insurance for at least one day 1975-1997. Self-employed, civil servants, students are not included. Data 1980-2003

Recipients of UA, UB, or MA 1980-2003.

Participants in further training, retraining, short programs (§41a EPA), German language courses and temporary wage subsidies 1980-1997.

Available information

Personal characteristics and history of employment.

Information about the receipt of benefits, mainly UB, UA, MA.

Personal characteristics of participants and information about training programs.

Important variables

Gender, age, nationality, education, profession, employment status, industrial sector, firm size, earnings, regional information

Type and amount of benefits received.

Type, duration and result of the program, type of income support paid during participation.

Structure Spells based on daily information. Spells based on daily info. Spells based on monthly information.Note: The merged data is based on monthly information. For detailed information on the merging and recoding procedures see Bender et al. (2004). The creation of this data base is a result of a three year joint project of research groups at the Universities of Mannheim (Bergemann, Fitzenberger, Speckesser) and St. Gallen (Lechner, Miquel, Wunsch) as well as the Institute for Employment Research of the FEA (Bender).


Sample definition

For each month 1986-1995 check:

• participants start training in that month

• nonparticipants do not enter training but are unemployed with receipt of UB/UA, no program also in the 11 following months

• age 20-55, no homeworkers/students, no part-time workers < ½ full-time eqiv.

• no program in the 4 years before

• eligibility: receipt of UB/UA in month before

multiple appearance of a person as participant or nonparticipant possible

pool participants/nonparticipants over 6 months to obtain sufficient sample sizes

check sensitivity w.r.t. these choices


Program starts in our sample (pooled)Number of participants

200

250

300

350

400

450

500

550

600

650

700

750

186 786 187 787 188 788 189 789 190 790 191 791 192 792 193 793 194 794 195 795

Time varying composition of participants and programmesComposition of participants held constantComposition of participants and programmes held constant


The formula that explains what we estimate

0: ( | 1, ) ( | 0, )t t t t t tCIA E Y D X x E Y D X x 1: ( | 0, ) ( | 1, )t t t t t tCIA E Y D X x E Y D X x

1 0|( | ) ( | 1, ) ( | 0, ) ( )t tt t t t t t t t t X PE Y Y P E Y D X x E Y D X x f x dx

1. participants in month t (ATET at t)

2. population which has same personal characteristics as pool of all participants 1986-1995 reduced to common support

3. population which has same personal characteristics and program mix as pool of all participants 1986-1995 reduced to common support

Vary population Pt in an interesting way:


Plausibility of the conditional independence assumption in our data: eligibility: ensured by sample definition selection of caseworkers: detailed personal, regional, employer information selfselection of UE: initial and remaining UE benefits claim, previous earnings 6 years of monthly pre-program employment history

Potentially important variables that are missing: jail and health status histories caseworker assessment of the UE (about motivation etc.)

unobservable factors captured to the extend to which they had impacts on

pre-program employment history focus on correlation of effects with labour market conditions: bias no problem

if uncorrelated with labour market conditions

Identification: selection on observables


Estimation: modified matching estimator as in LMW `05

Matching (for each month/6-month window):

• for each participant find one or more nonparticipants who are as similar as possible in all characteristics that jointly influence participation and the outcome of interest

• similarity within a prespecified radius

• comparisons are weighted according to their distance in characteristics

• characteristics can be summarised by participation probability to overcome curse of dimensionality (Rosenbaum and Rubin, 1983) and allow for semiparametric estimation of the effect

• common support: effect can only be estimated for the group of people for which there are comparable participants and nonparticipants

[apply – per period – matching estimator of Lechner, Miquel, Wunsch, 2005]


Outcomes of interest

• employment (subject to social insurance)

• cumulated employment

• registered unemployment (receipt of UB/UA, participation in training)

• cumulated registered unemployment

• (cumulated) monthly earnings

6 months after program start (average of months 5-7) [lock-in effect]

3 years after program start (average of months 34-39)

6 years after program start (average of months 61-72)

8 years after program start (average of months 85-96) [long-run effect]


Results: program effectsUnemployment and employment without minor employment(time varying composition of participants and programmes)

-0.2

-0.1

0

0.1

0.2

0.3

0.4

186 686 1186 487 987 288 788 1288 589 1089 390 890 191 691 1191 492 992 293 793 1293 594 1094 395

U6TH significant U96TH significant E6TH significant E96TH significant U6TH

U96TH E9TH E96TH Unemployment rate

Unemployment 6 months after program start

Employment 8 years after program start

Employment 6 months after program start

Unemployment 8 years after program start


Results: correlation with macro indicators

Outcome

Unemployment rate at

Quarterly GDP growth rate

# of participants in training programsprogram start

outcome measurement

Unemployment 6 months program after entry -43** -33* 3 19

3 years after entry -36* 21 8 10

6 years after entry -27* 24* 15 21

8 years after entry -1 26 17 17

Employment 6 months after entry 25* 5 8 -1

3 years after entry 45** -45** 2 -3

6 years after entry 43** -33** -3 -33**

8 years after entry 31** -47** -12 -50**

Cumulated unemployment 6 months after entry -43** -43** 8 15

8 years after entry -50** 27 17 22

Cumulated employment 6 months after entry 20 20 6 9

8 years after entry 52** -37** -4 -22Note: For the uncumulated outcomes the unemployment rate at outcome measurement is the rate measured in the respective month after program start. For the cumulated outcomes the unemployment rate at outcome measurement is the average unemployment rate over the respective period. Newey-West autocorrelation-robust standard errors: ** significant at the 1% level, * significant at the 5% level.


But: participants change over time

Characteristics of program participantsUnemployment rate

at program start

Woman -52**

Foreigner -24*

No professional degree -67**

University/college degree 7

Duration of last unemployment spell -51**

Fraction of months employed in the last 6 years 82**

Fraction of months unemployed in the last 6 years -46**

Note: Correlation of the monthly mean of the respective variable (six-month moving average) with the corresponding unemployment rate. ** significant at the 1% level, * significant at the 5% level.

...in a way which is correlated with labour market conditions

If effects are heterogenous w.r.t. these variables, then correlation may be due to them!

Adjust participants and nonparticipants to the same distribution of characteristics over time


Correlations with UE rate rather increase!

Outcome

Unemployment rate atPrevious specification:


program start outcome measurement program start

outcome measurement

Unemployment 6 months after entry -49** -45** -43** -33*

3 years after entry -48** 19 -36* 21

8 years after entry 19 15 -1 26

Employment 6 months after entry 36** 24 25* 5

3 years after entry 45** -56** 45** -45**

8 years after entry 31* -30** 31** -47**

Note: For the uncumulated outcomes the unemployment rate at outcome measurement is the rate measured in the respective month after program start. Newey-West autocorrelation-robust standard errors: ** significant at the 1% level, * significant at the 5% level.


But: composition of programs also changes over time

Note: Correlation of the monthly mean of the respective variable (six-month moving average) with the corresponding unemployment rate. ** significant at the 1% level, * significant at the 5% level.

...in a way which is correlated with labour market conditions

Characteristics of programs

Unemployment rate at program start

Participants “Stable” participants

Fraction of participants in short training 33** 24*

Fraction of participants in long training 33** 25**

Fraction of participants in retraining -1 6

Fraction of participants in job search assistance -42** -27*

Planned program duration 8 1

Planned duration of short training -17 -20*

Planned duration of long training -25** -21*

Planned duration of retraining -20* -50**

Planned duration of job search asssistance 40** 40**

keep program shares and planned duration constant over time(drop participants in job search assistance because of lack of support)


The correlations are still there!

Outcome


Previous specification: Unemployment rates at

program start

program startoutcome

measurementparticipants

constantparticipants

change

Unemployment 6 months after entry -61** -60** -49** -43**

3 years after entry -33* 33** -48** -36*

8 years after entry 6 14 19 -1

Employment 6 months after entry 51** 39** 36** 25*

3 years after entry 27 -58** 45** 45**

8 years after entry 42** -20 31* 31**

Note: For the uncumulated outcomes the unemployment rate at outcome measurement is the rate measured in the respective month after program start. Newey-West autocorrelation-robust standard errors: ** significant at the 1% level, * significant at the 5% level.


Sensitivity checks (final specification)

• merely seasonal pattern captured? -> no

• variation between low/high UE regions -> no contradictions

• stable over time? -> some changes depending on when outcome is measured but overall conclusions unchanged

• pooling of observations over 4/9 months:reduced/increased precision, correlation somewhat smaller/larger, conclusions unchanged

• no program in 6/12/24 months before program:no change since in common support no program before

• nonparticipants no program for 6/24 months after start date:conclusions unchanged

• future participation of nonparticipants uncorrelated with labour market cond.

• operational characteristics of matching estimator: LMW (2005) -> robust


Conclusions

• negative effects in the short run over the whole period

• positive employment effects in the long run most of the time

• almost no long-run effects on unemployment

confirms findings of previous studies


Conclusions

• short and long-run employment effects are positively correlated with unemployment at program start

• short-run unemployment effects are negatively correlated

• holding the composition of participants and program constant over time sharpens this finding

• seasonal correlation does not contradict this finding

• regional correlation does not contradict this finding

• patterns over time do not contradict this finding

• other sensitivity checks do not question this finding


Conclusions

Possible explanations for our findings:

• short run: lock-in effect less severe if UE is high or worsening

• long run: long-term consequences of lock-in effect?- worse employment record- human capital depreciation during longer UE

are training programs more effective when unemployment is high?

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