Are Training Programs More Effective When Unemployment is High?

1. Are Training Programs More EffectiveWhen Unemployment is High? October 2006 Michael Lechner and Conny Wunsch www.siaw.unisg.ch/lechner

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

3. 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

4. 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

5. The West German economy 1984-2003

6. Training as a part of German ALMP Ranked in ascending order of planned program duration: min. 1 weekmax. 48 months

7. Training as a part of German ALMP

8. 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

9. The data 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).

10. 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

11. Program starts in our sample (pooled)

12. The formula that explains what we estimate Vary population Ptin an interesting way: • participants in month t (ATET at t) • population which has same personal characteristics as pool of all participants 1986-1995 reduced to common support • population which has same personal characteristics and program mix as pool of all participants 1986-1995 reduced to common support

13. Identification: selection on observables • 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

14. 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]

15. 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]

16. 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: program effects

17. Results: correlation with macro indicators Note: 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.

18. Adjust participants and nonparticipants to the same distribution of characteristics over time But: participants change over time ...in a way which is correlated with labour market conditions 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. If effects are heterogenous w.r.t. these variables, then correlation may be due to them!

19. Correlations with UE rate rather increase! 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.

20. But: composition of programs also changes over time ...in a way which is correlated with labour market conditions 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. keep program shares and planned duration constant over time(drop participants in job search assistance because of lack of support)

21. The correlations are still there! 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.

22. 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

23. 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

24. 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

25. 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