1. Post-secondary vocational training courses: are they effective for Italian unemployed youth with a high school diploma? COMPIE 2014 Conference Rome, 27th November 2014 Paolo Severati Scientific Coordinator of the Project and Head of Training Policies Evaluation Unit ISFOL – Institute for the Development of Vocational Training for Workers Roma, Corso d’Italia, 33

2. A national project Ministry of Labour and Social Policies (applicant and provider of the co-financing of the project) ISFOL - Institute for the Development of Vocational Training for Workers ASVAPP & IRVAPP Coordinamento delle Regioni (National Coordination Committee of the Italian Regions)

3. Outline Evaluation design and methodology Average impact estimates Subgroup analysis Conclusions and policy implications References

4. EVALUATION DESIGN AND METHODOLOGY

5. Policy intervention and target population  The study is focused on ‘post-diploma’ (PD) training courses targeted mainly to 20-29 year old unemployed individuals with a high school diploma  Within the whole spectrum of the training programmes co-financed by ESF in Italy, the features of PD interventions are quite homogenous across the country  PD training courses are very intensive and have a strong focus on the acquisition specific job skills  So, they should reasonably have a positive effect on individual employability

6. Geographical scope Piedmont Trento Veneto Lazio

7. Time coverage Different from Region to Region, according to the programming choices and availability of data. For example, in Piedmont PD courses started between 2007 and 2011 were chosen (from 2-6 year coverage, outcome data being available until the end of 2013) PD courses last from less than one to two years Lock-in effect (generally absorbed after maximum 2 years)

8. Methodology Propensity score matching Kernel matching estimator Blocking with regression adjustment estimator (Imbens [2014])

9. Data requirement Propensity score matching is data hungry in terms of the number of variables to estimate participation and outcomes Three archives were used and merged for the analysis : 1.Regional archives on training policies (co-funded by ESF or not); 2.Public Employment Service Archives; 3.COB (Comunicazioni Obbligatorie) Archives, containing information that every private employer or agent is legally bound to communicate on- line to the COB archive in order to initiate, modify or terminate any work- related contract

10. Accurate description of the pre-intervention labour market history is crucial to derive a credible comparison group Pre-programme (un)employment is an important predictor of programme entry and employment outcomes (un)employment history can capture unobservable characteristics (as, for instance, motivation) which could influence participation and outcomes

11. Design of evaluation

12. AVERAGE IMPACT ESTIMATES

13. Average impact estimates Evidence from our analysis shows: 1.Significant and positive effects on individual employability 2.Lock-in effect (months after the beginning of the course the treatment group exhibits a lower probability of employment, but a positive effect on the probability of employment is evident in subsequent months)

14. Piedmont - Trainees employment rate and counterfactual estimate, months -24 to 48 since the beginning of the training courses, years 2007-2011

15. Trento - Probability to be at work for participants and controls after matching, months -36 to 27 since the beginning of the training courses, years 2010 and 2011 Trento - Probability to be at work for participants and controls after matching, months -36 to 27 since the beginning of the training courses, years 2010 and 2011

16. Veneto - Probability to be at work for participants and controls after matching, months -60 to 36 since the beginning of the training course, years 2008 and 2009

17. Effects on other outcomes and matching procedure It seems that participation to courses reduces the probability of finding an open-ended contract Positive effect on number of weeks spent working in a year Impact estimates do not depend on the matching procedure used

18. Piedmont – Average impact estimates

19. Piedmont - Sensitivity analysis

20. Trento - ATT on the probability to be at work estimating using kernel matching (KM) and blocking with regression adjustment (BRA)

21. Trento - ATT on the probability to get an open – ended contract and on the number of weeks at work estimating using kernel matching (KM) and blocking with regression adjustment (BRA)

22. Veneto - ATT on the probability to be at work estimating using kernel matching (KM) and blocking with regression adjustment (BRA)

23. Veneto - ATT on the probability to get an open – ended contract and on the number of weeks at work estimating using kernel matching (KM) and blocking with regression adjustment (BRA)

24. Lazio - ATT on the probability to be at work at 12, 24, 36 months after the beginning of the treatment

25. Lazio - ATT on the probability to get an open-ended contract at 12, 24, 36 months after the beginning of the treatment

26. SUBGROUP ANALYSIS

27. Effects on subgroups are different in each Region Age. In Piedmont, the impact seems to be higher for younger people (18-20) who just got a diploma. In Veneto the impact is larger for older participants. Gender. In Piedmont, few differences between women and men. In Veneto the effect is larger for females Length: in Piedmont some evidence on the existence of positive correlation between course length and employability. In Veneto slight evidence of a positive correlation between (short) length and the (short) size of the lock-in effect

28. Piedmont – Average impact estimates, by age

29. Piedmont - Average impact estimates, by gender and nationality

30. Trento - Average impact estimates, by age and gender

31. Veneto - Average impact estimates, by age and gender

32. Conclusions and policy implications Training courses have a positive effect on individual employability (and on number or weeks worked in a year) Effects on different target groups are different from Region to Region Length of courses and lock-in: interesting but slight evidence. More analysis is needed

33. References Ashenfelter,O. (1978), Estimating the effects of training programs on earnings, The Review of Economics and Statistics, Vol.60: 47-57 Crépon, B., Ferracci, M. and Fougère, D. (2007). Training the unemployed in France: how does it affect unemployment duration and recurrence? Bonn: IZA discussion paper n. 3215 Heckman, J.J., LaLonde, R.J. and Smith, J.A. (1999). The economics and econometrics of active labor market programs. In Ashenfelter, A. and Card, D. (Edited by) Handbook of Labour Economics, Volume 3: 1856- 2097. Elsevier Imbens G. (2014), ‘Matching Methods in Practice: Three Examples’, IZA Discussion Paper n. 8049 Lechner, M. (1999). Earnings and employment effects of continuous off-the-job training in East Germany after reunification. Journal of Business & Economic Statistics, 17(1): 74-90

34. References Lechner, M., Miquel, R. and Wunsch, C. (2007). The curse and blessing of training the unemployed in changing economy: the case of East Germany after unification. German Economic Review, 8(4): 468-509 van Ours, J.C. (2004). The locking-in effect of subsidized jobs. Journal of Comparative Economics, 32(1): 37-55 Sianesi, B. (2004). An evaluation of the Swedish system of active labor market programs in the 1990s. Review of Economics and Statistics, 86(1): 133-155

35. Thank you for your attentionp.severati@isfol.it