Hand held matching of jobs to LTU and job training

Transcription

1 Hand held matching of jobs to LTU and job training Peer Review in Sofia By Morten Biering, City of KØBENHAVNS KOMMUNE Beskæftigelses- og Integrationsforvaltning Side

2 Side 2 / Agenda 1 Who are we? Denmark/ 2 Hand held matching of jobs to LTU 3 Services to LTU: Job training vs. other ALMPs KØBENHAVNS KOMMUNE Beskæftigelses- og Integrationsforvaltningen

3 3 PES in Denmark / Labor market authorithy Ministry Supervised by 4 regional agencies : Mayor / Committee 94 municipal job centres us Administration 4 job centres

4 Side 4 / Hand held matching of jobs to LTU Who: Unskilled or low skilled LTU, both insured / uninsured How: Counselling session with job consultant Conversation based on actual job ad and resume Focus: What is required to apply for the job: training/qualifications, employer outlook, buzzwords, personal traits, etc. Effect: ~7% monthly integration rate for insured ~3% monthly integration rate for uninsured (which tends to be less than the monthly inflow into the group!) KØBENHAVNS KOMMUNE Beskæftigelses- og Integrationsforvaltningen

5 5 Effect of ALMP s in Some ALMP s are better than others: Integration rates of ALMP s for LTU s in : 2013 Integration rate after completion of ALMP Duration of ALMP 1 month 3 months 6 months Councelling, training and education 11% 21% 31% "Short" to "long" Internships 10% 19% 32% 4 weeks Job training: public sector 34% 41% 50% Usually 6 months Job training: private sector 47% 53% 63% 6 months Source: EIA internal statistics

6 6 Effect of ALMP s in But the results might be misleading! Job finding can be described as a binomial process the longer the period observed, the greater the chance of finding job (eg. the integration rate) Check the duration of the programmes: 1/3/6 months after 4 weeks < 1/3/6 months after 6 months thus normal job finding effect included in integration rates for job training. It s possible to check for some of this Model (logistic regression) to predict integration rates based on actual data for 2012/13 (all unemployed)

7 Effect relative to predicted (percentage-points) 7 Effect of ALMP s in Effect of job training: Observed (2012/13) vs. predicted by logistic regression 20% 15% 10% 5% 0% -5% Months after start of job training Job training: public CPH Job training: other public Job training: private Source: EIA internal statistics Note: Dotted lines imply insignificant results: Compared to predicted (all unemployed): Positive effects of job training (private>public other>cph) Different target groups can explain some of this difference Negative effect of public job training while in the program (called: a retention effect Implication: CPH; job training reduced to 4 month duration Further analysis available: Length/type of education & duration No retaining effect for LTU Bias in the model? Too much counselling might skew the results of the predicted model

8 8 Concluding remarks Unskilled LTU are difficult to reintegrate into the labour market even with a service as comprehensive as the hand held service: it might require additional measures to successfully integrate the group (labour market as well as social). Business related ALMPs tends to have better effect (after the end of the programme) but beware of retention in unemployment while in the programme. Job training requires that the employers utilise the trainee and helps develop skills and opportunities Jobs as a result of job training is not only in the same company as the training took place (only 1/3 in the same company!) Risk of private companies exploiting the rules for private job training o If hiring for an ordinary job: job training for 6 months then ordinary job = employer being reimbursed for part of salary for 6 months

9 Cumulative distribution function 9 Annex Job finding as a binomial process: Assumption: constant probability of finding a job The chance of finding job can then be described by a binomial function f(n=jobs applied for, p=probability of finding a job, q>0=the action of finding a job) The cumulative distribution function lets n vary from 1 to 100% 80% 60% 40% 20% 0% Time / # of jobs applied for