Presented at "Big Data and the complexity of Labour Market policies" Annual meeting of the European Network on Regional Labour Market Monitoring 15th - 16th October 2015, University of Milano-Bicocca, Milan, Italy during L’European Network on Regional Labour Market Monitoring (EN RLMM) è stato costituito nel 2006 ed ha come obiettivo la valorizzazione e l’attuazione di ricerche sul mercato del lavoro con attenzione ai territori regionali e locali, alle politiche pubbliche e ai sistemi di servizi per l’impiego e la formazione. Vi partecipano enti e istituzioni (pubbliche e private) di 27 paesi europei. Le attività dell’EN RLMM si sviluppano ogni anno attorno ad un argomento principale (Big Data and the Complexity of Labour Market Policies per il 2015). Negli anni, più di 400 persone sono state coinvolte nelle attività dell’EN RLMM, attraverso il trasferimento di conoscenze derivanti da studi e ricerche svolte in campo scientifico e la condivisione di ricerche sulle best practice degli osservatori sul mercato del lavoro europeo. Il network è diventato negli anni un punto di riferimento per policy-makers di livello regionale e locale ed ha in corso stabili contatti con organizzazioni Europee come la DG Employment, Cedefop, OECD LEED e EESC.

1. Big data for VET decision makers
Giampaolo Montalet
Direttore vicario ARIFL
Regione Lombardia

2. Outline
• Why big data for decision makers?
• Changing visions
• Understanding market dynamics
• Plan a new generation of VET/ALMP initiatives
• 3 use cases:
• Profiling people before enrolling them in VET/ALMP
initiatives
• Understanding transitions in the regional labour market
• Counterfactual model for policies evaluation
• conclusions

3. Helping policy makers to change
their vision of the market
• Very often vision is based on «common
sense/public opinion» perception of labour
market;
• Mainly based on static data while market
dynamics are more important to understand
change and policy planning.
• Introduce evaluation procedures with an
impact on resource distribution (avoid the
“evaluate for the sake of” effect).

4. The “big data” we are using
●
“Comunicazioni obbligatorie” (compulsory
communications) based on employers on-line
declarations about labour contracts start, close,
extension and transformation;
●
From 2008 up to 2015 contains for Lombardy
about 40 millions communications about 6 million
workers
●
Requires extensive data quality to avoid “garbage
in – garbage out” effect on statistics;
●
Used mainly for career, transition and longitudinal
analysis.

5. A new generation of VET/ALMP
initiatives
●
A radical change from the national/regional
tradition in Italy
●
From “projects” and “calls” to “personal
budgets” and “available on demand”
●
From “public one stop-shop” to “private-
public competition and/or cooperation”
●
From “paid for action” to “paid for results”
●
From “employability” to “employment
evaluation”

6. PROFILING USERS: WHO REALLY
NEEDS MORE HELP?
Use case n.1

7. Using profiling
●
Profiling is used to evaluate the probability
to find a new employment;
●
The lower the probability, the more intense
the support;
●
A more intense support is a better paid one;
●
Try to avoid creaming in the private sector
employment agencies working with public
fundings

8. profiling
●
Univariate logistic model to obtain individual
scores
●
Based on data about 927.681 people in
Lombardy with a job position ended in 2010
and 2011
●
357.890 people (38,5%) got a new job
before the end of 2012

9. re-employment probability

10. Profiling model variables
and tests
DF Wald Chi-square Pr > chi square
Nationality (italian/not
italian)
1 675,94 <.0001
full time/part time 1 3.021,01 <.0001
Age at the end of contract 4 28.145,61 <.0001
Skill level 2 152,05 <.0001
Industrial sector 3 4.638,73 <.0001
Last contract tipology
(open ended…)
4 23.314,12 <.0001
Gender 1 30,44 <.0001
ISCED level 2 893,58 <.0001

11. Quantile
Re-employment
probability
Support intensity
100% Max 63% from 48% of re-employment probability:
Minimal support.
99% 60%
95% 56%
90% 54%
75% Q3 48% From 40% to 48%: support required
50% Median 40% From 30% to 40%: intensive support required
25% Q1 30% Under 30% of re-employemnt probability: long term
intensive support required
10% 21%
5% 17%
1% 8%
0% Min 4%

12. UNDERSTANDING MARKET
DYNAMICS:
HOW LONG LASTING IS AN OPEN
ENDED CONTRACT?
Use case n.2

13. OE contracts - 2012 445.295 100,0%
closed after (duration):
a) 1 day to 1 month 30.080 6,8%
b) 1 month to 6 months 82.052 18,4%
c) 6 months to 1 year 53.824 12,1%
d) 1 year to 2 years 62.103 13,9%
e) More than 2 years 33.299 7,5%
Closed at the end of March
2015
261.358 58,7%
Still open 183.937 41,3%
Open ended contracts in 2012 by
duration

14. avviati a) Da 1 giorno a 1 meseb) Da 1 mese a 6 mesic) Da 6 mesi a 1 annod) Da 1 anno a 2 anni e) Piu' di 2 anni
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
Lavoro a progetto
Somministrazione
Tempo Determinato
Tempo Indeterminato
Contract’s tipology survival curves -
2012

15. UNDERSTANDING MARKET
DYNAMICS:
WHAT DO THEY DO AFTER
INTERNSHIP?
WHAT HAPPENS TO PEOPLE LOSING
A JOB? WHERE DO THEY GO?
Use case n.2

16. avviati a) Da 1 giorno a 1 meseb) Da 1 mese a 6 mesi c) Da 6 mesi a 1 anno d) Da 1 anno a 2 anni e) Piu' di 2 anni
0
5000
10000
15000
20000
25000
30000
35000
Tirocinio
Apprendistato
Internship and apprenticeship
survival curves

17. Internship ended in 2014 40.030
Contract after internship 20.943 52,3%
Apprenticeship 6.197 15,5%
Short term projects 2.320 5,8%
Temporary 2.230 5,6%
Closed terms 8.252 20,6%
Open ended 1.944 4,9%
Another internship 6.909 17,3%
Others 195 0,5%
NA 11.983 29,9%
Contracts after internship

18. From: Total To the same
tipology
To the same
tipology in %
Internship 133.292 33.638 25,2%
Apprenticeship 214.815 64.856 30,2%
Short term projects 919.409 656.875 71,4%
Temporary 1.217.997 896.527 73,6%
Closed term 3.885.306 2.939.686 75,7%
Open ended 1.725.021 1.035.127 60,0%
Total 8.095.840 5.626.709 69,5%
Transitions 2010-2014

19. Transitions

20. 30 gg 90 gg 180 gg 360 gg Oltre 360 gg
0
5000
10000
15000
20000
25000
Market acceptance after
apprentship

21. COUNTERFACTUALMODELFOR
POLICIES EVALUATION:
ARE WE REALLY MAKING A
DIFFERENCE FOR PEOPLE ENROLLED
IN OUR VET/ALMP PROGRAMS?
Use case n.3

22. People with a contract at Tn time
(%)
Treated group Non treated group
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7

23. Unemployed/first job seekers/subsidized
involuntary part time workes
Treated group Non treated group
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Disoccupato Inoccupato Occupato
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Disoccupato Inoccupato Occupato

24. Age groups
Treated group Non treated group
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Da 15 a 24 anni Da 25 a 34 anni Da 35 a 44 anni
Da 45 a 54 anni Oltre55 anni
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Da 15 a 24 anni Da 25 a 34 anni Da 35 a 44 anni
Da 45 a 54 anni Oltre55 anni

25. ISCED levels
Treated group Non treated group
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ISCED 0-2 ISCED 3 ISCED 5-6
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ISCED 0-2 ISCED 3 ISCED 5-6

26. Concluding remarks
●
Big data can be used for the usual “information to
support planning” game, but already available
statistics are much more better to write policy
paper introductions.
●
Don't let decision makers “play” with big data, it's
too expensive, too hard to understand, too risky.
Try to get simple bold evidence out of them.
●
Big data can be fruitfully used to sustain a change,
as to say in segmentation of targets, support to
transitions in the labour market and policy results
evaluation.