Big data for VET decision makers

Giampaolo MontaletDirettore vicario ARIFL

Regione Lombardia

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

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).

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.

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”

PROFILING USERS: WHO REALLY NEEDS MORE HELP?

Use case n.1

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

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

re-employment probability

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

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%

UNDERSTANDING MARKET DYNAMICS:HOW LONG LASTING IS AN OPEN ENDED CONTRACT?

Use case n.2

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

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 anni0

100000

200000

300000

400000

500000

600000

700000

800000

900000

Lavoro a progetto

SomministrazioneTempo Determinato

Tempo Indeterminato

Contract’s tipology survival curves - 2012

UNDERSTANDING MARKET DYNAMICS:WHAT DO THEY DO AFTER INTERNSHIP?WHAT HAPPENS TO PEOPLE LOSING A JOB? WHERE DO THEY GO?

Use case n.2

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 anni0

5000

10000

15000

20000

25000

30000

35000

TirocinioApprendistato

Internship and apprenticeship survival curves

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

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

Transitions

30 gg 90 gg 180 gg 360 gg Oltre 360 gg0

5000

10000

15000

20000

25000

Market acceptance after apprentship

COUNTERFACTUAL MODEL FOR

POLICIES EVALUATION:ARE WE REALLY MAKING A DIFFERENCE FOR PEOPLE ENROLLED IN OUR VET/ALMP PROGRAMS?

Use case n.3

People with a contract at Tn time (%)

Treated group Non treated group

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t120

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 t120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

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 t120

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 t120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Disoccupato Inoccupato Occupato

Age groups

Treated group Non treated group

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t120

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 t120

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

ISCED levels

Treated group Non treated group

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t120

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 t120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

ISCED 0-2 ISCED 3 ISCED 5-6

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.