Post on 26-Jul-2015
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Economic analysis of active labour market
measures
- testing applicability of simple decision
modelling
EU-HEMP workshop, 21 May 2015, Helsinki Aija
Aija Kettunen
aija.kettunen@diak.fi
Diaconia University of Applied Sciences
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Acknowledgements
Group
– Tuula Pehkonen-Elmi
– Anne Surakka
– Keijo Piirainen
Funding
– The European Social Fund
– The Centre for Economic Development, Transport
and Environment, North Karelia
Co-operation
– Researchers in the University of Eastern Finland
Aija Kettunen
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Content
• Long-term unemployment and active labour market
measures/services/intervention in Finland
• Simple decision modelling
• Testing with two labour market measure
• Conclusions, suggestion
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A lot of measures to improve
employability of long-term
unemployed
Employment has not advanced
It’s difficult to study effectiveness of employment measures
e.g.
– demand of labour differ widely between regions and
– same persons re-enter the intervention
But …
… should we be interested in social outcomes to prevent
exclusion – not only employment – of the long-term
employed?
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Number of persons in Employment measure in Finland
Persons, in average per year Pay subsidised employment
Job alternation leave primary
Work training / work life training
Employment training
Rehabilitative work activity
Self-motivated education
Other training (valmennuksessa)
Work or education trial
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Number of persons unemployed more
than 2 and 1 years unemployed
Unemployed more than 2
years
Unemployed more than 1
year
'000 persons, in average per
year
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A lot of money for employment
services
In Finland
State budget
- Labour market services about 600 million € / 2015
European Social Fund (2014-2020, with Finnish funding)
- Promoting employment and social inclusion, investing in
education and skills about 200 million € / year
+ Employment services by local municipalities
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Are the measures/services for
long-term unemployed ”good use
of public money”?
How to study?
Economic evaluation?
No possibility of RCT-study
In England simple-decision modelling to study economic
consequences of e.g.
– mental health promotion, community capacity
building and social care interventions
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Simple decision modelling to
evaluate employment services for
long-term unemployed
Test-measures/services:
- for long-term unemployed, who need a particularly great
deal of support
- aimed to prevent exclusion as well
Highest-level increased pay subsidy
– 7 employment projects, 273 participants
Rehabilitative work activity
– national level, 17 000 participants, 2011
Time horizon: the intervention and one year after it
2011 price level
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How it goes? The phases:
1. Pathways that produce the economic consequences justification for the model: literature and experts
– How clear are the pathways?
2. Data on costs and outcomes, probabilities of participants achieving different outcomes and the benefits and savings related to the outcomes: various sources, discussed the (missing) data with experts
– Are there data?
3. Populating the model and calculations
– estimates of the economic consequences of the evaluated services with available data
4. Describe what data should be produced for the model to provide better estimates
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The pathways producing economic
consequences
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Employment pathway
- Was possible to find follow-up data, but
- not on effectiveness
- Costs – challenging but possible
- Productivity gain - challenging
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Wellbeing and other pathways
Wellbeing pathway
- Little data on health and other
wellbeing outcomes,
qualitative
- The connection between
wellbeing and services
utilisation using national
survey data (Health 2011, Thl)
- Quality of life (EuroHIS-Quol
8) and Coherence scale
Antonovsky SOC-13
Connected to mental health
services
Didn’t include
Education pathway
- Benefits later
Didn’t include
Participation pathway
- Interesting – social inclusion
- didn’t find any data to populate
the model
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Costs and economic consequences of highest-
level increased pay subsidy case, n=273, the
period of the measure and the following year
Cost or consequence Calculation €
Measure1, (additional) cost (8.1 mth, n=273) 273 x 8,1 x (286.01 + 19.22) 674 955
Income transfers, additional cost (highest-level increased pay subsidy – labour market subsidy) 273 x 8.1 x (1051.48 - 609.59) 977 151
Savings after measure (one year)
-income transfers
13.7% in pay-subsidised employment2 (labour market subsidy - standard pay subsidy) 37 x 12 x (609.59 – 553.41) -24 944
7.1% in open labour market 19 x 12 x 609.59 -138 987
- mental health care services 3 -371
Net costs 1 487 804
Productivity gain
During measure (8.1 mth, n=273 )4 273 x 8.1 x 1051.48 2 325 138
After measure
- pay-subsidised employment (12 mth, n=37 )5 37 x 12 x (1071 x 1.04 x 1.2179) 602 306
- working in open labour market (12 mth, n=19) 19 x 12 x (1796.90 x 1.04 x 1.2179) 518 924
Productivity gain 3 446 368
Net benefit productivity gain - net cost 1 958 564
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Distribution of the income transfer and wage costs
and benefits of the highest-level increased pay
subsidy
(the period of the measure and the following year)
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Costs and economic consequences of
rehabilitative work activity case, n=17 000, the
period of the measure and the following year
Cost or consequence Calculation €
Measure1, (additional) cost (5.63 mth, n=17 000) 17 000 x 5.63 x 856.62 81 987 162
Additional income transfer cost2 17 000 x 5.63 x (733.15 – 609.59) 11 825 928
Costs after measure (one year)
- 25% continue in rehabilitative work activity 4 250 x 5.63 x 856.62 20 496 775
- additional income transfer cost 4 250 x 5.63 x (733.15 - 609.59) 2 956 482
- 3% employed under the highest-level increased pay subsidy system3 510 x 8.1 x (286.01 + 19.22) 1 260 905
- pay subsidy (highest-level increased) 510 x 8.1 x 1,051.48 4 343 669
Savings after measure (one year)
- income transfers
- 3% employed under the highest-level increased pay subsidy system 510 x 8.1 x 609.59 -2 518 216
- 1% employed in the open labour market 170 x 12 x 609.59 -1 243 564
- mental health care services 4 -23 102
Net costs 119 086 039
Productivity gain
During measure (5.63 mth, n=17 000 )5 17 000 x 5.63 x 0.3 x (1 450 x 1.04) x 1.2179 52 734 101
After measure
- in rehabilitative work activity (5.63 mth, n=4 250) 4 250 x 5.63 x 0.3 x (1 450 x 1.04) x 1.2179 13 183 525
- pay-subsidised employment (8.1 mth, n=510 )6 510 x 8.1 x 1 051.48 4 343 664
- employed in the open labour market7 170 x 12 x (0.7 x 2 774) x 1.04 x 1.2179 5 017 410
Productivity gain 75 278 700
Net benefit productivity gain - net cost -43 807 339
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Distribution of the income transfer and wage costs
and benefits of rehabilitative work activity
(the period of the measure and the following year)
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Conclusions, suggestions
Simple decision modelling - a beneficial approach
Decision-makers want this kind of information, but
- Big challenges with data … we need to …
Develop measures for other than employment outcomes
Follow-up other than employment outcomes
Robust effectiveness studies
Statistical analysis: combining national data sources
Evaluation on the project and service level (costs and
outcomes)
And then … again …
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Thank you!