Aija kettunen economic analysis

www.diak.fi www.diak.fi

Economic analysis of active labour market

measures

- testing applicability of simple decision

modelling

EU-HEMP workshop, 21 May 2015, Helsinki Aija

Aija Kettunen

[email protected]

Diaconia University of Applied Sciences


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


Content

• Long-term unemployment and active labour market

measures/services/intervention in Finland

• Simple decision modelling

• Testing with two labour market measure

• Conclusions, suggestion


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?


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


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


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


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


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


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


The pathways producing economic

consequences


Employment pathway

- Was possible to find follow-up data, but

- not on effectiveness

- Costs – challenging but possible

- Productivity gain - challenging


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


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


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)


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


Distribution of the income transfer and wage costs

and benefits of rehabilitative work activity

(the period of the measure and the following year)


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 …


Thank you!

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Aija kettunen economic analysis