1. Learning Seminar - Targeting employment policies
ESF Targeting
Learning Seminar -
Targeting employment policies
ESF Managing Authorities
Martin van der Ende
Brussels, 14 March 2013

2. ESF Targeting – Employment
Common result indicators
Benchmarking
Result equation
Frontier analysis
Without any data…

3. ESF Targeting – employment
1 Common result indicators
Common immediate result indicators on participants
inactive participants newly engaged in job searching upon leaving
participants in education/training upon leaving
participants gaining a qualification upon leaving
participants in employment upon leaving
Common longer-term result indicators on participants
participants in employment 6 months after leaving
participants in self-employment 6 months after leaving
participants with an improved labour market situation 6 months after leaving

4. 1 Common indicators: which one to choose for target setting?
Participants in employment 6 months after leaving?
Participants in employment upon leaving?
Job counselling / job search assistance:
Quality (duration) of job counts
Versus “work first”
Employment incentives:
Gaming: free workers for 1 month if job bonus > month wage
Training: long-term result indicator
Because of lock-in effect:
people don’t search jobs during training
Start-up incentives: long-term result indicator
Because of early business failures

5. 2 Benchmarking
Comparing results (benchmarking)
only works for “comparable” target groups
(here more important than for comparing costs)
- Result equation may be used for fair comparisons

6. 3 Result equation
Of all unemployed say 75% find a job in their first year
Should intervention target be >75%?
Not if targeted at specific groups
Result = a + b*(impact variables) + c*(participation variable)
Impact variables:
output indicators (target groups);
economic growth; …
If c>0 then intervention is effective
Result = 75% - 50% (if long-term unemployed) + 5% (if participant)
Intervention target% = 30%
Or not (is normative issue)

7. 3 Result equation pitfalls
How you measure participation variable, is important
… Employment incentives
use family income
after taxes
…Training
Use effect during training
And effect after training

8. 3 Result equation limitations
Result equations do not model wider impacts such as:
Substitution effect
(employers replace non-participants with participants)
Displacement effect
(employers without participants lose jobs to those with participants)
Macro-models are supposed to solve such problems but:
Modelling those effects is difficult
Macro-models tend to treat all target groups equal.

9. Also called Data Enveloping Analysis Leave out single “outliers”
4 Frontier analysis
Also called Data Enveloping Analysis
Leave out single “outliers”
How to deal with
grouped “outliers”
is normative
Employed 6 months after training
Number of training participants

10. 4 Frontier analysis with result equation
Predicted result =
a + b*(impact variables) + c*(participation indicator)
Choice between observed or predicted results is a normative issue

11. 5 Result targets without data on intervention results…
Start from % that find employment without intervention (=baseline)
For example: 25%
Assign a value to every job-finder,
For example: € 50,000
Relate unit cost of intervention to this value,
For example: € 5,000
5,000 / 50,000 = 10%
Add cost to value ratio to the baseline:
25% + 10% = 35%
Result target = (Percentage target) * (number of participants)
If 10,000 participants -> result target = 3,500.

12. 5 Result targets without any data
Previous method uses unit costs
If even these are missing, only “rule of thumb” remain, e.g.
75% job-finders in 1st year
25% job-finders in 2nd year
Reasonable target % for other groups depend on national / institutional settings
Wage cost of older workers;
Equal pay enforcement for minorities; …
Alternative: use unemployment distribution of other groups with “rule of thumb” percentages for short-term/long-term unemployed:
20% of older workers short-term unemployed and 80% long-term:
Target% = 20% * 75% % * 25% = 35%