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

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

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

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

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

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)

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

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.

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

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

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.

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% + 80% * 25% = 35%