1. 1 Counterfactual impact evaluation: What is it, why do it? Daniel Mouqué Evaluation Unit DG REGIO

2. 2 An example: support to enterprise & innovation Some €79 billion of cohesion in 2007-13: the largest broad category of expenditure Key instrument: investment/research grant But also significant spending on loans/venture capital, advice, networking, incubators With all this at stake, we should know exactly what we’re doing, right?

3. 3 What should we know about enterprise support? We’re managing a programme – what should we know? The context and needs (productive base, sectors, weaknesses etc…) What we plan to change (Investment? Productivity? Employment?) How we will change it: instruments, delivery, financial allocations Activity/outputs (number of enterprises assisted etc) Question: is this enough?

4. 4 In the long term, we want to know about impacts Do the instruments work? In terms of increasing long run investment, productivity, employment, etc? What is the optimum level of support? Different effects of different tools? Better single instrument or mixed? SMEs only or include larger enterprises?

5. 5 In other words, we want to know... What works? How much impact does it have? How to change/finetune it to get more impact? These questions apply to all cohesion policy fields: human resources, infrastructure, environment

6. 6 monitoring to track implementation efficiency (input-output) INPUTSOUTCOMESOUTPUTS MONITOR EFFICIENCY EVALUATE EFFECTIVENESS Plans, programmes Human behaviour  impact evaluation to measure effectiveness (output-outcome) Source: Arianna Legovini and the World Bank (modified) But impacts are the tricky bit

7. 7 How do we assess impacts? Traditionally in enterprise support: Monitoring (but: « before/after » problem) Beneficiary surveys Opinion And enterprise support is one of the « good » areas – situation no better in training, infrastructure, environment

8. 8 To truly know impacts, you must know… … What would have happened without the intervention Or in other words: The counterfactual

9. 9 How do we find this mysterious counterfactual? Can it be observed?

10. 10 A time machine?

11. 11 Sadly, only in Hollywood… Maybe someday?

12. 12 What do scientists do?

13. 13 One thing scientists do to find counterfactuals: Compare twins

14. 14 Source: www.webmd.com – smoking and sun are responsible herewww.webmd.com

15. 15 Twins in cohesion policy? Does this mean we can only provide training to twins? And only one of the two? And what about enterprises? Or urban neighbourhoods in crisis?

16. 16 Solution 1: large « n » - mobilising the power of statistics The « law of large numbers » As n increases, random differences tend to average out NB: « large » varies. But 20 or 50 may be enough

17. 17 Solution 2: clever statistical matching techniques Sometimes solution 1 is enough But sometimes we need to use statistical techniques to find matches between the treated and non-treated populations We’ll come back to how this is done tomorrow…

18. 18 But matching is not always straightforward

19. 19 Examples of counterfactuals in practice 100 innovation vouchers are randomly distributed between ~900 applicant firms, performance tracked 500 long term unemployed in poor mental health – 250 receive standard support, 250 receive extra counselling 70 deprived urban areas assisted. Performance on unemployment etc compared to neighbouring areas

20. 20 To recap It is crucial to know about impacts But measuring impacts is far from straightforward, depends on human behaviour « Traditional » techniques do not measure impact We need a counterfactual, comparing performance of treated and non-treated But counterfactuals are not the only useful technique…