1. Real-world impacts from research: Evidence & lessons David Pannell Centre for Environmental Economics and Policy School of Agricultural and Resource Economics For this PPT see www.davidpannell.net under Talks

2. Growing interest Perception: we need to do better at convincing government about benefits of research ARC discussing how to include real-world impact in ERA UKs Research Excellence Framework: 20% of funding based on impact from 2014.

3. Trial by universities, 2012 Group of Eight (Go8) and Aust Technology Network of Universities (ATN) Each university submitted cherry-picked case studies (165 submissions) Evaluated by people from industry & government 24 best selected

4. Plan An example research project Was selected in the GO8/ATN Some evidence about impact Measuring impact Strategies for having impact

5. Example

6. 2000: Salinity was a hot topic

7. $1.4 billion of public funding

8. I was shocked Poor design of the program Program developers seemed to have been unaware of crucial areas of salinity research and their implications No chance of any significant benefits

9. My response Media Discussion papers Presentations Submissions

10. Tried to help them Developed INFFER (Investment Framework for Environmental Resources) A tool for integrating the science with other info Develop logical, evidence-based environmental projects Assess value for money Prioritise projects

11. INFFER strategy Extensive input by users Make tools as simple as possible Provide training and help desk for users Readable documentation Public critiques of existing approaches Attempt to influence govt agencies to change the signals

12. Regional NRM application

14. Policy impacts Senate inquiry (2006) Recommended use of INFFER NRM Ministerial Council (2007) Endorsed new set of principles for investment in salinity Victorian Government, Biodiversity White Paper INFFER will be utilised for the next five years. Caring for our Country Influenced design of project template

15. Lessons: Use of science If you want people to use good science, the people issues are crucial Relationships Communication Most prospective users were happy with current (very poor) approach Didnt perceive that government would reward them for doing it better

16. Lessons: User capacity Lack of capacity to formally integrate disparate technical and socio-economic information for decision making Lack of expertise in economics and social science Lack of time to read things People misinterpret things easily

17. Research versus? Impact Has taken considerable effort beyond traditional research Time commitment New skills and knowledge New networks Satisfying but very challenging to make a difference Worth it? versus? and?

18. Research versus? Impact Various benefits for my research Interesting problems and issues arise Innovation - outside whats currently in journals Better understanding of research relevance Journal papers generated Directly part of the INFFER work: 17 Related/stimulated by: 16 Reputation for useful research easier to get funding (unsolicited approaches offering $) versus? and?

19. Evidence about impact

20. Evidence of high returns Estimated rates of return to R&D are typically very high Can indicate 30%, 50%, 100% annual rate of return Credible? $1 invested at 50% over 100 years = $4E17 (a million times Australias annual GDP) Sound analyses still show good returns For both applied and basic research

21. Heterogeneity The distribution of benefits is highly skewed Most research has low impact A small number of projects have huge impact More than enough to pay for the rest

22. Example: CRC program Benefits for 1991 to 2017 The CRC program generated a net economic benefit of $7.5 billion over the study period Annual contribution of $278 million BCR = 3.1

23. Impact is often slow Lags to impact usually measured in decades e.g. US agriculture From first investment to peak impact = 24 years Still generating benefits after 50 years Several lags Research lag Commercialisation lag Adoption lag Impact lag

24. Longer lags = lower net benefits Discounting allowing for interest costs on the up-front investment 30-year lag, 7% discount rate, benefits reduced by 87% The high measured rates of return occur despite the long time lags

25. Supply push vs demand pull Science push (Bush, 1945) Implicit in the linear model Basic R Applied R Technology Benefits Demand pull (Schmookler, 1966) Market demand Applied R Technology Benefits Big debate in the 1960s Resolved in the 1970s – innovation is an iterative process – both push and pull matter

26. Measuring impact

27. Determinants of benefits Scale of relevance Adoptability of the research Benefits per unit Probability of research success Share of the credit attributable to particular research Time lags

28. With vs without

29. Applicability? The theory is relatively straightforward It has been applied successfully in many case studies Especially agriculture

30. But … It takes resources and skills Easier … for physical products than for knowledge if the benefits arise in markets if the benefits occur quickly for applied than for basic research Much university research is not in the categories that are relatively easy to evaluate Knowledge, public goods, long time lags, basic

31. What will ERA do? Perhaps copy the UK Research Excellence Framework Two components Case studies of impact The submitting unit's approach to enabling impact from its research They wont expect an economic evaluation

32. If its case studies, youll need to Make the case/tell the story Link elements in chain from research to impact Provide evidence Note: in Go8/ATN trial, many nominations did this poorly The chain was incomplete The evidence was weak/unconvincing If you can do it well, youll stand out

33. Having an impact

34. How to have an impact? There is little research about this There are papers, but largely anecdotal Some resources at end of PPT

35. Chain from research to impact The chain varies widely from case to case Can have many links Understanding the chain for your research helps you to choose, design and deliver research for greater impact communicate impact provide evidence

36. A chain from research to impact: Technology Research and development Sell the IP Feasibility studies Design Manufacturing capacity Finance Marketing Sales

37. A chain from research to impact: Information for policy Research Something useful is learned (or isnt) New information influences policy (or doesnt) Policy change is implemented (or isnt) If policy aims to change behaviour, people respond as intended (or dont) Changes (relative to no research) result – social, environmental or economic benefits (or not)

38. Risk of low benefits from research to influence policy Nobody is listening You lack credibility with the decision maker The decision maker doesnt understand The new results are not different enough from what we already know The decision depends more on other factors The decision options have similar payoffs

39. Lessons: having impact Need some demand pull Understand and respect potential users Be prepared for opposition Need perseverance, continual marketing Need repetition – government has short memory Seek a product champion

40. Lessons: having impact Need absorptive capacity in the organisation The political circumstances need to be right. You cant change ideological positions of govt. Timing. Grasp opportunities. Good communication Simplicity, brevity, clarity Avoid jargon, maths, complex graphs Think about impact which choosing what to research

41. Conclusion We are going to be asked to demonstrate real- world impact Its not just about communicating what we do better – we may need to change what we do to have genuine impact Pursuing impact is exciting and worthwhile but challenging – spinoff benefits for research The earlier in the research process you start thinking about impact, the better

42. Resources Pannell, D.J. and Roberts, A.M. (2009). Conducting and delivering integrated research to influence land-use policy: salinity policy in Australia, Environmental Science and Policy 12(8), 1088-1099. http://dpannell.fnas.uwa.edu.au/dp0803.htm Pannell, D.J. (2004). Effectively communicating economics to policy makers. Australian Journal of Agricultural and Resource Economics 48(3), 535-555. http://dpannell.fnas.uwa.edu.au/j78ajare.pdf

43. Resources Weible et al. (2012). Understanding and influencing the policy process, Policy Science 45, 1-12. http://link.springer.com/article/10.1007%2Fs11077-011- 9143-5 http://link.springer.com/article/10.1007%2Fs11077-011- 9143-5

44. Pannell Discussions (Blog posts) 150 – Why dont environmental managers use decision theory? http://www.pannelldiscussions.net/2009/04/150-why- dont-environmental-managers-use-decision-theory/ http://www.pannelldiscussions.net/2009/04/150-why- dont-environmental-managers-use-decision-theory/ 136 – Engaging with policy: tips for researchers http://www.pannelldiscussions.net/2008/09/136- engaging-with-policy-tips-for-researchers/ http://www.pannelldiscussions.net/2008/09/136- engaging-with-policy-tips-for-researchers/

45. Resources A relevant blog post by ecologist Brian McGill on What it takes to do policy-relevant science http://dynamicecology.wordpress.com/2013/05/14/what -it-takes-to-do-policy-relevant-science/ http://dynamicecology.wordpress.com/2013/05/14/what -it-takes-to-do-policy-relevant-science/ Video: Ben Martin (U Sussex) Science Policy Research - Can Research Influence Policy? How? And Does It Make for Better Policy? http://upload.sms.csx.cam.ac.uk/media/747324

46. For this PPT see www.davidpannell.net under Talks