Real-world impacts from research: Evidence & lessons

1. Real-world impacts from research:Evidence & lessons David PannellCentre for Environmental Economics and PolicySchool 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 • UK’s Research Excellence Framework: 20% of funding based on “impact” from 2014.

3. Trial by universities, 2012 • Group of Eight (Go8) and AustTechnology 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 gov’t 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 • Didn’t 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. versus? and? 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?

18. versus? and? Research versus?Impact • Various benefits for my research • Interesting problems and issues arise • Innovation - outside what’s 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 $)

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 Australia’s 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 won’t expect an economic evaluation

32. If it’s case studies, you’ll 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, you’ll 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 isn’t) • New information influences policy (or doesn’t) • Policy change is implemented (or isn’t) • If policy aims to change behaviour, people respond as intended (or don’t) • 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 doesn’t 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 can’t 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 • It’s 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

44. Pannell Discussions (Blog posts) • 150 – Why don’t 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/

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/ • 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”