Yes the title of the book is The Australian Miracle.
This book is a few years old now but it still deserves a big plug. It is a very readable but factual book on the surprising success of Australia particularly during the 1800s. From a penal colony to one of the wealthiest economies in the world in just over a 100 years. That deserves the title 'miracle'. The book is for a popular audience and is rather polemical but as it is trying to act as a counterweight to the current tendencies in science policy worldwide this has a place in a debate which is surprisingly shallow at the moment.
Barlow rightly emphasises that success has been largely but not completely due to technological adoption and then adaption to Australia's particular environmental conditions has been critical to its success.
While he rightly points out that Australian mining and agriculture have relied on science and technological advance for a very long time, my only criticism is that he does not emphasise this enough. Australian agriculture is invented. It should not exist on one of the harshest environments on the planet. What I used to say when I worked for the Australian government was agriculture was a created advantaged in Australia. Certainly the perception from far away (overseas) and even in Australia is to take success for granted. Well of course we would have succeeded - even with the poorest soils and lowest rainfalls.
The other really strong point of the book is that it emphasises that that economies are very heterogeneous, and becoming more so, and thus require a diverse range of science and technology inputs. He points out that want is 'useful' science and technology is often a matter of time and perspective.
This book is a modern take on the traditional perspective that the best people to judge relevance are scientists themselves and the government should stay out of priority setting. I have some sympathy for this perspective.
I have a few disagreements with the book but they were surprisingly minor. It wasn't the purpose of the book to grapple with the political problem of justifying basic research to a public that wants to see more immediate relevance. Nor did the book go into the enormous issue of commercialising university research. I would have liked to have read more on both topics. My biggest criticism is that the book focusses on natural sciences, engineering and medicine and ignores completely the social sciences, humanities and the arts. In the heterogeneous economy that Barlow points to, that is a mistake.
It would also have been nice if there had been a few notes for more sources than the quite limited reference list.
Not all readers will accept the premise of the book that there are 10 myths that Australians hold about their science and innovation efforts but if you accept these as at least valid starting point to have a discussion about policy then it is well worth the read.
The book, although very oriented to the Australian market, should also be read by Canadians.
Wednesday, April 24, 2013
Wednesday, April 3, 2013
Disruptive Innovation Policy
In recent weeks the newsletter Re$earch Money which covers all things to do with innovation and research funding and policy in Canada published an opinion piece by me on innovation policy.
Here is a taste of what I said.
The point is; in a world that is increasingly being disrupted by innovation we can no longer expect that transitions will be smooth.
The recent economic crisis was as much triggered by conventional macro-economic variables (poor lending and risk practices by banks) as it was technological and innovation related. he banks didn't understand the innovations in risk 'management' and oil prices had hit $150 per barrel which appears to be some sort of trigger point. Yet, the conventional media continues to treat the whole GFC (Global Financial Crisis) as conventional macro-economics. Where are the innovations who can talk in broad terms about what is happening technologically.
I am more and more convinced we need to take Keynes' comment seriously.
• The long run is a misleading guide to current affairs. In the long run we are all dead. Economists set themselves too easy, too useless a task if in tempestuous seasons they can only tell us that when the storm is past the ocean is flat again. John Maynard Keynes, A Tract on Monetary Reform (1923) Ch. 3 English economist (1883 - 1946) http://www.quotationspage.com/quote/38202.html
• Neo-Schumpeterians (innovationists) set themselves too easy a task if they say that the maximisation of the production of innovation will be good for growth and competitiveness in the long run but can give no guide to governments on transitions in periods of disruption (Brian Wixted)
We need an macro-innov-nomics that worries about transitions – that is the destruction in Schumpeter’s creative destruction equation.
Here is a taste of what I said.
While traditional innovation policy remains
fixed on the production of knowledge and technology it focuses on the creative
Schumpeter, not the destructive Schumpeter.
Innovation policy needs to be Schumpeterian and Rogerian (Rogers – Diffusion of Innovations). When the
longed-for innovations disrupt industries and employment patterns, they become
the responsibility of ministries of health, human resources, employment,
industry etc across federal, provincial and municipal jurisdictions. When
governments are controlled by the neo-classical economic worldview which assumes
technology with appropriate transitions, and a legal profession that works on
the basis of precedence – it is hard to craft legislation and policies for
disruptive innovations.
The point is; in a world that is increasingly being disrupted by innovation we can no longer expect that transitions will be smooth.
The recent economic crisis was as much triggered by conventional macro-economic variables (poor lending and risk practices by banks) as it was technological and innovation related. he banks didn't understand the innovations in risk 'management' and oil prices had hit $150 per barrel which appears to be some sort of trigger point. Yet, the conventional media continues to treat the whole GFC (Global Financial Crisis) as conventional macro-economics. Where are the innovations who can talk in broad terms about what is happening technologically.
I am more and more convinced we need to take Keynes' comment seriously.
• The long run is a misleading guide to current affairs. In the long run we are all dead. Economists set themselves too easy, too useless a task if in tempestuous seasons they can only tell us that when the storm is past the ocean is flat again. John Maynard Keynes, A Tract on Monetary Reform (1923) Ch. 3 English economist (1883 - 1946) http://www.quotationspage.com/quote/38202.html
• Neo-Schumpeterians (innovationists) set themselves too easy a task if they say that the maximisation of the production of innovation will be good for growth and competitiveness in the long run but can give no guide to governments on transitions in periods of disruption (Brian Wixted)
We need an macro-innov-nomics that worries about transitions – that is the destruction in Schumpeter’s creative destruction equation.
Friday, February 1, 2013
7 Grand Challenges for Innovation Policy
For about the last year I have been thinking about how I would categorise the big challenges for science, education and innovation policy and policy oriented research over the coming decade. This is my 2013 list. This is not about answers it is about challenges, the problems are easier to see than the new frameworks.
1. Disconnect innovation policy from science policy.
We still overwhelmingly link science, technology and innovation policy. Innovation policy has essentially become the new language for science policy. Pick up any national innovation policy release, and it will still be overwhelmingly about the production of science, the production of technologies and the production of innovation (whatever that means). It is time to disconnect innovation from production and think more about its wholistic implications as innovations get used. Otherwise we get this. A few years ago the OECD released a series of documents branded 'innovation strategy'. The key findings documents contains comments such as
No word there on job losses, massive industry restructuring, industry location shifts etc.....Let's get serious, innovation has never been all upside, industries decline or transform. If we can break the close nexus of innovation policy from science policy we can better appreciate the 360 degree implications of innovations currently coming down the pipe.
2. Better knowledge investment statistics. We still use R&D data as the gold benchmark, but in many industries traditional R&D is not where they spend their knowledge investment dollars. We have known this for a long time but as manufacturing splitters around the globe and product architecture gets more centralised, the numbers become more meaningless. Further, a number of countries do not classify at great detail where the R&D is going anyway (in the university system for example), so the numbers do not tell us much. We cannot even cross classify research done in universities with research done in business as there are different classifications. Beyond R&D we have developed a classification of innovation (new product, new process etc), but what of developing an adoption based classification (innovation changeover costs, savings, job savings etc....)
3. The PhD system is broken. There is more and more evidence of this. In a zero growth university sector such as being experienced in many 'developed' economies then the continued assembly line production of PhDs trained for academia makes no logical sense at all. They are good labour for research grants but that seems a flawed relationship. PhDs could be more useful to society if the goals of their training were tweaked or overhauled to be more directed to being more valuable to non-profits, government agencies, business and starting their own businesses. For more reading on this see Nature 21 April 2011, vol 472 or a recent report on PhDs in Australia prepared by Toss Gascoigne and Associates (2012) 'Career Support for Researchers: Understanding Needs and Developing a best Practice Approach'.
4. Higher Education and Research. Disruptive change is headed for the university system. Lets accept that fact first. What those changes will look like it is very hard to say. That is the nature of disruptive innovation - technology change may have particular trajectories but their implementation is getting harder to predict. First, agree that change will happen and that what works today will probably not withstand the entrepreneurs of the next 15 years unchanged. That said, it is a massive system, with massive invested capital human and physical, with inherent location advantages so I am not saying it will all come crashing down. But we need to bring change conversations in from the fringe and begin to debate the future.
5. Reboot thinking on commercialisation. Commercialisation as currently typically defined is the transfer of knowledge (not people) from the universities to business. Our metrics are abysmal on proving or disproving this. So my point is simply takes a different approach. If commercialisation is such a problem is so many countries and has been for so many years then maybe its the concept that is bust. Put differently, if the treatment does not work there are two choices - the treatment is wrong or the diagnosis is wrong. I have been thinking for a number of years it is the latter and we are actually do the patient harm.
6. Embrace the role of failure. Failure is fundamental to learning and creativity. let us embrace it, so long as it leads to learning. Failure or knowing what does not work can be as big competitive advantage as knowing what does work. Can we work on incorporating failure into our schools and higher education more.
7. Macro-innonomics. The global financial crises has not abated yet, and this should give everyone in the innovation game pause to think. Old style economics of interest rates, savings, consumption and boring things like regulation still matter. How can we better integrate new thinking on innovation, with old economics alongside new imperatives such as sustainability. Such Macro-innonomics would be very useful for policy makers.
1. Disconnect innovation policy from science policy.
We still overwhelmingly link science, technology and innovation policy. Innovation policy has essentially become the new language for science policy. Pick up any national innovation policy release, and it will still be overwhelmingly about the production of science, the production of technologies and the production of innovation (whatever that means). It is time to disconnect innovation from production and think more about its wholistic implications as innovations get used. Otherwise we get this. A few years ago the OECD released a series of documents branded 'innovation strategy'. The key findings documents contains comments such as
- Ensure that framework conditions are sound and supportive of competition, conducive to innovation and are mutually reinforcing.
- These factors require rethinking innovation policy in order to move beyond supply-side policies focused on R&D and specific technologies to a more systemic approach that takes account of the many factors and actors that influence innovation performance, including demand-side policies. The policy objective should not be innovation as such, but its application to make life better for individuals and society at large. This is no easy task, especially as the scope for policies for innovation broadens. Effective policies will require priority setting and strategic decisions, safeguards against favouring a particular firm or region for political as opposed to economic or social reasons, and recognition that striving for “whole of government” co-ordination involves transaction costs. The objective of the OECD’s work to develop a strategy for developing policies for innovation is to support this process, avoid these pitfalls and provide guidance to achieve these goals.
- A more strategic focus on the role of policies for innovation in delivering stronger, cleaner and fairer growth.
- Broadening policies to foster innovation beyond science and technology in recognition of the fact that innovation involves a wide range of investments in intangible assets and actors.
- Education and training policies adapted to the needs of society today to empower people throughout society to be creative, engage in innovation and benefit from its outcomes.
- Greater policy attention to the creation and growth of new firms and their role in creating breakthrough innovations and new jobs.
- Improved mechanisms to foster the diffusion and application of knowledge through well-functioning networks and markets.
- New approaches and governance mechanisms for international cooperation in science and technology to help address global challenges and share costs and risks.
- Frameworks for measuring the broader, more networked concept of innovation and its impacts to guide policy making.
No word there on job losses, massive industry restructuring, industry location shifts etc.....Let's get serious, innovation has never been all upside, industries decline or transform. If we can break the close nexus of innovation policy from science policy we can better appreciate the 360 degree implications of innovations currently coming down the pipe.
2. Better knowledge investment statistics. We still use R&D data as the gold benchmark, but in many industries traditional R&D is not where they spend their knowledge investment dollars. We have known this for a long time but as manufacturing splitters around the globe and product architecture gets more centralised, the numbers become more meaningless. Further, a number of countries do not classify at great detail where the R&D is going anyway (in the university system for example), so the numbers do not tell us much. We cannot even cross classify research done in universities with research done in business as there are different classifications. Beyond R&D we have developed a classification of innovation (new product, new process etc), but what of developing an adoption based classification (innovation changeover costs, savings, job savings etc....)
3. The PhD system is broken. There is more and more evidence of this. In a zero growth university sector such as being experienced in many 'developed' economies then the continued assembly line production of PhDs trained for academia makes no logical sense at all. They are good labour for research grants but that seems a flawed relationship. PhDs could be more useful to society if the goals of their training were tweaked or overhauled to be more directed to being more valuable to non-profits, government agencies, business and starting their own businesses. For more reading on this see Nature 21 April 2011, vol 472 or a recent report on PhDs in Australia prepared by Toss Gascoigne and Associates (2012) 'Career Support for Researchers: Understanding Needs and Developing a best Practice Approach'.
4. Higher Education and Research. Disruptive change is headed for the university system. Lets accept that fact first. What those changes will look like it is very hard to say. That is the nature of disruptive innovation - technology change may have particular trajectories but their implementation is getting harder to predict. First, agree that change will happen and that what works today will probably not withstand the entrepreneurs of the next 15 years unchanged. That said, it is a massive system, with massive invested capital human and physical, with inherent location advantages so I am not saying it will all come crashing down. But we need to bring change conversations in from the fringe and begin to debate the future.
5. Reboot thinking on commercialisation. Commercialisation as currently typically defined is the transfer of knowledge (not people) from the universities to business. Our metrics are abysmal on proving or disproving this. So my point is simply takes a different approach. If commercialisation is such a problem is so many countries and has been for so many years then maybe its the concept that is bust. Put differently, if the treatment does not work there are two choices - the treatment is wrong or the diagnosis is wrong. I have been thinking for a number of years it is the latter and we are actually do the patient harm.
6. Embrace the role of failure. Failure is fundamental to learning and creativity. let us embrace it, so long as it leads to learning. Failure or knowing what does not work can be as big competitive advantage as knowing what does work. Can we work on incorporating failure into our schools and higher education more.
7. Macro-innonomics. The global financial crises has not abated yet, and this should give everyone in the innovation game pause to think. Old style economics of interest rates, savings, consumption and boring things like regulation still matter. How can we better integrate new thinking on innovation, with old economics alongside new imperatives such as sustainability. Such Macro-innonomics would be very useful for policy makers.
Friday, January 18, 2013
What is the velocity of policy
In the article by McCarthy, Lawrence, Wixted and Gordon - Academy of Management Review 2010, we defined velocity not only as the rate of change but also the direction of change. In the article we were concentrating on the characteristics of for profit organisational environments and thus defined the dimensions of their environments as technology, demand, regulation, competition and products.
It seems that our velocity concepts and analytics are perfectly suited to 'innovation policy'
But we need to come up with new dimensions.
We could ask.
So we could take as an example Napster and online file sharing really took off around 2000 and while existing copyright laws were somewhat useful they could not deal with digital realities.
The new Canada copyright laws were passed in 2011 and received ascent in 2012. Thus there was a time interval of 12 years. That is a reasonably slow velocity for the legislation. I make no observation regarding whether the new legislation actually is the correct direction - there is huge debates over that.
It seems that our velocity concepts and analytics are perfectly suited to 'innovation policy'
But we need to come up with new dimensions.
We could ask.
- when were broad innovation policy reviews were conducted
- when were particularly disruptive technologies first introduced into the market
- when did the technology noticeably disrupt?
- when was legislation introduced?
So we could take as an example Napster and online file sharing really took off around 2000 and while existing copyright laws were somewhat useful they could not deal with digital realities.
The new Canada copyright laws were passed in 2011 and received ascent in 2012. Thus there was a time interval of 12 years. That is a reasonably slow velocity for the legislation. I make no observation regarding whether the new legislation actually is the correct direction - there is huge debates over that.
Wednesday, March 28, 2012
Accrual accounting of commercialisation
I have a number of problems with policy conceptualisation of commericalisation, one of which is the notion that there is a gap which needs to be bridged by some institution; but I will address this in a later blog. But there is a more primary concern - a basic one about the economics of 'commercialisation'.
I should say that in general we do need knowledge developed in public institutions including unversities and government labs to have a positive impact on society. What concerns me is that if the wrong conceptual models are applied and with them the wrong metrics then we could easily have a perverse effect.
To avoid perverse effect we typically need to map larger sytems and their interactions than we are used to.
So let us start with the example of the national accounts of economies - we know that rapid economic growth can be created by simply strip mining all the natural assets and then selling them on the world market. A case of this might be the case of phosphate mining on Nauru http://en.wikipedia.org/wiki/Phosphate_mining_in_Nauru . Such an approach is obviously crazy in the long term. Short term cash traded off for long term unsustainability is non-sense. In response to the overarching criticism of GDP the Australian Bureau of Statistics (amongst a number of national statistical agencies) have begun working on net wealth indicators and has now expanded that even further to the measures of progress project. The point with net wealth indicators is that if you remove mineral resources now, (in a accrual accounting rather than cash accounting system) it is the eqvalent of selling assets.
So how can we apply this to knowledge which obviously has different properties to minerals in the ground as it is harder to trap and sell as it is there for re-use later. Commercialisation policy has exactly this goal to quarantine knowledge into saleable units for pushing into the market. Thus it does have certain dimensions where pushed to extreme the comparison is valid. The metrics look better the more that is sold, and that is achieved by packaging parcels of knowledge.
Now at this point I need to introduce another concept - reserve deposits. In mining as we exploit the easier to access resources we are driven to harder and harder locations (deep sea oil drilling and tar sands might be examples). The more we demand, the more the price goes up and the more rapidly we exploit the available resources, thus creating an incentive for accessing harder to obtain resources. So we end up and image like this one. The more we access more costly resources the more the price will go up.
Fig 1. We can illustrate this process.
In contrast to this what we see with Universities and government research is a different process. Government tend to want to create incentives for as much activity as possible to be commercialised. To achieve this it is necessary to set the selling price at a point where there will be buyers. In a competitive environment and knowing that university knowledge is often very expensive to develop to point where a business can make money then the only way to sell the knowledge is for the price to be driven down.
The only way this can happen is if the packets of knowledge get smaller and the projects thus less innovative. Unfortunately, what we know about kowledge is that the more complex the problem the more we need to draw up a rich base of knowledge from multiple fields.
In general this falling quality is exactly what the OECD has found The Science, Technology and Industry Scoreboard 2011 finds that patent quality has declined by an average of around 20 per cent between the 1990s and 2000s, a pattern seen in nearly all countries studied.
If we don't develop some metrics of net value of research and that isn't just costs and sales - that is still cash accounting but a sense of the assets and liabilities in terms of knowledge pools we will continue to have no real idea about the effects of science policy and it will continue to bouce around as it always does.
I should say that in general we do need knowledge developed in public institutions including unversities and government labs to have a positive impact on society. What concerns me is that if the wrong conceptual models are applied and with them the wrong metrics then we could easily have a perverse effect.
To avoid perverse effect we typically need to map larger sytems and their interactions than we are used to.
So let us start with the example of the national accounts of economies - we know that rapid economic growth can be created by simply strip mining all the natural assets and then selling them on the world market. A case of this might be the case of phosphate mining on Nauru http://en.wikipedia.org/wiki/Phosphate_mining_in_Nauru . Such an approach is obviously crazy in the long term. Short term cash traded off for long term unsustainability is non-sense. In response to the overarching criticism of GDP the Australian Bureau of Statistics (amongst a number of national statistical agencies) have begun working on net wealth indicators and has now expanded that even further to the measures of progress project. The point with net wealth indicators is that if you remove mineral resources now, (in a accrual accounting rather than cash accounting system) it is the eqvalent of selling assets.
So how can we apply this to knowledge which obviously has different properties to minerals in the ground as it is harder to trap and sell as it is there for re-use later. Commercialisation policy has exactly this goal to quarantine knowledge into saleable units for pushing into the market. Thus it does have certain dimensions where pushed to extreme the comparison is valid. The metrics look better the more that is sold, and that is achieved by packaging parcels of knowledge.
Now at this point I need to introduce another concept - reserve deposits. In mining as we exploit the easier to access resources we are driven to harder and harder locations (deep sea oil drilling and tar sands might be examples). The more we demand, the more the price goes up and the more rapidly we exploit the available resources, thus creating an incentive for accessing harder to obtain resources. So we end up and image like this one. The more we access more costly resources the more the price will go up.
Fig 1. We can illustrate this process.
In contrast to this what we see with Universities and government research is a different process. Government tend to want to create incentives for as much activity as possible to be commercialised. To achieve this it is necessary to set the selling price at a point where there will be buyers. In a competitive environment and knowing that university knowledge is often very expensive to develop to point where a business can make money then the only way to sell the knowledge is for the price to be driven down.
The only way this can happen is if the packets of knowledge get smaller and the projects thus less innovative. Unfortunately, what we know about kowledge is that the more complex the problem the more we need to draw up a rich base of knowledge from multiple fields.
In general this falling quality is exactly what the OECD has found The Science, Technology and Industry Scoreboard 2011 finds that patent quality has declined by an average of around 20 per cent between the 1990s and 2000s, a pattern seen in nearly all countries studied.
If we don't develop some metrics of net value of research and that isn't just costs and sales - that is still cash accounting but a sense of the assets and liabilities in terms of knowledge pools we will continue to have no real idea about the effects of science policy and it will continue to bouce around as it always does.
Wednesday, July 6, 2011
Cancer Society spends more on fundraising than research - Canada - CBC News
A couple of months back I noted that there is not necessarily a relationship between the deadliest conditions and fundraising - well here is another article on charities and the research systems.
I mentioned previously that I am working on analysis of the interaction between charities and research. Our team didn't get funding for the research project and given the reaction to the topic I have experienced my question is my iis not treated with ahigher priority.
Why is it not on the radar.
Tuesday, July 5, 2011
Canada's knowledge economy - not so much
This is worth reading.
In the same vein the Science and Technology coucil has released a report on Canad'a innovation performance.
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