Yet another thing we can do with the OECD patent database is study the benefits of collaboration quantitatively. I have heard more than one cynic claim that a major effect of the Performance Based Research Fund is to increase the number of co-authorships (I’ll put your name on my paper, if you put mine on yours). Is such an effect visible in the patent database? Do the big collaborative networks that we have found just arise from cynical self-interest?
Evidently not. In fact, what we find is a strong correlation between the mean number of collaborators in an inventor network and the mean productivity (in terms of the number of patents per inventor) of that network. This is shown in a plot on the left where I have used data from inventor networks in Germany and the USA. The data shows that the people who collaborate do tend to be more productive, although of course this is a correlation, not necessarily a causation. However, from my own experience, I know there is a cost to collaboration – in time, patience and comfort. So it should perhaps not be surprising that if people are collaborating they are gaining some benefit.
Do these measures correlate with inventor network size? Yes indeed. The plot on the right shows that both collaboration and productivity among inventors increase with the size of the collaborative network. Again, perhaps it is not surprising that productive, collaborative people will build large networks around themselves. However, one could still imagine such networks arising out of the cynical sharing of inventorships by inventors around a fixed pool of inventions – this doesn’t appear to be the case. The largest networks are both more collaborative and more productive.
How do these networks form (if not by cynical trading of inventorships)? That is something we are still studying and although I do have more to say on this subject, I will save it for a later post.
The MacDiarmid Institute held its annual meeting at the University of Auckland this year. This year’s theme was “How to make money from your $cience”. Our students and post-docs participated, along with a number of students from Singapore. The science students were taught how to write a business plan, then put into teams together with a student from the University’s business school. Each team was asked to brainstorm a business idea to present to a ‘dragon’s den’ panel.
Now, we don’t expect all our students to become hot shot entrepreneurs (although a good many of them do want to, and no doubt will), but even those who remain in academia will find the ability to present a sound business case to be essential.
On the day, the teams pitched concepts ranging from long-life ice cream to rapidly switchable, tintable coatings for glass. The team that won had an idea for a fetal health monitoring device. In the end, I think everyone enjoyed themselves immensely. We will be preparing a podcast based on interviews with the students during the event so you can hear what they really thought!
One session of the meeting was a talk from Basil Sharp, a University of Auckland economist who mused on why New Zealand’s patenting rate was so low when its number of researchers was so high. (He suggested ‘market failure’, but if you read this blog you will know that many of our ‘researchers’ are not scientists or engineers.)
We also had a talk by Brett Wells from Aeroqual who discussed the fun of successfully running a high tech start-up company in New Zealand. This was very interesting and generated a lot of discussion – it was evident from Brett’s talk that the support and patience of Aeroqual’s investors has been critical to its success. I hear many explanations for why New Zealand is not good at producing high tech start-ups, but those in the game such as Brett often don’t see many disadvantages at all.
My thanks go to David Williams from the University of Auckland for organising the event. David’s career is a case-study in making the transition from academia to industry and back (he has even done this more than once). He is certainly someone who knows how to make money from his science.
One of the interesting things we can do with the OECD patent database is look at how those patents are distributed among applicants. The applicant for a patent is often the organisation or company that employs the inventors rather than the inventors themselves. By looking at the distribution of patents among applicants we are looking at the size distribution of patent portfolios. Note that an organisation may apply for patents from multiple addresses – in this case I have treated each address as a separate applicant.
The plot on the right shows the distribution of European Patent Office patents among applicants from the USA, New Zealand, Australia and Finland. The data is shown on log-log axes – remarkably, the data in all four countries fall roughly on straight lines with slopes close to -2. In other words, the proportion of applicants with p patents is inversely proportional to p squared.
This appears to be yet another example of Zipf’s law, which is a frequency distribution that crops up in all sorts of strange places (none stranger than the popularity of opening moves in chess). One way such distributions can arise is through a process called “preferential attachment” (sometimes called a rich get richer process). In our case, such a distribution could be generated if an applicant’s probability of getting a new patent increases with the number of patents the applicant already has. The value of the exponent generated by such a process (close to -2 in the data shown) depends on the rate at which first-time applicants enter the population versus the rate at which new patents arise amongst existing applicants.
What is interesting is that the exponents are quite similar across the four countries, suggesting that the process that generates the distribution is the same in each. The main difference between countries is the absolute scale of the distribution rather than the slope.
What determines this scale? The best correlate I have found is the level of business expenditure on research and development (BERD) in each country. If we instead plot the number of applicants per million dollars of BERD, the distributions almost collapse on to one another. Actually, you can see that with this rescaling New Zealand comes out quite well – we get more patents for the dollars we spend than the other countries shown.
Despite the value for money New Zealand businesses appear to get for their R&D spend, the data show that you largely do get what you pay for. Indeed, the similarity of the exponents between countries also suggest that the innovation process itself does not vary widely – it is the amount of research you do rather than the way you do it that is important. Unfortunately in New Zealand, our focus is all too often on how we innovate, rather than how much we innovate.
I’m on my way to Helsinki at the moment to examine a Finnish PhD thesis. The University of Helsinki PhD exam is quite different from its New Zealand counterpart. To start with the thesis itself is a merciful 40 pages, although it is accompanied by 6 refereed journal articles. Overall the body of work is roughly comparable to a good New Zealand PhD thesis.
But most significantly, the oral exam is conducted in public. I am called the Opponent – after a short presentation from the candidate I am expected to question the candidate for approximately 2 hours. At the end of this period, questions can come from the public.
One thing the Finns have stressed is the dress code. This was mentioned in the official invite I was sent:
On such an important day it is worth dressing elegantly, and not to show up wearing jeans and a worn-out pullover, something that has occasionally been the case in Sweden.
Those crazy Swedes! Suffice to say that I had to send my measurements for a tailcoat and waistcoat a few weeks ago.
After the exam itself, I understand that I will be sequestered while I write my report on the exam.
Then comes the karonkka. I am told that karonkka literally means coronation, but on this occasion it is a dinner to celebrate the success of the candidate (apparently only two candidates in the 355 years of the University have failed the public exam, and having read the thesis it’s very unlikely there be a third by Friday). While I suspect the karonkka will not be good for my liver, my job as Opponent is (luckily) pretty much over at this point – it will just remain to toast the newly minted PhD.
It is a great privilege to be able to participate in such a tradition. This is also my first visit to Finland, a country that shows up strongly in my patent studies.
The All Blacks may have swept the Wallabies in the Bledisloe Cup this year, but how do New Zealand and Australia stack up on innovation? A few posts ago I looked at how New Zealand’s patents were distributed regionally. Using an OECD database of PCT patents from 1978-2008, I found that Auckland had the most patents per capita of any New Zealand city (naturally I was interviewed about this late last week by the Herald). But how do New Zealand cities compare to Australian cities in patents per capita?
To make the comparison, I have graphed the New Zealand and Australian city data together. Overall, the Australians are about a third ahead on patents per capita: there is one PCT patent for every 750 Australians compared to one for every 1000 New Zealanders.
However, we don’t do so badly when we compare cities. In fact Auckland compares well with the similar sized city of Adelaide, and even stacks up well against Brisbane. Where Australia gets ahead of us is through Sydney and Melbourne, with one patent for every 550 people.
As discussed earlier, this trend for larger cities to have more patents per capita has also been noted in data from the United States. It appears to hold in Australasia too, although Canberra bucks this trend with one patent for every 200 people. Thanks to CSIRO, Canberra is the most inventive city per capita in Australasia.
Nonetheless, why do bigger cities tend to have more patents per capita than smaller cities? I will look at this in later posts.
Evidently not. In fact, what we find is a strong correlation between the mean number of collaborators in an inventor network and the mean productivity (in terms of the number of patents per inventor) of that network. This is shown in a plot on the left where I have used data from inventor networks in Germany and the USA. The data shows that the people who collaborate do tend to be more productive, although of course this is a correlation, not necessarily a causation. However, from my own experience, I know there is a cost to collaboration – in time, patience and comfort. So it should perhaps not be surprising that if people are collaborating they are gaining some benefit. 
