z-Oped-090606

Two weeks into the campaign for the election of the next federal parliament on August 21^st the popular media and certain additional elements of the nation's architects of opinion continue to ruminate about the prime minister's marital status, disbelief in the existence of god(s), the state of her cabinet's plumbing, and the invasion via unseaworthy vessels of terrifying alien hoards (i.e. <4% of our annual intake of migrants).

Just exactly what policies would be promulgated by Ms Gillard remains an enigma, and if the media are any indication, are not of particular interest.

Of course as an editor of Time magazine observed decades ago, there are two types of news, important and interesting, and only occasionally are they synonymous.

The situation regarding the guiding principles the leader of the opposition, Mr Abbott, would bring, were the Coalition to achieve a majority of Members of the House of Representatives, is also far from evident, but there is the suspicion that much of John Howard's shadow would cover policy which, from the viewpoint of the research and university sectors, portends an ill and chilling wind.

Australia has experienced a 27% decline (between 1995 and 2007) in the number of high school students taking advanced maths, and a 15% decline during 2001-07 in the number of students majoring in maths at university. This comes at a time when demand for mathematics and statistics graduates is predicted to grow by 3.5% every year until 2013, despite supply trending in the opposite direction.

Australia is behind the pack internationally. We rank 20^th out of 30 OECD countries when it comes to the number of university graduates emerging with a science or engineering degree*.

While the world becomes increasingly reliant on science, technology, engineering and mathematics (STEM) we appear to be unable or unwilling to provide the resources to keep up, let alone improve, our relative position. And this despite preening that we are on top of the heap in regard to warding off the deleterious effects of the global economic crisis.

And prior to the GEC large surpluses were garnered by the Coalition government which could have utilised them to markedly improve the nation's infrastructure for learning, research and development. Instead it chose to bribe the Australian voters and flail the universities.

A fortnight ago the US President's Council of Advisors on Science and Technology met and about 15 minutes + discussion (11min) was devoted to co-chairman, Eric Lander, reporting on the progress his committee is making on issuing its report on fostering STEM education in the United States.

Click here and then move the pointer to 50 minutes into this webcast segment to access the beginning of the report.

Make no mistake, time is not on Australia's side to properly revitalize the nation's educational system "root and branch" to prevent it falling so far behind to be unable to recover.

Finally, we reprint here a contribution by Professor Peter Hall which was first published in the Australian Mathematical Society Gazette of July 2006 which makes it clear that the wheels of government can grind exceeding slow if not fine. And what is particularly worrying is the overly bureaucratic and constrictive approach by Labor as well as the Coalition toward providing resources. Australia's newest Nobel Laureate, Elizabeth Blackburn put it succinctly and forcefully: "I think there are tremendously good scientists in Australia but sometimes I just feel, are they really being able to run with it in the way they are capable of?" and went on to say that in the US, she benefited from a five-year grant that allowed her to follow her nose without having to write up "damn little" reports and catalogue milestones on a regular basis. "This was the perfect setting and I'm not aware that I would have been able to do that [here]."

Peter Hall: The Econometrics of Science, Research and Innovation

The following contribution is reprinted with the permission of the Australian Mathematical Society Gazette July 2006*

Peter Hall is developing classification tools for use with very small samples of very large vectors, arising in contexts ranging from genomics to the detection of covert signals.

How do we measure the value of science, education and research? How much do education, and university research, contribute to innovation? In a world where information and technology can be transferred rapidly, and economies are often interconnected, the answers to these questions are far from simple. They rely in part on the public policies of individual countries. For example, both the costs and the benefits of higher education depend significantly on a nation’s taxation system, among other facets of its economy (see e.g. Alstadsaeter, 2003).

However, if we are to gain a good understanding of the benefits of education, research and innovation then we are almost bound to combine data from different countries, and to attempt to assess (or model, as an econometrician would put it) both the similarities and the differences. If we work with information from just a single nation, such as Australia, we limit ourselves to the policy, institutional and other characteristics of that country.

Moreover, by confining ourselves to just one country we make it particularly difficult to infer the potentially massive effects of linkages with other economies. Would it have been possible to envisage and predict India’s burgeoning software industry without comparing the quality and price of programming skills there to those in other countries, such as the US?

There is substantial international literature on measuring the contributions made by education and research to innovation. Seven years ago, at a time of relative global optimism, the US Council on Competitiveness quantified and analysed the factors that drive innovation (Porter & Stern, 1999). It developed an econometric model for innovation, fitted the model to international data, and introduced an “innovation index” for assessing the strengths of innovation in different countries.

The Council concluded that America’s standing as an innovator was under threat, and argued in favour of a new US innovation strategy, including measures such as reversing “the downward slide of federal support for R&D” and “attending to the vitality of basic research at universities”. “The United States must rebuild its dwindling pool of scientists and engineers”, wrote Porter and Stern. It would also be necessary to make “a concerted effort to rebuild undergraduate and graduate training in technical disciplines”.

In a Science editorial in May last year, on benchmarks for science funding, Marburger (2005) called for “econometric models that encompass enough variables in a sufficient number of countries to produce reasonable simulations of the effect of specific policy choices”. Current econometric tools, such as those used by Porter & Stern (1999) and in the studies reported below, are admittedly rather primitive, although this may be a necessary reflection of the quality and quantity of the available data. More detailed work is needed, Marburger argued, to answer questions such as, “How much should a nation spend on science? What kind of science? How much from private versus public sectors?”

In Australia, similar questions are being asked with increasing frequency. The current Australian Productivity Commission enquiry “into the economic, social and environmental returns on public support for science and innovation in Australia” will be considering issues such as these as it develops methodology and prepares its report, due in March next year.

Independently of the Commission’s deliberations, a series of three papers on “Assessing Australia’s innovative capacity” has examined our position in the world of innovation, and discussed how our performance might be encouraged and improved (Gans & Stern, 2002; Gans & Hayes, 2004, 2006). The most recent report updates the earlier ones, using data for the years 1975 to 2004.

Each of these studies involves a statistical regression onto around a dozen variables. That is, the “response variable” representing innovation is expressed as a linear form in a dozen measurable quantities, such as expenditure on university research, which might help explain innovation; plus an error term representing other quantities impacting on innovation, but for which data are not readily available. The methodology used in all three reports is based on that of Porter & Stern (1999), and in particular, innovation is quantified, for any given country, as the logarithm of the number of patents granted.

Gans & Hayes (2006) note that “2004 saw Australia’s Innovation Index record a small decline. Together with Austria’s improved index this decline saw Australia’s OECD ranking fall from 14th in 2003 to 15th in 2004”. A feature that all three studies share is the very similar leverage they reveal for two key statistical variables, “Percentage of R&D funded by industry” and “Percentage of R&D performed by universities”.

Indeed, the relative leverage that these two items of expenditure have on innovation equals 1.4, 0.9 and 1.0 in the reports of Gans & Stern (2002) and Gans & Hayes (2004, 2006), respectively. Here we define “relative leverage” in terms of the ratio of the regression coefficients. In the case of the most recent report, where the coefficient ratio is almost identical to 1, the statistical significance of the respective coefficients is particularly high.

Reflecting results such as these, the conclusions and recommendations of the four reports (Porter & Stern, 1999; Gans & Stern, 2002; Gans & Hayes, 2004, 2006) argue strongly in favour of both private- and public-sector support for research. The reports stress the importance of interaction between both these parts of the economy. Resonating with the points made earlier by Porter & Stern (1999), each of the three Australian reports recommends that authorities “ensure a world-class pool of trained innovators by maintaining a high level of university excellence and providing incentives for students to pursue science and engineering careers”; and “enhance the university system so that it is responsive to the science and technology requirements of emerging cluster areas”.

We began this article by noting the challenges of comparing science, research and innovation among different countries. The 29 OECD nations whose data contribute to the work of Gans & Hayes (2006), vary greatly in terms of the ways they motivate and fund science and research. Some of these differences, as well as potential interactions among the explanatory variables, might be taken into account in a more complex model. However, in the absence of more detailed data it seems difficult to be significantly more definitive or more specific, and to respond adequately to Marburger’s (2005) call for a relatively sophisticated approach. This inherent limitation is bound to restrict the scope and authority of enquiries such as that by the Australian Productivity Commission.

Indeed, while more advanced econometric techniques would have the capacity to “torture the data until it confesses”, it is unlikely that fancier tools would alter the conclusions drawn by simpler arguments in all four of the reports discussed above — that R&D expenditure in both public and private sectors, education expenditure and IP protection all have very strong, positive impacts on innovation.

Acknowledgement. The author is grateful to Adonis Yatchew for helpful discussion.

Alstadsaeter, A. (2003). Does the tax system encourage too much education? Finanzarchiv 59, 27–48.

Porter, M.E. & Stern, S. (1999). The New Challenge to America’s Prosperity: Findings from the Innovation Index. Council on Competitiveness Publications Office, Washington, DC. www.compete.org/pdf/innovation.pdf

Peter Hall is Professor of Statistics at the Australian National University and the University of Melbourne, and is President-elect of the Australian Mathematical Society.