Viewpoint-06 July 2006
Econometrics of Science, Research and Innovation
The following contribution is
reprinted with the permission of the
Australian Mathematical Society Gazette July 2006*
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,
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
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.
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
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
The author is grateful to Adonis Yatchew for helpful discussion.
(2003). Does the tax system encourage too much education? Finanzarchiv
Gans, J. & Hayes, R.
(2004). Assessing Australia’s innovative capacity: 2004 update. IPRIA Report
Gans, J. & Hayes, R.
(2006). Assessing Australia’s innovative capacity: 2005 update. IPRIA Report
Gans, J. & Stern, S.
(2003). Assessing Australia’s innovative capacity in the 21st Century. IPRIA
www.mbs.edu/home/jgans/papers/Innovation Index Australia.pdf
(2005). Wanted: Better benchmarks. Science 308, 1087.
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.