Economics of Biotechnology Home Page
THE ECONOMICS OF BIOTECHNOLOGY
2.0 Structure
© Harry Hillman
Chartrand
Compiler Press, 2008
Index
2.0
Structure
2.3 Firm Size, Concentration, Clusters
& Alliances
2.5 National Innovation Systems
2.0 Structure
In Industrial Organization every industry has a distinct Structure or organizational character. The traditional elements of Structure are barriers to entry, the number and size distribution of firms, product differentiation and price elasticity of demand for its output. An industry may have barriers to new firms entering in competition to existing firms. Such barriers include economies of scale and of scope as well as exclusive possession of critical inputs to the production process. This may be physical or legal possession in the form of intellectual property rights. The number and size of firms also varies between industries. Some are competitive with many small firms. Others are oligopolies with a few large firms dominating the industry with a competitive fringe of smaller firms competing in niche markets. Some are effective monopolies with only one firm dominating the industry and a competitive fringe. Similarly there are industries in which the output of each and every firm is judged homogenous by consumers - final and/or intermediary. In other industries output by different firms is seen as distinct and different by consumers, e.g., through branding. Price elasticity of demand refers to the sensitive of demand to a 1% change in price. If a 1% change in price results in a 1% change in demand (up or down) then we have 'unitary' elasticity, i.e., 1:1. If a 1% change in price results in a greater than 1% change in demand we have elastic demand and if less than 1% then inelastic demand. Different industries display different price elasticity of demand.
According to the
Standard Model of economics - alternatively known as the Marshallian,
Neoclassical or Perfect Competition Model - if at least one factor of
production is fixed, i.e., we are in the
‘short-run’, then adding more and more of a variable input will eventually
result in the diminishing marginal product of that variable factor, i.e., the addition to final output will
decline and eventually turn negative.
This produces the classic
‘U’-shaped average cost curve with marginal
cost intersecting at the minimum of average cost. With this curve and a given price the profit
maximization point for the firm can be determined.
Furthermore,
when the price is just equal to the minimum point of the average variable cost
curve (where marginal cost intersects it) we have what is called the ‘shut-down’ point of the firm. At the minimum point of the average total
cost curve we have the ‘break-even’ point. Every point on the marginal cost curve above
shut-down represents the supply curve for the firm. Profit maximization will occur where the
going price is equal to the marginal cost of the last unit sold, i.e., where the marginal revenue earned
equals the cost of the last unit produced.
A profit is made on all previous units assuming a fixed price.
All of this became
possible with the
Marginalist Revolution of the 1870s with
the integration into economics of second order differential calculus,
specifically constrained maximization.
It is important to note that this new calculus began with demand and
Jeremy Betham’s ‘felicitous calculus’, i.e.,
the calculus of human happiness.
Unlike the other
humanities & social sciences, economic epistemology, i.e., its theory of knowledge, is rooted not in Platonic or
Aristotelian idealism but in Epicurean sensationalism. As noted by
Alfred Marshall (1920, 628), the most
influential successor of
Adam Smith
(1723-1790) was not an economist but rather
Jeremy Bentham (1748-1832), a radical reformer who
formalized Utilitarianism as a comprehensive philosophy (Clough 1964,
605). Bentham’s epistemology is based on
the atomic materialism of Epicurus (341-271 B.C.E.). He acquired this view from the De Rerum Natura (On the Nature of Things) by the Roman Epicurean poet Lucretius
(99-55 B.C.E.), whose work, unlike those of Epicurus, survived the fall of the
Roman Empire and the censorial fires of the Church.
Like Epicurus,
Bentham believed that physical sensation was the foundation of all
knowledge. Knowledge, including
preconceptions such as ‘body,’ ‘person,’ ‘usefulness,’ and ‘truth’, form in the
material brain as the result of repeated sense-experience of similar
objects. Ideas are formed by analogy
between or compounding such basic concepts (O’Keefe 2001).
For Bentham
sense experiences involved a unit measure of pleasure and pain called the
‘utile’ from which the philosophical school of thought known as
‘Utilitarianism’ emerged. Utiles would
eventually, according to Bentham, be subject to physical measurement and he
proposed a ‘felicitous calculus’ of human happiness. One corollary of the utile, however, is that
customs, traditions and taste cease to be independent variables. Compulsory standard education would ensure,
Bentham believed, that everyone’s customs, traditions and taste would
eventually become identical and therefore irrelevant.
Even aesthetics
shrank to analysis of pleasurable sensations evoked by a work of art. A thing is beautiful because it pleases, it
does not please because it is beautiful (Schumpeter 1954, 126-7). This, combined with Benthamite emphasis on
functionality, meant application of artistic effort was “irrational”. In industrial design and architecture, this
aesthetic reached its logical conclusion in the aphorism form follows function,
the Bauhaus and the glass and steel towers of the International School of
Architecture (Hughes 1981).
In the hands of
Francis Ysidro Edgeworth (1845-1926) Bentham’s felicitous calculus of human
happiness was married to Newtonian calculus of motion and reduced to geometric
expression subject to mathematical proof in his Mathematical Psychics (Edgeworth 1881). This geometry and its related calculus
permitted erection of what became the Standard Model in economics. It is important to note that use of calculus
defines the Standard Model as mechanical rather than biological in nature,
i.e., the calculus of motion, in this case, of human happiness.
The budget
(income and prices) constrains maximization of pleasure by the individual
consumer [U = f (x, y); I = PXX
+ PYY] yielding a demand curve while the cost constrained profit
maximization of the firm [Q = g (K,
L); C = PKK + PLL] yields a supply curve. When put together in the ‘Marshallian
scissors’ of supply and demand, a determinant geometric, mathematically precise
equilibrium emerges. It is an ideology
framed by an ‘X’ - the intersection of market supply and demand curves - marking
the spot where human happiness is to be found, where, at one and the same time,
consumers maximize their self-interest and producers their profits; everyone is
happy here - if one accepts certain very strict assumptions.
For our
purposes, three assumptions are relevant.
First assume all consumers and producers have ‘perfect knowledge’ in
which case, of course, there can be no market for knowledge since everyone has
it freely and perfectly. Second assume
that human beings are strictly rational, i.e.,
they are constantly calculating and weighing the relative probabilities of
present and future pleasure against present and future pain. Third, while utiles cannot be physically
measured let us assume they can be reified, i.e.,
an abstraction made concrete, in the form of money. The presence of money brings pleasure; its
absence brings pain. It is ironic that
the Standard Model in economics achieves what Plato, speaking about Art, feared
most, that: “not law and the reason of mankind, which by common consent have
ever been deemed best, but pleasure and pain will be the rulers in our State”
(Plato 1952, 433-434).
Unlike the
Standard Model in sub-atomic physics (Cottinham & Greenwood 1998), however,
the economic model is not empirical, i.e., it does not reflect nor pretend to
reflect observable reality. Furthermore,
it is not experimental, i.e., controlled conditions cannot be maintained nor
results replicated. Rather, the Standard
Model in economics is normative, specifying conditions under which perfection
can be attained, providing the benchmark against which economic reality can be
judged, e.g., the cost of
monopoly. It is therefore a ‘theory of
value’ reflecting the origins of economics as a branch of moral philosophy (Boulding 1969).
In this sense, the Standard Model of economics is indeed an ideology.
Nonetheless, the
Standard Model fulfils Rene Descartes’ requirement of a science in that it uses
deductive logic based on a set of key assumptions whose conclusions are subject
to geometric and mathematical proof. The
resulting ‘paradigm’ led, I infer, Thomas Kuhn to single out economics among
the other social sciences as best approximating ‘normal science’ (Kuhn 1996,
161).
The ‘U’-shaped average
cost curve of the Standard Model in fact reflects a manufacturing economy in
which fixed capital is spread out over increasing quantities of labour. It also reflects division and specialization
of labour available in manufacturing identified by Adam Smith and extended by
Charles Babbage inventor of the first computer (Rosenberg 1994, 22-46).
This cost
structure, however, is not general to the economy as a whole is evidenced by
the century-old existence of agricultural economics as a distinct
sub-discipline. In fact prior to Adam
Smith the French Physiocrats of the 18th century believed agriculture was the
source of economic surplus. Plant one
seed; harvest a thousand. The whole of
France was represented as a farm and the policy question was how to manage it
best. They gave us the name ‘economist’
as well as laissez faire and laissez passer. There were deeper policy implications to
their program which we will explore under 3.0 Conduct. At the time, however, they were not realized
because of the French Revolution. Unfortunately, Madame Guillotine separated
the Physiocratic head from the Physiocratic body in the Terror of the French
Revolution.
Waiting in the
wings on the other side of the English Channel, however, Adam Smith proposed
that economic surplus flowed from the division and specialization of labour in
manufacturing not from agriculture, i.e.,
from mechanics not from biology.
England, after winning the Napoleonic Wars, then adopted at least some
of Smith’s suggestions initiating the self-regulating marketplace (K. Polanyi [1944]
2001; see
Block’s Introduction to the 2001
edition). As they say: The rest is
history.
The cost structure
of a knowledge-based economy including biotechnology is not the ‘’U’-shaped
average cost curve of manufacturing but rather is ‘L’-shaped.
Consider, hypothetically, that the first unit of Windows VISTA
cost $500 million to develop but the second and all subsequent units cost a
$1.99 (once you have a CD/DVD burner).
This highlights the economic significance of copyright and IPRs. Without State-sponsored and enforced IPRs the
enormous initial investment required for many innovations would be
unprofitable. Arguably, however, the
same holds for the individual artist/author/creator. At the extreme, there is Van Gogh, the
epitome of the mad starving artist. He
cut off his ear and sent it to his girl friend; spent much of his life in an
insane asylum; and, in return, he gave us Sunflowers and Starry Nights
available for only $1.99 at your local dollar store.
There are implications to an ‘L’-shaped average cost
curve. The most obvious is that profit
maximization cannot be geometrically specified and only with great difficulty
and many assumptions can it be mathematically determined. One example is the success of Microsoft’s
Windows and Office programs. Through
Windows 1995, 1998, Millennium and 2000 until Windows XP in 2001 there was no
activation or anti-piracy devices built into the program. Instead it could be easily copied and only a
product key was required. Arguably
Microsoft wanted as many copies on as many desktops as possible – no matter the
loss in revenue. And, of course,
Microsoft does not produce computers but rather just software for ‘PC clones’
manufactured by many different producers.
This contrasts with Xerox and Apple that at the time required expensive
proprietary hardware and software to function.
In effect they limited the number of desktops using their operating
systems through high prices in an attempt to maximize profits.
Why would Microsoft, in effect, give away its property or
at least allow it to be so easily copied?
The answer is ‘network economies’.
Windows rapidly became the operating system for 95%+ of all PCs on the
planet. Once familiar with its operation
and having built up many documents and files using its sister Office products,
the user was essentially hooked. The
cost of conversion and skill acquisition locked customers into the Windows
brand. This is an example of what is
known as the ‘path dependency’ of techno-economic regimes (David 1990). In law, we call
it ‘precedent’. Both are cases of The Law of Primacy: What cones first
colours what comes after. The classic
example is the use of 110 vs. 220
volts in North America and Europe, respectively. Once a choice is made all appliance must
adapt to this foundational precedent. There
are real benefits in such standardization (Alder 1998) and to this degree at least Microsoft should be
praised.
Biotechnology also exhibits an ‘L’-shaped average cost
curve. Consider the goat genetically
engineered to produce spider silk in its milk.
Much time and effort but relatively moderate cost was required to get
the first goat but the second and all subsequent ‘copies’ is bred like any farm
animal at minimal cost because of the ‘natural purpose’ of living things – to
survive and reproduce. Given that it is
primarily new knowledge in biotechnology rather than huge capital plant and
equipment that plays the critical role of it should not be surprising that intellectual
property rights (IPRs – see 2.0 Conduct)
also plays a major role in determining its cost structure of biotechnology.
Under perfect
competition it is free entry and exit of consumers and firms from the
marketplace allowing: a long run equilibrium with
no excess profits: consumers and producer retaining
all of their respective surplus; all
firms operating at the same scale and using the same vintage of plant and
equipment; and, attaining technical and economic efficiency.
In this regard
it is important to appreciate the difference between technical and economic
efficiency. In general, efficiency
refers to the ratio of outputs to inputs. To measure efficiency one must
therefore be able to calculate both inputs and outputs. This is most easily
done in the production of goods rather than services, especially in
manufacturing, e.g. cars produced per
worker.
Technical
efficiency is achieved when it is not possible to increase output without
increasing inputs. Economic efficiency
on the other hand is achieved when the cost of production of a given output is
as low as possible. The final
determining factor is 'price' or marginal revenue received for the output. Thus all economically efficient solutions are
technically efficient but not all technically efficient solutions are
economically efficient, that is, something may be technically possible but
uneconomic. It does not pay its own way,
e.g., space exploration and the
military.
Finally, there
is allocative efficiency resulting from perfect competition. Allocative efficiency implies all factors of
production and all commodities demanded by consumers are in their best use and
receive their opportunity cost. Further,
it is assumed that there are no external costs or benefits, i.e. all external costs and benefits
have been ‘internalized’ in market price.
Three conditions must hold:
(i) Consumer Efficiency: when consumer cannot increase utility by reallocating budget;
(ii) Producer Efficiency: when firm cannot reduce cost by shifting input mix; and,
(iii) Exchange Efficiency: when all gains from trade have been exhausted. Gains from trade to consumer are called consumer surplus which measures the difference between what consumers are willing to pay and what they actually pay for a given quantity of a good or service at market price. Gains from trade to producers are called producer surplus which measures the difference between what producer are willing to accept and what they actually receive for providing a given quantity of output.
This ‘efficient’ outcome, however, cannot be achieved if there are barriers to entry/exit. Such barriers include economies of scale and of scope, possession of an exclusive input, IPRs or a government franchise.
Economies of scale mean that the larger the scale of production, i.e., capital plant and equipment, the lower the cost per unit. At the extreme this leads to one firm, i.e., a natural monopoly, able to supply all market demand at the lowest possible cost. Any firm that enters the industry does so at a smaller scale of production and hence at a higher cost that allows the monopolist to price below any entrant’s shutdown point. A monopolist can then raise price after the entrant has exited. The same outcome can occur under oligopoly when large firms attain scale economies and then, in collusion, use predatory pricing to drive entrants out of the market and then use price fixing to maintain excess profits (Labaton 2001).
Economies of scope, unlike economies of scale that generally operate in the production of a single product, are associated with the marketing and distribution of different types of goods and services. Economies of scope are apparent in product bundling, e.g., Internet Explorer and Media Player, product lining, e.g., offering low, medium and high cost output to different consumers as was first done by Josiah Wedgewood in the 18th century (McCraken 1988), and branding, e.g., Coca Cola using its brand name to introduce new products. As we will see two critical economies of scope in biotechnology concern legal tactics involving IPRs and regulatory testing of new products (2.0 Conduct). Large multi-product firms can spread such costs across many products while the single product firm cannot.
Exclusive possession of a critical input to the production process can take two forms – physical and legal. Exclusive possession of a physical input is generally straight forward, e.g., a firm owns the only mine from which the input can be extracted. Without access to that input no competitor can enter the market. Exclusive possession of an intellectual property right is less straight forward. Such intellectual property is sometimes formally protected by copyrights, patents, registered industrial designs and trademarks which are grants of temporary monopoly made by the State. As will be seen below (2 Conduct), through the courts the State enforces such monopoly rights. If any firm wishes access to the intellectual input it must purchase a license from its owner. The terms of such licenses can be, as will be seen, expensive, restrictive or even prohibitive thereby limiting entry.
Alternatively intellectual property is more informally but permanently protected as ‘know-how’ and trade secrets. Such rights are not formally recognized by many States but rather are protected through ‘confidentiality clauses’ in contracts with employees and users of the knowledge, e.g., franchises granted by McDonald’s.
Finally, some natural monopolies are judged by the State to be of such importance to the public good that either a private or publicly-owned firm is granted an exclusive franchise, e.g., local water, sewer and gas. The State uses its monopoly of coercive power including prison to prohibit any new entrants.
According to
Zucker et al (1998) the number of
American companies actively engaged in biotechnology grew from virtually none
in 1967 to 751 by 1990. Of these 511 or
68% were new entrants, 150 incumbents (20%), and 90 (12%) including 18 joint
ventures that could not be formally classified.
Furthermore, by 1990, 52 (7%) of the 751 had died or merged with other
firms (Zucker et al 1998: 292). Zucker et
al do not provide evidence regarding the size or concentration ratios for
biotech firms.
2.3 Firm Size, Concentration, Clusters
& Alliances
As evidenced in
the BIOTECanada
State of
the Industry Report 2004 and the
2003 U.S. Department of Commerce survey, biotechnology
in agriculture and medicine (the Green
and the Red of the
2003 Economist Survey) is essentially
oligopolistic with a few large firms surrounded by a ‘competitive fringe’ of
small firms. The situation for
industrial biotechnology (the White
of the 2003 Economist Survey) is unclear given the wide range of biotech
products involved. Formally ‘concentration’ in an industry is
measured by the per cent of sales (or other factors) contributed by the largest
– 3, 5, 10, etc. – firms. This is known as ‘the concentration ratio’. For my purposes, however, concentration in
the biotechnology industry has several other dimensions including: geographic
clusters and cross- as well as trans-industrial alliances.
Informally, high
tech industries exhibit a distinctive form of concentration called
‘clusters’. These are geographic rather
than financial in nature and the most famous example is ‘Silicon Valley’ in
California. Clusters and cluster
analysis has become a hot topic in economics (The Economist Part II – Cluster Analysis, 2003) since
introduced in the ‘New Economic Geography’ by Paul Kruman in the 1980s (Martin & Sunley 1996). Arguably, however, it is old wine in new
bottles. Alfred Marshal in 1919 analyzed
what were then called ‘industrial districts’.
While economies
of scale and scope are available within the firm, external economies, such as
those associated with clusters, are available only outside the firm. Some external economies are improved, less
costly inputs produced by suppliers. The
buying firm thereby benefits by improvements made by its suppliers. Some external economies are associated with
reduced transaction costs, e.g.,
using the ‘B-to-B’ internet, i.e.,
the business to business internet.
In the case of
clusters high tech especially small emerging firms operating in the same
industrial sector, e.g.,
biotechnology, benefit from the physical proximity of each other’s activities
in a number of ways. The highly
specialized talent and equipment involved means that if firms concentrate in
the same geographic area they can more easily draw upon each other’s
resources. Similarly, if some firms
focus on the production of instruments these can more easily be tailored to
meet the needs neighbouring firms involved in final production. For Marshall one of the most important
external economies generated by industrial districts was the most simple and
obvious: having coffee together. Managers
and workers in related fields can exchange ideas about products and practices
that sometimes lead to cost savings and/or innovative new products and methods.
A final form of
concentration in high tech industries including biotechnology is ‘alliances’. This too has become a hot subject in the discipline.
There are two forms. The first type of alliances involves firms
engaged in different parts of the industry, e.g.,
firms engaged in production of final goods and services ally with firms that
design instruments and production equipment.
These I will call ‘cross-industry’ alliances. The second type of alliances is
trans-industrial in nature. As noted
under
1.2.2 Instrumentation major
information technology companies have made significant commitments or ‘alliances’
with nascent biotechnology firms in the hope these will grow into a
multi-billion dollar market for biotech IT equipment and consulting . One can expect that similar trans-industrial
alliance will form as biotechnology matures offer new ways to produce existing
products.
Both clusters
and alliances also demonstrate a lesson learned in genomics – coevolution and
coconstruction. Life has burgeoned far
beyond single-celled creatures. Kauffman
notes there are some 265 different cell types in the human body (Kauffman 2000,
182). Each is an autonomous agent. Each, however, collectively combines to form
a higher order agent – an organ - that, in turn, forms a functioning part of a
yet higher order agent – the individual human being. Kauffman takes this hierarchy up from the
geosphere of chemistry to the biosphere to what he calls the 'econosphere'. The process I characterize as the increasing
diversity and complexity of autocatalytic systems pursuing Kantian natural
purpose.
The mechanism
driving increasing diversity and complexity is coevolution defined as the
mutual evolutionary influence of two species (molecular, organic or social)
that become dependent on each other.
Each exerts selective pressures on the other, thereby affecting each
others’ evolution. This often involves
morphological coconstruction, e.g.,
the shape of an orchid flower matching the bill of the hummingbird. Coevolution and conconstruction apply in both
symbiotic and predator/prey relationships between autonomous agents.
In fact,
Kauffman argues that the primary mechanism of molecular evolution is not the
template model of sequentially constructing DNA step-by-step up the
ladder. Rather it is through
coconstruction of its segments by sets of mutually dependent autocatalytic
molecules that then integrate the parts into a new coherent living whole. This catches the Kantian sense that “each
part is reciprocally means and end to every other. This involves a mutual dependence and
simultaneity that is difficult to reconcile with ordinary causality” (Grene
& Depew 2004, 94).
Arguably, in the
‘econosphere’ clustering, external externalities and alliances serve the
purpose of coevolution and coconstruction.
2.4 Innovators
There are
leading researchers or ‘stars’ who play a significant role as innovators within
the biotechnology sector of the economy.
Of some 207 biotech ‘stars’ identified by Zucker et al, 158 (76%) were
resident in universities, 44 (21%) in research institutes and only 5 (3%) in
commercial firms (Zucker et al 1998:
293). Like Watson, Crick and Berg such
‘stars’ have the talent, knowledge and experience that leads them to new
insights and breakthroughs. Their high
profile tends to attract the best students who, in turn, become the ‘stars’ of
the next generation. They also tend to
attract the attention of the large well established firms.
It has been argued,
using a life-cycle model, that most scientists invest in developing a
reputation early in their careers usually through publication in journals that
signal the value of their knowledge to the scientific community. With maturity they seek ways to appropriate
the economic value of their knowledge, e.g. through consultancy, work (full- or
part-time) with established enterprise outside of the university or by joining
or establishing a new firm (Audretsch and Stephan 1999). This appears to be especially true in
biotechnology.
In the case of
‘scientific founders’ of new firms in pharmaceutical biotechnology some 50%
followed the academic trajectory; 28% established their careers with large
pharmaceutical companies; 13% followed a mix of the two while 6% established
firms immediately following their academic training (Audretsch and Stephan
1998). It has also been argued that many
new biotech firms are founded with the specific intent of selling them to large
established firms (Arora and Gambardella 1990, p. 362).
2.5 National Innovation Systems
The final strand
in public support to the biotech sector is the national system of innovation
(NSI). Phillips and Khachatourians
(2001), quoting Metcalfe, define a NSI as “that set of distinct institutions
which jointly and individually contribute to the development and diffusion of
new technology and which provides the framework within which governments form
and implement policies to influence the innovation process. As such it is a system of interconnected
institutions to create, store and transfer the knowledge, skills and artifacts
which define new technologies.” The OECD
formalized the concept of NIS’s and has produced a blue print for its member (OECD
1997).
Governments
around the world are now consciously designing NSI’s in an effort to enhance
their competitiveness (Pagan 1999). As
we will see intellectual property rights regimes can arguably be considered a
critical part of the NIS. The biotech
sector is one of the chief objects of such NSI’s. However, the role of multinational
corporations is generating stresses and strains on the successful operation of
NIS’s (Patel and Pavitt 1998).
For my purposes,
the NIS can be defined a nonprofit academic institutions partnering with
government and private for-profit actors to create networks of specialized
research centres in priority knowledge domains, disciplines, sub-disciplines
and specialties. Such centres are
intended to facilitate commercial exploitation of new knowledge and enhance the
competitiveness of the nation. In the
process, three important structural changes are taking place.
First, the
mandate of the university is changing.
The medieval university was focused on interpretation of old
knowledge. This mandate changed little
following the Scientific Revolution of the 17th century. With religious wars waging, the university –
Protestant and Catholic – were busy defending religious doctrines and resisted
the new experimental philosophy. In
effect, the university remained a training ground for elites in traditional and
proper ways of knowing. It was not until
1809 that the first research university was founded in Berlin transforming the
mandate of the university - traditional and conservative heartland of Western
knowledge - from interpretation of old to the generation of new knowledge.
Today, the mandate of the university is arguably being enfolded within the NIS
transforming it to generation and commercial exploitation of new knowledge
(Nagy Nov. 3, 2005). As predicted, this
has produced a significant clash of cultures within the university itself
(Chartrand 1989).
Second, as
patron of the national knowledge-base, Government fosters and promotes
production of knowledge through arm’s length institutions. Such institutions generally direct funding
according to peer evaluation. In Canada,
for example, during the last decade the federal government has endowed a number
of quasi-public foundations to support knowledge production, e.g., “Canada Health Infoway Inc.,
received $500 million from the federal government; others have received
multiple payments amounting to, for example, $300 million to Genome Canada and
$250 million for the Green Municipal Funds” (Auditor-General of Canada Status
Report, April 2002, 1.9). In
the past foundations, endowments or grant-giving councils were involved in the
production of knowledge for knowledge sake.
Today, however, as part of the national innovation strategy these new
foundations are concerned with ‘knowledge for profit’. This means that commercial confidentiality
veils many of their activities from public scrutiny. This, in turn, raises serious questions about
the accountability of private interests serving the public purpose, i.e.,
Government by Moonlight: The Hybrid Parts of the State (Birkinshaw,
Harden and Lewis 1990) [Also see my
book review].
Third, to date,
the NIS has been restricted to the natural & engineering sciences. There is, however, no reason why it cannot be
extended to other knowledge domains and practices. For example, national cultural policy
corresponds to NIS in the Sciences. The
practices, with the notable exceptions of medicine and related engineering,
have not, however, been the subject of NIS.
Accounting and legal praxis are applied to develop NIS. They have not
themselves, however, been subjected to comparative advantage analysis, nor
networked into NIS nor held accountable for their contributions – positive and
negative – to competitiveness. I
suspect they will, formally or informally, shortly be enfolded within the NIS
framework. Arguably, heated political
debate in the United States concerning tort and product liability represents
the opening move towards seeing national legal systems from a competitiveness
perspective. Similarly, the accounting
profession in the United States is, under the terms of the Sarbanes-Oxley Act
of 2002, now subject to oversight unknown before the Enron scandal and the
collapse of Arthur Anderson & Co. This
too may be but a first step in enfolding accountancy within the NIS web.
Alder, K., “Making Things the Same: Representation, Tolerance and
the End of the Ancien Regime in France”, Social Studies of Science, 28
(4), Aug. 1998, 499-545.
Arora, A.,
Gambardella, A., “Complementarity and External Linkages: The Strategies of the
Large Firms in Biotechnology”, Journal of
Industrial Economics, 38 (4), June 1990, 361-379.
Auditor General
of Canada, Status Report: Chapter 1 - Placing the Public's Money Beyond Parliament's
Reach, April 2002.
Audretsch, D.
B., Stephan, P. E., “Company-Scientist Locational Links: The Case of
Biotechnology”, American Economic Review
, 86 (3), June 1996, 641-652.
Audretsch, D. B
and Stephan P. E., “Knowledge spillovers in biotechnology: sources and
incentives”, Journal of Evolutionary Economics, 1999, 9, 97-107.
BIOTECanada, State of
the Industry Report 2004.
Birkenshaw, P.,
Harden I. & Lewis, N., Government by Moonlight: The
Hybrid Parts of the State, Unwin Hyman, London, 1990. [Also see my book review]
Block, F., “Introduction” to Karl Polanyi’s The Great
Transformation: The Political and Economic Origins
of Our Time [1944], Beacon Press, 2001.
Boulding, Kenneth,
E., “Economics as a Moral Sciences”, American
Economic Review, 59 (1), March 1969, 1-12.
David, P.A., “The Dynamo and the Computer: An Historical
Perspective on the Modern Productivity Paradox”, American
Economic Review, 80 (2), May 1990, 355-36.
Economist, Survey:
Biotechnology, May 2003 - Part II: Cluster Analysis
Grene M.,&
Depew, D., The Philosophy of Biology: An Episodic History,
Cambridge University Press, 2004.
Kauffman S,, Investigations, Oxford University Press,
2000: see
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Elemental Economics
Economics of Biotechnology
