Elemental Economics

Economics of Biotechnology Home Page



2.0 Structure

© Harry Hillman Chartrand

Compiler Press, 2008


2.0  Structure

2.1 Cost Structure

2.2 Entry/Exit

2.3 Firm Size, Concentration, Clusters & Alliances

2.4 Innovators

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.


2.1 Cost Structure

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.


2.2 Entry/Exit

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.  One characteristic of high tech clusters is the role of the University as a nucleus for the location of firms and, in the case of biotechnology, the creation of firms byt the University itself or its 'Stars' (see 2.0 Structure: 2.4 Innovators).

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).  In turn this clash is causing the birth of the third age of the university - from interpretation to generation to commercialization of knowledge - including the teaching function (Chartrand 2008).  Biotech has been a major change agent in this process producing a new breed - the entrepreneurial scientist.  The process itself, however, began in the USA with a change of government policy concerning the use of federally funded research.  Until 1980 (the year of the first biotech patent), the federal government held all rights to to results of such research; after with passage of the Bayh-Dole Act, the university and its employees - the professoriate - retained such rights.

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.




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Elemental Economics

Economics of Biotechnology