science versus profit in research

SCIENCE VERSUS PROFIT IN RESEARCH

Carlo CarraroUniversity of Venice and FondazioneEni Enrico Mattei

Domenico SiniscalcoThe Treasury and Fondazione EniEnrico Mattei

AbstractThis paper deals with Science and Technology in research policy. Following recent literatureon the economics of knowledge, Science and Technology are defined as distinct institutionalarrangements, broadly corresponding to nonmarket and market allocation mechanisms.Previous analyses argued that Science and Technology can and should coexist within aneconomic system or society. This paper shows that Science and Technology tend to coexist—and should coexist on welfare grounds—also within the same research field, and even whenresearchers are perfectly identical. Our analysis was inspired by the race to sequence thehuman genome, but the proposed theoretical framework can also be used to assess the recentevolution of other research fields, e.g., research on GMOs. The paper also provides guidelinesfor policies designed to achieve the optimal size of public research within a given researchfield. (JEL: D78, H4, H23, O32, O38)

1. IntroductionIn economics there is a substantial literature that aims at explaining the dynam-ics of both technological change and scientific progress, although it has beenuncommon to analyze them in a unified framework. To achieve this goal,Dasgupta and David (1987) and Barba Naveretti, Dasgupta, Maler, and Sinis-calco (1996, 1998) start from the idea that knowledge can be produced throughdifferent institutions or allocation mechanisms, conventionally labelled as Sci-ence and Technology. Contrary to common language, Science and Technology,in this framework, are not defined according to the types of knowledge theyproduce (i.e., general principles vs. applied knowledge) nor on the methods ofinquiry they adopt (focused vs. broader perspective). Rather, Science andTechnology are defined according to the differences in the institutional arrange-

Acknowledgments: The authors are grateful to Kenneth Arrow, Partha Dasgupta, Alfonso Gam-bardella, Karl Goran Maler, Roberto Perotti, Alessandra Pome, Danny Quah, and an anonymousreferee for their comments. Carmen Marchiori and Jill Weinreich provided helpful assistance. Theusual disclaimers apply. In particular, examples referred to actual companies are simple illustra-tions taken from the literature, and do not imply any judgment on our part on the companiesthemselves.E-mail addresses: Carraro: [email protected]; Siniscalco: [email protected]

© 2003 by the European Economic Association

ments involving the allocation of resources and efforts in the production ofknowledge.

Science is defined as a nonmarket allocation mechanism, where knowledgeis treated as a pure public good and where fixed compensation, together withresearch grants and the rule of priority, gives scientists an incentive to work anddisclose their results. In this scheme, intentionally, there are no property rightson knowledge, the disclosure of results is complete, and positive externalitiesare relevant. In contrast, Technology is defined as an allocation mechanism inwhich intellectual property rights on knowledge can be sold to users on themarket for a profit (provided there is demand for it). In this scheme, knowledgecan be owned and researchers are compensated with profits related to revenuesand costs. Given the patent mechanism, revenues depend on success in theresearch activity.

Both Science and Technology are institutional arrangements with their ownshortcomings. Science, as an effort allocation mechanism, ensures full disclo-sure and positive externalities, but implies well-known agency problems (moralhazard, free riding, low effort). Technology, by nature, is a highly motivatingallocation mechanism, but seizes the main results of research and prevents manypositive spillovers related to the nature of knowledge. Conventional wisdomusually treats Science and Technology as alternative mechanisms, each with itsown benefits and shortcomings, and analyzes them separately. The referredtheory of knowledge (see the various chapters in Barba Navaretti, Dasgupta,Maler, and Siniscalco 1998) suggests that Science and Technology do coexistand should coexist in the society as a whole.

However, recent examples show that Science and Technology can coexisteven in the same research field. The race to sequence the human genomeconstitutes an interesting case as well as research on GMOs (these cases will bediscussed in the last section of this paper). Therefore, a first important questionarises. Why do researchers in the same field choose different institutionalmechanisms, private vs. public or market vs. nonmarket, to manage their ownresearch? An easy answer could be that researchers have different preferencesand skills. For example, some are more innovative and less risk-averse thanothers. However, there is a more fundamental explanation, derived from adeeper understanding of the role of spillovers and of the incentive mechanismsin research. Using this approach, in this paper we show that Science andTechnology as resource allocation mechanisms tend to coexist in the sameresearch field even when all researchers are perfectly identical.

Our analysis also provides useful hints for research policy. Some econo-mists and scientists recently argued that government funded research in biotech,as well as in other key areas such as GMOs, is now redundant on the basis ofefficiency considerations; they argued that the private sector knows (and per-forms) better. Other observers, mainly in Europe, believe that private researchis intrinsically risky because it is driven by profit rather than by the generalinterest. Therefore, the second question that we would like to address is as

577Carraro and Siniscalco Science versus Profit in Research

follows. Are there conditions under which market allocation mechanisms per-form better than public ones in maximizing the amount of knowledge producedwithin a given research field?

The answer is generally negative. This paper shows that Science andTechnology not only tend to coexist but their coexistence is also sociallyoptimal. Therefore, research policy should intervene to achieve the optimal sizeof public and private research in any research field. This size is often defined forthe whole economic system (in the United States the ratio is about 1:2 and thisis also the goal for Europe for the next decade as recently decided in Barcelonaby EU prime ministers. See Soete 2002). However, this paper shows that a moredetailed analysis—field by field—should also be undertaken to implement theoptimal ratio between public and private research.

The paper is divided into four sections. Following the Introduction, Section2 introduces the main definitions and presents a formal framework that showswhy (even identical) researchers may divide themselves into two groups—corresponding to Science and Technology as described here—within the sameresearch area. Section 3 discusses the social desirability of the coexistence ofScience and Technology. Section 4 recalls the application of our framework totwo interesting cases: the race to sequence the humane genome and the researchon GMOs.

With reference to the standard economic literature on research policy,R&D, and intellectual property rights, this paper innovates in several areas.First, it shows the importance of a unified framework where Science andTechnology (as distinct resource allocation mechanisms) are discussed together,on positive and normative grounds, even with reference to specific researchfields. Second, it provides a model to formally characterize the issues underreview, highlighting the key variables which influence researchers’ behavior aswell as social welfare. Third, the proposed model makes it possible to discussa welfare maximizing research policy where Science and Technology interact.

2. Science and Technology: A Positive Analysis

Consider N individuals—researchers—who have identical attributes (i.e., theyare symmetric players), work in the same scientific environment, and haveabsolute specialization (i.e., work only as researchers in a given field). Ofcourse, researchers can decide not to work if their expected compensation islower than their reservation wage. However, in the sequel we will assume thisnot to be the case. Hence, all N researchers work and the supply of researchersis perfectly inelastic.

Researchers produce knowledge, which is a partially public good withpositive externalities. More precisely, knowledge is a differentiated good, withnonrivalry, partial excludability and indivisibility in consumption, and positiveexternalities (or spillovers) in production which depend on intrinsic and insti-

578 Journal of the European Economic Association April–May 2003 1(2–3):576–590

tutional factors. The intrinsic characteristics of knowledge which generatepositive externalities are education, human capital mobility and leakage. Theinstitutional features which can internalize externalities are patents and copy-rights; at the opposite end, the peer review process. Given the existence ofknowledge externalities, each researcher i � 1, 2 . . . N produces knowledge bymeans of his/her own effort xi and the other researchers effort Xi, which spillsover his/her own production. Effort xi, is, of course, costly.

Imagine that researchers face two distinct institutions that govern theproduction and diffusion of knowledge: Science (S) and Technology (T).Assume, by now, that S and T are exogenously received institutions.

In S, knowledge is intentionally treated as a pure public good, with noproperty rights and full disclosure of results. In this institutional context,positive externalities are as large as possible. To deal with the incentive problemrelated to the production of public goods with externalities, researchers in S arecompensated through a complex remuneration structure: a fixed component Fi,unrelated to effort or to success, plus a “prize” component ki related to thediscovery. F and K (respectively, the sum of Fi and ki which are paid to thewhole scientific community) are usually provided by the government.

In T, the existence of intellectual property rights limits positive externalitiesto create a private incentive mechanism. Thanks to patents and copyrights,knowledge has a market and can be sold. Thanks to human capital mobility,leakage, etc., some positive externalities still exist (smaller than in S). Thecompensation mechanism of researchers in T is quite standard. Researchers inT produce knowledge and sell their product to the market. Their reward is theprofit from selling their product.

In both S and T, the production of knowledge is a risky activity with aprobability of success. Only winners get a prize (in S) or a patent (in T). Butknowledge, even in the same field, is a differentiated good so that more than oneprize and/or one patent are available even in the same field. Accordingly, thismodel can be considered a Dasgupta-Stiglitz type model with more than onewinner of the race towards invention (see Dasgupta and Stiglitz 1980). To sumup, in any field the probability of success for researchers, both in S and in T,depends on several elements: own effort, the effort by all other researchers in Sand T (with different externalities, greater from S than from T), the number ofresearchers N. Notice that the probability of success does not affect the fixedcomponent of compensation of researchers in S, while it affects the revenues ofresearchers in T.

Given the prior hypotheses, it is possible to write the payoff functions forresearchers that work in S and T, respectively. Let

�iS � Fi�n� � Pr

S�N, xiS, X�i

S , XT �ki � ciS�xi

S� (1)

be the expected payoff of researchers in S, where n is the number of researchersworking in T, and


�iT � Pr

T�N, xiT, XS, X�i

T �piT�i�xi

T� � ciT�xi

T� (2)

be the expected payoff of researchers in T, where piT � D[�i(xi

T)] is the inversedemand function and �i(xi

T) denotes total output. Hence �� is the productionfunction of knowledge in Technology. Let �� be the production function ofknowledge in Science. As usual, �� and �� are increasing and concave, with�(0) � 0, �(0) � 0. The cost function ci

S� is assumed to be convex withci

S(0) � 0. The fixed component Fi(n) positively depends on n, which is thenumber of researchers working in T. Therefore, Fi(n) does not depend on effort,but is positively related to the condition of the labor market (given N, the higherthe competition for researchers from T, the higher Fi, i.e., �Fi(n)/�n � 0).

The probability of success PrS� positively depends on own effort xi and on

the effort XiS by the other researchers in Science (because of positive spillovers

across researchers in S). It negatively depends on the size of the researchcommunity N and on the total effort XT undertaken by the n researchers workingin T. Hence, N and XT capture a competition or race effect (the more compet-itors in the overall field and in T, the lower the probability of being a winner,despite the own effort and the positive externalities flowing from the otherresearchers in S). In addition, increased competition may reduce the accuracy ofresearch thus further decreasing the probability of success.

Notice that, if Fi(n) is larger than ciS(xi

S) for all n � [0, N � 1] and allequilibrium effort levels, then �i

S is always greater than zero. In addition,whatever Fi(n) � 0, researchers working in S can always get a positive payoffif their effort is zero, because ci

S(0) � 0. For this reason, some free-riding effectsmay occur. Indeed, at no effort, �i

S � Fi(n) � 0 for all n � [0, N � 1]. Finally,let us assume decreasing returns from research efforts, i.e. �2Pr

S/�(xiS)2 � 0,

�2PrS/�(X�i

S )2 � 0, but increasing effects of competition �2PrS/�(XT)2 � 0.

In equation (2), the cost function ciT� is assumed to be convex with

ciT(0) � 0. The probability of success (only winners get a patent) positively

depends on own effort xiT and on the externality flowing from the total effort

XS of the researchers working in Science. It negatively depends on N andXi

T, which capture again a competition or race effect. Notice that, since thewhole revenue depends on Pr

T�, the payoff �iT may be negative. At no

effort, �iT � 0. Again, let us assume decreasing returns from research

efforts, i.e. �2PrT/�(xi

T)2 � 0, �2PrT/�(XS)2 � 0, but increasing effects of

competition �2PrT/�(X�i

T )2 � 0.As said above, absolute specialization prevails. Therefore, all N researchers

decide to work in this research field, because their reservation wage is assumedto be smaller than max[�i

S, �iT]. This condition is feasible because at worst

researchers get Fi(n) when they set their effort equal to zero.It is worth noting that for both groups of researchers in S and T, the sign of

knowledge externalities depends on the institutional arrangements (partial ex-cludability vs. nonexcludability). With knowledge as a public good in S, theeffect of XS on both �i

S and �iT is positive, as the positive spillover effect


dominates the negative competition effect. On the contrary, with knowledge asa private good in T, the effect of XT is negative as the competition effectdominates the positive spillover effect, which is smaller than in S but stillexisting.Let us assume that:

ASSUMPTION 1: A marginal change of own effort has an impact on the probabilityof success larger than the one of a marginal change of the total effort under-taken by the other researchers (both internal and external ).

ASSUMPTION 2: A marginal change of internal spillovers has an impact on theprobability of success larger than the one of a marginal change of externalspillovers.

These two assumptions define a hierarchy of knowledge spillovers. Aresearcher’s own effort has a larger impact on its own probability of successthan the total effort of the other researchers in his/her group, which is larger thanthe impact of the total effort of the researchers in the other group.

The emergence of the two institutions, S and T, is modelled as a two-stage,noncooperative game. In the first stage, symmetric researchers choose to workeither in S or T. This is a group formation game, where N researchers can all joinT or S or divide themselves into two groups. In the second stage, researcherschoose their optimal effort level, either xi

S or xiT.

Let us solve the game backward. The optimal effort level is obtained bymaximizing the payoff functions (1) and (2) with respect to xi

S and xiT, respec-

tively. This yields:

k�Pr

S�N, xiS, X�i

S , XT�

�xiS �

�ciS

�xiS i � n � 1, . . . N (3)

PrT�N, xi

T, XS, X�iT ��1 � �pi

T��i�xi

T�

�xiT � pi

T�i�xiT�

�PrT�N, xi

T, XS, X�iT �

�xiT �

�ciT

�xiT

(4)

i � 1, . . . n

where, without loss of generality, we have assumed that the first n researcherswork in T and where � � 1 is the inverse of demand elasticity. Theseequilibrium conditions simply says that the marginal cost of the research effortmust be equal to the marginal benefit yielded by this effort.

By solving (3) and (4) with respect to xiS and xi

T, we obtain, at the Nashequilibrium, xi

S, i � n 1, . . . N, and xiT, i � 1, . . . n, where:

xiS�n� � xi

S�n, N, k, ��xi

S

�n� 0 i � n � 1, . . . N (5)


and

xiT�n� � xi

T�n, N, k, ��xi

T

�n 0 i � 1, . . . n (6)

where N, k and � � 1 are given and n is determined in the first stage of the game.Total research effort is then (N � n)xi

S(n) in Science and nxiT(n) in Technology.

Let us also assume that:

ASSUMPTION 3: The effect of a positive change of the group size on the totalresearch effort produced by the group is positive, i.e. the positive size effectdominates the negative effort effect.

By replacing the equilibrium values (5) and (6) into (1) and (2), we obtainthe values of the payoff functions in the first stage of the game.

�iS�n� � Fi�n� � Pr

S �xiS�n�, �N � n � 1�xi

S�n�, nxiT�n�k � ci

S�xiS�n� (7)

i � n � 1, . . . N

�iT�n� � Pr

T�xiS�n�, �N � n�xi

S�n�,(8)

�n � 1�xiT�n�]D��xi

T�n��i�xiT�n�� ci

T�xiT�n� i � 1, . . . n

Notice that, in the first stage, expected payoffs depend only on the numberof researchers in each group, i.e., on n, because the size of the other group isgiven by N � n. In the first stage, the strategy space of each researcher isbinary—[S, T]—because he/she decides which institution to choose to carry outhis/her own research activity.

The equilibrium of the first stage is again a non-cooperative Nash equilib-rium. Following a widely accepted standard in coalition formation theory (cf.D’Aspremont et al. 1983; Carraro and Siniscalco 1993; Barrett 1994; Yi 1997),the Nash equilibrium is defined as follows: n* researchers choose Technologyat the equilibrium, and consequently N � n* researchers choose science, iff:

�iS�n*� 0 and �i

T�n*� 0 0 � n* � N (9)

and

�iT�n*� �i

S�n* � 1� and �iT�n* � 1� � �i

S�n*� 0 n* N (10)

Conditions (9) define the usual profitability of the equilibrium group size n*.Conditions (10) define the stability of the equilibrium group size. At theequilibrium, no researcher wants to leave Technology to join Science and noresearcher wants to leave Science to join Technology.

Let RiT � pi

T�i(xiT) be the revenue obtained by a winner in Technology. Let

us assume RiT � ki for all 0 � n � N. Using also Assumptions 1–3, it can easily

be shown, by differentiating (7) and (8), that d�iT/dn is negative for all n � [1,


N]. By contrast d�iS/dn can be positive or negative in the interval [0, N � 1].

In order to simplify the analysis and to reduce the number of cases to beanalyzed, let us assume that �i

S(n) is monotonic in the interval [0, N � 1].Moreover, we have:

d�iS

dn�

d�iT

dn� 0 for all 1 � n � N � 1 (11)

i.e., the slope of �iS(n) is always larger than the slope of �i

T(n), even when�i

S(n) is decreasing.1

When the reservation wage is normalized to zero, �iS(0) 0, �i

T(N) 0.�i

T(0) and �iS(N) are not defined. Then, three main conclusions can be derived:

(1) The equilibrium payoff in T is a negative function of n. Indeed, moreresearchers in T induce less spillovers from S and more competition in T.

(2) The slope of �iT(n) is always smaller than the slope of �i

S(n). As aconsequence, if �i

S(1) � �iT(1), then �i

S(n) is above �iT(n) for all n �

[1, N � 1] which implies that all N researchers are in Science.(3) In all other cases, the two institutions coexist unless the equilibrium

payoff in T is larger than in S even when almost all researchers are in T[i.e. �i

T(N � 1) � �iS(N � 1)].

From these conclusions, three types of equilibria of the above two-stage gameare possible:

(a) Science only.If �i

S(1) � �iT(1), then the payoff from choosing Science is higher than

the payoff from choosing Technology for all group sizes in the interval[1, N � 1]. Hence, all researchers choose Science which is the onlyinstitution which emerges at the equilibrium. This is the case whenproperty rights in T are weak or ill-defined and hence researchers in Tcannot market their discoveries; or when there is no demand for theoutput of research in T, e.g., because the research field focuses mostly onbasic research (e.g., mathematical theorems). Hence, there is no incentiveto move from S to T.

(b) Technology only.If �i

S(1) � �iT(1) and �i

S(N � 1) � �iT(N � 1), then the payoff from

choosing Technology is higher than the payoff from choosing Science forall group sizes in the interval [1, N � 1]. Hence, all researchers chooseTechnology, which is the only institution that emerges at the equilibrium.This is the case when researchers in S are badly paid, or when discoveriesin T are highly demanded and innovations can adequately be patented.

1. A detailed proof of this result and of the equilibria discussed here is in Carraro, Pome, andSiniscalco (2001).


(c) Science and Technology.If �i

S(1) � �iT(1) and �i

S(N � 1) � �iT(N � 1), then there exists a

value of n*, with 0 � n* � N, such that �iT(n*) �i

S(n* � 1) and�i

T(n* 1) � �iS(n*). Hence, n* defines the number of researchers who

choose Technology. The remaining N � n* researchers choose Science(see Figure 1 where two cases are shown: one where �i

S(n) is increasingand a second one where it is decreasing). Therefore, even identicalresearchers divide themselves into two groups. Consequently, S and Tpermanently coexist even in the same research field. In this case, Technologyis more profitable than Science when there are few researchers in T whobenefit from spillovers from S and from reduced competition in T. However,as the number of researchers in T increases, competition also increases andspillovers from S decrease, thus creating an incentive to belong to S.

3. Welfare Analysis

The analysis of the previous section shows that, on positive grounds, identicalresearchers may divide themselves into two groups, corresponding to Scienceand Technology as institutions. The obvious question at this point is whethersuch state of affairs is socially desirable, or welfare maximizing.

Assume for simplicity that the social cost of tax revenue collection is zeroor negligible (i.e., there are no distortionary effects of taxation). Then, theaggregate revenue of researchers in Science �i[Fi(n) ki] is equal to the costborn by the taxpayers.

FIGURE 1. Science and Technology Coexist


Given the above assumption, the social welfare function W(n) is:

W�n� � �i�iT�n� � CS�n� � E�Y�n� (12)

where �i�iT(n) is the aggregate profit of researchers in T, CS(n) is the consum-

ers’ surplus, and E[Y(n)] is the expected monetary social value of Y(n), whichis the flow of produced knowledge as a public good. Hence, E[Y(n)] is the valueof knowledge as a public good, whereas �i�i

T(n) CS(n) is the market valueof knowledge. The total production of knowledge is:

Y�n� � �N � n�PrS�N, xi

s, X�iS , XT��i�xi

S� � nPrT�N, xi

T, XS, X�iT ��i�xi

T�,

which becomes, by substituting equilibrium values:

Y�n� � �N � n�PrS�xi

S�n�, �N � n � 1�xiS�n�, nxi

T�n��i�xiS�n��

(13)� nPr

T�xiT�n�, �N � n�xi

S�n�, �n � 1�xiT�n��i�xi

T�n��

The consumers’ surplus CS(n) is, as usual, an increasing function of thenumber of researchers in Technology. More competition induces lower pricesand higher quantities, thus increasing surplus. The total profit �i�i

T(n) is bycontrast a decreasing function of n. In addition, by differentiating the totalproduction of knowledge Y(n) with respect to n, it can be shown that �Y(n)/�nis increasing with n for n close to zero and decreasing with n for n close to N.2

The reason is that, when n/N is small, an increase of producers in Technologyfosters individual effort in Science, whereas the negative effects on the proba-bility of success of an increase of n are still small. When n is close to N, mostresearchers work in Technology, research spillovers are low, the negativeeffects on the probability of success of excess competition are high, andindividual effort decreases in T.

Adding up the effects of a change of n, the welfare function W(n) can alsobe shown to be, in most cases, first increasing and then decreasing with respectto n in the interval [1, N � 1]. Of course, there may be cases in which W(n) isincreasing for all n � [1, N � 1]. For example, when the probability of successin T is much larger than in S, or when demand in T and therefore the consumers’surplus are very high. And there may be cases in which W(n) is decreasing forall n � [1, N � 1]. For example, when knowledge spillovers within S are verylarge, or when the negative race effects are very strong. However, if N issufficiently large (as it is the case because our model is a micro model ofindividual choices), then the most likely case (i.e., the one which occurs for thelargest range of functional forms and parameter values) is the one in which W(n)is first increasing and then decreasing with respect to n (the result is formallyshown in Carraro, Pome, and Siniscalco 2001). This implies that the welfaremaximizing value of n belongs to [2, N � 1]. Therefore, it is generally sociallyoptimal to have two groups of researchers and two knowledge allocation

2. Again a detailed proof of this result is in Carraro, Pome, and Siniscalco (2001).


mechanisms within the same research field even when researchers are identical.In other words, Science and Technology not only often coexists in the sameresearch area, but their coexistence is generally socially optimal.

Let n** be the maximand of W(n), i.e., n** is the socially optimal numberof researchers that should work in Technology. As a consequence, N � n**researchers should work in Science. The above conclusions implies 0 � n** �N. Depending on the whole set of parameters, the socially optimal value n**may be larger or smaller than the equilibrium value n* (see Figure 2 for a casein which there are too many researchers in Technology). Hence, there is scopefor a research policy that should reconcile individual behavior and socialoptimum.

Suppose that, given N, n* � n** (too many researchers in T vis a vis thesocial optimum). Research policy can induce a decrease of n* by increasing�i

S�, i.e., by increasing the fixed compensation of scientists, or their successrelated prize k, or by inducing higher spillovers among scientists through bettercooperation or more intense peer-review (in Figure 2, the function S movesupward). Alternatively, policy could decrease the payoff �i

T� in Technology,but this would reduce total welfare. The reason is that �i

S(s) does not entersocial welfare directly, whereas �i

T� does.Suppose now that, given N, n* � n** (too many researchers in S vis a vis

the social optimum). Research policy can increase the number of researchers inT either by reducing the payoff of researchers in S or by increasing the one inT. The second policy option is to be preferred because it shifts upward thewelfare function. Hence, total welfare increases both because n* becomes equalto n** and because W(n) moves upward. Examples of this type of policy can bea reinforcement of intellectual property rights on knowledge produced by T, an

FIGURE 2. The Optimal Size of Researchers in Science and Technology(W and �s on Different Scales)


enhancement of spillovers from S to T, an increased demand in T induced bypublic spending (the government buys products of researchers in T such as inthe case of vaccines), and tax exemptions for researchers in T.

4. Links with the Human Genome Project and Research on GMOs

The preceding analysis has been applied to two interesting cases: the race tosequence the human genome project and research on GMOs. Such cases arediscussed in specific papers, but the main argument can be briefly recalled.

The scientific research on the human genome began as a publicly fundedendeavor (i.e., as pure Science). In 1986, at the time of the Human Genome Project(HGP) kickoff meetings, federally funded genome-project scientists figured theycould move at their own pace and finish up in 2005 or thereabouts. But they figuredwrong. The private sector quickly discovered it could make billions of dollars byturning genome research into new drugs and treatments for a large number ofdiseases, given the legislation on property rights. And the companies that manage toget the results first—and lock up what they find with appropriate patents—will profitmost (Lemonick and Thompson 1999). It is no surprise, therefore, that private firmshave plunged into human-genome projects of their own. Nor is it surprising, giventhe potential payoff, that their researchers have found ways to speed up the decodingprocess. The most famous of these companies is Celera Genomics, led by scientistJ. Craig Venter (formerly working in the HGP), which decided to attack the problemwith an innovative approach using the most sophisticated computer technologyavailable. Venter’s decision was a clear example of Technology, as defined previ-ously. And Science and Technology in genomic research quickly began to coexistand challenge each other.

Celera’s initial announcement in 1998 described plans to sequence thehuman genome more rapidly and much more efficiently than the public HGP.The differences were astonishing: Celera’s budget was US$330 million versusHGP’s budget of US$3 billion; and completion of the project was set for theyear 2001, four years earlier than HGP’s deadline. Celera’s challenge to theacademic research community provoked a new sense of urgency and reality inpublic researchers. HGP doubled and redoubled its effort to both remaincompetitive and to guarantee public access to the large information databasesgenerated. HGP was also forced to adopt some of Venter’s ideas to avoid beingleft behind (Lemonick 2001). In the subsequent months, thanks to many inno-vations in the sequencing process, the productivity of sequencing grew signif-icantly to a turnover of 12,000 bases every minute. Another important differenceis disclosure policy. Celera announced that it had filed provisional patentapplications on thousands of newly discovered genes, charging millions ofdollars a year to wade through its data and computer services. HGP, by contrast,was publishing its results on the Web, which were free to all. One of the biggestusers of HGP data, incidentally, was Celera itself (Thompson 2000).


The race to sequence the humane genome is a case which can easily beinterpreted using the model described above (Carraro, Pome, and Siniscalco2001). In the human genome case, researchers in Technology worked withhigher effort, sometimes higher productivity, and constantly provided a com-petitive stimulus to researchers working in Science. On the other hand, Scienceprovided positive externalities, through human capital, knowledge spillovers,peer-review, and transparency. Therefore, each project contributed to the otherin a competitive environment, showing that their coexistence is a beneficialoutcome.

The case of research on GMOs can also be analyzed using our modellingframework. The story in this case it is derived from existing literature (e.g., seeEichenwald et al. 2001). According to such literature, agricultural research hasnever experienced the huge public funding of other research areas. And the lackof resources has remained unchanged even when the private sector beganinvesting private capital in plant and animal genetics. As a result, the increasingpredominance of Technology over Science created an unbalanced situation, withnegative feedbacks on the whole field. The story of the multinational Monsantomay capture the negative experience of the whole agri-biotech industry, whereshort-sight and inappropriate strategy produced, in the end, a negative outcome.

In the early days of the biogenetic industry, at the beginning of the 1980s,Monsanto management team said that, while they felt confident of the newfood’s overall safety, they also recognized that bioengineering raised concernsabout possible allergens, unknown toxins or negative environmental effects.Beyond that, they recognized a reasonable anxiety about human manipulation ofnature. For such reasons (as it emerges from massive published material)Monsanto believed that they had enormous obstacles from environmental andconsumer groups opposed to the new technology.

To make the business work, Monsanto strategy was to bring in scientistsand opponents and to discuss widely with such constituencies the results ofresearch and development, hoping their participation would ease the GM-food’spassage from the laboratory to the market. In the early 1990s, however,following a change in the top management, Monsanto’s strategy made a U-turn.This was favored by a new policy on bioengineered food—adopted in 1992 bythe U.S. administration—which meant that biotechnology companies would notneed government approval to sell the foods they were developing. The controlover quality and safety by FDA (Food and Drug Administration), USDA (U.S.Department of Agriculture), and EPA (Environmental Protection Agency), insome cases, was then substituted by “self-regulation.” As a consequence,Science in the field was no longer called as a credible counterpart to industryand agri-bio-food was not the subject of a thorough peer review.

In this context, the new Monsanto strategy, not surprisingly, generatedsevere and growing difficulties, primarily due to lack of control and transpar-ency. Meanwhile, the publicly funded scientists were displaying precisely theconcerns that Monsanto executives from the 1980s had anticipated, and indeed


had considered reasonable. But rather than trying to address those concerns,Monsanto were dismissing them as insignificant worries. Public research inagri-biotech was even less funded. Furthermore, labelling was ruled out aspotentially misleading to the consumer, since it might suggest that there wasreason for concern. Consultation and interaction with public science was basi-cally waved or rejected.

Soon, protests erupted all over and genetically modified foods became therallying point of a vast political opposition. Sales and exports of GMOs sloweddramatically. With plunging revenues and the stock price in the doldrumsbecause of its struggles with public opinion, in the late 1990s, the once-mightyMonsanto ended its existence as an independent company and was taken over byPharmacia, a New Jersey drug company. Similar backlash in stock pricesoccurred to other giants of GMO, who decided to dismiss that line of business.In recent months, the whole agri-biotech industry has been struggling with theconsequences of such errors. Leading food companies such as Frito-Lay andGerber said they will avoid most bioengineered crops. And grain companiessuch as Archer Daniels Midland and Cargill have asked farmers to separategenetically modified seeds from the traditional ones.

Quite obviously, the indisputable crisis of Monsanto as recalled in thebusiness literature can be due to many factors: among them, the lack of a strongscientific counterpart has probably played a serious role. Using our model, it isclear that research investments in Science were too low and that Technologycould not benefit from the positive spillovers (e.g., an accurate peer reviewprocess) that Science usually provides. For example, private business couldhave used interactions with public research and a careful peer review process asan effective marketing strategy. The GMO case also suggests that publicresearch policy was not adequate. Public authorities should have increased theamount of publicly funded research. This would have enhanced the positivespillovers from Science to Technology thus increasing both profits in the privatesector (consumers, re-assured by a transparent, careful and authoritative peerreview process carried out in Science, could have increased their demand) andsocial welfare (also through an increased value of knowledge). Therefore, in theGMO’s case, the size of Science has been suboptimal and adequate policiesdesigned to correct the inefficiencies in Technology have not been implemented.

References

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