Efficient Transfer of Public Scientific R&D to Private Firms
2.4 In Retrospect
34 T. V. S. Ramamohan Rao 5. ∂α/∂λσ2<0. Once again, it is plausible that a highly risk averse scientist will
not invest in his own startups.
6. ∂α/∂ p<0. It is expected that the scientist will license out more often if his share of revenues increases.
It can also be inferred that the scientist will be the sole entrepreneur ifδ is fairly large in comparison toλσ2and n, and/or k is fairly large.27
Consider the issue of the relationship between the number of startups (including licenses) and the net value generated by the process of commercialization. Ceteris paribus, it can be verified that ∂N/∂δ∗>0 and ∂n/∂δ∗>0. Consequently, an increase in the competence of the scientist will improve N as n increases. However, it cannot generally be shown that∂N/∂n>0 in all contexts. This is not surprising.
For, entrepreneurial success in generating a higher value of N is contingent on the entrepreneurs being supported by the availability of capital, finances, and so on.
It can be surmised that this result will hold even in the steady state. Hence, the entrepreneurship and growth nexus cannot be taken for granted.
2 Efficient Transfer of Public Scientific R&D 35 solution may also provide some clues to explain this phenomenon. Fourth, a new technology such as biotechnology may not totally replace the more conventional non-GM technologies even in a steady state. This will clearly have an effect on entrepreneurial development in the biotechnology sector. The present model may be extended to offer some insights.
A more dynamic analysis of the technology transfer process, such as in Rao (2006), therefore appears more promising.
Notes
1Arora (1996, p. 235) puts it this way. “Transfer of chemical technology will typically involve training the licensee in a variety of issues such as how to handle and store chemicals, how to control the production process and return it to operation after (an) unscheduled breakdown caused by (an) accident or impurities in the feedstock.” Such informal knowledge is necessary for better assimilation, utilization, and adaptation of technology. In the context of biotechnology it is also necessary to provide biological materials like cell lines. Further, it is necessary to train private firms in the use of laboratory tools for gene splicing, cloning, and so on. Informal knowledge refers to the demonstration by the scientist in the use of such biological materials and processes.
It may turn out that the informal knowledge transfer requirement is purely transitory. Private firms may develop these skills on their own as the technologies mature. This was probably the case in the context of other technologies. Even so, this may be a much longer run requirement as the analysis of Arora suggests.
2The fragmented development of knowledge clearly depends on the competence of any one, or a group of individuals, in developing knowledge to a point where a product of commercial value emerges. The high costs may in fact be a result of this feature.
3Sonka and Pueppke (1999) noted that “much of the market application of biotechnology, in general, has involved small, entrepreneurial firms driving innovation. Often, these firms exploited publicly available knowledge to overcome their lack of (skill). Relative to agricultural technology, an important role of publicly supported research may be to create knowledge that can be used to fuel innovation in the market place.”
4Several limitations of this approach have been recorded. One important aspect is that “patents and TRIPS agreement may exclude marginal farmers from benefits of biotech if all R&D and investment is private.” See Chaturvedi (2002). Public sector activity is necessary to maintain a social welfare perspective.
5This was the major theme in Just and Hueth (1993).
6In the area of biotechnology patents for knowledge were considered essential to achieve widespread knowledge diffusion. However, it is now obvious that this was not achieved. Several alternatives are under consideration. See, for example, Rai and Eisenberg (2004), Rai (2005b), and Rai (2005a).
7Note that licensing technology is not a new concept in itself though it is now extended to public science as well. The issue of providing informal knowledge must be squarely addressed as a part of the licensing agreement.
8There is a general feeling that public scientific R&D will be relevant and amenable to com- mercialization if a private firm is allowed to make the choice of appropriate products (of use to consumers) and the public firm and its scientists develop technologies oriented to this goal. See, for instance, Raina (2003). Based on this philosophy, the Government of India used to stipulate that at least 30% government funded programs must have a commercial partner who will be responsi- ble for directing R&D towards commercialization. Sonka and Pueppke (1999) also suggested that the private firm may be asked to finance a part of the R&D efforts of public institutions. How- ever, as noted above, this results in political bias. Hence, many otherwise worthwhile scientific
36 T. V. S. Ramamohan Rao developments may not be funded. In recognition of this limitation this institutional mechanism is generally not utilized. Instead, the process of development of scientific R&D is kept distinct from its commercialization.
9One good way of learning about the relative efficiency of policy options is to document the policies that various countries have been adopting and their relative success. However, this approach results in incremental thinking and offers suggestions for marginal adjustments in the existing public policy. It would be worthwhile to break free and attempt an analysis of the efficacy of more fundamental policy options.
10Three distinct organizational forms are discernible in the context of knowledge transfer in biotechnology industry.
1. Networks 2. Outsourcing
3. Open source architecture
Outsourcing is possible when the job can be divided into independent modules so that close col- laboration is not needed. Open sourcing is a peculiar network when no specific product is targeted (it evolves over time without any premeditation) and there is no clear a priori knowledge about which set of hackers will be in a position to add value. Refer to Rai and Eisenberg (2004) for details. Clearly these approaches are feasible only if there is no necessity for intense interaction and acquisition of informal knowledge in the transfer of scientific information. It was, however, observed that informal knowledge is critically important for the assimilation of biotechnology knowledge. See, for example, Visalakshi and Sandhya (1997). Hence, the option of a public sector firm developing scientific R&D and formally offering it to a private firm is fraught with limitations.
Networking and/or licensing a joint venture is by far the most efficient alternative in the context of the biotechnology industry.
11The coordination problems, between the scientists of a public institution and private firms, can be of three types. First, the development of knowledge is fragmentary and carried out by several scientists. These fragments of patented knowledge need to be coordinated to achieve a product of commercial value. For example, Byerlee and Fischer (2001) noted that enriched vitamin A rice (also known as the golden rice) is based on technology that spans 70 patents held by 31 different organizations. Private firms may feel inhibited while coordinating technology development with so many agents. Some centralized organizational mechanism may be more efficient. Second, a single aspect of knowledge developed may have many practical applications. The scientist may not be in a position to handle all of them if he becomes an entrepreneur. Hence, he may create some startups and license private firms to develop the others. Third, the context of creating germplasm and agri- cultural extension services appears to suggest that public sector institutions must be involved even at this stage. The basic problem here is that many recipients are involved and each of them needs specific help. In such cases the scientist, who initially discovered the technology, may not be in a position to handle the entire transfer process. Participation of public institutions may be more effi- cient. See, for instance, Gerpacio (2003). Detailed comparisons of several possible organizational arrangements were attempted in Berglund and Clarke (2000) and Fischer and Byerlee (2001).
12Even private firms may encounter financial constraints given the high risks of biotechnology projects. The Government of India (through its organ DBT) created BCIL (biotechnology con- sortium of India) with participation from IDBI, ICICI, and 30 other firms in the public sector.
“It guided startups, arranged technology transfer and supported their efforts to attract adequate finances.” For details see Ramani (2002). On the other hand, France allowed publicly supported scientists and institutions to become shareholders in the firms associated with their laboratories.
They may, as a result of such arrangements, gain control on knowledge leaks and performance of private firms. When the financial constraint is a problem the scientist may also be allowed to seek venture capital and/or equity financing. However, this will reduce his control to some extent. It may also place a limit on the discoveries that can be moved to the commercial stage due to differences in perceptions about appropriability.
13It was initially difficult to entice the scientist in a public institution to undertake commercial- ization. For, university science is often
2 Efficient Transfer of Public Scientific R&D 37 1. Directed to discovery and professional publication
2. Oriented to rewarding the scientists in the form of promotions, status, honors, and research funding
3. Supportive of free dissemination of information Public science research is also constrained by the inability to 1. Obtain necessary finances from the government 2. Have access to private financial institutions
3. Break free from political patronage that determines the nature of research.
Fundamentally, these difficulties manifest themselves in the form of low motivation and ability of the scientists to create startups or high costs of doing so.
14There is a possibility that developing any one product depends on n different patented scientific inventions. The analysis is somewhat different in such a case. For details see Rao (2005).
15A private firm may also induce a public institution to undertake specific research that it will eventually implement. This has implications for the nature of the product choice as well as the method of financing fixed expenditures on R&D. This variant will not be pursued further.
16Appropriability of the expected value through the market process is an important issue. This will not be considered explicitly in this study.
17Input measures of R&D activity may be inadequate for a more detailed analysis. It is diffi- cult to identify appropriate output measures and incorporate all the pertinent differences between technologies and products. Such a detailed analysis will be needed to define the efficient choice of instruments. However, such an analysis has hardly been initiated.
18The emphasis underlying this specification is that R&D and entrepreneurial activities are per- force subject to some element of surprise. Achieving unexpected success is as much a part of this process as failure. The alternative viewpoint is that no more than the targeted m can be achieved.
In such a case the actual output m may materialize with only a probability q. Such an alternative modeling framework is available in Filson and Morales (2005).
19The sources of this variance are factors outside the purview of the decision making process of private firms. However, it will be argued in the sequel that it may be a result of the decisions of the joint venture partners.
20Inclusion of fixed cost, and, in particular, sharing between the joint venture partners, requires some major modifications in the model presented in this study. See, for example, Rao (2004) and Sharma and Rao (2006).
21The assumption thatδ >δ∗may not hold in situations where the knowledge being trans- ferred is at the early stages, for the scientist may be more efficient in generating the fundamental knowledge necessary for the product development.
22The new technology may be embodied in the machine structure and the scientist may have to provide this to the private firm. In this case, the scientist has two options. He can provide the machinery and make the requisite investments. He then claims a royalty. Alternatively, the scientist may sell the equipment to the private firm. The scientist is generally reluctant to do this because patent rights are exhausted after such a first sale and this may result in reverse engineering by the private firm and eventual erosion of the advantage of the technological inventions of the scientist.
The alternative, of the scientist incurring the costs of private firms, may be considered analogously.
However, this appears unrealistic in practice.
23Low IPR protection may also make the scientist feel that the costs of licensing are higher.
24Note that the cost of licensing to a private firm is km2+m2/2δ. On the other hand, if the scientist creates his own startup it will be m2/2δ∗. Hence, licensing will be feasible only ifδ∗<
δ/(1+2kδ).
25k will also decrease as more private firms acquire competence either through learning-by- doing or hiring young scientists who acquired the skills through training and education. k may also decrease as n increases and/orαtends to unity. It should be noted that a private firm may consider σ2to be high because they are not sure about the extent and/or efficiency of informal knowledge transfer that they receive. To an extent, therefore,σ2may decrease with an increase in k and/orα.
38 T. V. S. Ramamohan Rao An efficient choice of k may also be conceptualized. Sharma and Rao (2005, 2006) contain some details of this alternative and its implications for the efficient transfer of technology.
26This corresponds to the spirit of the Coase theorem. That is, the principal-agent models posit that the issues pertaining to distribution of gains can be sorted out after achieving net value maximization.
27Similarly, the optimal choice of p will be such that−δ+α(1−p)δ2/δ∗+2(1−α)(1− p)kδ2+(1−α)(1−p)δn−2λnσ2(1−α)[α+p(1−α)] =0. Properties of the optimal choice of p can be explored as before.
28The analysis assumed complementarity between public and private R&D efforts towards com- mercialization. However, some studies emphasize the substitutability property of public vs. private R&D. Ishibashi and Matsumura (2005) and Alfranca and Huffman (2003) noted that in such situ- ations public policies affect private firm R&D and its use. In particular, public funding of activity is inhibitive of private R&D because
1. Too much political patronage, and the associated intervention, curtails the freedom of the scientists to pursue their curiosities and hence the nature and scope of R&D
2. Too much public investment crowds out private initiative and investment. Appropriability of private investment decreases with the volume of public investment. For instance, why would a farmer pay for extension services if he gets them free?
However, modeling public-private interaction in the presence of substitutability requires a some- what different conceptualization.
29The financial implications of this are also obvious. The private sector cannot continue making profits in the long run unless the finances provided by the government are paid back. The govern- ment should also accept the fact that the private firm will not produce goods of social value unless they recover costs adequately. Working out this delicate balance has been elusive for a long time and in the meantime any compromise solution can only be a second best. The present analysis suggests that the government must finance a greater share of the efforts of technology development and transfer whenever the above mentioned conditions hold.
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